U.S. patent application number 17/669691 was filed with the patent office on 2022-05-26 for calculating individual carbon footprints.
This patent application is currently assigned to Advanced New Technologies Co., Ltd.. The applicant listed for this patent is Advanced New Technologies Co., Ltd.. Invention is credited to Xue Bai, Huajing Jin, Zhenhua Li, Di Xu.
Application Number | 20220164273 17/669691 |
Document ID | / |
Family ID | 1000006135894 |
Filed Date | 2022-05-26 |
United States Patent
Application |
20220164273 |
Kind Code |
A1 |
Jin; Huajing ; et
al. |
May 26, 2022 |
CALCULATING INDIVIDUAL CARBON FOOTPRINTS
Abstract
Behavior data associated with a user is obtained. The behavior
data is generated when the user uses an Internet service and
includes a user identification and identification information
indicating the Internet service. At least one predefined
carbon-saving quantity quantization algorithm is determined based
on the identification information related to the Internet service.
A carbon-saving quantity associated with the user is calculated
based on the obtained behavior data and the determined at least one
predefined carbon-saving quantity quantization algorithm. Based on
the calculated carbon-saving quantity associated with the user and
the user identification, user data is processed. The user data is
related to the carbon-saving quantity associated with the user.
Inventors: |
Jin; Huajing; (Hangzhou,
CN) ; Xu; Di; (Hangzhou, CN) ; Li;
Zhenhua; (Hangzhou, CN) ; Bai; Xue; (Hangzhou,
CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Advanced New Technologies Co., Ltd. |
George Town |
|
KY |
|
|
Assignee: |
Advanced New Technologies Co.,
Ltd.
George Town
KY
|
Family ID: |
1000006135894 |
Appl. No.: |
17/669691 |
Filed: |
February 11, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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15684603 |
Aug 23, 2017 |
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17669691 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 10/00 20130101;
G06F 11/3058 20130101; G06F 11/3409 20130101; Y02D 10/00 20180101;
G06F 11/3013 20130101; Y02P 90/845 20151101; G06F 11/3438 20130101;
G06F 2219/10 20130101; Y02P 90/84 20151101 |
International
Class: |
G06F 11/34 20060101
G06F011/34; G06F 11/30 20060101 G06F011/30; G06Q 10/00 20060101
G06Q010/00 |
Foreign Application Data
Date |
Code |
Application Number |
Aug 24, 2016 |
CN |
201610717756.7 |
Claims
1.-20. (canceled)
21. A computer-implemented method comprising: receiving
pre-generated behavior data that is associated with use of a
particular online service by a user, wherein one or more carbon
emitting activities are avoided through the use of the particular
on-line service by the user; identifying, as an identified activity
type, an activity type that is associated with the particular
on-line service; identifying a particular algorithm that is
pre-associated with the identified activity type that is associated
with the particular on-line service; determining, based at least on
the particular algorithm and the pre-generated behavior data, a
quantity of uncollected points that represents an amount of carbon
that is calculated for the one or more carbon emitting activities
that were avoided through the use of the particular on-line service
by the user; determining that a user request has been received for
the quantity of uncollected points that represents an amount of
carbon that is calculated for the one or more carbon emitting
activities that were avoided through the use of the particular
on-line service by the user to be added to a total quantity of
collected points that represents an amount of carbon that is
calculated for one or more carbon emitting activities that were
avoided through the use of multiple on-line services by the user;
in response to determining that the user request has been received
for the quantity of uncollected points to be added to the total
quantity of collected points, adding (i) the quantity of
uncollected points to (ii) the total quantity of collected points;
and storing data that associates the updated total quantity of
collected points with the user.
22. The method of claim 21, wherein: the pre-generated behavior
data that is associated with use of the particular on-line service
by the user is received by an acquisition module of an application,
from a module of the application that is associated with the
particular on-line service, the activity type and the particular
algorithm are identified by a determination module of the
application, the quantity of uncollected points is determined by a
calculation module of the application, the user request is
determined to have been received, and the quantity of uncollected
points is added to the total quantity of collected points, and the
data is stored, by a processing module of the application.
23. The method of claim 21, wherein the identified activity type
comprises an on-line ticket purchasing activity type that avoids
the carbon emitting activities of driving and printing paper
tickets or receipts, and the quantity of uncollected points
includes both (i) uncollected points that represent an amount of
carbon that is saved by avoiding driving, and (ii) uncollected
points that represent an amount of carbon that is saved by avoiding
printing paper tickets or receipts.
24. The method of claim 21, wherein the identified activity type
comprises an on-line bill payment activity type that avoids the
carbon-emitting activity printing paper bills, and the quantity of
uncollected points includes uncollected points that represent an
amount of carbon that is saved by avoiding printing bills.
25. The method of claim 21, wherein the identified activity type
comprises an on-line reservation activity type that avoids the
carbon emitting activity of driving, and the quantity of
uncollected points includes uncollected points that represent an
amount of carbon that is saved by avoiding driving.
26. The method of claim 21, comprising purchasing, by the user, a
service from a service provider using a subset of the updated total
quantity of collected points as payment.
27. The method of claim 21, comprising: determining that a user
request has been received to acquire, from a different user, an
additional quantity of uncollected points that have not yet been
added to a total quantity of collected points that is associated
with the different user; in response to determining that the user
request has been received: adding the additional quantity of
uncollected points that are associated with the different user to
the updated total quantity of collected points that is stored for
the user, to generate a second updated total quantity of collected
points, subtracting the additional quantity of uncollected points
from a total quantity of uncollected points that are associated
with the different user; and storing data that associates the
second updated total quantity of collected points with the
user.
28. A non-transitory, computer-readable medium storing one or more
instructions executable by a computer system to perform operations
comprising: receiving pre-generated behavior data that is
associated with use of a particular online service by a user,
wherein one or more carbon emitting activities are avoided through
the use of the particular on-line service by the user; identifying,
as an identified activity type, an activity type that is associated
with the particular on-line service; identifying a particular
algorithm that is pre-associated with the identified activity type
that is associated with the particular on-line service;
determining, based at least on the particular algorithm and the
pre-generated behavior data, a quantity of uncollected points that
represents an amount of carbon that is calculated for the one or
more carbon emitting activities that were avoided through the use
of the particular on-line service by the user; determining that a
user request has been received for the quantity of uncollected
points that represents an amount of carbon that is calculated for
the one or more carbon emitting activities that were avoided
through the use of the particular on-line service by the user to be
added to a total quantity of collected points that represents an
amount of carbon that is calculated for one or more carbon emitting
activities that were avoided through the use of multiple on-line
services by the user; in response to determining that the user
request has been received for the quantity of uncollected points to
be added to the total quantity of collected points, adding (i) the
quantity of uncollected points to (ii) the total quantity of
collected points; and storing data that associates the updated
total quantity of collected points with the user.
29. The medium of claim 28, wherein: the pre-generated behavior
data that is associated with use of the particular on-line service
by the user is received by an acquisition module of an application,
from a module of the application that is associated with the
particular on-line service, the activity type and the particular
algorithm are identified by a determination module of the
application, the quantity of uncollected points is determined by a
calculation module of the application, the user request is
determined to have been received, and the quantity of uncollected
points is added to the total quantity of collected points, and the
data is stored, by a processing module of the application.
30. The medium of claim 28, wherein the identified activity type
comprises an on-line ticket purchasing activity type that avoids
the carbon emitting activities of driving and printing paper
tickets or receipts, and the quantity of uncollected points
includes both (i) uncollected points that represent an amount of
carbon that is saved by avoiding driving, and (ii) uncollected
points that represent an amount of carbon that is saved by avoiding
printing paper tickets or receipts.
31. The medium of claim 28, wherein the identified activity type
comprises an on-line bill payment activity type that avoids the
carbon-emitting activity printing paper bills, and the quantity of
uncollected points includes uncollected points that represent an
amount of carbon that is saved by avoiding printing bills.
32. The medium of claim 28, wherein the identified activity type
comprises an on-line reservation activity type that avoids the
carbon emitting activity of driving, and the quantity of
uncollected points includes uncollected points that represent an
amount of carbon that is saved by avoiding driving.
33. The medium of claim 28, comprising purchasing, by the user, a
service from a service provider using a subset of the updated total
quantity of collected points as payment.
34. The medium of claim 28, wherein the operations comprise:
determining that a user request has been received to acquire, from
a different user, an additional quantity of uncollected points that
have not yet been added to a total quantity of collected points
that is associated with the different user; in response to
determining that the user request has been received: adding the
additional quantity of uncollected points that are associated with
the different user to the updated total quantity of collected
points that is stored for the user, to generate a second updated
total quantity of collected points, subtracting the additional
quantity of uncollected points from a total quantity of uncollected
points that are associated with the different user; and storing
data that associates the second updated total quantity of collected
points with the user.
35. A system comprising: one or more computers; and one or more
computer memory devices storing one or more instructions that, when
executed by the one or more computers, perform one or more
operations comprising: receiving pre-generated behavior data that
is associated with use of a particular online service by a user,
wherein one or more carbon emitting activities are avoided through
the use of the particular on-line service by the user; identifying,
as an identified activity type, an activity type that is associated
with the particular on-line service; identifying a particular
algorithm that is pre-associated with the identified activity type
that is associated with the particular on-line service;
determining, based at least on the particular algorithm and the
pre-generated behavior data, a quantity of uncollected points that
represents an amount of carbon that is calculated for the one or
more carbon emitting activities that were avoided through the use
of the particular on-line service by the user; determining that a
user request has been received for the quantity of uncollected
points that represents an amount of carbon that is calculated for
the one or more carbon emitting activities that were avoided
through the use of the particular on-line service by the user to be
added to a total quantity of collected points that represents an
amount of carbon that is calculated for one or more carbon emitting
activities that were avoided through the use of multiple on-line
services by the user; in response to determining that the user
request has been received for the quantity of uncollected points to
be added to the total quantity of collected points, adding (i) the
quantity of uncollected points to (ii) the total quantity of
collected points; and storing data that associates the updated
total quantity of collected points with the user.
36. The system of claim 35, wherein: the pre-generated behavior
data that is associated with use of the particular on-line service
by the user is received by an acquisition module of an application,
from a module of the application that is associated with the
particular on-line service, the activity type and the particular
algorithm are identified by a determination module of the
application, the quantity of uncollected points is determined by a
calculation module of the application, the user request is
determined to have been received, and the quantity of uncollected
points is added to the total quantity of collected points, and the
data is stored, by a processing module of the application.
37. The system of claim 35, wherein the identified activity type
comprises an on-line ticket purchasing activity type that avoids
the carbon emitting activities of driving and printing paper
tickets or receipts, and the quantity of uncollected points
includes both (i) uncollected points that represent an amount of
carbon that is saved by avoiding driving, and (ii) uncollected
points that represent an amount of carbon that is saved by avoiding
printing paper tickets or receipts.
38. The system of claim 35, wherein the identified activity type
comprises an on-line bill payment activity type that avoids the
carbon-emitting activity printing paper bills, and the quantity of
uncollected points includes uncollected points that represent an
amount of carbon that is saved by avoiding printing bills.
39. The system of claim 35, wherein the identified activity type
comprises an on-line reservation activity type that avoids the
carbon emitting activity of driving, and the quantity of
uncollected points includes uncollected points that represent an
amount of carbon that is saved by avoiding driving.
40. The system of claim 35, wherein the operations comprise:
determining that a user request has been received to acquire, from
a different user, an additional quantity of uncollected points that
have not yet been added to a total quantity of collected points
that is associated with the different user; in response to
determining that the user request has been received: adding the
additional quantity of uncollected points that are associated with
the different user to the updated total quantity of collected
points that is stored for the user, to generate a second updated
total quantity of collected points, subtracting the additional
quantity of uncollected points from a total quantity of uncollected
points that are associated with the different user; and storing
data that associates the second updated total quantity of collected
points with the user.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 15/684,603, filed on Aug. 23, 2017, which claims priority to
Chinese Application No. 201610717756.7, filed on Aug. 24, 2016, the
entire contents of which are hereby incorporated by reference.
TECHNICAL FIELD
[0002] The present disclosure relates to computer-implemented
methods, software, and systems for calculating individual carbon
footprints.
BACKGROUND
[0003] Various human activities generate carbon emissions (such as,
greenhouse gases) that can have negative effects on the Earth's
environment. For example, driving a gasoline-powered car or
operating a thermal power station generates carbon emissions. To
control carbon emissions, it is important for an individual to be
aware of their measured carbon footprint based on daily
behaviors.
SUMMARY
[0004] The present disclosure describes methods and systems,
including computer-implemented methods, computer program products,
and computer systems for calculating individual carbon footprints,
and particularly for calculating how many carbon footprints can be
saved (that is, a carbon-saving quantity) from an individual
engaging in environment-friendly behaviors.
[0005] In an implementation, behavior data associated with a user
is obtained. The behavior data is generated when the user uses an
Internet service and includes a user identification and
identification information indicating the Internet service. At
least one predefined carbon-saving quantity quantization algorithm
is determined based on the identification information related to
the Internet service. A carbon-saving quantity associated with the
user is calculated based on the obtained behavior data and the
determined at least one predefined carbon-saving quantity
quantization algorithm. Based on the calculated carbon-saving
quantity associated with the user and the user identification, user
data is processed. The user data is related to the carbon-saving
quantity associated with the user.
[0006] The previously described implementation is implementable
using a computer-implemented method; a non-transitory,
computer-readable medium storing computer-readable instructions to
perform the computer-implemented method; and a computer-implemented
system comprising a computer memory interoperably coupled with a
hardware processor configured to perform the computer-implemented
method/instructions stored on the non-transitory, computer-readable
medium.
[0007] The subject matter described in this specification can be
implemented in particular implementations, so as to realize one or
more of the following advantages. First, the described approach can
be used to make individual people aware of their associated carbon
footprints, carbon-saving quantities, or both from their daily
behaviors. For example, fragmented behavior information associated
with a person (a user) within a particular period of time (for
example, a day, a month, or a year) can be aggregated. Based on the
aggregated behavior data and in combination with a corresponding
carbon-saving quantity quantization algorithm, a measurement of
carbon footprints reduced by the user (that is, a carbon-saving
quantity) can be calculated and provided to the user. Second, a
service provider can provide a particular service, such as a point
accumulation, an account upgrade, or other service, for the user
based on the carbon-saving quantity associated with the user. The
particular service can also provide incentives to the user to
encourage reduction of carbon footprints by, for example, adopting
more environmentally-friendly behaviors. Other advantages will be
apparent to those of ordinary skill in the art.
[0008] The details of one or more implementations of the subject
matter of this specification are set forth in the detailed
description, the claims, and the accompanying drawings. Other
features, aspects, and advantages of the subject matter will become
apparent from the detailed description, the claims, and the
accompanying drawings.
DESCRIPTION OF DRAWINGS
[0009] FIG. 1 is a flowchart illustrating an example method for
calculating individual carbon footprints, according to an
implementation of the present disclosure.
[0010] FIGS. 2A-2E are schematic diagrams illustrating methods for
generation of user behavior data in different scenarios, according
to an implementation of the present disclosure.
[0011] FIG. 3 is a block diagram illustrating a computing-based
architecture for calculating individual carbon footprints,
according to an implementation of the present disclosure.
[0012] FIGS. 4A-4C are illustrative screenshots related to point
accumulation, according to an implementation of the present
disclosure.
[0013] FIGS. 5A-5B are schematic diagrams of point acquisition
between users, according to an implementation of the present
disclosure.
[0014] FIG. 6 is a block diagram illustrating an example data
processing system for calculating individual carbon footprints,
according to an implementation of the present disclosure.
[0015] FIG. 7 is a block diagram illustrating an example computer
system used to provide computational functionalities associated
with described algorithms, methods, functions, processes, flows,
and procedures as described in the instant disclosure, according to
an implementation of the present disclosure.
[0016] Like reference numbers and designations in the various
drawings indicate like elements.
DETAILED DESCRIPTION
[0017] The following detailed description describes calculating
individual carbon footprints, particularly calculating a
carbon-saving quantity associated with a user based on collecting
and aggregating fragmented behavior data associated with the user
within a particular period of time, and is presented to enable any
person skilled in the art to make and use the disclosed subject
matter in the context of one or more particular implementations.
Various modifications, alterations, and permutations of the
disclosed implementations can be made and will be readily apparent
to those of ordinary skill in the art, and the general principles
defined may be applied to other implementations and applications,
without departing from the scope of the disclosure. In some
instances, details unnecessary to obtain an understanding of the
described subject matter may be omitted so as to not obscure one or
more described implementations with unnecessary detail and as such
details are within the skill of one of ordinary skill in the art.
The present disclosure is not intended to be limited to the
described or illustrated implementations, but to be accorded the
widest scope consistent with the described principles and
features.
[0018] Human activities can generate carbon emissions. To reduce
carbon emissions, it is important to make people aware of their
carbon footprints from their daily behaviors. In addition,
incentives can be provided to encourage enterprises or individuals
to take initiative to control their carbon emissions by adopting
environment-friendly behaviors. For enterprises, since enterprise
behaviors, in general, are relatively closely related to the goals
of the enterprise, each enterprise can calculate and control its
carbon footprints and carbon-saving quantities. However, for
individuals, as individual behaviors are often unrelated to each
other (that is, fragmented), carbon emissions can be generated from
many different unrelated human activities. As a result, it is
difficult for individual persons to calculate their associated
carbon footprints.
[0019] At a high-level, the described approach provides a mechanism
to automatically collect and aggregate behavior data associated
with a user within a period of time. Based on aggregated behavior
data and a particular carbon-saving quantity quantization
algorithm, carbon footprints associated with the user, a
carbon-saving quantity associated with the user, or a combination
of carbon footprints and a carbon-saving quantity associated with
the user can be calculated and provided to the user. The
carbon-saving quantity associated with the user can be further
processed by a service provider to provide a particular service,
such as point accumulation or account upgrade, for the user.
[0020] FIG. 1 is a flowchart illustrating an example method 100 for
calculating individual carbon footprints, according to an
implementation of the present disclosure. For clarity of
presentation, the description that follows generally describes
method 100 in the context of the other figures in this description.
However, it will be understood that method 100 may be performed,
for example, by any suitable system, environment, software, and
hardware, or a combination of systems, environments, software, and
hardware, as appropriate. In some implementations, various steps of
method 100 can be run in parallel, in combination, in loops, or in
any order.
[0021] At 105, behavior data associated with a user is acquired.
For example, the acquired behavior data can be generated when the
user uses an Internet service. In some implementations, the
behavior data can include a user identification (such as a user ID
or a user account) and identification information that indicates
the Internet service used when the behavior data is generated. The
Internet service can include, for example, at least one of an
Internet-based (or "online") electronic payment service, a
reservation service, a ticketing service, a payment service, a
health service, or other Internet service consistent with this
disclosure. In some implementations, the health service can be a
service associated with a mobile phone system or an application for
monitoring user movement behavior. In some implementations, the
health service can include, for example, at least one of a
step-counting service and a distance calculation service. In some
implementations, different Internet services can have different
identification information, which can include, for example, at
least one of a service type identification and a type
identification bit in an order number. As a result, a type of an
Internet service corresponding to the behavior data can be
determined based on the identification information in the behavior
data. In some implementations, behavior data from different
Internet services can be differentiated based on the identification
information included in the behavior data from the different
Internet services.
[0022] In some implementations, the acquired behavior data can
include fragmented behavior data. Each fragment of the behavior
data can include a user identification (such as a user ID or a user
account) and identification information indicating the Internet
service corresponding to the particular fragment of the behavior
data. To aggregate the fragmented behavior data, all the fragments
of the behavior data are toned to include identification related to
the same user in order to correlate the fragments of the behavior
data. In some implementations, a user can use different Internet
services through different applications or servers when generating
behavior data. For example, the user can use an online payment
service through a payment application on a mobile computing device
and use an online meal-ordering service through a meal-ordering
application. In some implementations, a user can use different
accounts when using the Internet services. To ensure that the
acquired behavior data is associated with the same user, the
different accounts (that is, user identifications) related to the
same user may be acquired. For example, the user can first input
information related to their different accounts to be stored. Then,
behavior data associated with an account can be acquired from a
corresponding application or server through, for example, an
account name of the user. In some implementations, data or
combinations of data identifying the user can be used to acquire
the behavior data. Note that the previously described method is not
limited to acquisition of different accounts related to a same
user, but also applicable to acquisition of different accounts
related to different users.
[0023] In some implementations, after the behavior data is
acquired, a field, representing a type of an Internet service, in
the behavior data can be determined based on a predefined rule.
Based on content in the field, the type of Internet service
corresponding to the behavior data can be determined. In some
implementations, types of Internet services provided by some
applications or servers are relatively fixed. For example, a
ticketing website server only provides a ticketing service. If
behavior data is acquired from such applications or servers, types
of Internet services corresponding to the behavior data can be
identified directly based on, for example, at least one of names,
domain names, Universal Resource Locators (URLs), and other
information associated with the applications or servers.
[0024] In some implementations, method 100 can be performed by an
application. In some implementations, method 100 can be performed
by a server. When performed by an application, the application may
be capable of providing various Internet services to the user. In
that case and in some implementations, the application can be
configured to generate behavior data for the user and to also
calculate carbon footprints for the user directly based on the
generated behavior data. In other words, by registering an account
in the application, behavior data generated by the user through use
of various Internet services in the application can be associated
with the user's registered account. As a result, the application
only needs to acquire behavior data related to the account to
calculate, for example, carbon footprints for the user.
[0025] However, if the application cannot itself provide an
Internet service to the user, the application can initiate a
request for acquiring behavior data from a third-party application
or a third-party server capable of providing an Internet service.
Then, the application may receive the behavior data from the
third-party application, or synchronize data with the third-party
application to receive the behavior data generated by the
third-party application. In that case, the user may input, in the
application, different third-party accounts associated with the
user, as well as third-party applications or third-party servers
corresponding to the different third-party accounts. The
application can then associate the third-party accounts with an
account registered by the user in the application. For example, to
acquire behavior data from a third-party application, the
application can determine, based on a third-party account, a
third-party application corresponding to the third-party account,
and send an acquisition request that carries the third-party
account to the third-party application. In response, the
third-party application can find behavior data related to the
third-party account and return the behavior data to the requesting
application. As a result, the application can acquire the user
behavior data.
[0026] In some implementations, when a third-party application is
involved in calculating individual carbon footprints, the
application that performs method 100 can register, in advance, with
the third-party application. By registering with the third-party
application, the application can receive behavior data from the
third-party application. In some implementations, when a
third-party server is involved in calculating individual carbon
footprints, the application that performs method 100 can acquire
behavior data from the third-party server through a data transfer
protocol that both the application and the third-party server agree
upon.
[0027] In some implementations, behavior data is generated, by a
third-party application or a third-party server, in a common data
format. For example, the behavior data can be generated in a
two-dimensional table format, a HyperText Markup Language (HTML)
format, or an Extensible Markup Language (XML) format. After
acquiring the behavior data, the application or server that
performs method 100 can read, analyze, or read and analyze the
behavior data based on the corresponding data format. In some
implementations, for a particular data format, the application that
performs method 100 and a third-party application can agree upon a
data transmission format (for example, a JavaScript Object Notation
(JSON) format). In addition, different methods for analyzing
different data formats can be added, in advance, to an Application
Programming Interface (API) of the application. In some
implementations, the acquired behavior data can be stored locally
or remotely. From 105, method 100 proceeds to 110.
[0028] At 110, at least one preset carbon-saving quantity
quantization algorithm is determined according to the
identification information of the Internet service in the acquired
behavior data. In some implementations, relationships between
Internet services and carbon-saving quantity quantization
algorithms are pre-established. Based on a particular Internet
service and the pre-established relationships, at least one
carbon-saving quantity quantization algorithm can be determined.
The at least one carbon-saving quantity quantization algorithm
includes, for example, a quantization formula, a quantization
model, or a combination of a quantization formula and a
quantization model.
[0029] In some implementations, behavior data including different
identification information from different Internet services can be
associated with different carbon-saving quantity quantization
algorithms. For example, an electronic payment service can save
paper products, and a walking trip can save carbon emissions
because a vehicle was not driven (such as a gasoline-powered car).
In some implementations, an Internet service can be associated with
multiple carbon-saving quantity quantization algorithms (that is,
multiple carbon-saving quantity quantization algorithms can be used
with one Internet service). For example, when a user uses an online
ticketing service, the user can buy a ticket without leaving, for
example, the user's home. As a result, carbon emissions caused by a
trip to a ticketing site by driving a vehicle can be saved. In
addition, buying tickets online can avoid printing paper products,
such as tickets, receipts, and reservation lists, during online
payment. As a result, carbon emissions caused by the used paper
products can be saved. To calculate a carbon-saving quantity
associated with the user by using the online ticketing service,
both a carbon-saving quantity quantization algorithm associated
with trips using a vehicle and a carbon-saving quantity
quantization algorithm associated with the use of paper products
are used.
[0030] In some implementations, the carbon-saving quantity
quantization algorithms can include a first preset algorithm and a
second preset algorithm. For example, the first preset algorithm
can be a carbon-saving quantity quantization algorithm associated
with the use of paper products (such as, a carbon-saving quantity
quantization algorithm for savings related to avoidance of printing
paper bills). The second preset algorithm can be a carbon-saving
quantity algorithm associated with use of a vehicle (such as, a
carbon-saving quantity quantization algorithm for savings related
to walking as opposed to driving).
[0031] For the first preset algorithm, a carbon-saving quantity can
be calculated based on a carbon emission corresponding to a paper
product. For example, the following formula can be used:
ER y = .SIGMA. i .function. ( F i .times. AD i , y ) .times. EF y
.times. 10 - 6 .times. ( 1 ) , ( 1 ) ##EQU00001##
where ER.sub.y is a carbon-saving quantity (unit: tons of CO2) of a
paper bill saved by each online payment in the y.sup.th year. In
other words, ER.sub.y represents, in essence, a carbon footprint
corresponding to a paper bill printed for each offline payment.
Since printing of a paper bill can be avoided if the user uses
online payment, the value of ER.sub.y can be used as a
carbon-saving quantity of a paper bill saved by each online
payment. i is a merchant type of offline payment. F.sub.i is a
proportion (that is, percentage) of i-type merchants using a point
of service (POS) machine for payment. AD.sub.i, y is the number of
times (unit: times) that the user makes payment offline at the
i-type merchants in the y.sup.th year. EF.sub.y is a baseline
emission factor (unit: g CO2/time) of offline payment in the
y.sup.th year. In some implementations, EF.sub.y can be determined
based on emission intensities of bill paper manufacturers in
different regions. For example, Table 1 shows emission intensities
of bill paper manufacturers in several provinces in China.
TABLE-US-00001 TABLE 1 Other Yunnan Zhejiang Shanxi provinces
Emission intensities (tons 1.9296 2.0072 1.8834 1.4622 of CO2/ton
of paper)
[0032] In some implementations, since the value of a carbon-saving
quantity corresponding to saving of a paper bill in each online
payment service (that is, ER) is too small, the carbon-saving
quantity is calculated on an annual basis (that is, ER.sub.y as in
Equation (1)). In some implementations, a threshold number can be
predefined to avoid calculating a carbon-saving quantity each time
the online payment service is being used. For example, after using
the online payment service by more than the threshold number, a
total carbon-saving quantity for using the online payment service
the threshold number of times can be calculated.
[0033] For the second preset algorithm, a carbon-saving quantity
can be calculated based on a carbon emission corresponding to a
trip by driving a vehicle to a service location (for example, a
bank, a shop, a restaurant). For example, the following formula can
be used:
P = L .times. W ; , ( 2 ) ##EQU00002##
where L is a geographical distance (unit: miles) between a user
location when using an online service and a nearest service
location, W is a mean value of carbon footprints generated by
driving a vehicle for a mile, and P is the carbon footprints
generated by driving the vehicle for the distance L. From 110,
method 100 proceeds to 115.
[0034] At 115, a carbon-saving quantity associated with the user is
calculated according to the behavior data and the determined at
least one preset carbon-saving quantity quantization algorithm. For
the first preset algorithm, production of paper products can be
reduced each time the user uses an online Internet service. As a
result, the carbon-saving quantity is related to the number of
times that the user uses the online Internet service. In addition,
since carbon emission standards are different in different regions,
the carbon-saving quantity is also related to the user's
geographical location. In some implementations, when the first
predefined algorithm is used to calculate a user carbon-saving
quantity, at least a number of times that the user uses an Internet
service and a user's geographical location when the user uses the
Internet service are first determined. Then, the carbon-saving
quantity is calculated based on the determined number of times that
the user uses the Internet service, the determined geographical
position of the user when the user uses the Internet service, and
the first predefined algorithm.
[0035] For the second preset algorithm, the carbon-saving quantity
associated with the user is related to, for example, a number of
walking steps or a walking distance associated with the user. In
some implementations, when the second predefined algorithm is used
to calculate a user carbon-saving quantity, at least a number of
walking steps or a walking distance associated with the user is
determined. Then, the carbon-saving quantity is calculated based on
the determined number of walking steps or walking distance, and the
second predefined algorithm.
[0036] In some implementations, when the Internet services and the
carbon-saving quantity quantization algorithms are in a one-to-one
relationship, a single carbon-saving quantity quantization
algorithm can be used to calculate a user carbon-saving quantity by
using a corresponding Internet service.
[0037] In some implementations, when the Internet services and the
carbon-saving quantity quantization algorithms are in a one-to-many
relationship, multiple carbon-saving quantity quantization
algorithms, corresponding to a particular Internet service, can be
used to calculate a user carbon-saving quantity by using the
particular Internet service.
[0038] In some implementations, the acquired behavior data may
include redundant or irrelevant data, such as data not required to
calculate individual carbon footprints. For example, acquired
behavior data for a user using an online ticketing service can
include data related to the amount of money the user paid. In some
implementations, the acquired behavior data may not be used
directly. For example, when a carbon-saving quantity is calculated
according to behavior data associated with a user using an online
ticketing service, the calculation may require a number of times
the user uses the online ticketing service. In some
implementations, the behavior data is processed to determine an
exact number of times the user uses the online ticketing service.
In other implementations, an approximate number of times the user
uses the online ticketing service can be used. As a result, before
calculating a carbon-saving quantity, data collating operations,
such as statistical collection, screening, and removal, can be
performed on the acquired behavior data. In some implementations,
an application or server that performs method 100 may perform the
data collating operations on the behavior data. In some
implementations, an application or server that performs method 100
and a behavior data provider can agree upon behavior data required
for calculation. As a result, the behavior data provider can
perform the data collating operations on behavior data associated
with a user before providing the processed behavior data to the
application or server.
[0039] In some implementations, a quantized value, calculated based
on the behavior data and the carbon-saving quantity quantization
algorithm, can represent a carbon footprint reduced by a user (that
is, a carbon-saving quantity associated with the user). In some
implementations, a carbon-saving quantity can be calculated on a
predefined cycle period. In some implementations, a carbon-saving
quantity can be calculated based on the number of times that the
user uses an Internet service. From 115, method 100 proceeds to
120.
[0040] At 120, specific user data is processed according to the
calculated carbon-saving quantity and the user identification. The
specific user data is related to a carbon-saving quantity. In some
implementations, after the carbon-saving quantity associated with
the user is calculated, data processing operations, such as
statistical collection and analysis, can be performed on the
carbon-saving quantity within a period of time (for example, a day,
a month, or a year). In some implementations, the calculated
carbon-saving quantity can be converted into points based on, for
example, a predefined conversion rule. The newly converted points
can be added to total points associated with the user to obtain an
updated total points value. As the value of the total points
increase, the carbon-saving quantity associated with the user
increases. Accordingly, a service provider can provide different
services to the user based on the value of the total points. For
example, virtual goods corresponding to the total points can be
assigned to the user. In some implementations, the virtual goods
have different display states corresponding to differing total
point values.
[0041] In some implementations, user identification can include a
user account. The specific user data can include data, such as
carbon-saving points, a carbon-saving level, a carbon-saving badge,
and carbon-saving related virtual goods, in the user account. After
120, method 100 stops.
[0042] FIGS. 2A-2E are schematic diagrams of methods 200, 210, 220,
230, and 240 for generation of user behavior data in different
scenarios, according to an implementation of the present
disclosure. Methods 200, 210, 220, 230, and 240 are presented as
detailed views of the operations of method 100 described in FIG. 1
in different scenarios. For clarity of presentation, the
description that follows generally describes methods 200, 210, 220,
230, and 240 in the context of the other figures in this
description. However, it will be understood that methods 200, 210,
220, 230, and 240 may be performed, for example, by any suitable
system, environment, software, and hardware, or a combination of
systems, environments, software, and hardware, as appropriate. In
some implementations, various steps of methods 200, 210, 220, 230,
and 240 can be run in parallel, in combination, in loops, or in any
order.
[0043] Scenario 1: a user uses an online ticketing service.
[0044] In some implementations, the online ticketing service can
include at least one of online booking, purchasing, and refunding
services for train tickets, plane tickets, ship tickets, movie
tickets, admission tickets, and other tickets consistent with this
disclosure. Compared to the traditional ticketing service (that is,
a user goes to a physical ticketing site to obtain a ticket), the
online ticketing service can save the user a trip to the physical
ticketing site. If the trip is taken to the physical ticketing
site, for example, by driving a vehicle, data related to carbon
emissions generated by the trip can be saved. In addition, by using
the online ticketing service, paper products (for example, printed
paper statements or receipts) used during ticket purchase or refund
can be reduced or eliminated.
[0045] After the user uses the online ticketing service, a service
provider that provides the online ticketing service (for example, a
ticketing website) can generate online ticketing data based on the
user's online ticketing behavior. The online ticketing data can be
used as behavior data for the user in using the online ticketing
service. A carbon-saving quantity associated with using the online
ticketing service can be calculated based on the behavior data.
[0046] The calculation of a carbon-saving quantity can be performed
by an application client having a carbon-saving quantity computing
function (hereinafter referred to as a computing application), or a
server having a carbon-saving quantity computing function. As an
example, FIGS. 2A-2E are described with the computing application
performing the carbon-saving quantity calculation. In general, the
online ticketing service is provided by a ticketing website. The
user can use the online ticketing service through an application
corresponding to the ticketing website (hereinafter referred to as
a ticketing application). Behavior data generated while the user is
using the online ticketing service can be generated by a server of
the ticketing website (hereinafter referred to as a ticketing
server).
[0047] FIG. 2A shows an example method 200 of acquiring and
calculating a user carbon-saving quantity purchasing a ticket
online. In general, when a user purchases a ticket online, the user
sends a ticket purchase request to a ticketing server through a
corresponding ticketing application. The ticket purchase request
can include user information (for example, user's ID card number,
name, a ticketing account registered in the ticketing application)
and ticket purchase information (for example, type, time, place of
a ticket to be purchased). After receiving the ticket purchase
request, the ticketing server can issue a ticket according to the
online ticket purchase request, generate ticketing data associated
with the user, and record the ticketing data as behavior data.
[0048] At 201, the computing application sends an acquisition
request, including user information to the ticketing server, to
acquire ticketing data associated with the user. In some
implementations, the acquisition request can include time
information indicating ticketing data associated with the user
within a predefined cycle period (for example, a day). In some
implementations, the acquisition request can include an account
registered by the user in the computing application (hereinafter
referred to as a computing account) and a ticketing account
associated with the user to the ticketing server. The ticketing
server, then, can dynamically acquire, according to the ticketing
account, ticketing data related to the ticketing account, and
actively push, according to the computing account, the ticketing
data related to the ticketing account to the computing application.
In some implementations, if the computing application itself has an
online ticketing service, and the user uses the online ticketing
service provided by the computing application, the computing
application can acquire ticketing data generated by the computing
application. From 201, method 200 proceeds to 202.
[0049] At 202, the ticketing server receives the acquisition
request, determines ticketing data corresponding to the user
information included in the acquisition request, and sends back the
determined ticketing data to the computing application. The
ticketing data includes at least a user ID, and identification
information that reflects a type of the ticketing service. In some
implementations, if the acquisition request includes time
information, the ticketing server can acquire, according to the
time information, ticketing data associated with the user matching
the time information. In some implementations, the ticketing server
can perform data collating operations on the ticketing data before
sending the collated ticketing data to the computing application.
For example, the ticketing data stored by the ticketing server may
include the amount of money for a purchased ticket, an origin of
the purchased ticket, and a destination of the purchased ticket.
The ticketing server can remove the amount of money for the
purchased ticket, the origin of the purchased ticket, and the
destination of the purchased ticket, from the ticketing data and
send the processed ticketing data to the computing application.
From 202, method 200 proceeds to 203.
[0050] At 203, after acquiring the ticketing data from the
ticketing server, the computing application determines, according
to the user ID included in the ticketing data, that the ticketing
data is associated with a user account. In addition, the computing
application determines, according to the identification information
included in the ticketing data, at least one carbon-saving quantity
quantization algorithm for calculating a carbon-saving quantity of
the ticketing data. The at least one carbon-saving quantity
quantization algorithm can be a carbon-saving quantity quantization
algorithm specific to reduction of trips by driving a vehicle, a
carbon-saving quantity quantization algorithm specific to savings
related to the use of paper products, or a combination of a
carbon-saving quantity quantization algorithm specific to reduction
of trips by driving a vehicle and a carbon-saving quantity
quantization algorithm specific to savings related to the use of
paper products. From 203, method 200 proceeds to 204.
[0051] At 204, a carbon-saving quantity associated with the user
using the online ticketing service is calculated according to the
determined carbon-saving quantity quantization algorithm and the
acquired ticketing data. In addition, specific user data can be
processed according to the calculated carbon-saving quantity
associated with the user. In some implementations, the ticketing
application includes a locating function capable of determining the
user's location information (for example, by using global
positioning system (GPS) or WIFI/cellular-triangulation
information) when the user sends an online ticketing instruction.
The computing application can determine, according to a ticketing
order number in the acquired ticketing data, the number of times
that the user uses the online ticketing service. The ticketing
order number uniquely identifies, for example, one online ticketing
service. In addition, the computing application can acquire,
through the ticketing application, user location information when
the user uses the online ticketing service, and determine EF.sub.y
in Equation (1) according to a geographic region corresponding to
the user location information. Accordingly, a carbon-saving
quantity specific to savings with respect to use of a printed paper
bill each time the user uses the online ticketing service can be
calculated. If the user uses the online ticketing service n times
in a same geographic region, corresponding to the same EF.sub.y, a
carbon-saving quantity associated with the user specific to savings
related to using printed paper bills can be calculated as
n.times.ER.sub.y. If the user uses the online ticketing service
multiple times in different geographic regions, corresponding to
different EF.sub.y, a carbon-saving quantity associated with the
user specific to savings related to using printed paper bills is an
accumulation of carbon-saving quantities associated with the user
in the different geographic regions.
[0052] In some implementations, the computing application can
determine, according to the user location information when the user
uses the online ticketing service, a ticketing site location (for
example, a railway station) closest to the user location. The
computing application, then, calculates a distance L between the
user location and the ticketing site location, and uses Equation
(2) to calculate a carbon-saving quantity specific to avoidance of
a trip by driving a vehicle for the distance L. In some
implementations, the calculated carbon-saving quantity associated
with the user can include both a carbon-saving quantity specific to
avoidance of a trip by driving a vehicle, and a carbon-saving
quantity specific to savings related to using printed paper
bills.
[0053] In some implementations, the calculated carbon-saving
quantity associated with the user using the online ticketing
service can be converted into points. After the user logs into the
computing application, the points, indicating reduced carbon
footprints by using an online ticketing service, can be presented
to the user. After 204, method 200 stops.
[0054] Scenario 2: a user uses an online payment service.
[0055] In some implementations, the online payment service can
include at least one of a face-to-face online payment service and
an online transfer service. Compared to the traditional payment
service, the online payment service can reduce consumption of paper
products (for example, printed paper bills), and thus can reduce
carbon footprints.
[0056] After the user uses the online payment service, a service
provider that provides the online payment service can generate
online payment data based on the user's online payment behavior.
The online payment data ca be used as behavior data for the user in
using the online payment service. A carbon-saving quantity
associated with using the online payment service can be calculated
based on the behavior data.
[0057] Similar to scenario 1, the calculation of a carbon-saving
quantity can be performed by a computing application or a server
having a carbon-saving quantity computing function. In some
implementations, a service provider capable of providing the online
payment service includes a commodity website, a payment platform,
and a bank. By taking the payment platform as an example, the user
can use the online payment service through an application
corresponding to the payment platform (hereinafter referred to as a
payment application). Behavior data generated while the user is
using the online payment service can be generated by a server of
the payment platform (hereinafter referred to as a payment
server).
[0058] FIG. 2B shows an example method 210 of acquiring and
calculating a user carbon-saving quantity making a payment online.
In general, when a user makes an online payment, the user sends a
payment request to a payment server through a corresponding payment
application. The payment request can include user information (for
example, a payment account registered by the user on the payment
platform), target user information (for example, a target account
registered by the target user on the payment platform), and payment
information (for example, the amount of payment). After receiving
the payment request, the payment server can acquire, according to
the received payment request, a fund matching the amount of payment
from the payment account associated with the user, assign the fund
to the target account of the target user, generate payment data
associated with the user, and record the payment data as behavior
data.
[0059] At 211, the computing application sends an acquisition
request, including user information to the payment server, to
acquire payment data associated with the user. In some
implementations, when a user pays a target user online through the
payment platform, the user and the target user each has a
corresponding account registered on the payment platform. In some
implementations, the acquisition request can include a payment
account registered by the user on the payment platform to acquire
payment data related to the payment account. In some
implementations, the acquisition request can include time
information indicating payment data associated with the user within
a predefined cycle period (for example, a day). In some
implementations, the computing application can send, in advance,
both a computing account registered by the user in the computing
application and the payment account associated with the user to the
payment server. The payment server, then, can dynamically acquire,
according to the payment account, payment data related to the
payment account, and actively push, according to the computing
account, the payment data related to the payment account to the
computing application. In some implementations, if the computing
application itself has an online payment service and the user uses
the online payment service provided by the computing application,
the computing application can acquire payment data generated by the
computing application. From 211, method 210 proceeds to 212.
[0060] At 212, the payment server receives the acquisition request,
determines payment data corresponding to the user information
included in the acquisition request, and sends back the determined
payment data to the computing application. The payment data
includes at least a user ID, and identification information that
reflects a type of the payment service. In some implementations, if
the acquisition request includes time information, the payment
server can acquire, according to the time information, payment data
associated with the user matching the time information. In some
implementations, the payment server can perform data collating
operations on the payment data before sending the collated payment
data to the computing application. For example, the payment data
stored by the payment server may include the amount of payment, and
the payment time. The payment server can remove the amount of
payment and the payment time from the payment data and send the
processed payment data to the computing application. From 212,
method 210 proceeds to 213.
[0061] At 213, after acquiring the payment data from the payment
server, the computing application determines, according to the user
ID included in the payment data, that the payment data is
associated with a user account. In addition, the computing
application determines, according to the identification information
included in the payment data, a carbon-saving quantity quantization
algorithm for calculating a carbon-saving quantity of the payment
data. The carbon-saving quantity quantization algorithm can be a
carbon-saving quantity quantization algorithm specific to savings
related to the use of paper products. From 213, method 210 proceeds
to 214.
[0062] At 214, a carbon-saving quantity associated with the user
using the online payment service is calculated according to the
determined carbon-saving quantity quantization algorithm and the
acquired payment data. In addition, specific user data can be
processed according to the calculated carbon-saving quantity
associated with the user. In some implementations, the payment
application includes a locating function capable of determining the
user's location information (for example, by using GPS or
WIFI/cellular-triangulation information) when the user sends an
online payment instruction. The computing application can
determine, according to a payment order number in the acquired
payment data, the number of times that the user uses the online
payment service. The payment order number uniquely identifies one
online payment service. In addition, the computing application can
acquire, through the payment application, user location information
when the user uses the online payment service, and determine
EF.sub.y in Equation (1) according to a geographic region
corresponding to the user location information. Accordingly, a
carbon-saving quantity specific to savings with respect to use of a
printed paper bill each time the user uses the online payment
service can be calculated. If the user uses the online payment
service n times in a same geographic region, corresponding to the
same EF.sub.y, a carbon-saving quantity associated with the user
specific to savings related to using printed paper bills can be
calculated as n.times.ER.sub.y. If the user uses the online payment
service multiple times in different geographic regions,
corresponding to different EF.sub.y, a carbon-saving quantity
associated with the user specific to savings related to using
printed paper bills is an accumulation of carbon-saving quantities
associated with the user in the different geographic regions.
[0063] In some implementations, the calculated carbon-saving
quantity associated with the user using the online payment service
can be converted into points. After the user logs into the
computing application, the points, indicating reduced carbon
footprints by using an online payment service, can be presented to
the user. After 214, method 211 stops.
[0064] Scenario 3: a user uses an online reservation service.
[0065] In some implementations, the online reservation service can
include at least one of online restaurant reservation, hotel
reservation, venue booking, and hospital registration services.
Compared to the traditional reservation service (that is, a user
goes to a physical service site to make a reservation), the online
reservation service can save the user a trip to the physical
service site. If the trip is taken to the physical service site,
for example, by driving a vehicle, data related to carbon emissions
generated by the trip can be saved.
[0066] After the user uses the online reservation service, a
service provider that provides the online reservation service (for
example, a hospital website) can generate online reservation data
based on the user's online reservation behavior. The online
reservation data can be used as behavior data for the user in using
the online reservation service. A carbon-saving quantity associated
with using the online reservation service can be calculated based
on the behavior data.
[0067] Similar to scenario 1, the calculation of a carbon-saving
quantity can be performed by a computing application or a server
having a carbon-saving quantity computing function. In some
implementations, a service provider capable of providing the online
reservation service includes a reservation platform, a hospital, a
hotel, and a restaurant. By taking the reservation platform as an
example, the user can use the online reservation service through an
application of the reservation platform (hereinafter referred to as
a reservation application). Behavior data generated while the user
is using the online reservation service can be generated by a
server of the reservation platform (hereinafter referred to as a
reservation server).
[0068] FIG. 2C shows an example method 220 of acquiring and
calculating a user carbon-saving quantity making a reservation
online. In general, when a user makes an online reservation, the
user sends a reservation request to a reservation server through a
corresponding reservation application. The reservation request can
include user information (for example, medical insurance
information of the user, user name, ID card number, a reservation
account registered by the user in the reservation application),
registration type information (for example, a specialist number, an
ordinary doctor number), and hospital information selected by the
user (for example, hospital level, hospital name). After receiving
the reservation request, the reservation server can register,
according to the received reservation request, a corresponding
hospital. After the registration succeeds, the reservation server
sends back an electronic registration form to the reservation
application, generates reservation data associated with the user,
and records the reservation data as behavior data.
[0069] At 221, the computing application sends an acquisition
request, including user information to the reservation server, to
acquire reservation data associated with the user. In some
implementations, the acquisition request can include a reservation
account registered by the user on the reservation platform to
acquire reservation data related to the reservation account. In
some implementations, the acquisition request can include medical
insurance information of the user, user name, ID card number, and a
reservation account registered by the user in the reservation
application. In some implementations, the acquisition request can
include time information indicating reservation data associated
with the user within a predefined cycle period (for example, a
day). In some implementations, the computing application can send
both a computing account registered by the user in the computing
application and the reservation account of the user to the
reservation server. The reservation server, then, can dynamically
acquire, according to the reservation account, reservation data
related to the reservation account, and actively push, according to
the computing account, the reservation data related to the
reservation account to the computing application. In some
implementations, if the computing application itself has an online
reservation service and the user uses the online reservation
service provided by the computing application, the computing
application can acquire reservation data generated by the computing
application. From 221, method 220 proceeds to 222.
[0070] At 222, the reservation server receives the acquisition
request, determines reservation data corresponding to the user
information included in the acquisition request, and sends back the
determined reservation data to the computing application. The
reservation data includes at least a user ID, and identification
information that reflects a type of the reservation service. In
some implementations, the reservation server can perform data
collating operations on the reservation data before sending the
collated reservation data to the computing application. For
example, the reservation data stored by the reservation server may
include a reservation type, and a date of hospital visit. The
reservation server can remove the reservation type and the date of
hospital visit from the reservation data, and send the processed
reservation data to the computing application. From 222, method 220
proceeds to 223.
[0071] At 223, after acquiring the reservation data from the
reservation server, the computing application determines, according
to the user ID included in the reservation data, that the
reservation data is associated with a user account. In addition,
the computing application determines, according to the
identification information included in the reservation data, a
carbon-saving quantity quantization algorithm for calculating a
carbon-saving quantity of the reservation data. The carbon-saving
quantity quantization algorithm can be a carbon-saving quantity
quantization algorithm specific to reduction of trips by driving a
vehicle. From 223, method 220 proceeds to 224.
[0072] At 224, a carbon-saving quantity associated with the user
using the online reservation service is calculated according to the
determined carbon-saving quantity quantization algorithm and the
acquired reservation data. In addition, specific user data can be
processed according to the calculated carbon-saving quantity
associated with the user. In some implementations, the reservation
application includes a locating function capable of determining the
user's location information (for example, by using GPS or
WIFI/cellular-triangulation information) when the user sends an
online reservation instruction. The reservation data acquired by
the computing application can include the location of the user when
the user uses the online reservation service. Based on a hospital
address included in the reservation data, the computing application
can determine the location of the hospital, calculate a distance L
between the user location and the hospital location, and uses
Equation (2) to calculate a carbon-saving quantity specific to
avoidance of a trip by driving a vehicle for the distance L.
[0073] In some implementations, the calculated carbon-saving
quantity associated with the user using the online reservation
service can be converted into points. After the user logs into the
computing application, the points, indicating reduced carbon
footprints by using an online reservation service, can be presented
to the user. After 224, method 220 stops.
[0074] Scenario 4: a user uses an online bill payment service.
[0075] In some implementations, the online bill payment service can
include at least one of paying water fees, electricity fees,
natural gas fees, and traffic fines online. Compared to the
traditional bill payment service, the online bill payment service
can save the user a trip to the physical bill payment site. If the
trip is taken to the physical bill payment site, for example, by
driving a vehicle, data related to carbon emissions generated by
the trip can be saved.
[0076] After the user uses the online bill payment service, a
service provider that provides the online bill payment service can
generate online bill payment data based on the user's online bill
payment behavior. The online bill payment data can be used as
behavior data for the use in using the online bill payment service.
A carbon-saving quantity associated with the user using the online
bill payment service can be calculated based on the behavior
data.
[0077] Similar to scenario 1, the calculation of a carbon-saving
quantity can be performed by a computing application or a server
having a carbon-saving quantity computing function. In some
implementations, a service provider capable of providing the online
bill payment service includes an online bill payment platform, a
bill payment website, and a bank. By taking the bill payment
platform as an example, the user can use the online bill payment
service through an application corresponding to the bill payment
platform (hereinafter referred to as a bill payment application).
Behavior data generated while the user is using the online bill
payment service can be generated by a server of the bill payment
platform (hereinafter referred to as a bill payment server).
[0078] FIG. 2D shows an example method 230 of acquiring and
calculating a user carbon-saving quantity making a bill payment
online. In general, when a user makes an online bill payment, the
user sends a bill payment request to a bill payment server through
a corresponding bill payment application. The bill payment request
can include user information (for example, driver's license number,
ID card number, a penalty ticket number of the user, a bill payment
account registered by the user on the bill payment platform). After
receiving the bill payment request, the bill payment server can
deduct, according to the received bill payment request, a
corresponding amount of fund from the account of the user, and pay
the bill to the corresponding bill payment website. After the bill
payment succeeds, the bill payment server sends back an electronic
payment voucher to the bill payment application, generates bill
payment data associated with the user, and records the bill payment
data as behavior data.
[0079] At 231, the computing application sends an acquisition
request, including user information to the bill payment server, to
acquire bill payment data associated with the user. In some
implementations, when a user makes an online bill payment, the user
first registers a corresponding account on the bill payment
platform. The account needs to have sufficient amount of money to
make the online bill payment successful. In some implementations,
the acquisition request can include at least one of a driver's
license number, ID card number, a penalty ticket number of the
user, and a bill payment account registered by the user on the bill
payment platform. In some implementations, the acquisition request
can include time information indicating bill payment data
associated with the user within a predefined cycle period (for
example, a day). In some implementations, the computing application
can send both a computing account registered by the user in the
computing application and the bill payment account associated with
the user to the bill payment server. The bill payment server, then,
can dynamically acquire, according to the bill payment account,
bill payment data related to the bill payment account, and actively
push, according to the computing account, the bill payment data
related to the bill payment account to the computing application.
In some implementations, if the computing application itself has an
online bill payment service and the user uses the online bill
payment service provided by the computing application, the
computing application can acquire bill payment data generated by
the computing application. From 231, method 230 proceeds to
232.
[0080] At 232, the bill payment server receives the acquisition
request, determines bill payment data corresponding to the user
information included in the acquisition request, and sends back the
determined bill payment data to the computing application. The bill
payment data includes at least a user ID, and identification
information that reflects a type of the bill payment service. In
some implementations, the bill payment server can perform data
collating operations on the bill payment data before sending the
collated bill payment data to the computing application. From 232,
method 230 proceeds to 233.
[0081] At 233, after acquiring the bill payment data from the bill
payment server, the computing application determines, according to
the user ID included in the bill payment data, that the bill
payment data is associated with a user account. In addition, the
computing application determines, according to the identification
information included in the bill payment data, at least one
carbon-saving quantity quantization algorithm for calculating a
carbon-saving quantity of the bill payment data. The at least one
carbon-saving quantity quantization algorithm can be a
carbon-saving quantity quantization algorithm specific to reduction
of trips by driving a vehicle, a carbon-saving quantity
quantization algorithm specific to savings related to the use of
paper products, or a combination of a carbon-saving quantity
quantization algorithm specific to reduction of trips by driving a
vehicle and a carbon-saving quantity quantization algorithm
specific to savings related to the use of paper products. From 233,
method 230 proceeds to 234.
[0082] At 234, a carbon-saving quantity associated with the user
using the online bill payment service is calculated according to
the determined carbon-saving quantity quantization algorithm and
the acquired bill payment data. In addition, specific user data can
be processed according to the calculated carbon-saving quantity
associated with the user. In some implementations, the bill payment
application includes a locating function capable of determining the
user's location information (for example, by using GPS or
WIFI/cellular-triangulation information) when the user sends an
online bill payment instruction. The computing application can
determine, according to a bill payment order number in the acquired
bill payment data, the number of times that the user uses the
online bill payment service. The bill payment order number uniquely
identifies one online bill payment service. In addition, the
computing application can acquire, through the bill payment
application, user location information when the user uses the
online bill payment service, and determine EF.sub.y in Equation (1)
according to a geographic region corresponding to the user location
information. Accordingly, a carbon-saving quantity specific to
savings with respect to use of a printed paper bill each time the
user uses the online bill payment service can be calculated. If the
user uses the online bill payment service n times in a same
geographic region, corresponding to the same EF.sub.y, a
carbon-saving quantity associated with the user specific to savings
related to using printed paper bills can be calculated as
n.times.ER.sub.y. If the user uses the online bill payment service
multiple times in different geographic regions, corresponding to
different EF.sub.y, a carbon-saving quantity associated with the
user specific to savings related to using printed paper bills is an
accumulation of carbon-saving quantities associated with the user
in the different geographic regions.
[0083] In some implementations, the computing application can
determine, according to the user location information when the user
uses the online bill payment service, a bill payment site location
(for example, a bank) closest to the user location. The computing
application, then, calculates a distance L between the user
location and the bill payment site location, and uses Equation (2)
to calculate a carbon-saving quantity specific to avoidance of a
trip by driving a vehicle for the distance L. In some
implementations, the calculated carbon-saving quantity associated
with the user can include both a carbon-saving quantity specific to
avoidance of a trip by driving a vehicle, and a carbon-saving
quantity specific to savings related to using printed paper
bills.
[0084] In some implementations, the calculated carbon-saving
quantity associated with the user using the online bill payment
service can be converted into points. After the user logs into the
computing application, the points, indicating reduced carbon
footprints by using an online bill payment service, can be
presented to the user. After 234, method 230 stops.
[0085] Scenario 5: a user goes out on foot, and walking data is
monitored by a health service.
[0086] In some implementations, walking can reduce carbon
footprints of a user. The user can walk to a physical service site.
For example, the user can walk to a hospital to register, walk to a
ticketing site to purchase a ticket, and walk to a bill payment
site to pay related fees.
[0087] Walking data can be produced by a health service application
(for example, a walking application) having a walking data
collection function. The walking data can be used as behavior data
for walking. A carbon-saving quantity associated with the user can
be calculated based on the behavior data. In some implementations,
the walking data can include at least one of the number of steps,
location information during walking, and a walking distance. In
some implementations, the walking data can include user information
(for example, an account registered by the user in the walking
application).
[0088] Similar to scenario 1, the calculation of a carbon-saving
quantity can be performed by a computing application or a server
having a carbon-saving quantity computing function. In some
implementations, the walking data can be obtained by a walking
application through a corresponding collection algorithm, a model,
or a sensing device (for example, a smart bracelet, a smart
watch).
[0089] FIG. 2E shows an example method 240 of acquiring and
calculating a user carbon-saving quantity by walking. At 241, the
computing application sends an acquisition request, including user
information to the walking application, to acquire walking data
associated with the user. In some implementations, the acquisition
request can include an account registered by the user on the
walking application. In some implementations, the walking
application can actively push, according to the registered account,
walking data related to the user information to the computing
application. In some implementations, if the computing application
itself has a walking data collection function, the computing
application can acquire walking data generated by the computing
application. From 241, method 240 proceeds to 242.
[0090] At 242, the walking application receives the acquisition
request, determines walking data corresponding to the user
information included in the acquisition request, and sends back the
determined walking data to the computing application. The walking
data includes at least a user ID, and identification information
that reflects a type of a walking behavior. From 242, method 240
proceeds to 243.
[0091] At 243, after acquiring the walking data from the walking
application, the computing application determines, according to the
user ID included in the walking data, that the walking data is
associated with a user account. In addition, the computing
application determines, according to the identification information
included in the walking data, a carbon-saving quantity quantization
algorithm for calculating a carbon-saving quantity of the walking
data. The carbon-saving quantity quantization algorithm can be a
carbon-saving quantity quantization algorithm specific to reduction
of trips by driving a vehicle. From 243, method 240 proceeds to
244.
[0092] At 244, a carbon-saving quantity associated with the user by
walking is calculated according to the determined carbon-saving
quantity quantization algorithm and the acquired walking data. In
addition, specific user data can be processed according to the
calculated carbon-saving quantity associated with the user. In some
implementations, if the walking data includes location information
of the user during walking, the computing application can
determine, based on the location information in the walking data, a
walking distance of the user. The carbon-saving quantity associated
with the user by walking, then, can be determined based on the
walking distance and the determined carbon-saving quantity
quantization algorithm. After 244, method 240 stops.
[0093] FIG. 3 is a block diagram illustrating a computing-based
architecture 300 for calculating individual carbon footprints,
according to an implementation of the present disclosure. As
illustrated in FIG. 3, an application client 301 acquires
fragmented user behavior data associated with a user 302, a
third-party application 303, a third-party server 304, or a
combination of a third-party application 303 and a third-party
server 304. The behavior data includes behavior data generated when
the user 302 uses different Internet services. After acquiring the
behavior data, the application client 301 sends the acquired
behavior data to an application server 305. The application server
305 calculates a carbon-saving quantity associated with the user
302, and returns the carbon-saving quantity to the application
client 301 for presentation to the user 302.
[0094] FIGS. 4A-4C are illustrative screenshots 400, 410, and 420
related to point accumulation, according to an implementation of
the present disclosure. For clarity of presentation, the
description that follows generally describes screenshots 400, 410,
and 420 in the context of the other figures in this description. In
some implementations, point accumulation can be made in response to
a confirming instruction sent by a user. For example, a control
component configured to accumulate points can be provided to the
user. The control component can be a suspension control component,
an embedded control component, or a popup window control component,
which can be implemented in hardware, software, or both.
[0095] In FIG. 4A, a control component is embedded in an
application interface 401. The user can request point accumulation
by clicking an "Accumulate" button 402. Based on this, accumulating
the converted points and total points of the user may include:
after receiving a confirming instruction (that is, clicking the
"Accumulate" button 402) sent by the user, points that can be
accumulated 403 are added to the total points 404 associated with
the user.
[0096] Total points 404 can be calculated and displayed
respectively according to different types of human behavior. As
shown in FIG. 4B, the application interface includes different
types of behavior items (for example, payment 411, ticketing 412,
walking 413, bill payment 414, reservation 415), and total points
for each type of behavior item is displayed in each type of
behavior item. In addition, total points for all types of behavior
items can be displayed, for example, by clicking "Click to view
total points" 416.
[0097] After clicking "Click to view total points" 416 in FIG. 4B,
the total points 421 for all types of behavior items is displayed
as shown in FIG. 4C.
[0098] In some implementations, different users can acquire
non-accumulated points from each other. For example, a user can
send an acquisition instruction to acquire at least part of
non-accumulated points of other users. As a result, the at least
part of non-accumulated points of other users are deducted from
accounts associated with the other users, and added to the total
points of the user. In some implementations, the other users are
related to the user. For example, the other users are listed in a
user's contact list.
[0099] FIGS. 5A-5B are schematic diagrams 500 and 510 of point
acquisition between users, according to an implementation of the
present disclosure. For clarity of presentation, the description
that follows generally describes diagrams 500 and 510 in the
context of the other figures in this description. As shown in FIG.
5A, non-accumulated points are displayed for each contact in a
user's contact list (Address list) 501. For example, contact
Xiaoming 502 has 50 non-accumulated points, contact Xiaohong 503
has 150 non-accumulated points 503, contact Xiaogang 504 has 360
non-accumulated points, and contact Er'ya 505 has 0 non-accumulated
points. The user can click on any contact in the contact list to
acquire non-accumulated points of the clicked contact. As shown in
FIG. 5B, after the user clicks on contact Xiaogang 504 in the
contact list 501, the user may be presented with a detailed view
511 of contact Xiaogang 504. The detailed view 511 shows
non-accumulated points corresponding to different types of
behaviors of contact Xiaogang 504. For example, Xiaogang 504 has
100 non-accumulated points in Payment 512, 50 non-accumulated
points in Ticking 513, 100 non-accumulated points in Walking 514,
50 non-accumulated points in Bill payment 515, and 60
non-accumulated points in Reservation 516. The user can click on a
particular behavior item (for example, Reservation) to acquire
non-accumulated points corresponding to the particular behavior
item (for example, 60 non-accumulated points in Reservation).
[0100] In some implementations, virtual goods matching total points
associated with a user can be assigned to the user. The virtual
goods can include a virtual tree, a virtual badge, and a virtual
medal. The virtual goods have different display states according to
differing total points. For example, according to pre-divided point
intervals, a point interval within which the total points fall can
be determined, and a display state of the virtual goods associated
with the user can be determined based on a predefined relationship
between the point intervals and display states of the virtual
goods. The display states of the virtual goods include, for
example, a size, a shape, and a color of the virtual goods. For
example, a virtual medal can be a bronze medal, a silver medal, or
a gold medal.
[0101] FIG. 6 is a block diagram illustrating an example data
processing system 600 for calculating individual carbon footprints,
according to an implementation. For clarity of presentation, the
description that follows generally describes system 600 in the
context of the other figures in this description. The system 600
can include an acquisition unit 601, a determination unit 602, a
calculation unit 603, a processing unit 604, a point acquisition
unit 605, and an assignment unit 606, which can be implemented in
hardware, software, or both.
[0102] The acquisition unit 601 can acquire behavior data
associated with a user, as discussed in step 105 of FIG. 1. The
determination unit 602 can determine at least one preset
carbon-saving quantity quantization algorithm based on
identification information of an Internet service, as discussed in
step 110 of FIG. 1 and steps 203, 213, 223, 233, and 243 of FIGS.
2A-2E. The calculation unit 603 can calculate a carbon-saving
quantity based on the acquired behavior data and the determined
preset carbon-saving quantity quantization algorithm, as discussed
in step 115 of FIG. 1 and steps 204, 214, 224, 234, and 244 of
FIGS. 2A-2E. The processing unit 604 can process specific user data
based on the calculated carbon-saving quantity and user
identification, as discussed in step 120 of FIG. 1. In addition,
the processing unit 604 can convert the calculated carbon-saving
quantity associated with the user into points based on a preset
conversion rule, and accumulate the converted points and total
points of the user to obtain updated total points of the user. The
point acquisition unit 605 can receive an acquisition instruction
sent by a user for non-accumulated points of other users, acquire
all or some points in the non-accumulated points of other users,
and accumulate the acquired all or some points and total points of
the user to obtain updated total points of the user. The assignment
unit 606 can determine updated total points of the user, and assign
virtual goods matching the updated total points to the user.
[0103] FIG. 7 is a block diagram of an example computer system 700
used to provide computational functionalities associated with
described algorithms, methods, functions, processes, flows, and
procedures, as described in the instant disclosure, according to an
implementation. The illustrated computer 702 is intended to
encompass any computing device such as a server, desktop computer,
laptop/notebook computer, wireless data port, smart phone, personal
data assistant (PDA), tablet computing device, one or more
processors within these devices, or any other suitable processing
device, including physical or virtual instances (or both) of the
computing device. Additionally, the computer 702 may comprise a
computer that includes an input device, such as a keypad, keyboard,
touch screen, or other device that can accept user information, and
an output device that conveys information associated with the
operation of the computer 702, including digital data, visual, or
audio information (or a combination of information), or a graphical
user interface (GUI).
[0104] The computer 702 can serve in a role as a client, network
component, a server, a database or other persistency, or any other
component (or a combination of roles) of a computer system for
performing the subject matter described in the instant disclosure.
The illustrated computer 702 is communicably coupled with a network
730. In some implementations, one or more components of the
computer 702 may be configured to operate within environments,
including cloud-computing-based, local, global, or other
environment (or a combination of environments).
[0105] At a high level, the computer 702 is an electronic computing
device operable to receive, transmit, process, store, or manage
data and information associated with the described subject matter.
According to some implementations, the computer 702 may also
include or be communicably coupled with an application server,
e-mail server, web server, caching server, streaming data server,
or other server (or a combination of servers).
[0106] The computer 702 can receive requests over network 730 from
a client application (for example, executing on another computer
702) and respond to the received requests by processing the
received requests using an appropriate software application(s). In
addition, requests may also be sent to the computer 702 from
internal users (for example, from a command console or by other
appropriate access method), external or third-parties, other
automated applications, as well as any other appropriate entities,
individuals, systems, or computers.
[0107] Each of the components of the computer 702 can communicate
using a system bus 703. In some implementations, any or all of the
components of the computer 702, hardware or software (or a
combination of both hardware and software), may interface with each
other or the interface 704 (or a combination of both), over the
system bus 703 using an application programming interface (API) 712
or a service layer 713 (or a combination of the API 712 and service
layer 713). The API 712 may include specifications for routines,
data structures, and object classes. The API 712 may be either
computer-language independent or dependent and refer to a complete
interface, a single function, or even a set of APIs. The service
layer 713 provides software services to the computer 702 or other
components (whether or not illustrated) that are communicably
coupled to the computer 702. The functionality of the computer 702
may be accessible for all service consumers using this service
layer. Software services, such as those provided by the service
layer 713, provide reusable, defined functionalities through a
defined interface. For example, the interface may be software
written in JAVA, C++, or other suitable language providing data in
extensible markup language (XML) format or other suitable format.
While illustrated as an integrated component of the computer 702,
alternative implementations may illustrate the API 712 or the
service layer 713 as stand-alone components in relation to other
components of the computer 702 or other components (whether or not
illustrated) that are communicably coupled to the computer 702.
Moreover, any or all parts of the API 712 or the service layer 713
may be implemented as child or sub-modules of another software
module, enterprise application, or hardware module without
departing from the scope of this disclosure.
[0108] The computer 702 includes an interface 704. Although
illustrated as a single interface 704 in FIG. 7, two or more
interfaces 704 may be used according to particular needs, desires,
or particular implementations of the computer 702. The interface
704 is used by the computer 702 for communicating with other
systems that are connected to the network 730 (whether illustrated
or not) in a distributed environment. Generally, the interface 704
comprises logic encoded in software or hardware (or a combination
of software and hardware) and is operable to communicate with the
network 730. More specifically, the interface 704 may comprise
software supporting one or more communication protocols associated
with communications such that the network 730 or interface's
hardware is operable to communicate physical signals within and
outside of the illustrated computer 702.
[0109] The computer 702 includes a processor 705. Although
illustrated as a single processor 705 in FIG. 7, two or more
processors may be used according to particular needs, desires, or
particular implementations of the computer 702. Generally, the
processor 705 executes instructions and manipulates data to perform
the operations of the computer 702 and any algorithms, methods,
functions, processes, flows, and procedures as described in the
instant disclosure.
[0110] The computer 702 also includes a database 706 that can hold
data for the computer 702 or other components (or a combination of
both) that can be connected to the network 730 (whether illustrated
or not). For example, database 706 can be an in-memory,
conventional, or other type of database storing data consistent
with this disclosure. In some implementations, database 706 can be
a combination of two or more different database types (for example,
a hybrid in-memory and conventional database) according to
particular needs, desires, or particular implementations of the
computer 702 and the described functionality. Although illustrated
as a single database 706 in FIG. 7, two or more databases (of the
same or combination of types) can be used according to particular
needs, desires, or particular implementations of the computer 702
and the described functionality. While database 706 is illustrated
as an integral component of the computer 702, in alternative
implementations, database 706 can be external to the computer
702.
[0111] The computer 702 also includes a memory 707 that can hold
data for the computer 702 or other components (or a combination of
both) that can be connected to the network 730 (whether illustrated
or not). Memory 707 can store any data consistent with this
disclosure. In some implementations, memory 707 can be a
combination of two or more different types of memory (for example,
a combination of semiconductor and magnetic storage) according to
particular needs, desires, or particular implementations of the
computer 702 and the described functionality. Although illustrated
as a single memory 707 in FIG. 7, two or more memories 707 (of the
same or combination of types) can be used according to particular
needs, desires, or particular implementations of the computer 702
and the described functionality. While memory 707 is illustrated as
an integral component of the computer 702, in alternative
implementations, memory 707 can be external to the computer
702.
[0112] The application 708 is an algorithmic software engine
providing functionality according to particular needs, desires, or
particular implementations of the computer 702, particularly with
respect to functionality described in this disclosure. For example,
application 708 can serve as one or more components, modules, or
applications. Further, although illustrated as a single application
708, the application 708 may be implemented as multiple
applications 708 on the computer 702. In addition, although
illustrated as integral to the computer 702, in alternative
implementations, the application 708 can be external to the
computer 702.
[0113] The computer 702 can also include a power supply 714. The
power supply 714 can include a rechargeable or non-rechargeable
battery that can be configured to be either user- or
non-user-replaceable. In some implementations, the power supply 714
can include power-conversion or management circuits (including
recharging, standby, or other power management functionality). In
some implementations, the power-supply 714 can include a power plug
to allow the computer 702 to be plugged into a wall socket or other
power source to, for example, power the computer 702 or recharge a
rechargeable battery.
[0114] There may be any number of computers 702 associated with, or
external to, a computer system containing computer 702, each
computer 702 communicating over network 730. Further, the term
"client," "user," and other appropriate terminology may be used
interchangeably, as appropriate, without departing from the scope
of this disclosure. Moreover, this disclosure contemplates that
many users may use one computer 702, or that one user may use
multiple computers 702.
[0115] Described implementations of the subject matter can include
one or more features, alone or in combination.
[0116] For example, in a first implementation, a
computer-implemented method, comprising: obtaining behavior data
associated with a user, wherein the behavior data is generated when
the user uses an Internet service, and the behavior data comprises
a user identification and identification information indicating the
Internet service; determining at least one predefined carbon-saving
quantity quantization algorithm based on the identification
information of the Internet service; calculating a carbon-saving
quantity associated with the user based on the obtained behavior
data and the determined at least one predefined carbon-saving
quantity quantization algorithm; and based on the calculated
carbon-saving quantity associated with the user and the user
identification, processing user data, wherein the user data is
related to the carbon-saving quantity associated with the user.
[0117] The foregoing and other described implementations can each,
optionally, include one or more of the following features:
[0118] A first feature, combinable with any of the following
features, wherein the at least one predefined carbon-saving
quantity quantization algorithm comprises: a first predefined
algorithm, wherein the first predefined algorithm is a
carbon-saving quantity quantization algorithm for targeting savings
of paper products; and a second predefined algorithm, wherein the
second predefined algorithm quantizes a carbon-saving quantity of
reduction of trips by taking vehicles.
[0119] A second feature, combinable with any of the previous or
following features, wherein, when the first predefined algorithm is
used to calculate the carbon-saving quantity, calculating a
carbon-saving quantity associated with the user comprises: based on
the behavior data, determining at least a number of times that the
user uses the Internet service and a geographical location of the
user when the user uses the Internet service; and calculating the
carbon-saving quantity associated with the user based on the
determined number of times that the user uses the Internet service,
the determined geographical position of the user when the user uses
the Internet service, and the first predefined algorithm.
[0120] A third feature, combinable with any of the previous or
following features, wherein, when the second predefined algorithm
is used to calculate the carbon-saving quantity, calculating a
carbon-saving quantity associated with the user comprises: based on
the behavior data, determining at least a number of walking steps
or a walking distance of the user; and calculating the
carbon-saving quantity associated with the user based on the
determined number of walking steps or walking distance of the user
and the second predefined algorithm.
[0121] A fourth feature, combinable with any of the previous or
following features, wherein determining at least one predefined
carbon-saving quantity quantization algorithm based on the
identification information of the Internet service comprises
determining at least one predefined carbon-saving quantity
quantization algorithm based on the identification information of
the Internet service and a plurality of pre-stored corresponding
relationships between a plurality of Internet services and a
plurality of carbon-saving quantity quantization algorithms.
[0122] A fifth feature, combinable with any of the previous or
following features, wherein the Internet service comprises at least
one of an electronic payment service, an online reservation
service, an online ticketing service, an online bill payment
service, and a health service.
[0123] A sixth feature, combinable with any of the previous or
following features, wherein processing user data comprises:
obtaining a plurality of carbon-saving quantities associated with
the user corresponding to a plurality of Internet services within a
predefined period; accumulating the obtained plurality of
carbon-saving quantities; and processing the user data based on the
accumulated carbon-saving quantity associated with the user.
[0124] A seventh feature, combinable with any of the previous or
following features, wherein processing the user data based on the
accumulated carbon-saving quantity associated with the user
comprises: adding the accumulated carbon-saving quantity associated
with the user and a total carbon-saving quantity associated with
the user together to obtain an updated total carbon-saving quantity
associated with the user; and processing the user data based on the
updated total carbon-saving quantity associated with the user.
[0125] An eighth feature, combinable with any of the previous or
following features, wherein adding the accumulated carbon-saving
quantity associated with the user and a total carbon-saving
quantity associated with the user together to obtain an updated
total carbon-saving quantity associated with the user comprises:
converting the accumulated carbon-saving quantity associated with
the user into points based on a predefined conversion rule; and
adding the converted points and total points of the user together
to obtain updated total points of the user.
[0126] A ninth feature, combinable with any of the previous or
following features, wherein a control component configured to
accumulate points is provided to the user, and adding the converted
points and total points of the user together to obtain updated
total points of the user comprises: receiving an instruction sent
by the user through the control component confirming points
accumulation; and adding the converted points and the total points
of the user together.
[0127] A tenth feature, combinable with any of the previous or
following features, further comprising: receiving an instruction
sent by the user to obtain non-accumulated points of other users;
obtaining at least part of the non-accumulated points of other
users in response to receiving the instruction sent by the user to
obtain non-accumulated points of other users; and adding the
obtained at least part of the non-accumulated points of other users
and the updated total points of the user together to obtain second
updated total points of the user.
[0128] An eleventh feature, combinable with any of the previous or
following features, further comprising determining the updated
total points of the user; and based on the updated total points of
the user, assigning, to the user, a virtual goods corresponding to
the updated total points of the user.
[0129] A twelfth feature, combinable with any of the previous or
following features, wherein the virtual goods has different display
states corresponding to different total points.
[0130] In a second implementation, a non-transitory,
computer-readable medium storing one or more instructions
executable by a computer system to perform operations comprising:
obtaining behavior data associated with a user, wherein the
behavior data is generated when the user uses an Internet service,
and the behavior data comprises a user identification and
identification information indicating the Internet service;
determining at least one predefined carbon-saving quantity
quantization algorithm based on the identification information of
the Internet service; calculating a carbon-saving quantity
associated with the user based on the obtained behavior data and
the determined at least one predefined carbon-saving quantity
quantization algorithm; and based on the calculated carbon-saving
quantity associated with the user and the user identification,
processing user data, wherein the user data is related to the
carbon-saving quantity associated with the user.
[0131] The foregoing and other described implementations can each,
optionally, include one or more of the following features:
[0132] A first feature, combinable with any of the following
features, wherein the at least one predefined carbon-saving
quantity quantization algorithm comprises: a first predefined
algorithm, wherein the first predefined algorithm is a
carbon-saving quantity quantization algorithm for targeting savings
of paper products; and a second predefined algorithm, wherein the
second predefined algorithm quantizes a carbon-saving quantity of
reduction of trips by taking vehicles.
[0133] A second feature, combinable with any of the previous or
following features, wherein, when the first predefined algorithm is
used to calculate the carbon-saving quantity, calculating a
carbon-saving quantity associated with the user comprises: based on
the behavior data, determining at least a number of times that the
user uses the Internet service and a geographical location of the
user when the user uses the Internet service; and calculating the
carbon-saving quantity associated with the user based on the
determined number of times that the user uses the Internet service,
the determined geographical position of the user when the user uses
the Internet service, and the first predefined algorithm.
[0134] A third feature, combinable with any of the previous or
following features, wherein, when the second predefined algorithm
is used to calculate the carbon-saving quantity, calculating a
carbon-saving quantity associated with the user comprises: based on
the behavior data, determining at least a number of walking steps
or a walking distance of the user; and calculating the
carbon-saving quantity associated with the user based on the
determined number of walking steps or walking distance of the user
and the second predefined algorithm.
[0135] A fourth feature, combinable with any of the previous or
following features, wherein determining at least one predefined
carbon-saving quantity quantization algorithm based on the
identification information of the Internet service comprises
determining at least one predefined carbon-saving quantity
quantization algorithm based on the identification information of
the Internet service and a plurality of pre-stored corresponding
relationships between a plurality of Internet services and a
plurality of carbon-saving quantity quantization algorithms.
[0136] A fifth feature, combinable with any of the previous or
following features, wherein the Internet service comprises at least
one of an electronic payment service, an online reservation
service, an online ticketing service, an online bill payment
service, and a health service.
[0137] A sixth feature, combinable with any of the previous or
following features, wherein processing user data comprises:
obtaining a plurality of carbon-saving quantities associated with
the user corresponding to a plurality of Internet services within a
predefined period; accumulating the obtained plurality of
carbon-saving quantities; and processing the user data based on the
accumulated carbon-saving quantity associated with the user.
[0138] A seventh feature, combinable with any of the previous or
following features, wherein processing the user data based on the
accumulated carbon-saving quantity associated with the user
comprises: adding the accumulated carbon-saving quantity associated
with the user and a total carbon-saving quantity associated with
the user together to obtain an updated total carbon-saving quantity
associated with the user; and processing the user data based on the
updated total carbon-saving quantity associated with the user.
[0139] An eighth feature, combinable with any of the previous or
following features, wherein adding the accumulated carbon-saving
quantity associated with the user and a total carbon-saving
quantity associated with the user together to obtain an updated
total carbon-saving quantity associated with the user comprises:
converting the accumulated carbon-saving quantity associated with
the user into points based on a predefined conversion rule; and
adding the converted points and total points of the user together
to obtain updated total points of the user.
[0140] A ninth feature, combinable with any of the previous or
following features, wherein a control component configured to
accumulate points is provided to the user, and adding the converted
points and total points of the user together to obtain updated
total points of the user comprises: receiving an instruction sent
by the user through the control component confirming points
accumulation; and adding the converted points and the total points
of the user together.
[0141] A tenth feature, combinable with any of the previous or
following features, further comprising: receiving an instruction
sent by the user to obtain non-accumulated points of other users;
obtaining at least part of the non-accumulated points of other
users in response to receiving the instruction sent by the user to
obtain non-accumulated points of other users; and adding the
obtained at least part of the non-accumulated points of other users
and the updated total points of the user together to obtain second
updated total points of the user.
[0142] An eleventh feature, combinable with any of the previous or
following features, further comprising: determining the updated
total points of the user; and based on the updated total points of
the user, assigning, to the user, a virtual goods corresponding to
the updated total points of the user.
[0143] A twelfth feature, combinable with any of the previous or
following features, wherein the virtual goods has different display
states corresponding to different total points.
[0144] In a third implementation, a computer-implemented system,
comprising: one or more computers; and one or more computer memory
devices interoperably coupled with the one or more computers and
having tangible, non-transitory, machine-readable media storing
instructions, that when executed by the one or more computers,
perform operations comprising: obtaining behavior data associated
with a user, wherein the behavior data is generated when the user
uses an Internet service, and the behavior data comprises a user
identification and identification information indicating the
Internet service; determining at least one predefined carbon-saving
quantity quantization algorithm based on the identification
information of the Internet service; calculating a carbon-saving
quantity associated with the user based on the obtained behavior
data and the determined at least one predefined carbon-saving
quantity quantization algorithm; and based on the calculated
carbon-saving quantity associated with the user and the user
identification, processing user data, wherein the user data is
related to the carbon-saving quantity associated with the user.
[0145] The foregoing and other described implementations can each,
optionally, include one or more of the following features:
[0146] A first feature, combinable with any of the following
features, wherein the at least one predefined carbon-saving
quantity quantization algorithm comprises: a first predefined
algorithm, wherein the first predefined algorithm is a
carbon-saving quantity quantization algorithm for targeting savings
of paper products; and a second predefined algorithm, wherein the
second predefined algorithm quantizes a carbon-saving quantity of
reduction of trips by taking vehicles.
[0147] A second feature, combinable with any of the previous or
following features, wherein, when the first predefined algorithm is
used to calculate the carbon-saving quantity, calculating a
carbon-saving quantity associated with the user comprises: based on
the behavior data, determining at least a number of times that the
user uses the Internet service and a geographical location of the
user when the user uses the Internet service; and calculating the
carbon-saving quantity associated with the user based on the
determined number of times that the user uses the Internet service,
the determined geographical position of the user when the user uses
the Internet service, and the first predefined algorithm.
[0148] A third feature, combinable with any of the previous or
following features, wherein, when the second predefined algorithm
is used to calculate the carbon-saving quantity, calculating a
carbon-saving quantity associated with the user comprises: based on
the behavior data, determining at least a number of walking steps
or a walking distance of the user; and calculating the
carbon-saving quantity associated with the user based on the
determined number of walking steps or walking distance of the user
and the second predefined algorithm.
[0149] A fourth feature, combinable with any of the previous or
following features, wherein determining at least one predefined
carbon-saving quantity quantization algorithm based on the
identification information of the Internet service comprises
determining at least one predefined carbon-saving quantity
quantization algorithm based on the identification information of
the Internet service and a plurality of pre-stored corresponding
relationships between a plurality of Internet services and a
plurality of carbon-saving quantity quantization algorithms.
[0150] A fifth feature, combinable with any of the previous or
following features, wherein the Internet service comprises at least
one of an electronic payment service, an online reservation
service, an online ticketing service, an online bill payment
service, and a health service.
[0151] A sixth feature, combinable with any of the previous or
following features, wherein processing user data comprises:
obtaining a plurality of carbon-saving quantities associated with
the user corresponding to a plurality of Internet services within a
predefined period; accumulating the obtained plurality of
carbon-saving quantities; and processing the user data based on the
accumulated carbon-saving quantity associated with the user.
[0152] A seventh feature, combinable with any of the previous or
following features, wherein processing the user data based on the
accumulated carbon-saving quantity associated with the user
comprises: adding the accumulated carbon-saving quantity associated
with the user and a total carbon-saving quantity associated with
the user together to obtain an updated total carbon-saving quantity
associated with the user; and processing the user data based on the
updated total carbon-saving quantity associated with the user.
[0153] An eighth feature, combinable with any of the previous or
following features, wherein adding the accumulated carbon-saving
quantity associated with the user and a total carbon-saving
quantity associated with the user together to obtain an updated
total carbon-saving quantity associated with the user comprises:
converting the accumulated carbon-saving quantity associated with
the user into points based on a predefined conversion rule; and
adding the converted points and total points of the user together
to obtain updated total points of the user.
[0154] A ninth feature, combinable with any of the previous or
following features, wherein a control component configured to
accumulate points is provided to the user, and adding the converted
points and total points of the user together to obtain updated
total points of the user comprises: receiving an instruction sent
by the user through the control component confirming points
accumulation; and adding the converted points and the total points
of the user together.
[0155] A tenth feature, combinable with any of the previous or
following features, further comprising: receiving an instruction
sent by the user to obtain non-accumulated points of other users;
obtaining at least part of the non-accumulated points of other
users in response to receiving the instruction sent by the user to
obtain non-accumulated points of other users; and adding the
obtained at least part of the non-accumulated points of other users
and the updated total points of the user together to obtain second
updated total points of the user.
[0156] An eleventh feature, combinable with any of the previous or
following features, further comprising: determining the updated
total points of the user; and based on the updated total points of
the user, assigning, to the user, a virtual goods corresponding to
the updated total points of the user.
[0157] A twelfth feature, combinable with any of the previous or
following features, wherein the virtual goods has different display
states corresponding to different total points.
[0158] Implementations of the subject matter and the functional
operations described in this specification can be implemented in
digital electronic circuitry, in tangibly embodied computer
software or firmware, in computer hardware, including the
structures disclosed in this specification and their structural
equivalents, or in combinations of one or more of them. Software
implementations of the described subject matter can be implemented
as one or more computer programs, that is, one or more modules of
computer program instructions encoded on a tangible,
non-transitory, computer-readable computer-storage medium for
execution by, or to control the operation of, data processing
apparatus. Alternatively, or additionally, the program instructions
can be encoded in/on an artificially generated propagated signal,
for example, a machine-generated electrical, optical, or
electromagnetic signal that is generated to encode information for
transmission to suitable receiver apparatus for execution by a data
processing apparatus. The computer-storage medium can be a
machine-readable storage device, a machine-readable storage
substrate, a random or serial access memory device, or a
combination of computer-storage mediums. Configuring one or more
computers means that the one or more computers have installed
hardware, firmware, or software (or combinations of hardware,
firmware, and software) so that when the software is executed by
the one or more computers, particular computing operations are
performed.
[0159] The term "real-time," "real time," "realtime," "real (fast)
time (RFT)," "near(ly) real-time (NRT)," "quasi real-time," or
similar terms (as understood by one of ordinary skill in the art),
means that an action and a response are temporally proximate such
that an individual perceives the action and the response occurring
substantially simultaneously. For example, the time difference for
a response to display (or for an initiation of a display) of data
following the individual's action to access the data may be less
than 1 ms, less than 1 sec., or less than 5 secs. While the
requested data need not be displayed (or initiated for display)
instantaneously, it is displayed (or initiated for display) without
any intentional delay, taking into account processing limitations
of a described computing system and time required to, for example,
gather, accurately measure, analyze, process, store, or transmit
the data.
[0160] The terms "data processing apparatus," "computer," or
"electronic computer device" (or equivalent as understood by one of
ordinary skill in the art) refer to data processing hardware and
encompass all kinds of apparatus, devices, and machines for
processing data, including by way of example, a programmable
processor, a computer, or multiple processors or computers. The
apparatus can also be, or further include special purpose logic
circuitry, for example, a central processing unit (CPU), an FPGA
(field programmable gate array), or an ASIC (application-specific
integrated circuit). In some implementations, the data processing
apparatus or special purpose logic circuitry (or a combination of
the data processing apparatus or special purpose logic circuitry)
may be hardware- or software-based (or a combination of both
hardware- and software-based). The apparatus can optionally include
code that creates an execution environment for computer programs,
for example, code that constitutes processor firmware, a protocol
stack, a database management system, an operating system, or a
combination of execution environments. The present disclosure
contemplates the use of data processing apparatuses with or without
conventional operating systems, for example LINUX, UNIX, WINDOWS,
MAC OS, ANDROID, IOS, or any other suitable conventional operating
system.
[0161] A computer program, which may also be referred to or
described as a program, software, a software application, a module,
a software module, a script, or code can be written in any form of
programming language, including compiled or interpreted languages,
or declarative or procedural languages, and it can be deployed in
any form, including as a stand-alone program or as a module,
component, subroutine, or other unit suitable for use in a
computing environment. A computer program may, but need not,
correspond to a file in a file system. A program can be stored in a
portion of a file that holds other programs or data, for example,
one or more scripts stored in a markup language document, in a
single file dedicated to the program in question, or in multiple
coordinated files, for example, files that store one or more
modules, sub-programs, or portions of code. A computer program can
be deployed to be executed on one computer or on multiple computers
that are located at one site or distributed across multiple sites
and interconnected by a communication network.
[0162] While portions of the programs illustrated in the various
figures are shown as individual modules that implement the various
features and functionality through various objects, methods, or
other processes, the programs may instead include a number of
sub-modules, third-party services, components, libraries, and such,
as appropriate. Conversely, the features and functionality of
various components can be combined into single components, as
appropriate. Thresholds used to make computational determinations
can be statically, dynamically, or both statically and dynamically
determined.
[0163] The methods, processes, or logic flows described in this
specification can be performed by one or more programmable
computers executing one or more computer programs to perform
functions by operating on input data and generating output. The
methods, processes, or logic flows can also be performed by, and
apparatus can also be implemented as, special purpose logic
circuitry, for example, a CPU, an FPGA, or an ASIC.
[0164] Computers suitable for the execution of a computer program
can be based on general or special purpose microprocessors, both,
or any other kind of CPU. Generally, a CPU will receive
instructions and data from and write to a memory. The essential
elements of a computer are a CPU, for performing or executing
instructions, and one or more memory devices for storing
instructions and data. Generally, a computer will also include, or
be operatively coupled to, receive data from or transfer data to,
or both, one or more mass storage devices for storing data, for
example, magnetic, magneto-optical disks, or optical disks.
However, a computer need not have such devices. Moreover, a
computer can be embedded in another device, for example, a mobile
telephone, a personal digital assistant (PDA), a mobile audio or
video player, a game console, a global positioning system (GPS)
receiver, or a portable storage device, for example, a universal
serial bus (USB) flash drive, to name just a few.
[0165] Computer-readable media (transitory or non-transitory, as
appropriate) suitable for storing computer program instructions and
data includes all forms of permanent/non-permanent or
volatile/non-volatile memory, media and memory devices, including
by way of example semiconductor memory devices, for example, random
access memory (RAM), read-only memory (ROM), phase change memory
(PRAM), static random access memory (SRAM), dynamic random access
memory (DRAM), erasable programmable read-only memory (EPROM),
electrically erasable programmable read-only memory (EEPROM), and
flash memory devices; magnetic devices, for example, tape,
cartridges, cassettes, internal/removable disks; magneto-optical
disks; and optical memory devices, for example, digital video disc
(DVD), CD-ROM, DVD+/-R, DVD-RAM, DVD-ROM, HD-DVD, and BLURAY, and
other optical memory technologies. The memory may store various
objects or data, including caches, classes, frameworks,
applications, modules, backup data, jobs, web pages, web page
templates, data structures, database tables, repositories storing
dynamic information, and any other appropriate information
including any parameters, variables, algorithms, instructions,
rules, constraints, or references thereto. Additionally, the memory
may include any other appropriate data, such as logs, policies,
security or access data, reporting files, as well as others. The
processor and the memory can be supplemented by, or incorporated
in, special purpose logic circuitry.
[0166] To provide for interaction with a user, implementations of
the subject matter described in this specification can be
implemented on a computer having a display device, for example, a
CRT (cathode ray tube), LCD (liquid crystal display), LED (Light
Emitting Diode), or plasma monitor, for displaying information to
the user and a keyboard and a pointing device, for example, a
mouse, trackball, or trackpad by which the user can provide input
to the computer. Input may also be provided to the computer using a
touchscreen, such as a tablet computer surface with pressure
sensitivity, a multi-touch screen using capacitive or electric
sensing, or other type of touchscreen. Other kinds of devices can
be used to provide for interaction with a user as well; for
example, feedback provided to the user can be any form of sensory
feedback, for example, visual feedback, auditory feedback, or
tactile feedback; and input from the user can be received in any
form, including acoustic, speech, or tactile input. In addition, a
computer can interact with a user by sending documents to and
receiving documents from a device that is used by the user; for
example, by sending web pages to a web browser on a user's client
device in response to requests received from the web browser.
[0167] The term "graphical user interface," or "GUI," may be used
in the singular or the plural to describe one or more graphical
user interfaces and each of the displays of a particular graphical
user interface. Therefore, a GUI may represent any graphical user
interface, including but not limited to, a web browser, a touch
screen, or a command line interface (CLI) that processes
information and efficiently presents the information results to the
user. In general, a GUI may include a plurality of user interface
(UI) elements, some or all associated with a web browser, such as
interactive fields, pull-down lists, and buttons. These and other
UI elements may be related to or represent the functions of the web
browser.
[0168] Implementations of the subject matter described in this
specification can be implemented in a computing system that
includes a back-end component, for example, as a data server, or
that includes a middleware component, for example, an application
server, or that includes a front-end component, for example, a
client computer having a graphical user interface or a Web browser
through which a user can interact with an implementation of the
subject matter described in this specification, or any combination
of one or more such back-end, middleware, or front-end components.
The components of the system can be interconnected by any form or
medium of wireline or wireless digital data communication (or a
combination of data communication), for example, a communication
network. Examples of communication networks include a local area
network (LAN), a radio access network (RAN), a metropolitan area
network (MAN), a wide area network (WAN), Worldwide
Interoperability for Microwave Access (WIMAX), a wireless local
area network (WLAN) using, for example, 802.11 a/b/g/n or 802.20
(or a combination of 802.11x and 802.20 or other protocols
consistent with this disclosure), all or a portion of the Internet,
or any other communication system or systems at one or more
locations (or a combination of communication networks). The network
may communicate with, for example, Internet Protocol (IP) packets,
Frame Relay frames, Asynchronous Transfer Mode (ATM) cells, voice,
video, data, or other suitable information (or a combination of
communication types) between network addresses.
[0169] The computing system can include clients and servers. A
client and server are generally remote from each other and
typically interact through a communication network. The
relationship of client and server arises by virtue of computer
programs running on the respective computers and having a
client-server relationship to each other.
[0170] While this specification contains many specific
implementation details, these should not be construed as
limitations on the scope of any invention or on the scope of what
may be claimed, but rather as descriptions of features that may be
specific to particular implementations of particular inventions.
Certain features that are described in this specification in the
context of separate implementations can also be implemented, in
combination, in a single implementation. Conversely, various
features that are described in the context of a single
implementation can also be implemented in multiple implementations,
separately, or in any suitable sub-combination. Moreover, although
previously described features may be described as acting in certain
combinations and even initially claimed as such, one or more
features from a claimed combination can, in some cases, be excised
from the combination, and the claimed combination may be directed
to a sub-combination or variation of a sub-combination.
[0171] Particular implementations of the subject matter have been
described. Other implementations, alterations, and permutations of
the described implementations are within the scope of the following
claims as will be apparent to those skilled in the art. While
operations are depicted in the drawings or claims in a particular
order, this should not be understood as requiring that such
operations be performed in the particular order shown or in
sequential order, or that all illustrated operations be performed
(some operations may be considered optional), to achieve desirable
results. In certain circumstances, multitasking or parallel
processing (or a combination of multitasking and parallel
processing) may be advantageous and performed as deemed
appropriate.
[0172] Moreover, the separation or integration of various system
modules and components in the previously described implementations
should not be understood as requiring such separation or
integration in all implementations, and it should be understood
that the described program components and systems can generally be
integrated together in a single software product or packaged into
multiple software products.
[0173] Accordingly, the previously described example
implementations do not define or constrain this disclosure. Other
changes, substitutions, and alterations are also possible without
departing from the spirit and scope of this disclosure.
[0174] Furthermore, any claimed implementation is considered to be
applicable to at least a computer-implemented method; a
non-transitory, computer-readable medium storing computer-readable
instructions to perform the computer-implemented method; and a
computer system comprising a computer memory interoperably coupled
with a hardware processor configured to perform the
computer-implemented method or the instructions stored on the
non-transitory, computer-readable medium.
* * * * *