U.S. patent application number 14/534778 was filed with the patent office on 2015-05-14 for system and method for allocating resources.
The applicant listed for this patent is Sharper Shape Ltd.. Invention is credited to Tero Heinonen, Juha Hyyppa, Anttoni Jaakkola, Ville Koivuranta.
Application Number | 20150134384 14/534778 |
Document ID | / |
Family ID | 53043441 |
Filed Date | 2015-05-14 |
United States Patent
Application |
20150134384 |
Kind Code |
A1 |
Heinonen; Tero ; et
al. |
May 14, 2015 |
SYSTEM AND METHOD FOR ALLOCATING RESOURCES
Abstract
Disclosed is a system for updating probability data of target
object property. The system comprises a database for storing
probability data, a property object data of a first target object,
a property object data of a second target object, a list of actions
and a cost of actions. The system also includes a processor having
an executable code configured to define a first probability of
accuracy of a property object data of a first target object, define
a second probability of the first target object having a negative
effect on a second target object, calculate a combined probability
from the first probability and the second probability and use the
combined probability to select to which at least one of the target
objects to allocate resources, and update the first probability and
the second probability in the database based on the action
performed with the allocated resource.
Inventors: |
Heinonen; Tero; (Helsinki,
FI) ; Koivuranta; Ville; (Helsinki, FI) ;
Hyyppa; Juha; (Espoo, FI) ; Jaakkola; Anttoni;
(Espoo, FI) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Sharper Shape Ltd. |
Helsinki |
|
FI |
|
|
Family ID: |
53043441 |
Appl. No.: |
14/534778 |
Filed: |
November 6, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61901490 |
Nov 8, 2013 |
|
|
|
Current U.S.
Class: |
705/7.12 |
Current CPC
Class: |
G06Q 10/0631 20130101;
G01S 17/08 20130101; G06Q 50/06 20130101; G01S 17/89 20130101 |
Class at
Publication: |
705/7.12 |
International
Class: |
G06Q 10/06 20060101
G06Q010/06; G06Q 50/06 20060101 G06Q050/06 |
Claims
1. A method for updating probability data of target object property
in a database, comprising the steps of defining a first probability
of accuracy of a property object data of a first target object;
defining a second probability of the first target object having a
negative effect on a second target object; using the first
probability and the second probability to calculate a combined
probability; using the combined probability to select to which at
least one of the target objects to allocate resources; using the
allocated resources to perform an action on the selected at least
one of the target objects; and updating the first probability and
the second probability in the database, based on the action.
2. A method of claim 1 wherein the property object data of the
first target object is selected from a group consisting of an
identifier, spatial information, attribute information and
structural information.
3. A method of claim 1 wherein the negative effect is the first
target object causing damage or failure to the second target
object.
4. A method of claim 1 wherein the combined probability is
calculated by multiplying the first probability with the second
probability.
5. A method of claim 1 wherein the action is selected from the
group consisting of measuring the property object data of the first
target object, measuring the property object data of the second
target object, performing action on the first target object,
performing action on the second target object, and allocating
equipment for performing actions.
6. A method of claim 5 where the action is prioritized by using
combined probability and cost of non-action.
7. A system for updating probability data of target object
property, the system comprising a database for storing probability
data, a property object data of a first target object, a property
object data of a second target object, a list of actions and a cost
of actions; and a processor having an executable code configured to
define a first probability of accuracy of a property object data of
a first target object; define a second probability of the first
target object having a negative effect on a second target object;
calculate a combined probability from the first probability and the
second probability; use the combined probability to select to which
at least one of the target objects to allocate resources; and
update the first probability and the second probability in the
database, based on the action performed with the allocated
resource.
8. A system of claim 7 wherein the property object data of the
first target object is selected from a group consisting of an
identifier, spatial information, attribute information and
structural information.
9. A system of claim 7 wherein the negative effect is the first
target object causing damage or failure to the second target
object.
10. A system of claim 7 wherein the combined probability is
calculated by multiplying the first probability with the second
probability.
11. A system of claim 7 wherein the action is selected from the
group consisting of measuring the property object data of the first
target object, measuring the property object data of the second
target object, performing action on the first target object,
performing action on the second target object, and allocating
equipment for performing actions.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to and the benefit of U.S.
provisional Patent Application No. 61/901,490, filed on 8 Nov.
2013; and is related to, and claims the benefit of, U.S. Patent
Application Ser. No. 61/901,489 filed on 8 Nov. 2013 entitled
System for Monitoring Power Lines (Sharpershape001); and U.S.
Patent Application Ser. No. 61/901,492, filed on 8 Nov. 2013,
entitled System and Method for Reporting Events (Sharpershape003);
the disclosures of which are incorporated herein by reference in
their entirety.
TECHNICAL FIELD
[0002] The general area of the disclosure is towards improved
resource utilization related to network maintenance, and, more
particularly, to a system and a method for allocating resources and
executing actions to maintain such network based on probability
data of target objects associated with the network and managing
such probability data.
BACKGROUND
[0003] Infrastructure networks (such as water pipes, oil and gas
pipes, electricity lines, etc.) are maintenance intensive, so
maintenance budgets are a substantial share of costs for such
businesses. Maintenance budgets are high, in part, due to the size
of the systems and the number of resources that it takes to
properly maintain the system to achieve the appropriate reliability
level. Substantial costs are involved in monitoring and identifying
of potential threats to objects in the infrastructure networks.
Traditionally this has been achieved primarily relying on on-site
manual inspection. However, such means have proven expensive,
time-consuming and often show inaccurate results over a period of
time. Further, independent monitoring and measurement analysis of
these extensive networks and keeping the information up to date in
a database is time and resource consuming task.
[0004] As fundamental economics teach, every organization,
commercial, non-profit, or governmental, has limited resources that
is, cash flow, capital assets, raw materials, equipment, personnel,
etc. These limited resources need to be optimally utilized to best
serve the organization's business goals. To comply with regulatory
requirements, with limited budgets, it is necessary to find tools
and techniques that can be used to optimally allocate resources to
maintain infrastructure networks. Such businesses must determine
how to allocate available resources for regular maintenance
activities. With increasingly competitive markets, the need of
optimum resource allocation further intensifies.
[0005] Considering an example of power line (PL) networks which
usually comprise of conductors, insulators, pylons and other
associated structures such as spacer, dead-lines, switch boxes,
etc. Generally, the PL networks are very large and are distributed
along large geographical area. For example, the power transmission
grid of United States consists of about 300,000 km of power lines
in total length, which is operated by 500 different power line
companies and involving thousands of personnel working to maintain
the network.
[0006] Such PL networks are often exposed to potential threats,
mainly caused by encroaching vegetation, structural changes between
"as-built" and "as-is" condition and violating clearance between
conductors and assets. FIG. 1 illustrates some examples showing
risks due to tree/vegetation growth close to the power lines. As
the tree grows it will be eventually so tall that in case it falls
down during a storm it would harm the power line. Additionally a
branch of a tree might have grown and is for example above the
power line, and during the winter time snow load might bend the
branch so much to touch the power line leading to some possible
damages to the line. In practice as the vegetation grows the
situation around the PL network changes and probability of possible
damages/failures to the PL network increases.
[0007] In case of a disaster such as a major storm a substantial
amount of damage may occur to the PL network causing massive
disruption to the power distribution and to the whole society
dependent on electricity. In all these circumstances, a quick and
accurate analysis of the damage is of utmost importance for the
electricity transmission and distribution operators, to manage the
repair work efficiently. Further a timely regular monitoring of the
network features and their spatial relations is required to have a
reference to the prior conditions of the power lines in such a
network.
[0008] According to an estimate, tree growth causes about 20
percent of sustained distribution outages, most of which are of
short duration. The percentage of tree growth caused distribution
outages is dependent on the portion of the forest of the land mass
and the type of forest and can reach 65 percent of all outages in
regions with boreal forest such as in Canada, Northern Europe and
Russia. Growth-related failures are maintainable and can be
effectively controlled through regular tree-trimming. Corrective
maintenance refers to repair activities done to restore the system
after a fault. Preventive vegetation management is done before a
failure actually occurs. The repair field force needs to be sent to
the locations where the repairs will have best impact on the number
of customers to get their power back, and the number of watts
transmitted or distributed.
[0009] The efficient allocation of the resources cannot be done
without accurate situational data of the target site. Traditionally
known methods for allocating resources can be classified as either
subjective, accounting, operations research/management science, or
the like. All of these methods try to address the same fundamental
issue faced by all organizations, which resources to allocate for
which purposes, and further how to prioritize and execute actions.
Generally, prices and costs are key factors driving such decisions.
Technological advancements have provided increased granularity for
resource utilization, and thus has enabled the businesses to make
more complex allocation decisions.
[0010] Therefore, there exists a need to devise a system that
solves the problem of measuring and identifying potential risks to
the infrastructure networks and provides optimum allocation of
resources for infrastructure maintenance, and that overcomes the
above-mentioned limitations of existing systems.
BRIEF SUMMARY
[0011] The present disclosure provides a system and method for
allocating resources to maintain a network. More specifically, the
present disclosure relates to a system and a method for allocating
resources and executing actions to maintain such network based on
probability data of target objects associated with the network; and
managing such probability data.
[0012] In one aspect, embodiments of the present disclosure provide
a system for updating probability data of target object property.
The system comprises a database for storing probability data, a
property object data of a first target object, a property object
data of a second target object, a list of actions and a cost of
actions. The system also includes a processor having an executable
code configured to define a first probability of accuracy of a
property object data of a first target object, define a second
probability of the first target object having a negative effect on
a second target object, calculate a combined probability from the
first probability and the second probability and use the combined
probability to select to which at least one of the target objects
to allocate resources, and update the first probability and the
second probability in the database based on the action performed
with the allocated resource.
[0013] In another aspect, embodiments of the present disclosure
provide a method for updating probability data of target object
property in a database. The method comprises defining a first
probability of accuracy of a property object data of a first target
object; defining a second probability of the first target object
having a negative effect on a second target object; using the first
probability and the second probability to calculate a combined
probability; using the combined probability to select to which at
least one of the target objects to allocate resources; using the
allocated resources to perform an action on the selected at least
one of the target objects and updating the first probability and
the second probability in the database based on the action.
[0014] According to an embodiment, the system and the method enable
a user, such as electricity transmission and distribution
operators, to order resources in order to execute a mission related
to infrastructure maintenance.
[0015] Further, the system and the method are configured for
generating resource requests which are allocated based on
configurable business goals.
[0016] Additional aspects, advantages, features and objects of the
present disclosure would be made apparent from the drawings and the
detailed description of the illustrative embodiments.
[0017] It will be appreciated that features of the disclosure are
susceptible to being combined in various combinations or further
improvements without departing from the scope of the disclosure and
this provisional application.
DESCRIPTION OF THE DRAWINGS
[0018] The summary above, as well as the following detailed
description of illustrative embodiments, is better understood when
read in conjunction with the appended drawings. For the purpose of
illustrating the present disclosure, exemplary constructions of the
disclosure are shown in the drawings. However, the disclosure is
not limited to specific methods and instrumentalities disclosed
herein. Wherever possible, like elements have been indicated by
identical numbers.
[0019] FIG. 1 illustrates a pictorial representation of exemplary
potential threats to an infrastructure network, in accordance to an
embodiment of the present disclosure;
[0020] FIG. 2 illustrates a high level architecture of a system for
managing target object property data to allocate resources and
execute actions to maintain an infrastructure network, in
accordance with an embodiment of the present disclosure;
[0021] FIG. 3 illustrates a block diagram of a system for managing
probability data of target objects to allocate resources and
execute actions to maintain an infrastructure network, in
accordance with an embodiment of the present disclosure;
[0022] FIG. 4 illustrates a graphical representation of probability
distribution of failure in power lines of various areas in an
infrastructure network, in accordance with an exemplary embodiment
of the present disclosure; and
[0023] FIG. 5 illustrates steps of a method for updating
probability data of target object property in a database, in
accordance with an embodiment of the present disclosure.
[0024] In the accompanying drawings, an underlined number is
employed to represent an item over which the underlined number is
positioned or an item to which the underlined number is adjacent. A
non-underlined number relates to an item identified by a line
linking the non-underlined number to the item. When a number is
non-underlined and accompanied by an associated arrow, the
non-underlined number is used to identify a general item at which
the arrow is pointing.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0025] Referring now to the drawings, particularly by their
reference numbers, FIG. 1 illustrates a pictorial representation of
exemplary potential threats to an infrastructure network, in
accordance to an embodiment of the present disclosure. As an
example, FIG. 1 illustrates risks due to tree/vegetation growing
close to an infrastructure network, such as power lines (PL).
[0026] Referring now to FIG. 2, illustrated is a high level
architecture of a system 100 for managing target object property
data of target objects constituting an infrastructure network. The
term "managing" of the target object property data means acquiring,
sending, analyzing and storing of such the target object property
data. The system 100 further enables in allocating resources and
executing actions to maintain the infrastructure network based on
the target object property data. The target object property data is
related to the properties of the target objects, explained in
detail herein later.
[0027] According to an embodiment, the system 100 includes a
mission control unit 110. The system 100 also includes a mission
target 114, associated with a mission 112. In an example, the
mission 112 may be related to mitigating risks to a PL network. The
PL network is usually extensive and distributed along a large
geographical area. Further, the PL network are often exposed to
potential threats, typically caused by encroaching vegetation, and
probability of possible failures of the PL network increases due to
such threats. Therefore, it is required to timely and regularly
monitor the network features (such the target object property data)
and spatial relations there-between.
[0028] The mission target 114 includes of one or more target
objects 118. The target objects 118 are physical world objects of
interest, including man-made target objects such as infrastructure
(buildings, roads, pipelines, grids including electricity power
stations and power lines); natural target objects which are of
interest such as ground, trees, river, lake, hills, undergrowth;
spatial target objects, such as boundaries, areas, e.g. country,
region, municipality and the like. In an example, the mission
target 114 includes electricity power lines having pylons and
conductors and all trees in a defined area. Alternatively, the
mission target 114 may include a vehicle convoy consisting of a
defined set of target objects 118 and the like.
[0029] Further, each of the target objects 118 includes at least
one target object property 119. The target object property 119
includes but not limited any of identifier(s) of target objects
118, spatial information (such as location or pose) of target
objects 118, attribute information (such as height, type, or size)
of target objects 118, or structural information (such as
topological relation) of the target objects 118 with respect to
each other. The target object property 119 can be quantified as
data relates to the various properties of the target objects 118,
accordingly the term "target object property data" and the term
"target object property 119" is used interchangeably based on
appropriate situations.
[0030] As shown in FIG. 2, the mission control unit 110 includes a
user terminal 116, one or more surveying modules 120, an analyzing
module 130, an action module 160, an action plan optimization means
170 and a record means 180. The mission control unit 110 also
includes an action type 164 and one or more action resources 166
for monitoring, guiding, or overriding the mission 112.
[0031] The user terminal 116 includes one of a laptop, a personal
computer, a desktop computer, a web tablet, wireless devices
including, although are not limited to, smart phones, Mobile
Internet Devices (MID), wireless-enabled tablet computers,
Ultra-Mobile Personal Computers (UMPC), phablets, tablet computers,
Personal Digital Assistants (PDA), web pads, smart phones, and
iPhone.RTM. etc. Further, in an example, the user terminal 116 may
be associated with electricity transmission and distribution
operators.
[0032] The surveying modules 120 of the system 100 are configured
to perform utility monitoring task, for example, monitoring of the
target object 118, and collecting remote sensing data 122 about the
target object 118 and the target object property 119.
[0033] Each of the surveying modules 120 includes at least one
remote sensing equipment 126. In an embodiment, the remote sensing
equipment 126 may include digital remote sensing equipment and
instruments such as LiDAR, SAR radar, thermal camera, camera, or
video camera, x-ray radar, etc. The remote sensing equipment 126
may be located near by the mission target site or may be located
remotely to the target site gathering information by remote
communication means. In a preferred embodiment, the remote sensing
equipment 126 includes LiDAR systems which have been gaining
popularity as a primary information source. LiDAR (also written
LIDAR) is a remote sensing technology that measures distance by
illuminating a target with a laser and analyzing the reflected
light. The term "LiDAR" comes from combining the words light and
radar. This emerging data acquisition tool provides an opportunity
to classify a utility corridor scene more reliably and thus
generate accurate 3D models of infrastructure features due to
LiDAR's ability of make highly dense and accurate data collection
as well as and multiple-echo data acquisition, which can also
provide information on the internal structure of vegetation.
[0034] LiDAR uses ultraviolet, visible, infrared, or near infrared
light to image objects and can be used with a wide range of
targets, including non-metallic objects, rocks, rain, chemical
compounds, aerosols, clouds and even single molecules. LiDAR
systems employ a narrow laser beam which can be used to map
physical features with very high resolution. Wavelengths from about
10 micrometers to the UV (ca. 250 nm) are used to suit the target.
Typically light is reflected via backscattering. Different types of
scattering are used for different LiDAR applications; most common
are Rayleigh scattering, Mie scattering, Raman scattering, and
fluorescence. Based on different kinds of backscattering, the LiDAR
can be accordingly called Rayleigh LiDAR, Mie LiDAR, Raman LiDAR,
Na/Fe/K Fluorescence LiDAR, and so on. Suitable combinations of
wavelengths can allow for remote mapping of atmospheric contents by
looking for wavelength dependent changes in the intensity of the
returned signal.
[0035] In an embodiment, the remote sensing equipment 126 is
installed and operated from a mobile platform 128. In an example,
the mobile platform 128 includes but not limited to a copter, fixed
wing plane, an Unmanned Aerial Vehicle (UAV), Unmanned Aerial
System (UAS), satellite, wheel drive terrain vehicle such as a car,
forest machine; or on a person backpack, helmet and the like. The
surveying modules 120, consisting of mobile platform 128 with
sensing equipment 126, is essentially a logical unit in terms of
dispatching the mission 112 to the mission target object 118.
[0036] The remote sensing equipment 126 is configured to collect
remote sensing data 122 and mission prior data 124 about the
mission target 114. The mission prior data 124 includes any
available data related to the mission target 114 prior to a
mission. The mission prior data 124 may be related to
useful/relevant information about one or more target object 118.
More specifically, the mission prior data 124 essentially contains
one or more target object property 119 of the target objects
118.
[0037] It may be contemplated by a person ordinarily skilled in the
art that the mission prior data 124 can be absolute (as in specific
coordinates), relative (to other mission prior data 124), or
structural (e.g. topology or proximity between the target objects
118). Further, the mission prior data 124 may be discrete, or
probabilistic (in a sense of probability distribution of the target
object property 119, or joint probability distribution of several
target object properties 119). Moreover, the mission prior data 124
may be in a paper form or, preferably, in an electronic form. The
mission prior data 124, in electronic form, may be stored locally
or over the Intranet or Internet, and in any suitable storage
medium like hard-drives, network drive, servers, discs, tapes, or
any combination thereof. Additionally, the collection of the remote
sensing data 122 and mission prior data 124 involves regular
monitoring of the target object 118, and obtaining data about the
target object property 119.
[0038] The analyzing module 130 is primarily a computing device
having standard functional elements, such as, a processor, storage
memory, flash memory, input means, output means, a set of programs,
etc. In an embodiment, the analyzing module 130 includes a target
recognition means 140 and observation rules 150 associated with
observations 152.
[0039] The analyzing module 130 is configured to extract relevant
target object property 119 from the remote sensing data 122 and
associating the extracted target object property 119 with the
corresponding target object 118. The analyzing module 130 is also
configured to update the target objects 118 and the target object
properties 119 using the obtained remote sensing data 122 and
comparing that with existing mission prior data 124 of the
corresponding target objects 118 and target object properties 119.
The comparison may be achieved by using the already detected
identity of the target objects 118 by the target recognition means
140.
[0040] The target recognition means 140 is configured to identify
target object 118 of relevance to the mission target 114. In an
example, target recognition means 126 may be configured for
recognition of the target object property 119, associated with the
target object 118, which is of relevance to the mission target 114.
The recognition of the target object property 119 is a process of
analyzing mission prior data 124 consisting of stored data related
to target objects 118 and target object properties 119.
[0041] The observation rules 150 are basically in the form of a
data structure, or the like. The observation rules 150 are
specification of events in terms of target objects 118 and target
object properties 119 which are associated with operative,
strategic or business goals. For example, in case of PL networks
the observation rules 150 may include a tree growing in close
proximity to a power line conductor, and in turn may pose a threat
to the network.
[0042] Further, the observations 152 are occurrences in target
object properties 119 which match the observation rules 150. The
analyzing module 130 is configured to associate the observations
152 with the observation rules 150 (rule instance, i.e. which rule
was triggered), target objects 118 (one, or multiple) and their
associated target object properties 119 matching the rule. The
analyzing module 130 may be further configured to optionally
provide timestamp, classification, priority, likelihood/confidence,
etc. for the each observation 152.
[0043] According to an embodiment, the action module 160 includes
action determination means 162 which is configured to select an
action type 164. The action determination means 162 is configured
to select an appropriate action type 164 for each observation 152.
The action type 164 is a type of strategic, operational or business
action which is targeted to manage one or more action resource 166
based on certain observation 152. The action resources 166 are any
physical, operational, organizational means to perform the action
type 164 with respect to certain target object(s) 118.
[0044] It may be contemplated by a person skilled in the art that
the action resources 166 may be either identified physical entities
(certain person or machine such as a guard, a field Engineer, a
contractor, etc.) or logical (a service provided by a
subcontractor). Further, in accordance with an embodiment, the
action type 164 might be a call to security personnel to the
location, an alarm to dispatch the field personnel in a specified
area, reminder to initiate procurement of dispatch work for field
maintenance, or the like.
[0045] In an embodiment, the action module 160 is associated with
the action plan optimization means 170. The action plan
optimization is the process of assigning one or more action
resources 166 to execute an action type 164 with respect to some
observation 152. The process may be continuous, one time, or
discrete (e.g. hourly, weekly, monthly, yearly). The action plan
optimization means 170 provides an action plan 172. The action plan
172 includes assigning action resources 166 for a set or subset of
observations 152. In an example, the plan 172 includes selecting
the appropriate subcontractor to perform field engineering based on
their costs and availability. Further, the action plan 172 may
contain additional attributes such as deadline, pricing/cost,
supplementary information like images, media, free text, etc.
[0046] According to an embodiment of the present disclosure, the
action plan optimization means 170 employs simple ranking procedure
for preparing action plans 172 which can be used for network-level
maintenance scheduling. In one embodiment, the action module 160
may generally rank those in the worst condition as the highest
priority without regard to the return on the funds invested. The
advantage of this method is its easy-to-use feature. However, the
resulting funding allocation is not optimal. In another embodiment,
the action module 160 may consider some type of measure of
cost-effectiveness in the selection process, if the goal is to
provide the best service for the available funds. It may be
contemplated by a person skilled in the art that alternate
allocation schemes could be found by employing other methods.
[0047] The record means 180 of mission control unit 110 is
configured for maintaining action results 182 out of a certain
action plan 172. The action results 182 are the results as reported
by action resources 166 of the execution of the action plan 172 on
the target objects 118. The action results 182 may be provided
continuously, discretely (e.g., every one min, one month), or one
time (after completion of the action plan 172). Further, action
results 182 are expressed in terms of target object properties 119,
for example, status=cut down (tree), or shape=new shape (of a
tree). Moreover, the action results 182 may optionally contain
supplementary information such as images, video, media, free text
or the like.
[0048] The system 100 of the present disclosure is operable to
perform the mission 112. The system 100 includes defining the
mission target 114, which may be a task dispatched to one or
multiple surveying modules 120 to remotely sense mission target 114
and to produce remote sensing data 122. Optionally, the system 100
is operable to process the remote sensing data 122 into target
object properties 119. Further, the system 100 is operable to
evaluate observation rules 152 for target object properties 119 of
the mission target 114 and produce any matching observations 150.
Moreover, the system 100 is operable to send the remote sensing
data 122, target object properties 119 and/or observations 152 to
parties (users such as electricity transmission and distribution
operators) needing the data.
[0049] According to an embodiment of the present disclosure, the
mission 112 could be to mitigate risks to power lines (PL) networks
of a target site. In such a situation, the mission 112 includes
corridor clearance analysis for the target site. Further, the
mission target 114 would be to check current state of all
components of the power network including all target objects 118
such as, trees and buildings near the network in the target site.
The target object property 119 could be aspects like height, type,
species, or size of the trees, buildings, poles for the power
lines. Furthermore, the mission prior data 124 may for example be
the approximate location of power line and its expected topology
(in a sense of a graph). Further, in such instance, the observation
rules 150 may include a tree growing in close proximity to a power
line conductor, and in turn may pose a threat to the network, and
observation 152 can be one or several occurrences needing
vegetation management.
[0050] Further, in such a case, the action plan 172 could be
vegetation management plans such as what to do immediately and what
for example next year, taking in consideration the growth.
Furthermore, the action type 164 can be undergrowth cutting, tree
cutting, maintenance staff field observations, whereas action
resources 166 can be for example trimming helicopter, contractors,
and/or forest workers. Moreover, the action result 182 can be
reported as, for example, results of the tree cutting.
[0051] In another exemplary embodiment, the system 100 of the
present disclosure provides automatic selection of the mission
target 114 based on quality of the mission prior data 124. It may
be understood that often the mission prior data 124 is
heterogeneous, that is, for some part the data is accurate and
recent such that the current state of the target object 118 and
target object property 119 can be forecasted; and for some parts
the data 124 may be missing or it is likely to have changed. For
example, the various possible scenarios could be: 1) the power line
corridor has just been surveyed and data is up-to-date, 2) power
line corridor was surveyed last year, and the vegetation growth can
be forecasted accurately, say over period of one year, 3) part of
the power line has never been surveyed, and there are no reliable
data, 4) part of the power line is attached to an area where based
on satellite imagery/SAR images, all the trees have been recently
cut in a large area and which may have caused new or eliminated
risks to the power line.
[0052] Based on embodiments, the system 100 is configured to
analyze quality of the mission prior data 124. In case of finding
data elements which need updating or checking, a mission is
allocated to perform that update or check. The present system 100
automatically forms missions 112 based on the quality of the
mission prior data 124 to perform missions where it is necessary to
improve the quality of the data 124. Optionally the analysis is
done continuously, and the mission is always selected which will
best improve the quality of the mission prior data 124 or which
provide the best cost/quality improvement ratio.
[0053] The system 100 is configured to estimate the needed time and
resources for the task. If there are overlaps with other orders of
the same resources, the resource allocation is done by prioritizing
the missions. For example, if there is a power line that has been
damaged, this could be allocated with higher priority than annual
maintenance. Such service may overcome the problems with known
project business models which take long time to start the work
because of the various steps like plan, tender, select, order,
execute, receive data, and accept. The process can take months
before any results are received and changing the Mission parameters
trigger complex renegotiation and planning process.
[0054] According to an embodiment of the present disclosure, the
users of the system 100 can set a service level for the resource
requests/mission targets. For example, users can set for the
mission tag of "no urgency" this can be done when the mission
control unit 110 or at least one surveying module 120 or service
personnel is free or close by, or "very urgent" where the mission
control unit 110 or at least one surveying module 120 or service
personnel needs to be immediately dispatched. Users can also
allocate funds for each mission. The allocated funds can be used by
the system to determine which of the missions is carried out
first.
[0055] As explained above, the system 100 is associated with
managing target object property data to allocate resources and
execute actions to maintain an infrastructure network. According to
an embodiment of the present disclosure, the system 100,
particularly, the analyzing module 130 may be configured for
executing performance models, which are used to predict future
conditions of the target object 118, and more particularly the
target object property 119 associated with the target object 118.
The performance models can be classified into two types:
deterministic or probabilistic. In deterministic models, the future
condition of an infrastructure network predicted as an exact value
based on the past information collected about the facility (as
explained in conjunction with the FIG. 2). In probabilistic models,
the performance of an infrastructure network is predicted by
estimating the probability with which the infrastructure network
would change to a particular condition state, from a predefined set
of possible facility conditions of the random process.
[0056] The probabilistic models are usually associated with
discretization of the condition states. Moreover, the probabilistic
models can also be used to describe the deterioration of the whole
infrastructure network. In general, the deterioration process of an
infrastructure network is a function of various factors affecting
the mechanistic or electric characteristics of the infrastructure
network, such as design, environment, materials, construction, age,
and the degree of maintenance. This in turn may help to schedule
maintenance activities for the considered infrastructure networks.
The effectiveness of maintenance planning in infrastructure
management depends on the accuracy of the predicted future
condition (such as the target object property 119) of the
infrastructure network, particularity the target objects 118. If
the performance models used in determining the maintenance policies
cannot effectively represent the actual deterioration process, the
planned maintenance activities might not yield the expected
results, which leads to suboptimal use of resources.
[0057] Referring now to FIG. 3, illustrated is a block diagram of a
system 300 for practicing an embodiment of the present disclosure.
According to an embodiment of the present disclosure, the system
300 is configured to be operable on the probabilistic performance
model. For example, the system 300 is operable to manage (acquire,
send, analyze and store) probability data of target objects (such
as the target objects 118) associated with an infrastructure
network. Further, the system 300 is operable to update probability
data of target object property in a database. Moreover, based on
the probability data of the target object property, the system 300
allocates resources and executes actions to maintain such
infrastructure network.
[0058] The system 300 includes a processor (or server) 310 and a
database 320 operatively connected to the server 310. The system
300 could be accessed over a communication network 330. In an
example, the communication network 330 includes but not limited to
Internet, Intranet, MAN, LAN, and WAN. The system further includes
a web-enabled device 340 associated with the user, such as, a power
line operator can for using the system 300. The user can use the
system 300 via a remote method such as using a web interface with
the help of the web-enabled device 340. Alternatively, the user use
the system 300 through direct digital integration of the processor
310 and the customer's ERP (Enterprise Resource Planning) or
similar system. The system 300 also includes a communication
terminal 350 for a survey unit or module (such the surveying module
120) to send and collected data, i.e., the target object property
data. The system 300 also includes a communication terminal 360
associated with action resources (such as the action resources 166)
i.e. people that update status or data (such as the target object
property data) over the communication network 330. In an example,
the communication terminal 360 can include but not limited to a
smart phone.
[0059] Based on embodiments, the system 300 is operated in a manner
that information on the missions (such as the mission 112) and
available resources (such as the action resources 166) is
maintained in the database 320. Further, the database 320 stores
probability data, a target object property data (such as the remote
sensing data 122 and mission prior data 124 of the target objects)
of plurality of target objects (such as a first target object and a
second target object), a list of actions (such as the action type
164) and a cost of actions, and the like.
[0060] According to an embodiment of the present disclosure, the
processor 310 includes an executable code configured to define a
first probability of accuracy of a property object data of a first
target object; define a second probability of the first target
object having a negative effect on a second target object;
calculate a combined probability from the first probability and the
second probability; use the combined probability to select to which
at least one of the target objects to allocate resources; and
update the first probability and the second probability in the
database, based on the action performed with the allocated
resource.
[0061] It may be contemplated by a person ordinarily skilled in the
art that the processor 310 can be a processor, of the analyzing
module 130 of the system 100, containing the executable code
(defined above) having intrusions to update the probability data of
target object property in the database 320, such as a database of
the analyzing module 130.
[0062] The first probability of accuracy of the property object
data of the first target object includes a probability value i.e.
correctness of available property object data with respect to the
first target object. In an example, if we consider the first target
object to be a tree and a property object data to be a height of
the tree, in that case the accuracy of the property object data
would depend on when the height of the tree was monitored last
time. Therefore, the first probability of accuracy of the property
object data of the first target object would be more if the
property object data is new (i.e. the first target object is
monitored recently, for example, with the help of the surveying
module 120) as compared to old property object data. Further, the
first probability of accuracy of the property object data of the
first target object could be as high as 1 (if the property object
data is very recent), otherwise the first probability of accuracy
could be low as 0.5 (if the property object data is one year or two
year old data) or even 0 (if the property object data is very old).
According to an embodiment, the property object data of the first
target object is selected from a group consisting of an identifier,
spatial information, attribute information and structural
information.
[0063] The second probability of the first target object having a
negative effect on a second target object includes a probable value
i.e. a threat level associated with the second target object that
the first target object would have a negative effect on the second
target. According to an embodiment, the negative effect includes
the first target object causing damage or failure to the second
target object. In an example, if we consider the second target
object to be a power line (or pylori), in that case negative effect
can be falling of the tree on the power line to break such power
line.
[0064] In an example, the second probability of the first target
object (such as the tree) having the negative effect on the second
target object (such as the power line) would be more if the tree is
located near to the power line (or about to touch the power line
very soon) as compared to when the tree is located far. Further,
the second probability of the first target object having the
negative effect on the second target object could be as high as 1
(if the tree is located very near to the power line), otherwise the
second probability could be low as 0 (if the tree is located far or
located at a safer distance from the power line).
[0065] The system 300, as mentioned above, is operable to calculate
a combined probability from the first probability and the second
probability. According to embodiment of the present disclosure, the
combined probability is calculated by multiplying the first
probability with the second probability. According to embodiment of
the present disclosure, the combined probability distribution is
calculated by multiplying the first probability distribution with
the second probability distribution.
[0066] The system 300, as mentioned above, is also operable to use
the combined probability to select to which at least one of the
target objects to allocate resources. It may be contemplated by a
person ordinarily skilled in the art that the system 300 may be
employed or executed with respect to a power line network having a
plurality of first target objects (such as trees) and a plurality
of second target objects (such as power lines or pylons).
Therefore, the at least one of the target objects (to be allocated
with resources) are those target objects (such as trees) for which
the combined probability is high. For example, based on a high
combined probability, trees belonging to a specific zone of a
corridor of the power line network may need immediate attention as
compared to other trees belonging to other zones and having low
combined probability.
[0067] The system 300, as mentioned above, is further operable to
update the first probability and the second probability in the
database 320, based on the action performed with the allocated
resource. According to an embodiment, the action is selected from
the group consisting of measuring the property object data of the
first target object, measuring the property object data of the
second target object, performing action on the first target object,
performing action on the second target object and allocating
equipment for performing actions. As mention herein, the action of
measuring the property object data of the first and second target
objects, can include collecting property object data of the first
and second target objects (for example, with the help of the
surveying module 120). Further, performing action on the first and
second target objects can relate to any maintenance or correction
work performed to correct the functional and/or structural aspects
of the target objects (for example, cutting the trees having high
combined probability).
[0068] Once the action is performed on target objects with the
allocated resource, the property object data of such target objects
changes with such actions. Accordingly, the first and second
probability of the target object changes with such actions. For
example, when a first target object (such as the tree) is having a
high probability of falling into a second target object (such as
the power line) is cut recently, for example, last week. In such
instance, the first probability of accuracy of the property object
data of the first target object can be as high as 1 (since it is
done recently). Further, the second probability of the first target
object having the negative effect on the second target object
becomes as low as 0. Therefore, such changes in the first
probability and the second probability are updated in the database
320 with respect to the target objects.
[0069] In an embodiment, the system 300 is operable to allocate and
handle continuous operations as a Mission Priority Queue (MPQ). As
per the MPQ, users, such as power line operators can add their
missions via the web-enabled device 340 based on the priority of
the mission. The MPQ is made accessible over the communication
network 330 directly to the users (for the part of their own
Missions, authentication and identification of the customer is
needed for the access). The MPQ is operated in the server 310 and
dispatched to surveying module 120; or the MPQ may be distributed
(over telecommunication) between central server 310 and all active
surveying modules 120, where surveying modules 120 independently
choose their missions from the shared MPQ.
[0070] Further, based on the MPQ the action plan optimization means
170 can help to optimize a mission (such as the mission 112) based
on some predefined criteria, such as, cost of execution (proximity,
logistics), value of the mission, cost of the mission, and
opportunity cost combined. Further, optimization can include a set
up for an auction of survey resources, say by, the customers
willing to pay the highest price gets the service now; those
looking at a lower price need to wait till capacity is
available.
[0071] The benefits of such a system 300, as taught by the present
disclosure includes fast response in case of value based
prioritization criteria as the user such as a network
infrastructure operator can get guaranteed immediate service by
bidding the highest price to their mission. Further, the system 300
provides flexibility in changes, as continuous optimization process
takes all changes into account immediately as they become available
in the system, such as new, changed, or cancelled missions.
Moreover, the system 300 provides improved asset utilization by
employing schemes such as when there is no high priority tasks, low
priority tasks could be taken at lower costs which would otherwise
not realize due to preventive costs, and therefore results in
better utilization of assets by survey operator.
[0072] In another exemplary embodiment, the system 300 of the
present disclosure provides optimization of mission by setting
priority queues. FIG. 4 shows a graphical representation of
probability distribution of failure in power lines of various areas
in an infrastructure network. Further, FIG. 4 illustrates an
exemplary probability distribution of failure in power lines for a
mission 112 required to measure all power lines in areas A, B and C
(constituting an infrastructure network). Therefore, the various
priority queues (sequence of attending to the areas A, B and C) can
include sequences such as ABC, ACB, BAC, BCA, CAB and CBA.
[0073] According to an embodiment of the present disclosure, the
processor 310 is operable to determine the priority order (or
priority queues) in a manner that the combined cost of probable
failure of the plurality of target objects with respect to the
priority order is minimized.
[0074] In an example, the database 320 stores information
pertaining to probability of failure of the target objects and cost
of failure of the target objects. The processor 310 is operable to
calculate a combined cost of probable failure of the plurality of
target objects as a function of at least one of: the probability of
failure, the cost of failure, and/or a time required by the
resources to perform an action. The processor 310 is thereafter
operable to select, from the plurality of target objects, at least
one target object to which to allocate the resources, based on the
combined cost of probable failure.
[0075] Further, the processor 310 is operable to calculate the
combined cost of probable failure by summing individual costs of
probable failure of the plurality of target objects as a function
of time. According to an embodiment, the processor 310 is operable
to calculate an individual cost of probable failure for a given
target object by using following equation.
C(1-P T)
[0076] wherein, C is the cost of failure of the given target
object;
[0077] P is a probability of non-failure of the given target object
in a time unit; and
[0078] T is the time (in time units) required by the resources to
perform an action on the given target object.
[0079] In an example, assuming number of infrastructure segments
(particularly target objects associated with the segments) in the
area A as 1, the area B as 2 and in the area C as 3. Further,
measurement time/verification time per segment is assumed to be one
week per segment. Therefore, total time required to check (or to
perform action) the entire network (constituted by the areas A, B
and C) is 1+2+3=6 weeks. Further, defining, for example, cost of
failure per segment in area A is 10 units, in area B is 20 units
and in area C is 5 units. Moreover, defining, for example,
probabilities of failure in a week in segments in area A, B and C
are 1%, 3% and 10%, respectively. Therefore, the probabilities of
non-failure would be 0.99, 0.97 and 0.9 with respect to the
probabilities of respective failure 1%, 3% and 10%. Example of a
segment is a power line between points X and Y i.e. if there is a
failure in the said segment between X and Y electricity can not be
distributed thru the segment (i.e the electricity network or grid
can be considered to consist of multiple connected segments).
[0080] Based on conventional methodology, a mission would be
implemented in round robin manner i.e., area A would be measured
first, followed by area B and further followed by area C. In that
case, based on the above equation probable cost failure associated
with the areas A, B and C can be calculated as:
Cost A=10.times.(1-0.99 1)=0.1 units
Cost B=20.times.(1-0.97 3)=1.74 units
Cost C=5.times.(1-0.9 6)=2.34 units
Therefore, the combined cost of probable failure=4.2 units
[0081] However, based on mission optimization, as employed by the
present system 300, the order of work will be optimized, for
example, by changing order of maintenance work to be area B, area A
and then area C. In that case, based on the above equation probable
cost failure associated with the areas A, B and C can be calculated
as:
Cost A=10.times.(1-0.99 3)=0.58 units
Cost B=20.times.(1-0.97 2)=0.29 units
Cost C=5.times.(1-0.9 6)=2.34 units
Therefore, the combined cost of probable failure=3.8 units, which
is lower than the first case where no optimization of mission was
employed.
[0082] The system 300 of the present disclosure enables to set cost
of failure for each of the segments and also possible profits for
each of the segments. Said information can be used to select which
of the segments will be measured/surveyed first in the order of the
minimum total cost. Further, in case there are observations 152,
the system 300 provides which segment needs to be managed through
which action type 164. In an example, the action type 164 includes
the tree cutting crew to be sent to the site to cater to an
immediate threat to the PL, when the observations become available,
without the need to wait for all results. The said cost model can
be also used to determine pricing for the resource allocation of
mobile platforms 128 or other mission related resources.
[0083] In another aspect, the system 300 of the present disclosure
is further configured for selecting which of missions should be
performed or should be allocated resources. For example the system
can be configured to determine purchase price (from network owners
respective) or selling price (from perspective of vendor performing
missions) related to purchasing/selling of resources to missions.
The determination of the purchase and/or selling price can be based
on quality of mission data and can be optimized based on the
probability of failure in each of the segments. The probability of
failure is initially assumed to have arbitrary value such as 1%.
The probability is adjusted each time the missions are performed.
For example, based on embodiments, probability is decreased if
corridor clearing analysis demonstrates that likelihood of having
trees interfering with the power line is low, otherwise the
probability is increased.
[0084] Further, if there are areas where last check has been made a
long time ago, the probability of failure can be set to increase as
function of time since the vegetation grows and environment can
change. Further, the parameters of this function can be based on
prior measurements gathered from the same area i.e. empirical
growth model in the locality. The probability of failure is
construed as a function of time from last measurement of the
probability where the probability increases over time. Therefore,
the present system 300 enables to provide improved model to define
the probability of failure based on the mission data.
[0085] Referring now to FIG. 5, illustrated is a method 500 for
updating probability data of target object property in a database,
in accordance with an embodiment of the present disclosure.
[0086] At step 502, a first probability of accuracy of a property
object data of a first target object is defined. In an example, the
property object data of the first target object is selected from a
group consisting of an identifier, spatial information, attribute
information and structural information.
[0087] At step 504, a second probability of the first target object
having a negative effect on a second target object is defined. In
an example, the negative effect includes the first target object
causing damage or failure to the second target object.
[0088] At step 506, a combined probability is calculated using the
first probability and the second probability. In an example, the
combined probability is calculated by multiplying the first
probability with the second probability.
[0089] At step 508, the combined probability is used to select to
which at least one of the target objects is to allocate
resources.
[0090] At step 510, the allocated resources is used to perform an
action on the selected at least one of the target objects. In an
example, the action is selected from the group consisting of
measuring the property object data of the first target object,
measuring the property object data of the second target object,
performing action on the first target object, performing action on
the second target object, and allocating equipment for performing
actions. In an embodiment, the action is prioritized by using
combined probability and cost of non-action.
[0091] At step 512, based on the action the first probability and
the second probability are updated in the database.
[0092] The steps 502 to 512 are only illustrative and other
alternatives can also be provided where one or more steps are
added, one or more steps are removed, or one or more steps are
provided in a different sequence without departing from the scope
of the claims herein.
[0093] It is intended that all matter contained in the above
description or shown in the accompanying drawings shall be
interpreted as illustrative only and not limiting of the scope of
the disclosure. Expressions such as "including", "comprising",
"incorporating", "consisting of", "have", "is" used to describe the
present disclosure are intended to be construed in a non-exclusive
manner, namely allowing for items, components or elements not
explicitly described also to be present. Reference to the singular
is also to be construed to relate to the plural.
[0094] While certain exemplary embodiments have been described and
shown in the accompanying drawings, it is to be understood that
such embodiments are merely illustrative of and not restrictive on
the broad present disclosure, and that this present disclosure is
not limited to the specific constructions and arrangements shown
and described, since various other modifications and/or adaptations
may occur to those of ordinary skill in the art. It is to be
understood that individual features shown or described for one
embodiment may be combined with individual features shown or
described for another embodiment.
* * * * *