U.S. patent application number 16/790840 was filed with the patent office on 2020-08-27 for flexible automated sorting and transport arrangement (fast) asset monitor.
The applicant listed for this patent is Walmart Apollo, LLC. Invention is credited to Matthew D. Alexander, Jason Bellar, Chris Ford, William Mark Propes, Amy Savaiinaea.
Application Number | 20200272918 16/790840 |
Document ID | / |
Family ID | 1000004689098 |
Filed Date | 2020-08-27 |
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United States Patent
Application |
20200272918 |
Kind Code |
A1 |
Bellar; Jason ; et
al. |
August 27, 2020 |
FLEXIBLE AUTOMATED SORTING AND TRANSPORT ARRANGEMENT (FAST) ASSET
MONITOR
Abstract
A disclosed system for transport asset monitoring, for example
monitoring truck trailer unloading progress at large retail
locations, includes an artificial intelligence (AI) solution for
managing operations at a facility. The AI solution analyzes current
load percentage and other data to predict availability for moving
the transport asset and ability to accept a new incoming transport
asset. Predictions of availability can reduce response times,
resulting in higher utilization rates for assets, thereby improving
efficiency. An exemplary system includes a sensor configured to
sense operation progress parameter data for a transport asset; and
logic to receive the operation progress parameter data from the
sensor; determine, using the AI solution and based at least on the
operation progress parameter data, a predicted milestone parameter;
and report the predicted milestone parameter to a remote node.
Inventors: |
Bellar; Jason; (Bella Vista,
AR) ; Propes; William Mark; (Bentonville, AR)
; Ford; Chris; (Rogers, AR) ; Alexander; Matthew
D.; (Rogers, AR) ; Savaiinaea; Amy;
(Bentonville, AR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Walmart Apollo, LLC |
Bentonville |
AR |
US |
|
|
Family ID: |
1000004689098 |
Appl. No.: |
16/790840 |
Filed: |
February 14, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62811187 |
Feb 27, 2019 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 67/12 20130101;
G05D 2201/0213 20130101; G06N 20/00 20190101; G06N 5/04 20130101;
G05D 1/0088 20130101; G05D 2201/0216 20130101 |
International
Class: |
G06N 5/04 20060101
G06N005/04; H04L 29/08 20060101 H04L029/08; G06N 20/00 20060101
G06N020/00; G05D 1/00 20060101 G05D001/00 |
Claims
1. A system for transport asset monitoring, the system comprising:
a first sensor configured to sense a first operation progress
parameter data for a first transport asset; a processor; and a
non-transitory computer-readable medium storing instructions that
are operative when executed by the processor to: receive the first
operation progress parameter data from the first sensor; determine,
using an artificial intelligence (AI) solution and based at least
on the first operation progress parameter data, a predicted
milestone parameter; and report the predicted milestone parameter
to a remote node.
2. The system of claim 1 wherein the transport asset comprises a
trailer.
3. The system of claim 1 wherein the first sensor comprises at
least one sensor selected from the list consisting of: an RFID
sensor, a barcode scanner, and a computer vision (CV) sensor.
4. The system of claim 1 wherein the first operation progress
parameter data comprises identification of items unloaded from the
transport asset.
5. The system of claim 1 further comprising: a second sensor
configured to sense a second operation progress parameter data for
the transport asset, wherein the second operation progress
parameter data comprises identification of items remaining on the
transport asset; and wherein the instructions are further operative
to: receive the second operation progress parameter data from the
second sensor; and wherein determining the predicted milestone
parameter comprises determining, using the AI solution and based at
least on the first operation progress parameter data and the second
operation progress parameter data, the predicted milestone
parameter.
6. The system of claim 1 wherein the instructions are further
operative to: receive, from a user interface (UI), confirmation or
correction of the predicted milestone parameter.
7. The system of claim 1 further comprising: a machine learning
(ML) component to generate the AI solution using at least
historical operation progress parameter data.
8. The system of claim 1 further comprising: a wireless
communication module; and an automated ground vehicle (AGV) in
communication with the processor via the wireless communication
module.
9. The system of claim 1 wherein the instructions are further
operative to: generate, using the AI solution and based at least on
the first operation progress parameter data, logistical
instructions for a second transport asset.
10. A method of transport asset monitoring, the method comprising:
receiving a first operation progress parameter data for a first
transport asset from a first sensor, wherein receiving the first
operation progress parameter data from the first sensor data
comprises receiving the first operation progress parameter data
from at least one sensor selected from the list consisting of: an
RFID sensor, a barcode scanner, and a computer vision (CV) sensor;
determining, using an artificial intelligence (AI) solution and
based at least on the first operation progress parameter data, a
predicted milestone parameter; and reporting the predicted
milestone parameter to a remote node.
11. The method of claim 10 wherein receiving the first operation
progress parameter data for the first transport asset comprises
receiving the first operation progress parameter data for a
trailer.
12. The method of claim 10 wherein the first operation progress
parameter data comprises identification of items unloaded from the
transport asset.
13. The method of claim 10 further comprising: receiving a second
operation progress parameter data from a second sensor, wherein the
second operation progress parameter data comprises identification
of items remaining on the transport asset; and wherein determining
the predicted milestone parameter comprises determining, using the
AI solution and based at least on the first operation progress
parameter data and the second operation progress parameter data,
the predicted milestone parameter.
14. The method of claim 10 further comprising: receiving, from a
user interface (UI), confirmation or correction of the predicted
milestone parameter.
15. The method of claim 10 further comprising: generating, with a
machine learning (ML) component, the AI solution using at least
historical operation progress parameter data.
16. The method of claim 10 further comprising: wirelessly
communicating, with an automated ground vehicle (AGV), logistical
data related to the AGV.
17. The method of claim 10 further comprising: generating, using
the AI solution and based at least on the first operation progress
parameter data, logistical instructions for a second transport
asset.
18. One or more computer storage devices having computer-executable
instructions stored thereon for transport asset monitoring, which,
on execution by a computer, cause the computer to perform
operations comprising: receiving a first operation progress
parameter data for a first transport asset from a first sensor,
wherein receiving the first operation progress parameter data for
the first transport asset comprises receiving the first operation
progress parameter data for a trailer, wherein the first operation
progress parameter data comprises identification of items unloaded
from the transport asset, and wherein receiving the first operation
progress parameter data from the first sensor data comprises
receiving the first operation progress parameter data from at least
one sensor selected from the list consisting of: an RFID sensor, a
barcode scanner, and a computer vision (CV) sensor; determining,
using an artificial intelligence (AI) solution and based at least
on the first operation progress parameter data, a predicted
milestone parameter; receiving, from a user interface (UI),
confirmation or correction of the predicted milestone parameter;
reporting the predicted milestone parameter to a remote node;
generating, with a machine learning (ML) component, the AI solution
using at least historical operation progress parameter data; and
generating, using the AI solution and based at least on the first
operation progress parameter data, logistical instructions for a
second transport asset.
19. The one or more computer storage devices of claim 18 wherein
the operations further comprise: receiving a second operation
progress parameter data from a second sensor, wherein the second
operation progress parameter data comprises identification of items
remaining on the transport asset; and wherein determining the
predicted milestone parameter comprises determining, using the AI
solution and based at least on the first operation progress
parameter data and the second operation progress parameter data,
the predicted milestone parameter.
20. The one or more computer storage devices of claim 18 wherein
the predicted milestone parameter comprises an expected completion
time of unloading the trailer.
Description
BACKGROUND
[0001] In large retail settings, the delivery, unloading, and
sorting of items can be a significant aspect of operational
efficiency. Under-utilization of assets, caused by delays, faulty
planning assumptions, and resource limitations, can degrade
efficiency and negatively impact profitability. In some
conventional asset monitoring approaches, delivery assets (e.g.,
truck trailers) are monitored during transit and for arrival at
destinations, and then visibility into activities affecting the
asset's return to service abruptly cease.
SUMMARY
[0002] A disclosed system for transport asset monitoring, for
example monitoring truck trailer unloading progress at large retail
locations, includes an artificial intelligence (AI) solution for
managing operations at a facility. The AI solution analyzes current
load percentage and other data to predict availability for moving
the transport asset and ability to accept a new incoming transport
asset. Predictions of availability can reduce response times,
resulting in higher utilization rates for assets, thereby improving
efficiency. An exemplary system includes a sensor configured to
sense operation progress parameter data for a transport asset; and
logic to receive the operation progress parameter data from the
sensor; determine, using the AI solution and based at least on the
operation progress parameter data, a predicted milestone parameter;
and report the predicted milestone parameter to a remote node.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] The disclosed examples are described in detail below with
reference to the accompanying drawing figures listed below:
[0004] FIG. 1 illustrates an exemplary arrangement for transport
asset monitoring;
[0005] FIG. 2 illustrates a finer level of detail for some elements
of the exemplary arrangement of FIG. 1;
[0006] FIG. 3 illustrates a finer level of detail for some elements
of the exemplary arrangement of FIG. 1;
[0007] FIG. 4 shows a flow chart of operations associated with the
exemplary arrangement of FIG. 1; and
[0008] FIG. 5 is a block diagram of an example computing node for
implementing aspects disclosed herein.
[0009] Corresponding reference characters indicate corresponding
parts throughout the drawings. Elements in the figures are
illustrated for simplicity and clarity and have not necessarily
been drawn to scale. For example, the dimensions and/or relative
positioning of some of the elements in the figures may be
exaggerated relative to other elements to help to improve
understanding. Also, common but well-understood elements that are
useful or necessary in a commercially feasible embodiment may not
be depicted, in order to facilitate a less obstructed view.
DETAILED DESCRIPTION
[0010] A more detailed understanding may be obtained from the
following description, presented by way of example, in conjunction
with the accompanying drawings. The entities, connections,
arrangements, and the like that are depicted in, and in connection
with the various figures, are presented by way of example and not
by way of limitation. As such, any and all statements or other
indications as to what a particular figure depicts, what a
particular element or entity in a particular figure is or has, and
any and all similar statements, that may in isolation and out of
context be read as absolute and therefore limiting, may only
properly be read as being constructively preceded by a clause such
as "In at least some embodiments, . . . ." For brevity and clarity
of presentation, this implied leading clause is not repeated ad
nauseum.
[0011] In large retail settings, the delivery, unloading, and
sorting of items can be a significant aspect of operational
efficiency. Under-utilization of assets, caused by delays, faulty
planning assumptions, and resource limitations, can degrade
efficiency and negatively impact profitability. In some
conventional asset monitoring approaches, delivery assets (e.g.,
truck trailers) are monitored during transit and for arrival at
destinations, and then visibility into activities affecting the
asset's return to service abruptly cease.
[0012] Therefore, a disclosed system for transport asset
monitoring, for example monitoring truck trailer unloading progress
at large retail locations, includes an artificial intelligence (AI)
solution for managing operations at a facility. The AI solution
analyzes current load percentage and other data to predict
availability for moving the transport asset and ability to accept a
new incoming transport asset. Predictions of availability can
reduce response times, resulting in higher utilization rates for
assets, thereby improving efficiency. An exemplary system includes
a sensor configured to sense operation progress parameter data for
a transport asset; and logic to receive the operation progress
parameter data from the sensor; determine, using the AI solution
and based at least on the operation progress parameter data, a
predicted milestone parameter; and report the predicted milestone
parameter to a remote node.
[0013] In some examples, the AI solution manages trailer operations
at a retail facility. Some example AI solutions analyze trailer
current unloading progress, remaining load, trailer route, driver
log, driver instructions. This permits predicting when the trailer
can move, and thus, some examples also determine driver
availability to transport the trailer, so that driver availability
and trailer availability can be coordinated. Additional efficiency
is achieved when multi-stop trailers are routed to another
destination when the retail facility will not be ready for the
arrival at the expected time. Also, in some examples, when trailer
unloading is finished, additional activity can be coordinated, for
example stowing a modular conveyor assembly, such as the mechanized
portions of a flexible automated sorting and transport arrangement
(FAST). Some examples also instruct loaders to load return or
transfer items on a trailer, or empty pallets or breakpack
cartons.
[0014] The result is enhanced efficiency, from leveraging
automation to coordinate services and activities. By informing
remote nodes of progress, activity schedules for dispersed assets
can be more tightly integrated. Information is shared with incoming
trucks and a driver logistics system to pair drivers (who are in
the vicinity and have a budget of driving time remaining) with
trailers, being able to schedule the trailer departure in advance.
A driver management rules assists locating a driver to transport a
completed trailer to its next destination.
[0015] FIG. 1 illustrates an exemplary arrangement 100 for
transport asset monitoring. Arrangement 100 includes several
transport assets 102a-102e. In general, a transport asset may
include a trailer, a box truck, a flatbed truck, a pickup truck, an
automated ground vehicle (AGV), a railway vehicle, an aerial
vehicle, or any other motorized or towed conveyance. For example,
transport asset 102a is a trailer connected to a tractor 104. As
illustrated, a retail facility 112 has an unloading bay 114 with
several dock positions 106a-106d. Transport asset 102a is occupying
dock position 106a and transport asset 102b is occupying dock
position 106d. Dock positions 106b and 106c are unoccupied.
[0016] Also as illustrated, transport asset 102c is departing
retail facility 112, navigating toward either a distribution center
130 to retrieve another load, or if transport asset 102c is on a
multi-stop route, transport asset 102c is navigating toward a
nearby retail facility 112a. Transport asset 102d is inbound to
retail facility 112, and will occupy one of dock positions 106b and
106c upon arrival. Although there are two available dock positions
(106b and 106c) the advantageous operations of the teachings herein
have predicted that unloading operations will be inefficient if
transport asset 102e arrives as expected. Such a determination can
be made, as described below, using information related to current
operation progress parameter data (e.g., unloading status) for
transport assets 102a and 102b, labor force profile (e.g.,
absences, workers available), equipment status (e.g., mechanized
equipment failures and downtime), and other information. Therefore,
despite the predicted availability of at least one of dock
positions 106b and 106c, transport asset 102e is diverted to a
different destination. For example, transport asset 102e may be
diverted to nearby retail facility 112a using a wireless
communication link 526. Such efficiencies are achievable because
transport assets 102a-102e report their position and other
operation progress parameter data to a central hub 380, where it
can be retrieved for advantageous use.
[0017] Within unloading bay 114, there is a backup of item (such as
item 124) that are awaiting removal from unloading bay 114 for
stocking on a retail floor space 116 or some other location within
retail facility 112. In some examples, an AGV 204a is tasked with
transporting item 124 from unloading bay 114 to retail floor space
116, such as delivering featured items on a pallet nearby a front
door 126 of retail floor space 116. This, however, requires time.
Due to prior experience, when all four dock positions 106b and 106c
had been occupied simultaneously, with a labor force comparable to
current labor force 122, the unloading operations were
significantly slowed. As a result, a machine learning (ML)
component (see FIG. 3) generated an AI solution (using historical
operation progress parameter data) that diverted transport asset
102e.
[0018] AGV 204a is in communication with a wireless communication
module 374, permitting it to share data with, and receive
instructions from, FAST computing node 210 and/or a FAST asset
monitor computing node 300. FAST computing node 210 is described in
greater detail with respect to FIG. 2, and asset monitor computing
node 300 is described in greater detail with respect to FIG. 3. In
some examples, asset monitor computing node 300 is a stand-alone
node; in some examples, asset monitor computing node 300 is
integrated within FAST computing node 210.
[0019] Asset monitor computing node 300 predicts operational
milestone parameters (e.g., percentage complete and expected
completion time for an unloading operation) and reports one or more
predicted milestone parameters to a remote node, for example
central hub 380. FAST computing node 210 and Asset monitor
computing node 300 communicate with central hub 380 over a network
530. In some examples, nearby retail facility 112a has the same set
of assets as retail facility 112, and so also communicate with
central hub 380 over network 530. A warehouse management system
(WMS) 382, which includes a WMS asset monitor component 384 also
communicates with central hub 380 over network 530. This permits
WMS 382 to monitor predicted availability for transport assets 102a
and 102b, providing lead time for scheduling use of transport
assets 102a and 102b for future delivery tasks. More detail is
provided for WMS 382 and WMS asset monitor component 384 in
relation to FIG. 3.
[0020] FIG. 2 illustrates a finer level of detail for some elements
of arrangement 100. A conveyor assembly 202 is used in unloading
bay 114 for unloading operations for transport asset 102a in dock
position 106a. In some examples, conveyor assembly 202 is a FAST
modular conveyor assembly that can be disassembled and stowed when
not in use. The disassembly operation requires a labor force, which
can be beneficially scheduled in advance, if the completion time of
the unloading operation can be predicted. As illustrated, items
206a, 206b, and 206c have been unloaded from transport asset 102a;
item 208 remains on transport asset 102a.
[0021] A first sensor 370a, which can be any of an RFID sensor, a
barcode scanner, and a computer vision (CV) sensor, senses a first
operation progress parameter data for transport asset 102a,
specifically, identification of item 206c that is being unloaded
from transport asset 102a. A second sensor 370b, which can be any
sensor suitable for the task, senses a second operation progress
parameter data for the transport asset, specifically,
identification of item 208 remaining on transport asset 102a. Asset
monitor computing node 300 receives the first operation progress
parameter data from first sensor 370a and receives the second
operation progress parameter data from second sensor 370b. As
illustrated, transport asset has its own wireless communication
module 374 for communicating data from second sensor 370b to asset
monitor computing node 300.
[0022] Asset monitor computing node 300 uses application logic 310
and data 340 (described in more detail in relation to FIG. 3), to
determine a predicted milestone parameter, for example the
completion time of unloading transport asset 102a. For example, a
cargo manifest 220, that lists items 206a-206c and 208 can be used,
along with the identification of items 206a-206c (by sensor 370a)
as having been unloaded, enables determination that item 208
remains on board. Alternatively, sensor 370b can, in some
situations, preclude the need for calculating the remaining items,
by sensing item 208 directly. In order to facilitate determining a
predicted milestone parameter, asset monitor computing node 300
uses a variety of data sets. As illustrated, these include a retail
facility inventory 222, product information 224, a labor force
profile 226, equipment status 228, and weather information 230.
[0023] Product information 224 includes data regarding container
sizes, weights, and other parameters that can affect delivery
speed. Such information is relevant when, historically, delivery of
products having certain characteristics slowed unloading operation
speed for subsequent transport assets that arrived within a certain
period of time. Labor force profile 226 can be used to determine
whether there are sufficient labor assets available to unload
additional expected incoming transport assets. If not, then
incoming transport assets may be diverted, or their predicted
departure availability will be delayed. Equipment status 228
indicates if unloading equipment, such as a forklift, is out of
operation, and thus may cause unloading operation delays. Weather
information 230 can be used when past inclement weather incidents
are correlated with delayed operations. For example, snow storms
can affect labor asset availability and preclude use of some dock
locations.
[0024] AGVs 204a and 204b are tasked by FAST computing node 210 or
asset monitor computing node 300 with transporting items 124, 206a,
206c, 206c, and 208 from unloading bay 114 to retail floor space
116, automated storage and retrieval system (ASRS) 216, or another
location. In some examples, asset monitor computing node 300
wirelessly communicates with AGVs 204a and 204b to provide
logistical data related to the AGV. The logistical data can include
item delivery prioritization, delivery instructions, and navigation
instructions. For example, asset monitor computing node 300 may
instruct AGV 204b to retrieve and deliver item 124 prior to
retrieving and delivering item 206. As illustrated, FAST computing
node 210 includes item data 212 that identifies delivery
destinations for items 124, 206a, 206c, 206c, and 208, an AGV
navigation and control component 214 for directing AGVs 204a and
204b, and a communication logic 318 for communicating with various
remote nodes (e.g., central hub 380).
[0025] FIG. 3 illustrates a finer level of detail for asset monitor
computing node 300. Asset monitor computing node 300 includes a
processor 514 that executes operations encoded as logic and stored
in a memory 512. Memory is a non-transitory computer-readable
medium storing data 340 and instructions (application logic 310)
that are operative, when executed by processor 514, to perform
operations described herein. Asset monitor computing node 300
receives operation progress parameter data from one or more of
sensors 370a, 370b, and 370c, for example, via wireless
communication module 374. In the illustrated example, sensor 370a
is depicted as a CV sensor, although it should be understood that
sensor 370a can be a different type of sensor, that any of sensors
370b and 370c can be a CV sensor, and also that a different number
of sensors can be used.
[0026] Asset monitor computing node 300 calculates, using a
calculation logic 312 the remaining load of a transport asset,
using cargo manifest 220 and subtracting items unloaded 344, to
determine items remaining 346. This provides a percentage complete.
Using the scenario depicted in FIG. 2, items unloaded 344 lists
items 206a-206c and items remaining 346 lists item 208. From this
data, a prediction logic 314 uses AI solution 352 to determine,
based at least on operation progress parameter data (e.g., one or
more of items unloaded, items remaining, percentage complete), a
predicted milestone parameter (e.g., expected completion time or
departure-ready time). A timer 372 provides the elapsed time for
reaching the current state of unloading percentage, and as a simple
prediction example, if the unloading progress has reached 50% (half
way), the remaining time is equal to the elapsed time. However,
such a simplified calculation does not leverage historical data
350, which may indicate that the second half of unloading
operations were either faster or slower than the first half in most
situations matching current data 348. Using AI solution 352 with
operation progress parameter data and current data 348 permits a
more reliable prediction. In some examples, AI solution 352 is a
neural net that had been programmed by an ML component 326 using
historical data 350.
[0027] A user interface (UI) 316, permits a human to confirm or
correct the predicted milestone parameter using a presentation
component 516 (described in more detail with respect to FIG. 5). In
some examples, the milestone parameter is departure-ready time,
which can differ from unloading compete time. For example, in
multi-stop situation, a transport asset is only partially unloaded.
Also, if an empty transport asset is to be loaded with return
items, prediction logic 314 may predict unloading complete time
accurately, but may not factor in the reloading time. In such a
situation, a human can over-ride estimate of the time that the
transport asset will be ready to depart, using UI 316. In some
examples, the progress (measured as completion percentage) is
displayed on presentation component 516 as a bar graph, to provide
workers in the vicinity of unloading bay 114 with a visual
indication of progress. The barograph representing progress can
also be displayed at central hub 380, WMS 382 (using WMS asset
monitor component 384 within WMS 382 to communicate with asset
monitor computing node 300), and/or a driver dispatch 386 that will
be scheduling and sending a driver to retrieve the transport asset
being unloaded.
[0028] Communication logic 318 uses network 530 to report the
predicted milestone parameter to a remote node, for example central
hub 380, driver dispatch 386, and/or WMS 382 (specifically, WMS
asset monitor component 384 within WMS 382). An inbound asset
management logic 320 determines whether to divert expected incoming
transport assets. For example, AI solution 352 may predict that an
inbound transport asset will not be unloaded efficiently, and may
therefor instruct a delay or diversion to provide a minimum arrival
interval between transport assets, even when there is an empty dock
position. Some of the factors that AI solution 352 operates on are
the type of items in the current load and expected arriving load,
labor force profile, weather, equipment status, and other factors.
In some situations, for example a multi-stop route, a transport
asset may be loaded such that changing an expected route is not
practical because the item positions in the truck would require
unloading and reloading to access the items out of the order that
was expected when the transport asset had been loaded. Such
determinations can leverage cargo manifest 220. A logistics logic
322 is operative to generate, using AI solution 352 and based at
least on operation progress parameter data, logistical instructions
for a transport asset. Logistics logic 322 can work with, or
include, inbound asset management logic 320, although logistics
logic 322 also includes planning for outbound transport assets.
[0029] A driver management logic 324 assists locating a driver to
transport a completed trailer to its next destination for when the
transport asset is ready to depart, in order to provide lead time.
In some examples, driver management logic 324 and inbound asset
management logic 320 are included within logistics logic 322.
Driver management logic 324 helps avoid situations in which a
transport asset sits in a dock position empty and unused, because
efforts to locate a driver and tractor had not begun early enough.
In some examples, driver management logic 324 include driver
management rules such as: [0030] driver hours worked during the
current cycle (e.g., current day); [0031] driver hours worked
during recent cycles (e.g., current week); [0032] driver proximity;
[0033] return load priority; [0034] driver wait time; and [0035]
predicted driver task completion time.
[0036] ML component 326 generates AI solution 352 using at least
historical operation progress parameter data stored in historical
data 350. AI solution 352 is then able to use current data 348 as
input data. Current data 348 includes data obtained from sensors
(e.g., sensors 370a, 370b, and 370c, which are configured to sense
operation progress parameter data for a transport asset), and also
a variety of data sets, including data available through FAST
computing node 210. Examples of the data stored in current data 348
for use by AI solution 352 include (see FIG. 2): retail facility
inventory 222, product information 224, labor force profile 226,
equipment status 228, and weather information 230. Historical data
350 keeps records of past contents of current data 348, along with
actual operational activity timing (determined at least partially
using timer 372), so that AI solution 352 can be trained and
improve with time.
[0037] Other executable instructions 330 and other data 360 include
other logic and data that are useful in the operations disclosed
herein. Other facility asset monitor node 300a may be a duplicate
of asset monitor computing node 300, although located elsewhere
(e.g., nearby retail facility 112a of FIG. 1). In some examples,
other facility asset monitor node 300a share data with each other,
central hub 380, and WMS 382 to permit fleet-wide operations
planning, thereby enhancing efficiency in the utilization of
transport assets.
[0038] FIG. 4 shows a flow chart 400 of operations associated with
arrangement 100. In some examples, some or all of flow chart 400 is
performed as computer-executable instructions on a computing node
500 (see FIG. 5). Flow chart 400 commences when a first transport
arrives in operation 402. Current data (e.g., current data 348) is
obtained in operation 404, and the cargo manifest is obtained in
operation 406. In some examples, FAST computing node 210 provides
at least a portion of the data obtained in operations 404 and 406.
In some examples, data is obtained both locally from retail
facility 112 and remotely from central hub 380 and/or WMS 382.
[0039] Transport asset operations, such as unloading the first
transport asset, begin in operation 408, and tracking of progress
begins in operation 410. Operation 412 includes receiving a first
operation progress parameter data for a first transport asset from
a first sensor. In some examples, the first operation progress
parameter data comprises identification of items unloaded from the
transport asset. In some examples, receiving the first operation
progress parameter data for the first transport asset comprises
receiving the first operation progress parameter data for a
trailer. In some examples, receiving the first operation progress
parameter data from the first sensor data comprises receiving the
first operation progress parameter data from at least one sensor
selected from the list consisting of: an RFID sensor, a barcode
scanner, and a CV sensor. As indicated in FIG. 4, operation 412 may
repeat for additional sensor input. Some examples of repeating
operation 412 include receiving a second operation progress
parameter data from a second sensor, wherein the second operation
progress parameter data comprises identification of items remaining
on the transport asset.
[0040] Operation 414 includes determining progress, for example,
determining or calculating the remaining load on a trailer, and
calculating the percentage complete for the transport asset
operation. The elapsed time is determined in operation 416.
Operation 418 includes determining, using an artificial
intelligence (AI) solution and based at least on the first
operation progress parameter data, a predicted milestone parameter.
In some examples, determining the predicted milestone parameter
comprises determining, using the AI solution and based at least on
the first operation progress parameter data and the second
operation progress parameter data, the predicted milestone
parameter. In some examples, determining the predicted milestone
parameter comprises determining when the transport asset operation
will be completed and the transport asset will be ready to
depart.
[0041] Operation 420 includes receiving, from a UI, confirmation or
correction of the predicted milestone parameter. In some examples,
UI 316 [provides logic and control and a human operator interfaces
with UI 316 via presentation component 516. Thus, a human can
verifies or human over-ride (correct) the prediction, based on
conditions known to the human. For example, a new equipment
malfunction may be known to the human, but not yet processed by AI
solution 352. Operation 422 includes reporting the predicted
milestone parameter to a remote node, for example central hub 380.
In some examples, a completion (or ready-to-depart) prediction is
pushed out via an application programming interface (API) to a
messaging service. Operation 424 includes wirelessly communicating,
with an AGV, logistical data related to the AGV. This includes
prioritization instructions for item removal from unloading bay
114, and in some cases navigational instruction.
[0042] Operation 426 includes determining inbound transport assets,
for example a second transport asset. Decision operation 428
includes determining, using the AI solution and based at least on
the first operation progress parameter data, whether to divert the
second transport asset, delay it, or provide any other logistical
instructions. If yes, then operation 430 includes generating, using
the AI solution and based at least on the first operation progress
parameter data, logistical instructions for a second transport
asset.
[0043] Operation 432 includes contacting or alerting drivers (e.g.,
via driver dispatch 386) so that a driver (and possibly also a
needed tractor) will be available as soon as possible after the
first transport asset is ready to depart. This reduces the time
that the first transport asset occupies a dock position
unnecessarily, relative to waiting until the transport asset is
ready to depart before alerting a driver. Additionally, it can
improve the utilization percentage of the transport asset.
Operation 434 includes generating a route plan for the driver to
use, for example returning the transport asset to a distribution
center or sending it elsewhere for another task.
[0044] Operation 436 includes adding the current data and the
recent historical operation progress parameter data (e.g.,
unloading progress, time to unload and predicted completion versus
actual completion) to historical data, such as historical data 350.
Operation 438 includes generating, with an ML component, the AI
solution using at least historical operation progress parameter
data (e.g., ML component 326 and AI solution 352). This includes
improving a prior-existing AI solution. Operation 440 permits
advantageous use of historical data by assessing operations. For
example, chronic delays can be correlated with certain events
and/or conditions. This information can be used to improve
efficiency by informing decision-makers of issues to address, using
quantifiable metric data.
Exemplary Operating Environment
[0045] FIG. 5 is a block diagram of an example computing node 500
for implementing aspects disclosed herein and is designated
generally as computing node 500. Computing node 500 is one example
of a suitable computing environment and is not intended to suggest
any limitation as to the scope of use or functionality of the
invention. Neither should the computing node 500 be interpreted as
having any dependency or requirement relating to any one or
combination of components/modules illustrated. The examples and
embodiments disclosed herein may be described in the general
context of computer code or machine-useable instructions, including
computer-executable instructions such as program components, being
executed by a computer or other machine, such as a personal data
assistant or other handheld device. Generally, program components
including routines, programs, objects, components, data structures,
and the like, refer to code that performs particular tasks, or
implement particular abstract data types. The disclosed examples
may be practiced in a variety of system configurations, including
personal computers, laptops, smart phones, mobile tablets,
hand-held devices, consumer electronics, specialty computing nodes,
etc. The disclosed examples may also be practiced in distributed
computing environments, where tasks are performed by
remote-processing devices that are linked through a communications
network.
[0046] Computing node 500 includes a bus 510 that directly or
indirectly couples the following devices: memory 512, one or more
processors 514, one or more presentation components 516,
input/output (I/O) ports 518, I/O components 520, a power supply
522, and a network component 524. Computing node 500 should not be
interpreted as having any dependency or requirement related to any
single component or combination of components illustrated therein.
While computing node 500 is depicted as a seemingly single device,
multiple computing nodes 500 may work together and share the
depicted device resources. That is, one or more computer storage
devices having computer-executable instructions stored thereon may
perform operations disclosed herein. For example, memory 512 may be
distributed across multiple devices, processor(s) 514 may provide
housed on different devices, and so on.
[0047] Bus 510 represents what may be one or more busses (such as
an address bus, data bus, or a combination thereof). Although the
various blocks of FIG. 5 are shown with lines for the sake of
clarity, in reality, delineating various components is not so
clear, and metaphorically, the lines would more accurately be grey
and fuzzy. For example, one may consider a presentation component
such as a display device to be an I/O component. Also, processors
have memory. Such is the nature of the art, and the diagram of FIG.
5 is merely illustrative of an exemplary computing node that can be
used in connection with one or more embodiments. Distinction is not
made between such categories as "workstation," "server," "laptop,"
"hand-held device," etc., as all are contemplated within the scope
of FIG. 5 and the references herein to a "computing node" or a
"computing device." Memory 512 may include any of the
computer-readable media discussed herein. Memory 512 may be used to
store and access instructions configured to carry out the various
operations disclosed herein. In some examples, memory 512 includes
computer storage media in the form of volatile and/or nonvolatile
memory, removable or non-removable memory, data disks in virtual
environments, or a combination thereof.
[0048] Processor(s) 514 may include any quantity of processing
units that read data from various entities, such as memory 512 or
I/O components 520. Specifically, processor(s) 514 are programmed
to execute computer-executable instructions for implementing
aspects of the disclosure. The instructions may be performed by the
processor, by multiple processors within the computing node 500, or
by a processor external to the client computing node 500. In some
examples, the processor(s) 514 are programmed to execute
instructions such as those illustrated in the flowcharts discussed
below and depicted in the accompanying drawings. Moreover, in some
examples, the processor(s) 514 represent an implementation of
analog techniques to perform the operations described herein. For
example, the operations may be performed by an analog client
computing node 500 and/or a digital client computing node 500.
[0049] Presentation component(s) 516 present data indications to a
user or other device. Exemplary presentation components include a
display device, speaker, printing component, vibrating component,
etc. One skilled in the art will understand and appreciate that
computer data may be presented in a number of ways, such as
visually in a graphical user interface (GUI), audibly through
speakers, wirelessly among multiple computing nodes 500, across a
wired connection, or in other ways. Ports 518 allow computing node
500 to be logically coupled to other devices including I/O
components 520, some of which may be built in. Example I/O
components 520 include, for example but without limitation, a
microphone, keyboard, mouse, joystick, game pad, satellite dish,
scanner, printer, wireless device, etc.
[0050] In some examples, the network component 524 includes a
network interface card and/or computer-executable instructions
(e.g., a driver) for operating the network interface card.
Communication between the computing node 500 and other devices may
occur using any protocol or mechanism over any wired or wireless
connection. In some examples, the network component 524 is operable
to communicate data over public, private, or hybrid (public and
private) using a transfer protocol, between devices wirelessly
using short range communication technologies (e.g., near-field
communication (NFC), Bluetooth.RTM. branded communications, or the
like), or a combination thereof. Network component 524 communicates
over communication link 526 to a cloud resource 528. Various
different examples of communication link 526 include a wired
connection, wireless connection, and/or a dedicated link, and in
some examples, at least a portion is routed through the
internet.
[0051] Although described in connection with an example computing
node 500, examples of the disclosure are capable of implementation
with numerous other general-purpose or special-purpose computing
system environments, configurations, or devices. Examples of
well-known computing systems, environments, and/or configurations
that may be suitable for use with aspects of the disclosure
include, but are not limited to, smart phones, mobile tablets,
mobile computing nodes, personal computers, server computers,
hand-held or laptop devices, multiprocessor systems, gaming
consoles, microprocessor-based systems, set top boxes, programmable
consumer electronics, mobile telephones, mobile computing and/or
communication devices in wearable or accessory form factors (e.g.,
watches, glasses, headsets, or earphones), network PCs,
minicomputers, mainframe computers, distributed computing
environments that include any of the above systems or devices,
virtual reality (VR) devices, holographic device, and the like.
Such systems or devices may accept input from the user in any way,
including from input devices such as a keyboard or pointing device,
via gesture input, proximity input (such as by hovering), and/or
via voice input.
[0052] Examples of the disclosure may be described in the general
context of computer-executable instructions, such as program
modules, executed by one or more computers or other devices in
software, firmware, hardware, or a combination thereof. The
computer-executable instructions may be organized into one or more
computer-executable components or modules. Generally, program
modules include, but are not limited to, routines, programs,
objects, components, and data structures that perform particular
tasks or implement particular abstract data types. Aspects of the
disclosure may be implemented with any number and organization of
such components or modules. For example, aspects of the disclosure
are not limited to the specific computer-executable instructions or
the specific components or modules illustrated in the figures and
described herein. Other examples of the disclosure may include
different computer-executable instructions or components having
more or less functionality than illustrated and described herein.
In examples involving a general-purpose computer, aspects of the
disclosure transform the general-purpose computer into a
special-purpose computing device or computing node when configured
to execute the instructions described herein.
[0053] By way of example and not limitation, computer readable
media comprise computer storage media and communication media.
Computer storage media include volatile and nonvolatile, removable
and non-removable memory implemented in any method or technology
for storage of information such as computer readable instructions,
data structures, program modules, or the like. Computer storage
media are tangible and mutually exclusive to communication media.
Computer storage media are implemented in hardware and exclude
carrier waves and propagated signals. Computer storage media for
purposes of this disclosure are not signals per se. Exemplary
computer storage media include hard disks, flash drives,
solid-state memory, phase change random-access memory (PRAM),
static random-access memory (SRAM), dynamic random-access memory
(DRAM), other types of random-access memory (RAM), read-only memory
(ROM), electrically erasable programmable read-only memory
(EEPROM), flash memory or other memory technology, compact disk
read-only memory (CD-ROM), digital versatile disks (DVD) or other
optical storage, magnetic cassettes, magnetic tape, magnetic disk
storage or other magnetic storage devices, or any other
non-transmission medium that can be used to store information for
access by a computing device. In contrast, communication media
typically embody computer readable instructions, data structures,
program modules, or the like in a modulated data signal such as a
carrier wave or other transport mechanism and include any
information delivery media.
Exemplary Operating Methods and Systems
[0054] An exemplary system for transport asset monitoring
comprises: a first sensor configured to sense a first operation
progress parameter data for a first transport asset; a processor;
and a non-transitory computer-readable medium storing instructions
that are operative when executed by the processor to: receive the
first operation progress parameter data from the first sensor;
determine, using an AI solution and based at least on the first
operation progress parameter data, a predicted milestone parameter;
and report the predicted milestone parameter to a remote node.
[0055] An exemplary method of transport asset monitoring comprises:
receiving a first operation progress parameter data for a first
transport asset from a first sensor, wherein receiving the first
operation progress parameter data from the first sensor data
comprises receiving the first operation progress parameter data
from at least one sensor selected from the list consisting of: an
RFID sensor, a barcode scanner, and a CV sensor; determining, using
an AI solution and based at least on the first operation progress
parameter data, a predicted milestone parameter; and reporting the
predicted milestone parameter to a remote node.
[0056] One or more exemplary computer storage devices having
computer-executable instructions stored thereon for transport asset
monitoring, which, on execution by a computer, cause the computer
to perform operations comprising: receiving a first operation
progress parameter data for a first transport asset from a first
sensor, wherein receiving the first operation progress parameter
data for the first transport asset comprises receiving the first
operation progress parameter data for a trailer, wherein the first
operation progress parameter data comprises identification of items
unloaded from the transport asset, and wherein receiving the first
operation progress parameter data from the first sensor data
comprises receiving the first operation progress parameter data
from at least one sensor selected from the list consisting of: an
RFID sensor, a barcode scanner, and a CV sensor; determining, using
an AI solution and based at least on the first operation progress
parameter data, a predicted milestone parameter; receiving, from a
UI, confirmation or correction of the predicted milestone
parameter; reporting the predicted milestone parameter to a remote
node; generating, with an ML component, the AI solution using at
least historical operation progress parameter data; and generating,
using the AI solution and based at least on the first operation
progress parameter data, logistical instructions for a second
transport asset.
[0057] Alternatively, or in addition to the other examples
described herein, examples include any combination of the
following: [0058] the transport asset comprises a trailer; [0059]
the first sensor comprises at least one sensor selected from the
list consisting of: an RFID sensor, a barcode scanner, and a CV
sensor; [0060] the first operation progress parameter data
comprises identification of items unloaded from the transport
asset; [0061] a second sensor configured to sense a second
operation progress parameter data for the transport asset, wherein
the second operation progress parameter data comprises
identification of items remaining on the transport asset; and
wherein the instructions are further operative to: receive the
second operation progress parameter data from the second sensor;
and wherein determining the predicted milestone parameter comprises
determining, using the AI solution and based at least on the first
operation progress parameter data and the second operation progress
parameter data, the predicted milestone parameter; [0062] the
instructions are further operative to receive, from a UI,
confirmation or correction of the predicted milestone parameter;
[0063] an ML component to generate the AI solution using at least
historical operation progress parameter data; [0064] a wireless
communication module; and an AGV in communication with the
processor via the wireless communication module; [0065] the
instructions are further operative to generate, using the AI
solution and based at least on the first operation progress
parameter data, logistical instructions for a second transport
asset; [0066] receiving the first operation progress parameter data
for the first transport asset comprises receiving the first
operation progress parameter data for a trailer; [0067] the first
operation progress parameter data comprises identification of items
unloaded from the transport asset; [0068] receiving a second
operation progress parameter data from a second sensor, wherein the
second operation progress parameter data comprises identification
of items remaining on the transport asset; and wherein determining
the predicted milestone parameter comprises determining, using the
AI solution and based at least on the first operation progress
parameter data and the second operation progress parameter data,
the predicted milestone parameter; [0069] receiving, from a UT,
confirmation or correction of the predicted milestone parameter;
[0070] generating, with an ML component, the AI solution using at
least historical operation progress parameter data; [0071]
wirelessly communicating, with an AGV, logistical data related to
the AGV; [0072] generating, using the AI solution and based at
least on the first operation progress parameter data, logistical
instructions for a second transport asset; and [0073] the predicted
milestone parameter comprises an expected completion time of
unloading the trailer.
[0074] The order of execution or performance of the operations in
examples of the disclosure illustrated and described herein may not
be essential, and thus may be performed in different sequential
manners in various examples. For example, it is contemplated that
executing or performing a particular operation before,
contemporaneously with, or after another operation is within the
scope of aspects of the disclosure. When introducing elements of
aspects of the disclosure or the examples thereof, the articles
"a," "an," "the," and "said" are intended to mean that there are
one or more of the elements. The terms "comprising," "including,"
and "having" are intended to be inclusive and mean that there may
be additional elements other than the listed elements. The term
"exemplary" is intended to mean "an example of" The phrase "one or
more of the following: A, B, and C" means "at least one of A and/or
at least one of B and/or at least one of C."
[0075] Having described aspects of the disclosure in detail, it
will be apparent that modifications and variations are possible
without departing from the scope of aspects of the disclosure as
defined in the appended claims. As various changes could be made in
the above constructions, products, and methods without departing
from the scope of aspects of the disclosure, it is intended that
all matter contained in the above description and shown in the
accompanying drawings shall be interpreted as illustrative and not
in a limiting sense. While the disclosure is susceptible to various
modifications and alternative constructions, certain illustrated
examples thereof are shown in the drawings and have been described
above in detail. It should be understood, however, that there is no
intention to limit the disclosure to the specific forms disclosed,
but on the contrary, the intention is to cover all modifications,
alternative constructions, and equivalents falling within the
spirit and scope of the disclosure.
* * * * *