CN109074539A - Cold chain overall cost and quality software as service module - Google Patents
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Abstract
Provide a kind of system and method for cold chain route for analyzing and selecting cold chain system.The described method includes: determining to form the route of the cold chain from the current location of cold haulage vehicle to destination using cold chain network；The route data about identified route being collected at cold chain network；Prediction route data is calculated based on the route data being collected into using cold chain network；Using display, the route data being collected into and prediction route data are shown using graphical user interface (GUI)；And the route selection based on the shown route data being collected into and prediction route data is received from user input apparatus.
Description
Background technique
This disclosure relates to which a kind of cold chain system, collects and analyzes more particularly, to being used to provide route management tool
Method of the route information in order to route selection, system and computer program product.
Cold chain is the supply chain of controlled temperature.Specifically, cold chain be it is a series of do not interrupt and it is continual storage and
Dispatching activity is positive and maintains given temperature range along the product that chain moves.For example, it helps to extend using cold chain and really
Protect product shelf-life, such as fresh agricultural product, seafood, frozen food, film, fluid, chemicals, drug and other to temperature
Sensitive article.
Map Services can be used to provide the supply of the route used in this supply chain.However, this Map Services base
Route is provided in general standard.Therefore, cold chain route manager executes the route selection of cold chain, cold without considering specifically to influence
The factor of chain.
Therefore, it is necessary to a kind of roads possibility Geng You because usually determining cold chain transportation vehicle for by considering influence cold chain
The system and method for line.
Summary of the invention
According to an embodiment, a kind of method of cold chain route for analyzing and selecting cold chain system is provided.Institute
The method of stating includes: to determine to form the route of the cold chain from the current location of cold haulage vehicle to destination using cold chain network；
The route data about identified route being collected at cold chain network；Using cold chain network based on the road being collected into
Line number is according to calculating prediction route data；Using display, the route number being collected into is shown using graphical user interface (GUI)
According to prediction route data；And it receives from user input apparatus based on the shown route data being collected into and prediction route
The route selection of data.
Other than one or more of feature as described above, or alternatively, further embodiment can wrap
It includes: the route data being collected into is stored in first database, the route data includes for calculating prediction route data
Refrigeration unit data comprising refrigerating efficiency；And the route data being collected into is stored in the second database, the road
Line number according to include for calculates prediction route data Driver data comprising driver's validity, wherein be based on and prediction
Actual path data that route data is compared determine driver's validity.
Other than one or more of feature as described above, or alternatively, further embodiment can
It include: that a plurality of route is determined using cold chain network, every route forms the cold chain from current location to destination；In cold chain net
The route data about identified a plurality of route being collected at network；Using cold chain network based on the route number being collected into
According to calculating prediction route data；The route data being collected into and prediction route number are shown using the display of cold haulage vehicle
According to；And the route choosing based on the shown route data being collected into and prediction route data is received from user input apparatus
It selects.
Other than one or more of feature as described above, or alternatively, further embodiment can
Route data including being wherein collected into includes weather data and refrigerating box data, and wherein weather data includes temperature, atmospheric pressure
Power, wind speed, wind direction, cloud layer, storm warning, one or more of humidity, ozone content and pollen amount, and wherein refrigerate
Case data include time-varying temperature value, the energy using one or more of data and current temperature value.
Other than one or more of feature as described above, or alternatively, further embodiment can
Including, wherein prediction route data be from by weather to refrigerating box influence, the predicting the weather of different route, weather is to refrigeration
At least one selected in the group of calculating, efficiency, the freshness of prediction and quality and route the risk composition of influence.
Other than one or more of feature as described above, or alternatively, further embodiment can
Route data including being wherein collected into is made of Driver data, and wherein Driver data includes the previous driving for route
Time, speed average, whole velocity amplitudes, driving time, parking length, parking position and one of habit or more of refueling
Person.
Other than one or more of feature as described above, or alternatively, further embodiment can
Route data including wherein predicting be from by gasoline mileage value range, semi-mounted truck routes gasoline mileage, efficiency, prediction it is new
At least one selected in freshness and quality and the group of route risk composition.
Other than one or more of feature as described above, or alternatively, further embodiment can
Route data including being wherein collected into is made of route data, route delay, path length and semi-mounted truck data, Road
Line number according to include route velocities limit data, route tilt data, gas station's map datum, maintenance factory's map datum, generally
Diagram data and road type data, and wherein semi-mounted truck data include tankage, tire pressure, mileage, engine temperature
Degree, engine sensor data, cabin sensing data, battery data and alternator data.
Other than one or more of feature as described above, or alternatively, further embodiment can
Route data including wherein predicting is from the influence by gasoline mileage value range, refrigerating box efficiency, weather to refrigerating box, difference
The predicting the weather of route, semi-mounted truck routes gasoline mileage, the calculating of influence of the weather to refrigeration, efficiency, the freshness of prediction
At least one selected in the group formed with quality and route risk.
According to an embodiment, a kind of route management tool for cold chain system is provided.The route manages work
Tool includes cold haulage vehicle, and the cold haulage vehicle includes: display, is configured with graphical user interface (GUI)
Show the route data being collected into and prediction route data；And user input apparatus, it is configured as receiving based on shown
The route data being collected into and prediction route data route selection；And cold chain network, be configured to determine that be formed from
The current location of cold haulage vehicle to destination cold chain route, receive the route number about identified route being collected into
According to, and prediction route data is calculated based on the route data being collected into, cold chain network includes: first database, is configured
To store the route data being collected into, including including the refrigeration unit data for predicting route data for calculating comprising refrigeration
Efficiency；And second database, it is configured as the route data that storage is collected into, including predicting route data for calculating
Driver data comprising driver's validity, wherein according to based on the actual path number compared with estimated route data
According to determining driver's validity.
Other than one or more of feature as described above, or alternatively, further embodiment can
Including, wherein cold chain network is additionally configured to determine that a plurality of route, every route form the cold chain from current location to destination,
And the route data about identified a plurality of route being collected into.
Other than one or more of feature as described above, or alternatively, further embodiment can
Including wherein the route data being collected into is made of weather data and refrigerating box data, wherein weather data includes temperature, atmosphere
One or more of pressure, wind speed, wind direction, cloud layer, storm warning, humidity, ozone content and pollen amount, and it is wherein cold
Hiding case data include time-varying temperature value, the energy using one or more of data and current temperature value.
Other than one or more of feature as described above, or alternatively, further embodiment
May include wherein prediction route data be from by weather to refrigerating box influence, the predicting the weather of different route, weather is to refrigeration
Influence calculating, efficiency, the freshness of prediction and quality and route risk composition group in select at least one.
Other than one or more of feature as described above, or alternatively, further embodiment can
Route data including being wherein collected into is made of Driver data, and wherein Driver data includes being used for route, speed
Average value, whole velocity amplitudes, driving time, parking length, parking position and one or more of habit of refueling.
Other than one or more of feature as described above, or alternatively, further embodiment can
Including wherein predicting that route data is from by gasoline mileage value range, semi-mounted truck routes gasoline mileage utilization rate, efficiency, prediction
Freshness and quality and route risk composition group in select at least one.
Other than one or more of feature as described above, or alternatively, further embodiment can
Route data including being wherein collected into is made of route data, route delay, path length and semi-mounted truck data, Road
Line number is according to including route velocities limitation data, route tilt data, gas station's map datum, repair shop's map datum, generally
Diagram data and road type data, and wherein semi-mounted truck data include tankage, tire pressure, mileage, engine temperature
Degree, engine sensor data, cabin sensing data, battery data and alternator data.
Other than one or more of feature as described above, or alternatively, further embodiment can
Route data including wherein predicting is from the influence by gasoline mileage value range, refrigerating box efficiency, weather to refrigerating box, difference
The predicting the weather of route, semi-mounted truck routes gasoline mileage, the calculating of influence of the weather to refrigeration, efficiency, the freshness of prediction
At least one selected in the group formed with quality and route risk.
According to another embodiment, provide a kind of system for cold chain route management, including with one or more classes
The one or more processors of the computer readable storage medium communication of type, the computer readable storage medium has and one
Act the program instruction realized.Described program instruction can be performed by one or more processors to cause the processor: using cold
Chain network to form the route of the cold chain from the current location of cold haulage vehicle to destination to determine；It receives and receives at cold chain network
The route data about identified route collected；Using cold chain network, prediction road is calculated based on the route data being collected into
Line number evidence；Using the display of cold haulage vehicle, the route data being collected into and pre- is shown using graphical user interface (GUI)
Survey route data；And from the reception of the user input apparatus of cold haulage vehicle based on the shown route data being collected into and in advance
Survey the route selection of route data.
Other than one or more of feature as described above, or alternatively, further embodiment can
Additional program instructions including that can be performed by one or more processors to cause processor to perform the following operation: by what is be collected into
Route data is stored in first database, including the refrigeration unit data for calculating prediction route data comprising refrigeration
Efficiency；And the route data being collected into is stored in the second database, including the driving for calculating prediction route data
Member's data comprising driver's validity, wherein determining driving based on the actual path data compared with predicting route data
Member's validity.
Other than one or more of feature as described above, or alternatively, further embodiment can
Including that can be performed by one or more processors to cause processor to carry out the additional program instructions of the following terms: being wherein collected into
Route data by route data, route delay, path length, semi-mounted truck data, Driver data, weather data and refrigeration
Case data composition；Wherein route data includes route velocities limitation data, route tilt data, gas station's map datum, repairs
Factory site diagram data, General maps data and road type data；Wherein semi-mounted truck data include tankage, tire pressure,
Mileage, engine temperature, engine sensor data, cabin sensing data, battery data and alternator data；Wherein drive
Member's data include previous driving time, speed average, whole velocity amplitudes, the driving time, parking length, parking position of route
With one or more of habit of refueling；Wherein prediction route data is from by gasoline mileage value range, refrigerating box efficiency, weather
In influence to refrigerating box, the predicting the weather of different routes, the calculating of influence of the weather to refrigeration, semi-mounted truck routes gasoline
It is selected in the group of calculating, efficiency, the freshness of prediction and quality and route the risk composition of the influence of journey, weather to refrigeration
At least one；Wherein weather data includes temperature, atmospheric pressure, wind speed, wind direction, cloud layer, storm warning, humidity, ozone content
One or more of with pollen amount；And wherein refrigerating box data include with the time and become temperature value, the energy is using number
According to one or more of with current temperature value.
Detailed description of the invention
According in conjunction with attached drawing carry out the following specifically describes the foregoing and other feature and advantage of the disclosure are aobvious and easy
See, in the accompanying drawings:
Fig. 1 depicts the cloud computing environment of the embodiment according to the disclosure.
Fig. 2 depicts the abstract model layer of the embodiment according to the disclosure；
Fig. 3 is the exemplary block diagram shown for practicing the processing system instructed herein；
Fig. 4 depicts the block diagram for showing the system of the embodiment according to the disclosure；
Fig. 5 depicts the flow chart of the route planning software according to the embodiment of the disclosure；
Fig. 6 depicts the flow chart of the alignment analysis software of the route planning software according to the embodiment of the disclosure；
Fig. 7 depicts the flow chart of the weather effect software according to the route planning software of the embodiment of the disclosure；
Fig. 8 depicts the flow chart of the risk analysis software of the route planning software according to the embodiment of the disclosure；
Fig. 9 depicts the flow chart of the product quality software of the route planning software according to the embodiment of the disclosure；
Figure 10 is depicted to be connect according to the route management tool graphical user of the route planning software of the embodiment of the disclosure
Mouth (GUI)；
Figure 11, which is depicted, manages pass cost risk GUI according to the route of the route planning software of the embodiment of the disclosure；
And
Figure 12 depicts the collection according to the embodiment of the disclosure and handles the method for the route information of cold chain system
Flow chart.
Specific embodiment
It can be understood in advance that, although the disclosure includes the detailed description about cloud computing, the implementation of teachings described herein
It is not limited to cloud computing environment.But the embodiment of the disclosure can be in conjunction with any other class currently known or develop later
The calculating environment of type is implemented.
Cloud computing is a kind of service variable values, for realizing shared pool (such as network, the net to configurable computing resource
Network bandwidth, server, processing, memory, storage, application program, virtual machine and service) convenient on-demand network access, can
It quickly provides and provides by least management work or with the interaction of service provider.This cloud model may include at least five
A feature, at least three service models and at least four deployment models.
Feature is as follows:
On-demand Self-Service: cloud consumer can automatic one-sided provision computing capability, such as server time as needed
And network storage, without carrying out man-machine interactively with the provider of service.
Extensive network access: ability is available via network, and is accessed by standard mechanism, and this facilitate the thin visitors of isomery
The use of family end or Fat Client platform (for example, mobile phone, notebook computer and PDA).
Resource pool: the computing resource of provider pools together, and multiple consumers are serviced using multi-tenant model, according to
Different physics and virtual resource are dynamically assigned and be reassigned to demand.There are position feeling of independence, because consumer is usually right
The accurate location of provided resource do not have control or and be unaware of, but can higher levels of abstraction (for example, country,
State or data center) designated position.
It is quickly elastic: can quickly and flexibly to provide ability, extend to the outside rapidly automatically in some cases, and quickly hair
It puts with inwardly extended rapidly.For consumers, can be used for provide function usually look like it is unlimited, and can at any time with
Any quantity purchase.
Measurement service: cloud system, which passes through to utilize, is suitable for service type (for example, storage, processing, bandwidth and active user account
Family) certain levels of abstraction metrology capability, automatically control and optimization resource use.Can monitor, control and report resource use
Rate, to provide the transparency for the supplier of service and consumer utilized.
Service model is as follows:
Software services (SaaS): the ability for being supplied to consumer is running in cloud infrastructure using provider
Application program.It can be for example, by the thin-client interface of web browser (for example, network-based Email) etc, from each
Kind client terminal device accesses the application program.Consumer does not manage or controls bottom cloud architecture, including network, service
Device, operating system, the even individual application program abilities of storage, possible exception are limited user's application-specific with installing
It sets.
Platform is to service (PaaS): the ability for being supplied to consumer will be used by the programming language and work of provider's support
In consumer's creation of tool creation or the application deployment to cloud infrastructure obtained.Consumer does not manage or controls bottom
Cloud infrastructure, including network, server, operating system or storage, but can be to the application program and possible application disposed
The configuration of program hosting environment is possessed of control power.
Infrastructure is to service (IaaS): the ability for being supplied to consumer is provision processing, is stored, network is basic with other
Computing resource, consumer can dispose and run wherein any software, may include operating system and application program.Consumer
Bottom cloud infrastructure is not managed or controls, but to operating system, it storing, the application program disposed is possessed of control power,
And selected networking component (for example, host firewall) may be weighed with constrained control.
Deployment model is as follows:
Private clound: cloud infrastructure is only tissue operation.It can be managed by tissue or third party, and may be present in it is local or
It is external.
Community cloud: cloud infrastructure is shared by several tissues, and supports have shared focus (for example, task, is wanted safely
Ask, strategy and compliance consider) particular community.It can be managed by tissue or third party, and may be present in local or external.
Public cloud: this cloud infrastructure uses for general public or large-scale industry group, and by the group of sale cloud service
It knits all.
Mixed cloud: this cloud infrastructure is made of two or more clouds (private clound, community cloud or public cloud), these clouds
It is still unique entity, but is bound together by standardization or proprietary technology, so that realizes data and application program can
Transplantability (for example, cloud burst for the load balance between cloud).
Cloud computing environment be it is service-oriented, lay particular emphasis on stateless, lower coupling, modularization and semantic interoperability.Cloud meter
The core of calculation is the architecture for including interconnecting nodes network.
Referring now to Figure 1, depicting illustrative cloud computing environment 50.As shown, cloud computing environment 50 include one or
Multiple cloud computing nodes 10, local computing de used in cloud consumer, for example, personal digital assistant (PDA) or honeycomb electricity
54A, desktop PC 54B are talked about, laptop computer 54C and/or Automotive Computer System 54N can be logical with cloud computing node 10
Letter.Node 10 can communicate with one another.They can in one or more networks physics or virtual group (not shown), the network example
Private clound for example as described above, community cloud, public cloud or mixed cloud, or combinations thereof.This allows cloud computing environment 50 to provide base
Plinth framework, platform and/or software are as service, so that cloud consumer does not need the resource on maintenance local computing device.It should
Understand, the type of calculating equipment 54A to 54N shown in Fig. 1 is set only illustrative, and calculate node 10 and cloud computing
Environment 50 can be via any kind of network and/or network addressable connection (for example, using web browser) and any type
Computerized device communication.
Referring now to Figure 2, showing the one group of functional abstraction layer provided by cloud computing environment 50 (Fig. 1).It should be understood in advance that,
Component shown in Fig. 2, layer and function are set only illustrative, and the embodiment of the disclosure is without being limited thereto.As retouch
It draws, provides with lower layer and corresponding function:
Hardware and software layer 60 includes hardware and software component.The example of hardware component includes: host 61；Based on RISC
The server 62 of (Reduced Instruction Set Computer) framework；Server 63；Blade server 64；Storage device 65；And network
With networking component 66.In some embodiments, software component includes network application server software 67 and database software 68.
Virtualization layer 70 provides level of abstraction, can provide the following example of pseudo-entity: virtual server 71 from level of abstraction；It is empty
Quasi- storage 72；Virtual network 73, including virtual private network；Virtual applications and operating system 74；And virtual client
75。
In one example, management level 80 can provide function described below.Resource is provided 81 and is provided in cloud computing
The dynamic procurement of the computing resource of execution task and other resources in environment.Metering and price 82 provide resource in cloud computing environment
In cost tracing when being utilized, and for these resources consumption charging or draw a bill.In one example, these are provided
Source may include application software license.Safety provides authentication, and protection data and other moneys for cloud consumer and task
Source.Portal user 83 provides the access to cloud computing environment for consumer and system manager.Service level management 84 provides cloud
Computational resource allocation and management, so that the grade of service needed for meeting.85 offer pair of service level agreement (SLA) planning and realization
Cloud computing resources being presetted and purchasing, and for the cloud computing resources, are expected tomorrow requirement according to SLA.
Workload layer 90 provides the functional example that can utilize cloud computing environment.The workload that can be provided from this layer
Example with function includes: map and navigation 91；Software development and life cycle management 92；Virtual classroom education dispatching 93；Number
According to analysis processing 94；Trading processing 95；And the Message Processing across multiple communication systems 96.
According to the exemplary implementation scheme of the disclosure, provide for being prioritized message across the dispatching of multiple communication systems
Method, system and computer program product.In an exemplary embodiment, messaging system is configured as across personal institute's benefit
Multiple communication systems are received for personal message.Messaging system be additionally configured to based on to message analysis and
Personal user profiles determine priority level associated with each message.Based on identified priority level and user
Message is dispensed into desired communication device via desired messaging system by profile, messaging system.In exemplary reality
It applies in scheme, is receiving after personal feedback, messaging system updates user profiles at once, wherein feedback includes
The message dispatching preference and message priority preference of people.
With reference to Fig. 3, the embodiment that shows the processing system 100 for implementing to instruct herein.In this embodiment, it is
System 100 has (general designation or the commonly referred to as processor such as one or more central processing unit (processor) 101a, 101b, 101c
101).In one embodiment, each processor 101 may include Reduced Instruction Set Computer (RISC) microprocessor.Processing
Device 101 is coupled to system storage 114 and various other components via system bus 113.Read-only memory (ROM) 102 couples
It to system bus 113 and may include basic input/output (BIOS), with certain basic functions of control system 100.
Fig. 3 is further depicted as being coupled to input/output (I/O) adapter 107 of system bus 113 and network adapter
106.I/O adapter 107, which can be, to be communicated with hard disk 103 and/or tape storage drive 105 or any other like
Small computer system interface (SCSI) adapter.I/O adapter 107, hard disk 103 and magnetic tape strip unit 105 are in this collectively
For massive store 104.Operating system 120 for executing in processing system 100 is storable in massive store 104.
Network adapter 106 interconnects bus 113 and external network 116, enable data processing system 100 and other as be
System communication.Screen (for example, the display monitor) 115 is connected to system bus 113 by display adapter 112, and the display is suitable
Orchestration 12 may include the graphics adapter and Video Controller to improve the performance of graphic intensive application program.At one
In embodiment, adapter 107,106 and 112 may be connected to one or more I/O buses, these I/O buses pass through intermediate total
Line bridge (not shown) is connected to system bus 113.For connecting such as hard disk controller, network adapter and graphics adapter
The suitable I/O bus of peripheral unit generally includes shared protocol, such as peripheral parts interconnected (PCI).Other input/output dress
It sets and is shown as being connected to system bus 113 via user interface adapter 108 and display adapter 112.Keyboard 109, mouse
110 and loudspeaker 111 via user interface adapter 108 be all interconnected to bus 113, user interface adapter 108 may include
Such as super I/O chip, multiple device adapters are integrated into single integrated circuit.
In an exemplary embodiment, processing system 100 includes graphics processing unit 130.Graphics processing unit 130 is special
With electronic circuit, it is designed to manipulate and change memory to make the image in the set frame buffer for being output to display
Creation accelerates.In general, graphics processing unit 130 is highly effective in terms of maneuvering calculation machine figure and image procossing, and
Structure with highly-parallel, so that it is more more effective than the universal cpu of the algorithm of the processing for completing chunk data parallel.
Therefore, as configured in Fig. 3, system 100 includes the processing capacity in the form of processor 101, including system is deposited
The storage capacity of reservoir 114 and massive store 104, such as the input unit of keyboard 109 and mouse 110, and including loudspeaking
The fan-out capability of device 111 and display 115.In one embodiment, the one of system storage 114 and massive store 104
Partially common storage program area, to coordinate the function of various parts shown in Fig. 3.
According to one or more embodiments, a kind of route management tool is provided, determines one based on prediction route
The route of a or multiple cold haulage vehicles, the prediction route includes such as weather and delay, gasoline mileage, weather to refrigeration
It influences, product freshness/quality of efficiency and prediction.The database of refrigeration unit and driver and its respective validity
For determining route.
In addition, being additionally provided a kind of for assessing and managing refrigerated transportation vehicle according to one or more embodiments
The system of risk.Software collects fuel, spare part, stand-by power source, the data of nearest repair shop, and transfers data to and sell
Quotient.Dealer's software systems identify the risk of chain related to cold chain, and allow dealer via with planning and management tool
User interface reacts to the risk identified.
According to one or more embodiments, a kind of method of route information for collecting and analyzing cold chain system is provided.
The method includes collection and efficiency, product quality and the related information of risk management.For example, the method is collected and temperature
Spend control efficiency, route running time efficiency and the related efficiency information of fuel/battery service efficiency.In addition, product quality is believed
Breath may include time-varying deterioration rate calculated, as include temperature, temperature fluctuation, pressure, humidity, Sunlight exposure,
The influence of driving conditions including air filtration data and other factors.In addition, the method collects described in information and processing
Information calculates value-at-risk related from different routes.
According to one or more specific embodiments, tool, system and or method is implementable to be and refrigeration semi-mounted truck one
It rises using to determine what route it should take.Such as gasoline mileage, refrigerating box efficiency, shadow of the weather to refrigerating box are not considered
The factors such as sound.
In addition, one or more embodiments permission route managers determine the route of refrigerator car based on following: different
Route and delay predict the weather, route truck gasoline mileage, the calculating of influence of the weather to refrigeration, efficiency, prediction it is fresh
The risk of degree and quality and route.In addition, according to another embodiment, for collecting and analyzing the route phase of cold chain system
The SaaS embodiment for closing data may include and use the unique first database for tracking the refrigeration unit of fleet, thus more accurate
Ground calculates refrigerating efficiency.In addition, according to another embodiment, for collecting and analyzing the route related data of cold chain system
SaaS embodiment may include and unique second database of the validity using driver in terms of meeting, can also
It is included in route planning manager.According to embodiment, the combination of the validity of refrigerating efficiency and specific driver can be used
Best timing is determined to calculate.
According to one or more embodiments, the risk of potential route is determined by SaaS system.For example, SaaS system
On software collect cold haulage vehicle data, such as fuel, spare part and stand-by power source and route data, such as repair shop
Position and riving condition.In addition, the software in system identifies risk relevant to cold chain, and shown in route planning management system
Show risk data, to help to inform route planning decision.The system also allows dealer via with planning and management tool
User interface react to the risk identified.Thus, for example system can be implemented for cold chain transportation vehicle, have
For multiple potential routes of its dispatching, to help a cold chain transportation vehicle selection wherein route.
According to one or more embodiments, it is possible to provide the advantages of be from market angle improve be worth, because of cold chain conduct
The first aim of service software is management route planning, but also using the cold chain data of influence quality and cost (for example, day
Gas, risk).According to one or more embodiments, another advantage includes risk involved in system help management cold chain
Ability.
Turning now to attached drawing, Fig. 4 depicts the block diagram for showing the system according to exemplary implementation scheme, and the system is had
It is known as such as route management tool 400 to body.Route management tool 400 includes at least one cold haulage vehicle as shown in the figure
410.According to another embodiment, route management tool 400 may include multiple cold haulage vehicles.In addition, route management tool 400
It further include cold chain network 420.Cold chain network 420 is communicatively connect to cold haulage vehicle 410 by cloud resource 405.Cold chain
Network 420 is configured as the route data value for helping to collect and predict the different routes determined for cold haulage vehicle 410.It uses
User/driver is used to select the display and GUI of route, and the route data value that these are collected into and are predicted is supplied to cold fortune
User/driver of defeated vehicle 410.
According to one or more embodiments, cold haulage vehicle 410 includes Global Positioning Service (GPS) device 411, fuel
Sensor 412, stand-by power source sensor 415, replacement part databases (DB) 413 and route management tool 414.GPS device 411, combustion
Material sensor 412 and stand-by power source sensor 415 can be used for collection path data, then provide it to cold chain by cloud 405
Network 420.In addition, spare part DB 413 stores information related with the spare part of cold haulage vehicle 411.414 quilt of route management tool
It is configured to the influence of weather and delay, gasoline mileage, weather to refrigeration based on prediction, the product freshness of efficiency and prediction/
Quality determines the route of cold haulage vehicle.
According to one or more embodiments, cold chain network 420 includes several disparate databases (DB).For example, cold
Chain network 420 includes repair shop DB 421, traffic DB 422, weather DB 423, driver DB 424 and refrigeration unit DB 429.
These databases are for storing and each of the associated databases related route data for being collected into and predicting.
Cold chain network 420 further includes risk analysis software 428 and risk GUI 429, is configured to assess and manage refrigeration
The risk of haulage vehicle.Specifically, risk analysis software 428 is configured with the different numbers in cold chain network 420
Value-at-risk is generated according to the route data and the route data that receives from old haulage vehicle 410 in library.Risk GUI 429 is by wind
Danger value and calculated route data are display together to user/driver, so that user is when what route determination will select
Value-at-risk can be used.
Cold chain network 420 further includes route planning software 426 and alignment analysis software 427.Route planning software 426 is matched
It is set to and determines one or more route, and alignment analysis software 427 is configured with the route data from all databases
And the route data that receives from cold haulage vehicle 410 analyzes route.
Fig. 5 depicts the flow chart of the route planning software 500 of one or more embodiments according to the disclosure.Route
Planning software 500 determines the different potential routes from current location to dispatching destination, and these routes are saved in route rule
Draw DB (operation 510).Route planning software 500 executes each software then to collect and calculate the data about different routes.
Specifically, route planning software 500 executes alignment analysis software (operation 520), weather influences software (operation 530), risk
Analyze software (operation 540) and product quality software (operation 550).Software 500 then shows route pipe on transport display
Science and engineering tool (its can be vehicle a part or it can be the isolated system held by driver or Delivery Co., Ltd), fill
There are the data (operation 560) collected and calculated from several softwares previously executed.The user of display is transported (that is, driver
Or Delivery Co., Ltd) route selected based on the information shown in route management tool.
Fig. 6 depicts the alignment analysis software of the route planning software of one or more embodiments according to the disclosure
600 flow chart.Alignment analysis software 600 determines the total distance of every route, and route data is stored to route planning DB
In (operation 605).Alignment analysis software 600 will then be used based on distance and the average miles per gallon data retrieved to calculate
In the amount of gasoline (operation 610) of every route of driving, and calculated gasoline gallonage is stored in route planning DB (operation
615).Alignment analysis software 600 then accesses traffic DB, to retrieve traffic data for every route and to be saved into route
Plan DB (operation 620).Alignment analysis software 600 is then by carrying out all different traffic data entries related from route
Traffic data storage (is operated 625) in route planning DB to calculate the retardation as caused by traffic by summation.Alignment analysis
Software 600 then accesses driver DB, to retrieve traveled distance length (time) and estimation of the specific driver about them
Stroke length is compared to data how (operation 630).In one embodiment, the data from each run are combined into reality
One value on border and estimation journey time length.For example, described value is traveled distance time span divided by all drivers
The quotient of the estimation journey time length of previous run.Next, alignment analysis software 600 by by route distance divided by average speed
Degree limitation, in addition traffic delays, and by summation multiplied by the journey time length data of the reality and estimation retrieved, it is every to calculate
The estimation journey time length (operation 635) of route.
Fig. 7 depicts the flow chart that software 700 is influenced according to the weather of the route planning software of exemplary implementation scheme.It
Gas influences software 700 and retrieves weather data relevant to potential route.Weather data includes precipitation (such as avenging, rain) and environment temperature
Degree.It may also include the data such as sunlight, wind, humidity or air pressure.Weather is calculated to the efficiency of refrigerated cargo container
Influence, to generate energy utilization rate.Energy utilization rate multiplied by estimation journey time length, as a result, refrigeration cost.
Freeze cost for every route calculation in route planning DB, and is saved into route planning DB.
Specifically, weather, which influences software 700, accesses weather DB, and retrieve the day destiny of every route of route planning DB
According to, and store data into route planning DB (operation 705).Weather influences the refrigeration DB that software 700 also accesses network, with retrieval
The efficiency data (operation 710) of refrigerated cargo container.In addition, it is that every route calculation predicts environment that weather, which influences software 700,
The average influence (operation 715) of temperature and precipitation to the efficiency of refrigerated cargo container.Weather influences software 700 also by by institute
The efficiency of calculating calculates the refrigeration cost of every route multiplied by the estimation journey time length of route.(operation 720).Finally,
Weather influences software 700 and the refrigeration cost of calculated every route is saved in route planning DB (operation 725).
Fig. 8 depicts the flow chart of the risk analysis software 800 of the route planning software according to exemplary implementation scheme.This
Range of driving range of the risk analysis software 800 based on vehicle, the position of gas station, the position of repair shop and the fuel of stand-by power source
Level analyzes the risk of route.The range of driving range of vehicle is calculated based on fuel tank size and per gallon average mileage.From adding
Petrol station DB is retrieved along the position of the gas station of every route, and calculates the maximum distance between gas station and average distance, to accuse
Know the risk to run out of gas on every route of user.From repair shop DB retrieval along the position of the repair shop of every route, and
And maximum distance and average distance between gas station are calculated, to inform that user brakes and wind not near repair shop
Danger.
For example, according to the embodiment of the disclosure, as shown in figure 8, risk analysis software 800 accesses vehicle DB, and
Retrieve the fuel tank size and per gallon average mileage (operation 805) of vehicle.Risk analysis software 800 further includes based on vehicle
Fuel tank size and per gallon average mileage calculate the range of driving range of vehicle.Calculated range of driving range is saved in wind
Dangerous DB (operation 810).Risk analysis software 800 further includes access gas station DB, and determine every route of route planning DB
Maximum distance and average distance (operation 815) between gas station, and the storage of this route data (is operated to route planning DB
820).Risk analysis software 800 further includes access repair shop DB, and between the repair shop of every route of determining route planning DB
Maximum distance and average distance (operation 825), and by this route data storage to route planning DB (operation 830).Risk point
Analysis software 800 further includes obtaining stand-by power source fuel sensor reading, and the route data is saved in risk DB (operation
835)。
Fig. 9 depicts the product quality software of the route planning software of one or more embodiments according to the disclosure
900 flow chart.(it is directed to product class to deterioration rate of the product quality software 900 based on its current production quality classification, deterioration DB
The temperature that type and refrigerated cargo container are maintained at), and the journey time length of estimation determines estimating for conveyed products
Count product quality grading.According to an embodiment, for example, the product quality grading of estimation is equal to current production quality classification and subtracts
Go the deterioration rate being multiplied with the journey time length of estimation.
For example, according to one or more embodiments of the disclosure, as shown in figure 9, product quality software 900 includes
Product DB is accessed, and retrieves product quality grading (operation 905), and the product deterioration DB (operation 910) on access network.This
Outside, product quality software 900 includes, for every route of route planning DB, based on the product quality grading retrieved, coming from
The deterioration rate of DB, and the journey time length of estimation are deteriorated to calculate the product quality grading (operation 915) of estimation.Product matter
Amount software 900 further includes that all route datas for being collected into and predicting are stored into route planning DB (operation 920).
Figure 10 depicts the route management tool graphical user for route planning software according to exemplary implementation scheme
Interface (GUI).As shown, GUI can show the information of two or more routes (such as route 1 and route 2).As schemed
Show, every route may include several different values, show these values to help user's route to make a choice.Citing comes
It says, route includes precipitation, temperature, traffic delays, gasoline gallonage, cost of freezing, distance, the estimation time span of route and 10
In take one prediction product quality.And, for example, the button for operation risk analysis.According to one or more embodiments,
GUI can be shown in vehicle display by route planning software.GUI can be shown by the data of each software acquisition/calculating.This
Outside, according to embodiment, " risk analysis " button opens route management tool-risk GUI.User is (for example, driver, dispatching
Company) it helps to determine the route for dispatching using route management tool GUI.
Figure 11 depicts route management tool-risk GUI according to the route planning software of exemplary implementation scheme.Pass through
" risk analysis " button on route management tool GUI is selected to open this risk GUI.Risk GUI is shown from risk analysis
The data of software.Risk GUI may also display the data from spare part DB.Select the Close button that will close route management tool-
Risk GUI, and make user back to route management tool GUI.As shown, risk GUI can provide one or more route
Risk information.As shown, showing two lines.Some examples of the risk relevant routes data shown include but is not limited to vehicle
Journey range (in Full Tanks), stand-by power source, maximum distance-gas station, average distance-gas station, maximum distance-repair shop and
Average distance-repair shop.GUI can also disclose component count table as shown in the figure.
The method that Figure 12 depicts the collection according to the embodiment of the disclosure and handles the route information of cold chain system
1200 flow chart.Method 1200 includes determining to be formed from the current location of cold haulage vehicle to destination using cold chain network
Cold chain route (operation 1205).Method 1200 further includes being collected at cold chain network about identified route
Route data (operation 1210).In addition, method 1200 includes using cold chain network, calculated based on the route data being collected into
Predict route data (operation 1215).In addition, method 1200 includes being connect using the display of cold haulage vehicle using graphical user
Mouthful (GUI) shows the route data being collected into and prediction route data (operation 1220), and defeated from the user of cold haulage vehicle
Enter device and receives the route selection (operation 1225) based on the shown route data being collected into and prediction route data.
One or more embodiments of the disclosure can be system, method and/or computer program product.Computer program
Product may include computer readable storage medium (or media) above with computer-readable program instructions, and described instruction causes
Processor carries out several aspects of one or more embodiments of the disclosure.
Computer readable storage medium, which can be, to be able to maintain and store instruction is tangible so that instruction executing device uses
Device.Computer readable storage medium can be such as but not limited to electronic storage device, magnetic storage device, optical storage dress
It sets, any appropriate combination of electromagnetic storage device, semiconductor storage or aforementioned device.Computer readable storage medium is more
The non-exhaustive list of specific example includes the following terms: portable computer diskette, hard disk, random access memory (RAM), only
Read memory (ROM), Erasable Programmable Read Only Memory EPROM (EPROM or flash memory), static random access memory (SRAM), just
Take formula compact disk read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanical coding device (such as
Bulge-structure in punched card or groove, thereon record have instruction) and aforementioned storage medium any appropriate combination.Such as this
Computer readable storage medium used in text is not necessarily to be construed as being temporary signal, such as radio wave or other freedom in itself
The electromagnetic wave of propagation, the electromagnetic wave (for example, the light pulse for passing through fiber optic cables) propagated by waveguide or other transmission medias, or
Pass through the electric signal of wire transmission.
Computer-readable program instructions described herein can download to corresponding meter from computer readable storage medium
Calculation/processing unit, or via network (such as internet, local area network, wide area network and/or wireless network) download to outer computer or
External memory.Network may include copper transmission cable, optical transmission fibers, wireless transmission, router, firewall, interchanger,
Gateway computer and/or Edge Server.Network adapter cards or network interface in each calculating/processing unit connect from network
Computer-readable program instructions are received, and forward computer-readable program instructions, to be stored in corresponding calculating/processing unit
In interior computer readable storage medium.
The computer-readable program instructions of operation for carrying out the disclosure can be assembly instruction, instruction set architecture (ISA)
Instruction, machine instruction, machine-dependent instructions, microcode, firmware instructions, condition setup data, or with one or more programming languages
Any combination source code or object code write, the programming language includes the programming language of object-oriented, such as
Smalltalk, C++ etc., and conventional procedural, such as " C " programming language or similar programming language.Computer
Readable program instructions can execute on the user's computer completely, partly execute on the user's computer, as independent
Software package executes, and partly executes on the remote computer on the user's computer and partly, or completely in remote computation
It is executed on machine or server.In latter, remote computer can pass through any kind of network (including local area network
(LAN) or wide area network (WAN)) be connected to the computer of user, or may be connected to outer computer (for example, by using mutual
The internet of the Internet services provider).In some embodiments, including such as programmable logic circuit, field programmable gate
The electronic circuit of array (FPGA) or programmable logic array (PLA) can be by being believed using the state of computer-readable program instructions
It ceases so that electronic circuit personalization executes computer-readable program instructions, to execute several aspects of the disclosure.
Herein with reference to the flow chart according to the method for the embodiment of the disclosure, equipment (system) and computer program product
Diagram and/or block diagram describe the aspect of the disclosure.It should be understood that each frame and flow chart of flow chart diagram and/or block diagram
The combination of diagram and/or the frame in block diagram can be realized by computer-readable program instructions.
These computer-readable program instructions can be provided to general purpose computer, special purpose computer or other programmable datas
The processor of processing equipment carrys out production machine, so that executing via computer or the processor of other programmable data processing devices
Instruction generate the means of function action specified in one or more frames for implementation flow chart and/or block diagram.These
Computer-readable program instructions are also storable in computer readable storage medium, at bootable computer, programmable data
Reason equipment and/or other devices work in a specific way, so that being wherein stored with the computer readable storage medium packet of instruction
Product is included, the product includes each side of function action specified in one or more frames of implementation flow chart and/or block diagram
The instruction in face.
Computer-readable program instructions can also be loaded into computer, other programmable data processing devices or other devices
On, to cause series of operation steps to execute on computer, other programmable devices or other devices, to generate computer
The process of implementation, so that the instruction implementation flow chart and/or frame that are executed on computer, other programmable devices or other devices
Specified function action in one or more frames of figure.
Flow chart and block diagram in schema show system, method and the computer of the various embodiments according to the disclosure
The architecture, functionality and operation of the possible embodiment of program product.In this regard, each frame in flowchart or block diagram can table
Show the module, segment or part of instruction comprising for implementing one or more executable instructions of specified logic function.?
In some alternate embodiments, function described in frame can occur by order described in figure is different from.For example, continuously show
Two frames out actually can substantially simultaneously execute or the frame can be executed in the reverse order sometimes, this depends on institute
The functionality being related to.It will additionally note that, in each frame and block diagram and or flow chart diagram of block diagram and or flow chart diagram
The combination of frame can be implemented by the system based on specialized hardware, the system based on specialized hardware executes specified function or dynamic
Make, or carries out the combination of specialized hardware and computer instruction.
Claims (20)
1. a kind of method of the cold chain route for analyzing and selecting cold chain system, which comprises
It determines to form the route of the cold chain from the current location of cold haulage vehicle to destination using cold chain network；
The route data for the route about the determination being collected at the cold chain network；
Using the cold chain network, prediction route data is calculated based on the route data being collected into；
Using display, the route data being collected into and the prediction route are shown using graphical user interface (GUI)
Data；And
The route selection based on the shown route data being collected into and prediction route data is received from user input apparatus.
2. according to the method described in claim 1, its further include:
The route data being collected into is stored in first database, the route data includes for calculating prediction route data
Refrigeration unit data, including refrigerating efficiency；And
The route data being collected into is stored in the second database, the route data includes for calculating prediction route data
Driver data, including driver's validity,
Wherein driver's validity is determined based on the actual path data compared with predicting route data.
3. according to the method described in claim 1, its further include:
Determine that a plurality of route, every route are formed from the current location to the cold of the destination using the cold chain network
Chain；
The route data of a plurality of route about the determination is received at the cold chain network；
Using the cold chain network, prediction route data is calculated based on the route data being collected into；
The route data and the prediction route number being collected into is shown using the display of the cold haulage vehicle
According to；And
The route of the route data being collected into and prediction route data based on the display is received from the user input apparatus
Selection.
4. according to the method described in claim 1,
Wherein the route data being collected into is made of weather data and refrigerating box data,
Wherein the weather data include temperature, atmospheric pressure, wind speed, wind direction, cloud layer, storm warning, humidity, ozone content and
One or more of pollen amount, and
Wherein refrigerating box data include time-varying temperature value, the energy using one of data and current temperature value or more
Person.
5. according to the method described in claim 4,
Wherein the prediction route data is from the influence by weather to the refrigerating box, the predicting the weather of different route, weather
At least one selected in the group of calculating, efficiency, the freshness of prediction and quality and route the risk composition of influence to refrigeration
Person.
6. according to the method described in claim 1,
Wherein the route data being collected into is made of Driver data,
Wherein Driver data includes previous driving time, speed average, the whole velocity amplitudes, driving time, parking of route
Length, parking position and one or more of habit of refueling.
7. according to the method described in claim 6,
Wherein the prediction route data is from by gasoline mileage value range, semi-mounted truck routes gasoline mileage, efficiency, prediction
At least one selected in freshness and quality and the group of route risk composition.
8. according to the method described in claim 1,
Wherein the route data being collected into is made of route data, route delay, path length and semi-mounted truck data,
Wherein route data includes route velocities limitation data, route tilt data, gas station's map datum, repair shop's map number
According to, General maps data and road type data, and
Wherein semi-mounted truck data include tankage, tire pressure, mileage, engine temperature, engine sensor data, vehicle
Cabin sensing data, battery data and alternator data.
9. according to the method described in claim 1,
Wherein the prediction route data is from the influence, no by gasoline mileage value range, refrigerating box efficiency, weather to refrigerating box
With the predicting the weather of route, semi-mounted truck routes gasoline mileage, the calculating of influence of the weather to refrigeration, efficiency, prediction it is fresh
At least one selected in the group that degree and quality and route risk form.
10. a kind of route management tool for cold chain system, the route management tool include:
Cold haulage vehicle comprising:
Display is configured with graphical user interface (GUI) to show the route data being collected into and prediction route number
According to；And
User input apparatus is configured as receiving the route data being collected into based on the display and predicts route data
Route selection；And
Cold chain network is configured to determine that form the route of the cold chain from the current location of cold haulage vehicle to destination, connect
The route data for the route about the determination being collected into, and prediction road is calculated based on the route data being collected into
Line number evidence, the cold chain network include:
First database, is configured as the route data that storage is collected into, and the route data includes for calculating prediction road
The refrigeration unit data of line number evidence, including refrigerating efficiency；And
Second database, is configured as the route data that storage is collected into, and the route data includes for calculating prediction road
The Driver data of line number evidence, including driver's validity,
Wherein driver's validity is determined based on the actual path data compared with the route data of estimation.
11. route management tool according to claim 10, wherein the cold chain network is additionally configured to determine a plurality of road
Line, every route form the cold chain from the current location to the destination, and be collected into about the determination
The route data of a plurality of route.
12. route management tool according to claim 10,
Wherein the route data being collected into is made of weather data and refrigerating box data,
Wherein the weather data include temperature, atmospheric pressure, wind speed, wind direction, cloud layer, storm warning, humidity, ozone content and
One or more of pollen amount, and
Wherein refrigerating box data include time-varying temperature value, the energy using one of data and current temperature value or more
Person.
13. route management tool according to claim 12,
Wherein the prediction route data is from the influence by weather to the refrigerating box, the predicting the weather of different route, weather
At least one selected in the group of calculating, efficiency, the freshness of prediction and quality and route the risk composition of influence to refrigeration
Person.
14. route management tool according to claim 10,
Wherein the route data being collected into is made of Driver data, and
Wherein Driver data includes previous driving time, speed average, the whole velocity amplitudes, driving time, parking of route
Length, parking position and one or more of habit of refueling.
15. route management tool according to claim 14,
Wherein the prediction route data be from by gasoline mileage value range, semi-mounted truck routes gasoline mileage utilization rate, efficiency,
At least one selected in the group of freshness and quality and route the risk composition of prediction.
16. route management tool according to claim 10,
Wherein the route data being collected into is made of route data, route delay, path length and semi-mounted truck data,
Wherein route data includes route velocities limitation data, route tilt data, gas station's map datum, repair shop's map number
According to, General maps data and road type data, and
Wherein semi-mounted truck data include tankage, tire pressure, mileage, engine temperature, engine sensor data, vehicle
Cabin sensing data, battery data and alternator data.
17. route management tool according to claim 10,
Wherein the prediction route data is from the influence, no by gasoline mileage value range, refrigerating box efficiency, weather to refrigerating box
With the predicting the weather of route, semi-mounted truck routes gasoline mileage, the calculating of influence of the weather to refrigeration, efficiency, prediction it is fresh
At least one selected in the group that degree and quality and route risk form.
18. a kind of system for cold chain route management comprising:
One or more processors are communicated with the computer readable storage medium of one or more types, and the computer can
Reading storage medium has the program instruction therewith embodied, and described program instruction can be executed by one or more of processors
To cause the processor:
It determines to form the route of the cold chain from the current location of cold haulage vehicle to destination using cold chain network；
The route data for the route about the determination being collected at the cold chain network；
Using the cold chain network, prediction route data is calculated based on the route data being collected into；
Using the display of the cold haulage vehicle, the route number being collected into is shown using graphical user interface (GUI)
According to the prediction route data；And
The route data being collected into and prediction road based on the display are received from the user input apparatus of the cold haulage vehicle
The route selection of line number evidence.
19. system according to claim 18 further includes that can be executed by one or more of processors to cause
State the additional program instructions that processor performs the following operation:
The route data being collected into is stored in first database, the route data includes for calculating prediction route data
Refrigeration unit data, including refrigerating efficiency；And
The route data being collected into is stored in the second database, the route data includes for calculating prediction route data
Driver data, including driver's validity,
Wherein driver's validity is determined based on the actual path data compared with predicting route data.
20. system according to claim 18 further includes that can be executed by one or more of processors to cause
State the additional program instructions that processor performs the following operation:
Wherein the route data being collected into is by route data, route delay, path length, semi-mounted truck data, driver
Data, weather data and refrigerating box data composition,
Wherein route data includes route velocities limitation data, route tilt data, gas station's map datum, repair shop's map number
According to, General maps data and road type data,
Wherein semi-mounted truck data include tankage, tire pressure, mileage, engine temperature, engine sensor data, vehicle
Cabin sensing data, battery data and alternator data,
Wherein Driver data includes previous driving time, speed average, the whole velocity amplitudes, driving time, parking of route
Length, parking position and one or more of habit of refueling,
Wherein the prediction route data be from by gasoline mileage value range, refrigerating box efficiency, weather to the shadow of the refrigerating box
It rings, the predicting the weather of different routes, the calculating of influence of the weather to refrigeration, semi-mounted truck routes gasoline mileage, weather is to refrigeration
Influence calculating, efficiency, the freshness of prediction and quality and route risk composition group in select at least one.
Wherein the weather data include temperature, atmospheric pressure, wind speed, wind direction, cloud layer, storm warning, humidity, ozone content and
One or more of pollen amount, and
Wherein refrigerating box data include the temperature value changed over time, the energy using one of data and current temperature value or more
Person.
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| US62/314010 | 2016-03-28 | ||
| PCT/US2017/023823 WO2017172484A1 (en) | 2016-03-28 | 2017-03-23 | Cold chain overall cost and quality software as a service module |
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| CN109074539A true CN109074539A (en) | 2018-12-21 |
| CN109074539B CN109074539B (en) | 2022-04-26 |
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| CN201780022840.XA Active CN109074539B (en) | 2016-03-28 | 2017-03-23 | Cold chain overall cost and quality software as a service module |
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| WO2017172484A1 (en) | 2017-10-05 |
| EP3437039A1 (en) | 2019-02-06 |
| CN109074539B (en) | 2022-04-26 |
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