CN114186938A - Internet of things data processing method, device, equipment, medium and program product - Google Patents
Internet of things data processing method, device, equipment, medium and program product Download PDFInfo
- Publication number
- CN114186938A CN114186938A CN202111527380.0A CN202111527380A CN114186938A CN 114186938 A CN114186938 A CN 114186938A CN 202111527380 A CN202111527380 A CN 202111527380A CN 114186938 A CN114186938 A CN 114186938A
- Authority
- CN
- China
- Prior art keywords
- data
- internet
- things
- warehousing
- resource
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000003672 processing method Methods 0.000 title claims description 7
- 230000008859 change Effects 0.000 claims abstract description 89
- 238000000034 method Methods 0.000 claims abstract description 50
- 238000012545 processing Methods 0.000 claims abstract description 37
- 230000004044 response Effects 0.000 claims abstract description 12
- 230000000007 visual effect Effects 0.000 claims description 33
- 238000013507 mapping Methods 0.000 claims description 21
- 238000004590 computer program Methods 0.000 claims description 20
- 230000008569 process Effects 0.000 claims description 9
- 238000007726 management method Methods 0.000 description 41
- 238000007405 data analysis Methods 0.000 description 16
- 238000010586 diagram Methods 0.000 description 15
- 238000012937 correction Methods 0.000 description 11
- 230000015654 memory Effects 0.000 description 10
- 230000006870 function Effects 0.000 description 8
- 239000000463 material Substances 0.000 description 6
- 238000004891 communication Methods 0.000 description 5
- 238000012800 visualization Methods 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 3
- 229910052737 gold Inorganic materials 0.000 description 3
- 239000010931 gold Substances 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 229910052709 silver Inorganic materials 0.000 description 3
- 239000004332 silver Substances 0.000 description 3
- 230000003068 static effect Effects 0.000 description 3
- 230000033228 biological regulation Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000013439 planning Methods 0.000 description 2
- 238000012384 transportation and delivery Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000000758 substrate Substances 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/23—Updating
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Databases & Information Systems (AREA)
- Development Economics (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Data Mining & Analysis (AREA)
- Educational Administration (AREA)
- General Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a method for processing data of an internet of things, and relates to the field of the internet of things. The method comprises the steps of receiving the data of the internet of things of each resource library in N resource libraries, wherein the data of the internet of things comprises resource library identification, warehousing change data and operation change data of transportation equipment, each resource library is used for supplying resources to M service points, and the data of the internet of things is generated in response to resource scheduling requests of the M service points; determining a corresponding internet of things model based on the resource library identification, wherein the internet of things model of each resource library is the same or different, and comprises a warehousing model and a transportation equipment model; and updating the corresponding Internet of things model and/or the resource scheduling request based on the Internet of things data of each resource library. The disclosure also provides an internet of things data processing device, equipment, a storage medium and a program product.
Description
Technical Field
The present disclosure relates to the field of internet of things, and more particularly, to a method, an apparatus, a device, a medium, and a program product for processing internet of things data.
Background
The internet of things is, for example, a huge network formed by combining various information sensing devices with the internet, and exchanges and communicates internet of things data through an information propagation medium, so that the interconnection and intercommunication of people, machines and objects are realized, and functions such as intelligent positioning, tracking, management and the like are facilitated.
At present, a resource library is generally used as an independent system, for example, warehousing equipment, transportation equipment, various resources and the like in the resource library are combined with the internet to exchange and communicate internet data. Different dispatching systems may need to be configured for transportation equipment of different models or manufacturers to achieve the effect of the internet of things.
In implementing the disclosed concept, the inventors found that there are at least the following problems in the related art:
at present, the method of processing the internet of things data independently for each resource library can only be limited to the interconnection and intercommunication inside each resource library, and the requirement of unified intelligent management for all resource libraries cannot be met.
Disclosure of Invention
In view of the foregoing problems, embodiments of the present disclosure can incorporate all resource pools into a unified internet of things, and provide an internet of things data processing method, apparatus, device, medium, and program product.
One aspect of the embodiments of the present disclosure provides an internet of things data processing method, including: receiving the data of the Internet of things of each resource library in N resource libraries, wherein the data of the Internet of things comprises resource library identifications, warehousing change data and operation change data of transportation equipment, each resource library is used for supplying resources to M service points, the data of the Internet of things is generated in response to resource scheduling requests of the M service points, and N or M is an integer greater than or equal to 1; determining a corresponding internet of things model based on the resource library identification, wherein the internet of things model of each resource library is the same or different, the internet of things model comprises a warehousing model and a transportation equipment model, the warehousing model comprises warehousing data of each resource library before change, and the transportation equipment model comprises operation data of the transportation equipment before change; and updating the corresponding Internet of things model and/or the resource scheduling request based on the Internet of things data of each resource library.
According to an embodiment of the present disclosure, further comprising: responding to resource scheduling requests of M service points, and establishing a corresponding relation between the Internet of things data of each resource library and the resource scheduling requests; wherein the resource scheduling request corresponding to the update of the internet of things data based on each resource library comprises: and updating the execution condition of the resource scheduling request based on the warehousing change data and the job change data.
According to the embodiment of the disclosure, the warehousing model comprises visualized warehousing graphs of resource libraries, the transportation equipment model comprises transportation equipment operation graphs, and updating the corresponding internet of things model based on the internet of things data of each resource library comprises: updating the visual warehousing graph based on warehousing change data; updating the transportation equipment operation graph based on the operation change data; and mapping the transport equipment operation graph to the visual storage graph for displaying.
According to an embodiment of the present disclosure, before updating the visual warehousing graph based on the warehousing change data, the method further includes obtaining the visual warehousing graph, specifically including: obtaining a map graph of the resource library and a graph of warehousing equipment, wherein the warehousing equipment is used for bearing resources in the resource library; and mapping the warehousing equipment graph to the map graph based on the first coordinate of the warehousing equipment in the resource library to obtain the visual warehousing graph.
According to an embodiment of the present disclosure, the updating the visual warehousing graph based on warehousing change data includes: obtaining a second coordinate of the warehousing equipment in the warehousing change data; and mapping the warehousing equipment graph to the map graph based on the second coordinate so as to update the visual warehousing graph.
According to an embodiment of the present disclosure, before updating the transportation device operation graph based on the job change data, the method further includes obtaining the transportation device operation graph, specifically including: acquiring a transportation equipment graph, a third coordinate of the transportation equipment in a resource library and a runnable path; the method further comprises the following steps: mapping the runnable path to the visual warehousing graph; and/or mapping the transport device graph to the runnable path based on the third coordinate.
According to an embodiment of the present disclosure, the updating the transportation device operation graph based on the job change data includes: and obtaining fourth coordinates of the transportation equipment in the operation change data, and mapping the transportation equipment graph to the runnable path based on the fourth coordinates.
According to an embodiment of the present disclosure, the receiving the internet of things data of each of the N resource pools includes: acquiring operation change data reported by the transportation equipment, wherein the operation change data comprises data generated in the operation process of the transportation equipment; and under the condition that the operation change data is correction-type data, correcting based on the data reported by the resource library, wherein the data reported by the resource library comprises the warehousing change data, and the correction-type data comprises data corrected by referring to external data.
Another aspect of the embodiments of the present disclosure provides an internet of things data processing apparatus, including: the data receiving module is used for receiving the data of the Internet of things of each resource library in N resource libraries, wherein the data of the Internet of things comprises resource library identification, warehousing change data and operation change data of transportation equipment, each resource library is used for supplying resources to M service points, the data of the Internet of things is generated in response to resource scheduling requests of the M service points, and N or M is an integer greater than or equal to 1; the model determining module is used for determining a corresponding internet of things model based on the resource library identification, wherein the internet of things model of each resource library is the same or different, the internet of things model comprises a warehousing model and a transportation equipment model, the warehousing model comprises warehousing data of each resource library before change, and the transportation equipment model comprises operation data of the transportation equipment before change; and the data updating module is used for updating the corresponding Internet of things model and/or the resource scheduling request based on the Internet of things data of each resource library.
Another aspect of the disclosed embodiments provides an electronic device, including: one or more processors; memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method as described above.
Yet another aspect of the embodiments of the present disclosure provides a computer-readable storage medium having stored thereon executable instructions, which when executed by a processor, cause the processor to perform the method as described above.
Yet another aspect of the disclosed embodiments provides a computer program product comprising a computer program that when executed by a processor implements the method as described above.
One or more of the above embodiments have the following advantageous effects: firstly, the data of the internet of things of each resource library in the N resource libraries can be received, and resource scheduling requests of the M service points can be received to enable the resource libraries to be scheduled to generate the data of the internet of things. Secondly, the corresponding internet of things models can be respectively adapted to specific conditions of different resource libraries, and the internet of things models are determined based on the resource library identifications so as to process the internet of things data. And finally, the Internet of things data can be processed in time to update the Internet of things model, and the Internet of things data can be combined with the resource scheduling request to realize the real-time update of the resource scheduling request from generation and execution to completion. Therefore, the limitation of the original internet of things to the resource library itself can be broken through, the interconnection and intercommunication among the service point, the resource scheduling request and all the resource libraries can be realized, and more data references can be provided for intelligent management.
Drawings
The foregoing and other objects, features and advantages of the disclosure will be apparent from the following description of embodiments of the disclosure, which proceeds with reference to the accompanying drawings, in which:
FIG. 1 schematically illustrates an architecture diagram of an Internet of things data processing system in accordance with an embodiment of the present disclosure;
FIG. 2 schematically illustrates a data processing flow diagram for an Internet of things data processing system according to an embodiment of the disclosure;
fig. 3 schematically illustrates a flow chart of a method of processing internet of things data according to an embodiment of the present disclosure;
FIG. 4 schematically illustrates a flow chart for updating an Internet of things model according to an embodiment of the disclosure;
FIG. 5 schematically illustrates a flow chart for obtaining the visual warehousing graph according to an embodiment of the present disclosure;
FIG. 6 schematically illustrates a flow chart of a method of updating a visual warehousing graphic according to an embodiment of the present disclosure;
FIG. 7 schematically illustrates a flow chart for updating a transportation device operational diagram according to an embodiment of the present disclosure;
FIG. 8 schematically shows a flow chart of data correction according to an embodiment of the present disclosure;
fig. 9 schematically shows a block diagram of a configuration of an internet of things data processing apparatus according to an embodiment of the present disclosure;
fig. 10 schematically shows a block diagram of an electronic device adapted to implement a method of processing internet of things data according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
In the technical scheme of the disclosure, the processes of acquisition, collection, storage, use, processing, transmission, provision, disclosure, application and the like of the data of the Internet of things all accord with the regulations of related laws and regulations, necessary confidentiality measures are taken, and the good custom of the public order is not violated.
Taking an Automatic Guided Vehicle (AGV) in a resource library as an example, an AGV scheduling system is mostly provided by an AGV hardware manufacturer at present, is deployed inside the resource library, and mainly undertakes tasks such as AGV management and task scheduling, so as to provide service for controlling the AGV inside the resource library. However, there is still no effective support for remote managers outside the resource libraries, such as management departments, to remotely master the running load and operation efficiency of AGVs in each resource library, and even the real-time running information of each AGV. Similarly, the implementation of the internet of things in the resource library is limited to the inside of a certain resource library, and even the AGV itself in the resource library. In actual use, the data which are mutually connected cannot be combined, the connection among all resource libraries cannot be reflected on the whole, and the scheduling efficiency of the whole resources is reduced.
The embodiment of the disclosure provides an internet of things data processing method, which comprises the following steps: the method comprises the steps of receiving the internet of things data of each resource library in N resource libraries, wherein the internet of things data comprise resource library identification, warehousing change data and operation change data of transportation equipment, each resource library is used for supplying resources to M service points, the internet of things data are generated in response to resource scheduling requests of the M service points, and N or M is an integer greater than or equal to 1. And determining a corresponding internet of things model based on the resource library identification, wherein the internet of things model of each resource library is the same or different, the internet of things model comprises a warehousing model and a transportation equipment model, the warehousing model comprises warehousing data of each resource library before change, and the transportation equipment model comprises operation data of the transportation equipment before change. And updating the corresponding Internet of things model and/or resource scheduling request based on the Internet of things data of each resource library.
Firstly, the data of the internet of things of each resource library in the N resource libraries can be received, and resource scheduling requests of the M service points can be received to enable the resource libraries to be scheduled to generate the data of the internet of things. Secondly, the corresponding internet of things models can be respectively adapted to specific conditions of different resource libraries, and the internet of things models are determined based on the resource library identifications so as to process the internet of things data. And finally, the Internet of things data can be processed in time to update the Internet of things model, and the Internet of things data can be combined with the resource scheduling request to realize the real-time update of the resource scheduling request from generation and execution to completion. Therefore, the condition that the original Internet of things is limited to the resource libraries per se can be broken through, interconnection and intercommunication among service points, resource scheduling requests and all the resource libraries are achieved, and more data references are provided for intelligent management.
Fig. 1 schematically illustrates an architecture diagram of an internet of things data processing system according to an embodiment of the present disclosure.
As shown in fig. 1, the internet of things data processing system 100 according to the embodiment may include an operation resource management unit 110, an internet of things data analysis unit 120, an internet of things device management unit 130, an edge resource management unit 140, a warehouse management unit 150, and a transportation device scheduling unit 160.
The operating resource management unit 110 may be configured to send macro job data, such as allocation, inventory, warehouse-out, etc., to the edge resource management unit 140 through the internet of things device management unit 130 in response to a resource scheduling request of a service point.
The branch edge repository management unit 140 may drive the transportation device scheduling unit 160 to complete transportation according to the macro operation data, and the warehouse management unit 150 records real-time inventory changes.
The warehouse management unit 150 may acquire warehouse change data, and the transport scheduling unit 160 may acquire job change data of the transport, such as AGV position, status, and the like.
The edge-side vault management unit 140 may collect the data of the warehouse management unit 150 and the transportation equipment scheduling unit 160, and send the data to the data analysis unit 120 through the equipment management unit 130.
The internet of things data analysis unit 120 may generate internet of things data that may be used for a manager to refer to, based on a pre-established storage model and a transportation equipment model, in combination with storage change data and operation change data of transportation equipment. The internet of things data may be real-time data, that is, the internet of things data is generated by the internet of things data analysis unit 120 in real time in response to the internet of things data uploaded by the warehouse management unit 150 and the transportation device scheduling unit 160. The data may also be quasi-real-time data, for example, after receiving the internet of things data sent by the warehouse management unit 150 and the transportation equipment scheduling unit 160, the processes of model matching, visualization processing, client display and the like are performed, which results in a certain time delay (e.g., 10 to 30 seconds), and therefore the data is called quasi-real-time data.
The internet of things data processing system 100 may be implemented based on a cloud edge architecture, which may include a cloud computing layer (e.g., the operation resource management unit 110, the internet of things data analysis unit 120, the internet of things device management unit 130), an edge layer (e.g., the edge vault management unit 140), and a device layer (e.g., the warehouse management unit 150 and the transportation device scheduling unit 160). The edge layer executes the local data processing flow of each resource library to improve the data processing efficiency of the whole framework. The internet of things data processing system 100 may be used in financial institutions with warehouse management requirements, logistics institutions or institutions that produce and sell physical products, and the like.
According to an embodiment of the present disclosure, the operation resource management unit 110 may send a resource scheduling request to the internet of things data analysis unit 120 for processing in response to the resource scheduling request of one or more service points. The edge-side resource management unit 140, the warehouse management unit 150, and the transportation device scheduling unit 160 in fig. 1 may have one or more. For example, each resource pool is correspondingly configured with an edge resource pool management unit 140, a warehouse management unit 150 and a transportation device scheduling unit 160.
Fig. 2 schematically illustrates a data processing flow diagram of an internet of things data processing system according to an embodiment of the present disclosure. The edge domain may include areas where the edge vault management unit 140, the warehouse management unit 150, and the transportation device scheduling unit 160 are located.
As shown in fig. 2, when the transport facility is an AGV, the AGV operation data and the storage change information generated in the edge domain are transmitted to the internet of things data analysis unit 120 via the internet of things management unit 130. The internet of things data analysis unit 120 may match the AGV operation data with the corresponding AGV operation model and match the storage change information with the corresponding storage model based on the repository identification, and perform the update operation respectively. The updating operation refers to, for example, generating quasi-real-time running data through a streaming computing method, so that management personnel can refer to the quasi-real-time running data in a cloud. The management personnel include, for example, an operator in the resource repository, a resource scheduler, an equipment purchaser, a financial staff, and the like.
The method for processing the internet of things data according to the embodiment of the present disclosure will be described in detail below with reference to fig. 3 to 8 based on fig. 1 and 2.
Fig. 3 schematically shows a flowchart of an internet of things data processing method according to an embodiment of the present disclosure.
As shown in fig. 3, the method for processing the internet of things data according to this embodiment includes operations S310 to S330.
In operation S310, the data of the internet of things of each of N resource pools is received, where the data of the internet of things includes a resource pool identifier, warehousing change data, and job change data of transportation equipment, each resource pool is used to supply resources to M service points, the data of the internet of things is generated in response to resource scheduling requests of the M service points, and N or M is an integer greater than or equal to 1.
The warehousing change data comprises data such as the number of resources in a resource library, the position of warehousing equipment, the state of the warehousing equipment and the like. For example, the data in the vault for indicating the increase or decrease of the vault inventory, the change of the cash storage position, the change of the shelf position, the change of the functional area, and the like. The operation change data includes, for example, data such as the position of the transport facility, the load, the operation state, the power amount, the carried material information, the task to be executed, and the start position of the transport task.
Taking bank a as an example, the resource bank may be a vault of bank a, and resources such as cash, securities, important certificates, gold and silver and the like may be stored in the vault. For example, in a city a, bank a has several branch points (i.e., service points), and several vaults. Wherein each vault may supply cash, securities, important vouchers, gold and silver, etc. to one or more branch outlets. Each branch node can send a resource scheduling request, wherein the resource scheduling request can be used for storing the resources of the node in a vault or requesting the resources from the vault.
In operation S320, a corresponding internet of things model is determined based on the resource library identifications, where the internet of things model of each resource library is the same or different, the internet of things model includes a warehousing model and a transportation device model, the warehousing model includes pre-change warehousing data of each resource library, and the transportation device model includes pre-change operation data of a transportation device.
For example, in city a, city b and city c, bank a has several branch points (i.e., service points) and several vaults in each city. The national treasury building structures, storage resources and internal storage plans in the cities A, B and C may be different, and even the national treasury building structures, storage resources and internal storage plans in one city may be different. In addition, the transportation equipment in each vault may be purchased from different manufacturers, and is suitable for different systems. Therefore, the implementation of the internet of things in the related art is limited to the inside of each resource library and is independent of each other.
In an alternative mode, based on the actual situation of each resource pool, a corresponding internet of things model is configured in advance for each resource pool, and because there may be differences between the resource pools, the corresponding internet of things models are the same or different. In addition, the corresponding relationship between the resource library and the internet of things model can be established in advance based on the resource library identifier, and when the model is determined, for example, the resource library with the same resource library identifier and the internet of things model can be matched.
In operation S330, the corresponding internet of things model and/or resource scheduling request is updated based on the internet of things data of each resource pool.
The method for processing the data of the internet of things can provide quasi-real-time data such as the current running position of the transport equipment, the AGV state, the AGV load and the like in the resource library, and managers can also perform intelligent management through updated internet of things models or resource scheduling requests.
In some embodiments, before performing operation S330, the method for processing internet of things data of these embodiments may further include establishing a correspondence between the internet of things data of each resource pool and the resource scheduling request in response to the resource scheduling requests of the M service points. In operation S330, updating the execution status of the resource scheduling request based on the warehousing change data and the job change data may be included.
Referring to fig. 1, after receiving the resource scheduling request, the operating resource management unit 110 may generate a corresponding resource scheduling task, and send the request, the task, and the resource pool information for executing the task to the internet of things data analysis unit 120. The internet of things data analysis unit 120 may establish a corresponding relationship between the request, the task and the resource library executing the task, and obtain specific cloud task information, such as distribution task information in a money allocation stage, a check stage, a delivery stage, a collection application stage, and the like, and information such as an inventory register, an accounting register, and the like. After the data of the internet of things returned by the corresponding resource library is received, the execution condition of the corresponding request can be updated. For example, the transport equipment can be updated in an operating state, a load state or an idle state through the operation change data, and the currently requested delivery task information can be updated through the warehousing change data. Finally, the manager can call the relevant data for reference, for example, through a resource scheduling request, which resource pool to execute, which transportation devices in the resource pool to execute, the current scheduling stage, etc. can be called. Therefore, the Internet of things data and the resource scheduling request can be combined, and data support is provided for improving the scheduling efficiency through the execution condition of the request.
Fig. 4 schematically shows a flowchart of updating the internet of things model in operation S330 according to an embodiment of the present disclosure.
As shown in fig. 4, the updating of the corresponding internet of things model based on the internet of things data of each resource pool in operation S330 in this embodiment may include operations S410 to S430. The warehousing model comprises a visual warehousing graph of the resource library, and the transportation equipment model comprises a transportation equipment operation graph.
In operation S410, the visual warehousing graph is updated based on the warehousing change data.
In operation S420, the transportation device operation diagram is updated based on the job change data.
In operation S430, the transportation device operation graph is mapped to the visual warehousing graph for displaying.
In the related art, the dispatching system of the transportation equipment only monitors the transportation equipment, and the internet of things of each resource pool is only limited to the resource pool. According to the embodiment of the disclosure, on the one hand, on the basis of visualizing the warehousing graph and the transportation equipment operation graph, the warehousing change data and the operation change data are combined and displayed in a visualized mode, for example, data such as articles carried by the transportation equipment, warehouse positions of transportation starting points and end points, and inventory distribution, warehousing and ex-warehousing tasks and the like of the whole resource base are displayed, so that a more convenient reference mode and a more intuitive observation result can be provided. On the other hand, all resource libraries and service point data are integrated, so that a manager can conveniently schedule resources from the perspective of the system.
FIG. 5 schematically illustrates a flow chart for obtaining a visual warehousing graphic according to an embodiment of the present disclosure.
Before operation S310, obtaining a visual warehouse graph may further be included, and as shown in fig. 5, obtaining a visual warehouse graph according to this embodiment may include operations S510 to S520.
In operation S510, a map graph of a resource library and a graph of warehousing equipment are obtained, where the warehousing equipment is used for bearing resources in the resource library.
The map graph of the resource library is used for displaying the internal structure, the function partition, the equipment arrangement and the like of the resource library. For example, the map graph of the vault may be a static vault space graph, including functional areas such as a vault internal storage area, a money allocation area, a clearing area and the like, and the storage location and the shelf arrangement in the areas. The warehouse device pattern is, for example, a device pattern of a shelf, a tray, a container, etc. storing resources, and the warehouse device in the vault is, for example, a shelf, a tray, a container, or a safe carrying resources such as cash, gold, silver, securities, etc.
In operation S520, the warehousing equipment graph is mapped to the map graph based on the first coordinates of the warehousing equipment in the repository to obtain a visual warehousing graph.
Taking the vault as an example, the execution of operations S510 to S520 may include the following steps:
first, the entity vault is abstracted into a graphic model, such as a two-dimensional plan, a three-dimensional map, and the like, to obtain a map graphic.
Secondly, the warehousing equipment is subjected to primitive transformation, and warehousing equipment graphs are obtained. For example, the trays, shelves and containers are represented by different shapes, and the carrying state, such as empty or full space, can also be represented by different colors.
And thirdly, establishing a space coordinate system in the entity vault, and identifying the position in the vault by using the ordered points or XYZ-axis coordinates.
Next, a mapping relationship between the coordinates and the graphical model is established. The warehousing equipment graphic is mapped to the map graphic, for example, based on first coordinates of the warehousing equipment in the repository.
Finally, elements such as shelves, pallets, goods, storage locations, etc. are pre-registered in the internet of things data analysis unit 120, and an identification and an initial state are assigned to the graph of each entity.
In some embodiments, the visual warehousing graphic may also include graphics or text for characterizing dynamic data such as material carried by the pallet, stock level material information, shelf material information, and the like.
FIG. 6 schematically illustrates a flow chart of a method of updating a visual warehouse graph according to an embodiment of the present disclosure.
As shown in fig. 6, operation S410 may include operations S610 to S620.
In operation S610, second coordinates of the warehousing equipment in the warehousing change data are obtained.
In operation S620, the warehousing equipment graphic is mapped to the map graphic based on the second coordinate to update the visual warehousing graphic.
For example, a tray in a vault is carried by the transport apparatus from the home position (i.e., first coordinates) to the vault doorway (i.e., second coordinates), and accordingly, the warehouse management unit 160 uploads its position data as part of warehouse change data. The internet of things data analysis unit 120 may move the corresponding graphic of the tray from the first coordinate to the second coordinate on the map graphic based on the mapping relationship between the actual coordinate system and the map graphic.
According to an embodiment of the present disclosure, obtaining a transportation device operation graph, for example, obtaining the transportation device graph, the third coordinate of the transportation device in the resource library, and the runnable path, may be further included before operation S320. In some embodiments, mapping the runnable path to a visual warehousing graphic may also be included. And/or mapping the transport device graph to the runnable path based on the third coordinate. Such as mapping the transport device graph onto a runnable path.
The transportation device operation graph may include a static operation graph, such as a graph of the transportation device, a position (e.g., a third coordinate), a runnable path, a charging station position, a position where the AGV may park, and a special channel node of an elevator, a door access, and the like, and may further include a dynamic operation graph, such as a visual operation state, an electric quantity, material carrying information, a currently executed task, a start position of a currently carried task, and the like. The transportation equipment operation graph can be mapped to the visual storage graph for displaying.
FIG. 7 schematically illustrates a flow chart for updating a transportation device operation graph according to an embodiment of the present disclosure.
As shown in fig. 7, updating the transportation device operation diagram based on the job change data in operation S420 includes operations S710 to S720.
In operation S710, a fourth coordinate of the transport apparatus in the job change data is obtained.
In operation S720, the transportation device graphic is mapped to the runnable path based on the fourth coordinate.
For example, during the movement of the transport device, its position information, i.e. the fourth coordinates, can be collected. The fourth coordinates are transmitted to the internet of things data analysis unit 120 as part of the data content in the job change data by the transportation device scheduling unit 170. The position of the transport device graphic on the travelable path is moved from the third coordinate to the fourth coordinate on the map graphic by the internet of things data analysis unit 120. Taking an AGV as an example, the method may include the following steps:
firstly, combining static data, processing single dynamic data in real time, and forming a time section snapshot of an AGV view angle in a whole database model. Taking the position of the AGV as an example, after the position is reported by the AGV, the big data platform combines all the AGV in the fund to form the overall distribution of the AGV of the time point on the runnable path according to the reporting time.
And then, mapping the AGV dynamic data to a map graph by combining graph definition and mapping, and calculating the AGV coordinate system through the mapping relation between the actual coordinate system of the resource library and the graph model to form the position information displayed on the image by the AGV.
Finally, the AGV statistics may be processed in time intervals, such as the daily average operating mileage of the AGV, the operating time after charging, and the like.
FIG. 8 schematically shows a flow chart of data correction according to an embodiment of the disclosure.
As shown in fig. 8, the receiving of the internet of things data of each of the N resource pools in operation S310 includes operations S810 to S820 for data correction.
In operation S810, job change data reported by the transportation device is obtained, where the job change data includes data generated during the operation of the transportation device.
New data generated during the operation of the transportation equipment or changes of the existing data can be used as operation change data. For example, the transportation equipment itself has data such as electric quantity change, newly-added operation mileage, position movement, material transportation, etc. during the operation process.
In operation S820, in the case that the job change data is correction-type data, the correction is performed based on data reported by the resource library, where the data reported by the resource library includes warehousing change data, and the correction-type data includes data corrected by referring to external data.
The job change data may include non-correction class data and correction class data. The non-correction class data may not require external data references. The above-mentioned correction with reference to the external data means, for example, that the correction can be confirmed or adjusted by the external data, and the correction data can be determined to be correct or the error can be corrected. Taking the location information of the transportation device as an example, for example, coordinate data uploaded by the transportation device when passing through an elevator may be corrected by referring to the elevator sensor data reported by the warehouse management unit 150, and the final location data of the device may be formed after edge service preprocessing (multi-party data consistency detection, deduplication, early warning, fault handling). Taking the transportation device executing the carrying task as an example, the transportation device scheduling unit 160 may monitor that a certain tray is loaded on a certain transportation device, and at this time, the tray position information (i.e., the storage change data) reported by the storage management unit 150 may be referred to each other.
Taking an AGV as an example, the uncorrected class data for the AGV may include at least one of: the system comprises AGV running state data, AGV charging time data, AGV charging balance data, AGV mileage data, AGV running mileage data and the like, and transportation task planning data received by AGV running starting point coordinates, end point coordinates, planning paths, task mileage and the like. The correction class data for the AGV may include at least one of: the system comprises AGV current space coordinates, position data such as floor coordinates and two-dimensional coordinates in floors, event data such as AGV fault occurrence positions and occurrence reasons, and carrying information such as AGV carrying goods information, bound tray information, total tray amount, luggage number, cash ticket surface and bundle number.
In some embodiments, the transport equipment scheduling unit 160 may collect data according to the frequency with which the data occurs. The state, position and electric quantity of the transportation equipment need to be monitored in real time or change frequently and are classified as high-frequency data, and the transmission is acquired at regular time intervals (which can be defined by 10s and 20 s) in a regular acquisition mode. The fault details, the operation data, the task data, the cargo data and the like of the transportation equipment are classified into low-frequency data, and an event-driven transmission mode is adopted, for example, the fault details are transmitted when a fault occurs, the task and the operation data are transmitted when the transportation task starts and stops, and the cargo data of the transportation tray are transmitted when the transportation is changed.
In some embodiments, the internet of things data analysis unit 120 may provide presentation and query of the original model data when presenting the data, and may also provide a visual dynamic presentation of the transportation device data. The visualization process also comprises two steps, namely firstly requesting a map graph, then requesting and displaying the mapping data of the transportation equipment graph according to time steps, and drawing a final visualization effect graph by the display front end. Therefore, in the cloud edge architecture, the data is collected in the internet of things data analysis unit 120 on the cloud, which not only can be used in the resource library, but also a remote manager can observe the quasi-real-time operation dynamics of the internet of things data, and support history backtracking and data statistics.
Based on the above method for processing the data of the internet of things, the present disclosure also provides a device for processing the data of the internet of things. The apparatus will be described in detail below with reference to fig. 9.
Fig. 9 schematically shows a block diagram of a structure of an internet of things data processing apparatus according to an embodiment of the present disclosure.
As shown in fig. 9, the internet of things data processing apparatus 900 of this embodiment includes a data receiving module 910, a model determining module 920, and a data updating module 930.
The data receiving module 910 may perform operation S310, for example, to receive the data of the internet of things of each of N resource pools, where the data of the internet of things includes a resource pool identifier, warehousing change data, and job change data of transportation equipment, each resource pool is used to supply resources to M service points, the data of the internet of things is generated in response to resource scheduling requests of the M service points, and N or M is an integer greater than or equal to 1.
The model determining module 920 may perform operation S320, for example, to determine a corresponding internet of things model based on the resource pool identifications, where the internet of things model of each resource pool is the same or different, the internet of things model includes a warehousing model and a transportation equipment model, the warehousing model includes warehousing data of each resource pool before being changed, and the transportation equipment model includes operation data of transportation equipment before being changed.
The data updating module 930 may perform operation S330, for example, to update the corresponding internet of things model and/or resource scheduling request based on the internet of things data of each resource pool.
According to an embodiment of the present disclosure, the data updating module 930 may be further configured to perform operations S410 to S430, operations S610 to S620, and operations S710 to S720, for example, refer to the related embodiments described above, which are not described herein again.
According to the embodiment of the present disclosure, the device 900 for processing internet of things may further include a visualization module, configured to perform operations S510 to S520, which may refer to the above related embodiments and are not described herein again.
According to the embodiment of the present disclosure, the data receiving module 910 may be further configured to perform operations S810 to S820, for example, which refer to the above related embodiments and are not described herein again.
According to an embodiment of the present disclosure, any plurality of the data receiving module 910, the model determining module 920, and the data updating module 930 may be combined into one module to be implemented, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the data receiving module 910, the model determining module 920, and the data updating module 930 may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware. Alternatively, at least one of the data receiving module 910, the model determining module 920 and the data updating module 930 may be at least partially implemented as a computer program module, which when executed, may perform a corresponding function.
Fig. 10 schematically shows a block diagram of an electronic device adapted to implement a method of processing internet of things data according to an embodiment of the present disclosure.
As shown in fig. 10, an electronic device 1000 according to an embodiment of the present disclosure includes a processor 1001 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)1002 or a program loaded from a storage section 1008 into a Random Access Memory (RAM) 1003. Processor 1001 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 1001 may also include onboard memory for caching purposes. The processor 1001 may include a single processing unit or multiple processing units for performing different actions of a method flow according to embodiments of the present disclosure.
In the RAM 1003, various programs and data necessary for the operation of the electronic apparatus 1000 are stored. The processor 1001, ROM 1002, and RAM 1003 are connected to each other by a bus 1004. The processor 1001 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 1002 and/or the RAM 1003. Note that the program may also be stored in one or more memories other than the ROM 1002 and the RAM 1003. The processor 1001 may also perform various operations of method flows according to embodiments of the present disclosure by executing programs stored in one or more memories.
The present disclosure also provides a computer-readable storage medium, which may be embodied in the devices/apparatuses/systems described in the above embodiments. Or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM 1002 and/or the RAM 1003 described above and/or one or more memories other than the ROM 1002 and the RAM 1003.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method illustrated in the flow chart. When the computer program product runs in a computer system, the program code is used for causing the computer system to realize the item recommendation method provided by the embodiment of the disclosure.
The computer program performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure when executed by the processor 1001. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In one embodiment, the computer program may be hosted on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted in the form of a signal on a network medium, distributed, downloaded and installed via the communication part 1009, and/or installed from the removable medium 1011. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In such an embodiment, the computer program may be downloaded and installed from a network through the communication part 1009 and/or installed from the removable medium 1011. The computer program performs the above-described functions defined in the system of the embodiment of the present disclosure when executed by the processor 1001. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.
Claims (12)
1. An internet of things data processing method comprises the following steps:
receiving the data of the Internet of things of each resource library in N resource libraries, wherein the data of the Internet of things comprises resource library identifications, warehousing change data and operation change data of transportation equipment, each resource library is used for supplying resources to M service points, the data of the Internet of things is generated in response to resource scheduling requests of the M service points, and N or M is an integer greater than or equal to 1;
determining a corresponding internet of things model based on the resource library identification, wherein the internet of things model of each resource library is the same or different, the internet of things model comprises a warehousing model and a transportation equipment model, the warehousing model comprises warehousing data of each resource library before change, and the transportation equipment model comprises operation data of the transportation equipment before change;
and updating the corresponding Internet of things model and/or the resource scheduling request based on the Internet of things data of each resource library.
2. The method of claim 1, further comprising:
responding to resource scheduling requests of M service points, and establishing a corresponding relation between the Internet of things data of each resource library and the resource scheduling requests;
wherein the resource scheduling request corresponding to the update of the internet of things data based on each resource library comprises:
and updating the execution condition of the resource scheduling request based on the warehousing change data and the job change data.
3. The method of claim 1, wherein the warehousing model comprises a visual warehousing graph of resource pools, the transportation equipment model comprises a transportation equipment operation graph, and the updating of the corresponding internet of things model based on the internet of things data of each resource pool comprises:
updating the visual warehousing graph based on warehousing change data;
updating the transportation equipment operation graph based on the operation change data;
and mapping the transport equipment operation graph to the visual storage graph for displaying.
4. The method according to claim 3, wherein before updating the visual warehousing graph based on the warehousing change data, further comprising obtaining the visual warehousing graph, specifically comprising:
obtaining a map graph of the resource library and a graph of warehousing equipment, wherein the warehousing equipment is used for bearing resources in the resource library;
and mapping the warehousing equipment graph to the map graph based on the first coordinate of the warehousing equipment in the resource library to obtain the visual warehousing graph.
5. The method of claim 4, wherein said updating said visual warehousing graph based on warehousing change data comprises:
obtaining a second coordinate of the warehousing equipment in the warehousing change data;
and mapping the warehousing equipment graph to the map graph based on the second coordinate so as to update the visual warehousing graph.
6. The method according to claim 4, wherein before the updating the transportation device operation graph based on the job change data, further comprising obtaining the transportation device operation graph, specifically comprising:
acquiring a transportation equipment graph, a third coordinate of the transportation equipment in a resource library and a runnable path;
the method further comprises the following steps:
mapping the runnable path to the visual warehousing graph; and/or
Mapping the transport device graph to the runnable path based on the third coordinate.
7. The method of claim 6, wherein the updating the transportation device operational graphic based on the job change data comprises:
obtaining a fourth coordinate of the transportation device in the operation change data;
mapping the transport device graph to the runnable path based on the fourth coordinate.
8. The method of claim 1, wherein the receiving of the internet of things data for each of the N resource pools comprises;
acquiring operation change data reported by the transportation equipment, wherein the operation change data comprises data generated in the operation process of the transportation equipment;
and under the condition that the operation change data is correction-type data, correcting based on the data reported by the resource library, wherein the data reported by the resource library comprises the warehousing change data, and the correction-type data comprises data corrected by referring to external data.
9. An internet of things data processing device comprising:
the data receiving module is used for receiving the data of the Internet of things of each resource library in N resource libraries, wherein the data of the Internet of things comprises resource library identification, warehousing change data and operation change data of transportation equipment, each resource library is used for supplying resources to M service points, the data of the Internet of things is generated in response to resource scheduling requests of the M service points, and N or M is an integer greater than or equal to 1;
the model determining module is used for determining a corresponding internet of things model based on the resource library identification, wherein the internet of things model of each resource library is the same or different, the internet of things model comprises a warehousing model and a transportation equipment model, the warehousing model comprises warehousing data of each resource library before change, and the transportation equipment model comprises operation data of the transportation equipment before change;
and the data updating module is used for updating the corresponding Internet of things model and/or the resource scheduling request based on the Internet of things data of each resource library.
10. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-8.
11. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 8.
12. A computer program product comprising a computer program which, when executed by a processor, implements a method according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111527380.0A CN114186938A (en) | 2021-12-14 | 2021-12-14 | Internet of things data processing method, device, equipment, medium and program product |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111527380.0A CN114186938A (en) | 2021-12-14 | 2021-12-14 | Internet of things data processing method, device, equipment, medium and program product |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114186938A true CN114186938A (en) | 2022-03-15 |
Family
ID=80604988
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111527380.0A Pending CN114186938A (en) | 2021-12-14 | 2021-12-14 | Internet of things data processing method, device, equipment, medium and program product |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114186938A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117816578A (en) * | 2024-01-08 | 2024-04-05 | 北京智联弘盛科技发展有限公司 | Intelligent bank vault system and operation method thereof |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107358388A (en) * | 2016-11-03 | 2017-11-17 | 厦门嵘拓物联科技有限公司 | A kind of WMS based on Internet of Things and the storage quality risk appraisal procedure based on the system |
| CN108876242A (en) * | 2018-06-01 | 2018-11-23 | 中国人民解放军第三〇九医院 | A kind of intelligence goods and material handling method and device |
| CN109308593A (en) * | 2018-09-13 | 2019-02-05 | 吉林化工学院 | A kind of machine-building product storage transportation system and method based on Internet of Things |
| CN109426934A (en) * | 2017-08-25 | 2019-03-05 | 甘肃国通大宗商品供应链管理股份有限公司 | Metal staple commodities warehousing management control system |
| CN111144632A (en) * | 2019-12-19 | 2020-05-12 | 深圳供电局有限公司 | Prediction management and control model for power storage materials |
| CN111626670A (en) * | 2020-05-20 | 2020-09-04 | 贵州省人工影响天气办公室 | Weather modification ammunition tracking and monitoring system based on Internet of things |
| CN111695788A (en) * | 2020-05-25 | 2020-09-22 | 智强通达科技（北京）有限公司 | Emergency material management system based on Internet of things |
| CN111724094A (en) * | 2019-03-22 | 2020-09-29 | 上海赛印供应链管理有限公司 | Supply chain management system and method based on Internet of things |
| CN112243023A (en) * | 2020-08-05 | 2021-01-19 | 宁夏无线互通信息技术有限公司 | Product tracing system and method based on industrial internet identification analysis |
| CN112508471A (en) * | 2020-08-28 | 2021-03-16 | 山东新兴集团有限公司 | Petrochemical logistics storage and tank area management system and method, storage medium and terminal |
| KR20210073158A (en) * | 2019-12-10 | 2021-06-18 | 동명대학교산학협력단 | Inventory Management System Using IoT |
-
2021
- 2021-12-14 CN CN202111527380.0A patent/CN114186938A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107358388A (en) * | 2016-11-03 | 2017-11-17 | 厦门嵘拓物联科技有限公司 | A kind of WMS based on Internet of Things and the storage quality risk appraisal procedure based on the system |
| CN109426934A (en) * | 2017-08-25 | 2019-03-05 | 甘肃国通大宗商品供应链管理股份有限公司 | Metal staple commodities warehousing management control system |
| CN108876242A (en) * | 2018-06-01 | 2018-11-23 | 中国人民解放军第三〇九医院 | A kind of intelligence goods and material handling method and device |
| CN109308593A (en) * | 2018-09-13 | 2019-02-05 | 吉林化工学院 | A kind of machine-building product storage transportation system and method based on Internet of Things |
| CN111724094A (en) * | 2019-03-22 | 2020-09-29 | 上海赛印供应链管理有限公司 | Supply chain management system and method based on Internet of things |
| KR20210073158A (en) * | 2019-12-10 | 2021-06-18 | 동명대학교산학협력단 | Inventory Management System Using IoT |
| CN111144632A (en) * | 2019-12-19 | 2020-05-12 | 深圳供电局有限公司 | Prediction management and control model for power storage materials |
| CN111626670A (en) * | 2020-05-20 | 2020-09-04 | 贵州省人工影响天气办公室 | Weather modification ammunition tracking and monitoring system based on Internet of things |
| CN111695788A (en) * | 2020-05-25 | 2020-09-22 | 智强通达科技（北京）有限公司 | Emergency material management system based on Internet of things |
| CN112243023A (en) * | 2020-08-05 | 2021-01-19 | 宁夏无线互通信息技术有限公司 | Product tracing system and method based on industrial internet identification analysis |
| CN112508471A (en) * | 2020-08-28 | 2021-03-16 | 山东新兴集团有限公司 | Petrochemical logistics storage and tank area management system and method, storage medium and terminal |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117816578A (en) * | 2024-01-08 | 2024-04-05 | 北京智联弘盛科技发展有限公司 | Intelligent bank vault system and operation method thereof |
| CN117816578B (en) * | 2024-01-08 | 2024-05-28 | 北京智联弘盛科技发展有限公司 | Intelligent bank vault system and operation method thereof |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Dutta et al. | Managing a big data project: the case of ramco cements limited | |
| US20220019204A1 (en) | Intelligent data object model for distributed product manufacturing, assembly and facility infrastructure | |
| US10181111B1 (en) | Electronic device communications for item handoffs | |
| CN109074539A (en) | Cold chain overall cost and quality software as service module | |
| CN103998897A (en) | Geocoding points of interest and service route delivery and audit field performance and sales method and apparatus | |
| CN108549565A (en) | A kind of visualization sale management system and method | |
| US11734739B2 (en) | Methods for sample presentation using autonomous vehicles | |
| Luo et al. | Physical Internet-enabled customised furniture delivery in the metropolitan areas: digitalisation, optimisation and case study | |
| US20140164264A1 (en) | System and method for identifying and learning actionable opportunities enabled by technology for urban services | |
| Pepper et al. | Cross-border data flows, digital innovation, and economic growth | |
| CN114186938A (en) | Internet of things data processing method, device, equipment, medium and program product | |
| US11727351B2 (en) | Systems and methods for automated information collection and processing | |
| US20190180209A1 (en) | Interactive system for optimizing logistics in moving items from a first location to second location | |
| US20200219049A1 (en) | Delivery load management method and system | |
| Sharma et al. | Cloud Computing for Supply Chain Management and Warehouse Automation: A Case Study of Azure Cloud | |
| Cao et al. | Digital twin-driven warehouse management system for picking path planning problem | |
| KR20170126602A (en) | Cyber physical logistics Integration control system using image analysis | |
| RU2755520C1 (en) | Automated logistical intelligent information decision-making system in manufacturing and logistical complex | |
| AL‐Shboul | RFID technology usage and supply chain global positioning information sharing system: An enablers of manufacturing enterprises' supply chain performance‐fresh insights from the Middle East region as developing countries | |
| KR20220113303A (en) | real estate investment curation system based on artificial neural network and method therefor | |
| US20210334716A1 (en) | Information processing device and program | |
| Luan et al. | A data-based opportunity identification engine for collaborative freight logistics based on a trailer capacity graph | |
| KR102617902B1 (en) | Product pickup information provision method and system | |
| US12008592B1 (en) | Strategic and tactical intelligence in dynamic segmentation | |
| CN116306759B (en) | Label-bearing balancing weight, and label-based balancing weight allocation method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |