Knowledge graph and cross-system integrated warehousing and sales management automation method and system

Abstract

The invention relates to a knowledge graph and cross-system integrated warehousing and sales management automation method and system, and belongs to the technical field of warehousing and sales management. The method comprises the following steps: acquiring cross-system storage and sales data, and constructing a passenger car part storage and sales knowledge graph according to the cross-system storage and sales data; performing modeling processing on the passenger car part warehousing and selling knowledge graph through a dynamic rule to generate an RPA dynamic script; according to the RPA dynamic script, processing a passenger car part warehousing and selling event and outputting a passenger car part warehousing and selling management abnormal event; the passenger car part warehousing and selling management billing information is obtained through closed-loop exception handling of the passenger car part warehousing and selling management exception event, autonomous decision management and closed-loop exception handling can be carried out according to preset business knowledge, and automatic, scientific and intelligent passenger car part warehousing and selling management is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of warehousing and sales management, and in particular relates to a warehousing and sales management automation method and system integrating knowledge graph and cross-system. Background Art

[0002] In the current warehousing and sales management of passenger car parts, although RPA technology is partially applied to the financial invoicing process, it still faces three major bottlenecks: multi-system data silos, complex and rigid rules, and inefficient exception handling, and lacks unified semantic expression and intelligent decision-making capabilities.

[0003] First, manual data separation is required in warehousing scenarios to align inventory and orders. In invoicing scenarios, due to data semantic differences between the email system, Yibao system, and tax system (such as inconsistent customer names), manual mapping is required, which can easily lead to mapping errors. At the same time, the existing processes are difficult to adapt to dynamic changes in rules because traditional RPA relies on static scripts. For example, the parts compatibility rules in warehousing scenarios (such as part A adapting to model B) and dynamic inventory allocation, special template verification in invoicing scenarios, and order splitting and merging all need to adapt to the rules for dynamic adjustment. In addition, inventory anomalies in warehousing (such as part offsets) or supplier delays, tax number verification failures in invoicing, or shared document usage all require manual recovery, resulting in long process interruption cycles and high costs.

[0004] Therefore, there is an urgent need to study a warehouse sales management automation method and system that integrates knowledge graphs and cross-systems to achieve a leap from "manual experience-driven" to "knowledge-autonomous decision-making". Summary of the Invention

[0005] In order to solve the above problems existing in the prior art, the present invention proposes a warehouse sales management automation method and system with knowledge graph and cross-system integration.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A warehouse sales management automation method integrating knowledge graph and cross-systems, including: S1: Obtain cross-system warehousing and sales data, and construct a passenger vehicle parts warehousing and sales knowledge graph based on the cross-system warehousing and sales data; S2: Generate an RPA dynamic script by processing the passenger car parts warehousing and sales knowledge graph through dynamic rule modeling; S3: Processing the passenger car parts warehousing and sales event according to the RPA dynamic script to output a passenger car parts warehousing and sales management abnormality event; S4: Obtaining passenger vehicle parts warehousing and sales management invoicing information by processing the passenger vehicle parts warehousing and sales management abnormal event through closed-loop exception processing.

[0007] Preferably, the process of constructing the passenger car parts warehousing and sales knowledge graph in step S1 is as follows: S101: Processing the cross-system warehouse sales data through cross-system field mapping alignment to obtain preliminary cross-system warehouse sales alignment data; S102: Processing the cross-system warehousing and sales preliminary alignment data through data consistency verification to obtain cross-system warehousing and sales alignment data; S103: Processing the cross-system warehousing and sales alignment data through entity resolution to obtain a passenger vehicle parts warehousing and sales knowledge graph.

[0008] Preferably, the process of dynamic rule modeling in step S2 is: S201: Preset passenger car parts warehousing and sales business rules; S202: Obtaining a passenger vehicle parts warehousing and sales business structure rule base by structurally processing the passenger vehicle parts warehousing and sales business rules; S203: Associating the passenger vehicle parts warehousing and sales business structure rule base with the passenger vehicle parts warehousing and sales knowledge graph; S204: Obtain a business event monitoring signal, and dynamically analyze and match the business event monitoring signal and the passenger car parts warehousing and sales knowledge graph through business rule dynamic analysis to obtain an RPA dynamic script.

[0009] Preferably, the generation process of the RPA dynamic script in step S204 is: S204-1: Processing the business event monitoring signal through knowledge graph context extraction to obtain warehousing and sales business dynamic context data; S204-2: Processing the warehousing and sales business dynamic context data through dynamic rule matching to obtain a rule action type; S204-3: Process the rule action type through the RPA script template engine to obtain an RPA dynamic script.

[0010] Preferably, the process of RPA dynamic script processing in step S3 is: S301: Processing the passenger vehicle parts warehousing and sales knowledge graph through RPA dynamic script parameter parsing to obtain an executable RPA dynamic script; S302: Obtain a passenger car parts warehousing and sales event, and process the passenger car parts warehousing and sales event using an executable RPA dynamic script to obtain a warehousing and sales operation log; S303: Outputting abnormal events in the storage and sales management of passenger vehicle parts according to the storage and sales operation log.

[0011] Preferably, the output process of abnormal events in the storage and sales management of passenger vehicle parts in step S303 is as follows: S303-1: Preset warehouse sales abnormal pattern library; S303-2: Processing the warehousing sales operation log and the warehousing sales abnormality pattern library through log streaming analysis to obtain structured warehousing sales abnormality events; S303-3: Processing the structured warehousing and sales abnormal events through knowledge graph association query to obtain passenger car parts warehousing and sales management abnormal events.

[0012] Preferably, the process of closed-loop exception handling in step S4 is: S401: Preset exception type and root cause mapping rules; S402: Outputting an abnormal event handling solution instruction according to the abnormality type and root cause mapping rule; S403: Perform RPA repair according to the abnormal event handling solution instruction to obtain an RPA repair script; S404: Outputting passenger vehicle parts warehousing and sales management invoicing information according to the RPA repair script.

[0013] A warehouse sales management automation system integrating knowledge graphs and cross-systems, the system being applied to the above-mentioned warehouse sales management automation method, comprising a graph construction module, a business rule processing module, a warehouse sales management process module, and a closed-loop exception handling module; The graph construction module is used to obtain cross-system warehousing and sales data and construct a passenger car parts warehousing and sales knowledge graph based on the cross-system warehousing and sales data; The business rule processing module is used to generate an RPA dynamic script by processing the passenger car parts warehousing and sales knowledge graph through dynamic rule modeling; The warehousing and sales management process module is used to process passenger car parts warehousing and sales events according to RPA dynamic scripts and output passenger car parts warehousing and sales management abnormal events; The closed-loop exception processing module is used to obtain passenger vehicle parts warehousing and sales management invoicing information by processing the passenger vehicle parts warehousing and sales management exception event through closed-loop exception processing.

[0014] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned warehouse sales management automation method is implemented.

[0015] A storage medium containing computer-executable instructions, which are used to execute the above-mentioned warehouse sales management automation method when executed by a computer processor.

[0016] The beneficial effects of the present invention are: By constructing a knowledge graph for passenger vehicle parts warehousing and sales through cross-system field mapping alignment, data consistency verification, and entity resolution, we achieve automated cross-system data integration and alignment, and scientifically normalize multi-system data, resolving the problem of existing methods relying on manual mapping, which is prone to mapping errors.

[0017] By building a passenger car parts warehousing and sales business structure rule base and associating it with the passenger car parts warehousing and sales knowledge graph; through dynamic analysis and matching of business rules, business event monitoring signals and the passenger car parts warehousing and sales knowledge graph are processed to obtain RPA dynamic scripts, the passenger car parts warehousing and sales management process is automated, and relevant business process rules can be preset according to business needs, simplifying the management process and improving the scientific and intelligent management.

[0018] (3) By presetting the mapping rules between exception types and root causes and outputting the abnormal event handling solution instructions, RPA repair is performed according to the abnormal event handling solution instructions to obtain an RPA repair script, and the passenger car parts warehousing and sales management invoicing information is output according to the RPA repair script, thus realizing closed-loop exception handling and improving the abnormal event handling capability of the passenger car parts warehousing and sales management process. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0020] Figure 1 This is a flow chart of a knowledge graph and cross-system integrated warehouse sales management automation method of the present invention. DETAILED DESCRIPTION

[0021] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0022] See also Figure 1 , a warehouse sales management automation method integrating knowledge graph and cross-system, including: S1: Obtain cross-system warehousing and sales data, and construct a passenger vehicle parts warehousing and sales knowledge graph based on the cross-system warehousing and sales data; S2: Generate an RPA dynamic script by processing the passenger car parts warehousing and sales knowledge graph through dynamic rule modeling; S3: Processing the passenger car parts warehousing and sales event according to the RPA dynamic script to output a passenger car parts warehousing and sales management abnormality event; S4: Obtaining passenger vehicle parts warehousing and sales management invoicing information by processing the passenger vehicle parts warehousing and sales management abnormal event through closed-loop exception processing.

[0023] Example 1 In this embodiment, the acquisition of cross-system warehousing and sales data and the construction of a passenger vehicle parts warehousing and sales knowledge graph based on the cross-system warehousing and sales data are specifically implemented by the following steps: The cross-system warehouse sales data includes ERP system data, WMS system data, TMS system data, CRM system data, and supplier system data; Specifically, the ERP system data includes sales order number, customer ID, parts list, quantity, and price; the WMS system data includes warehouse ID, part ID, change quantity, current inventory, and storage location; the TMS system data includes logistics waybill number, order number, current location of the package, estimated arrival time, and package receipt status; the CRM system data includes customer ID, customer credit limit, and customer historical arrears; the supplier system data includes passenger car part ID, passenger car part name, passenger car part specifications, passenger car part supplier ID, and passenger car part alternative part relationship data.

[0024] S101: Processing the cross-system warehouse sales data through cross-system field mapping alignment to obtain preliminary cross-system warehouse sales alignment data; S102: Processing the cross-system warehousing and sales preliminary alignment data through data consistency verification to obtain cross-system warehousing and sales alignment data; S103: Processing the cross-system warehousing and sales alignment data through entity resolution to obtain a passenger vehicle parts warehousing and sales knowledge graph.

[0025] It should be noted that the data consistency verification process performs data verification through preset data quality rules, and the data quality rules include non-null check, range check, and consistency check; the data that passes the verification is processed by the entity resolution service, thereby resolving the identification differences of the same entity in different systems, obtaining a globally unique entity identification and association relationship, and storing it in a standardized data pool.

[0026] In this embodiment, the generation of an RPA dynamic script by processing the passenger vehicle parts warehousing and sales knowledge graph through dynamic rule modeling is specifically implemented by the following steps: S201: Preset passenger car parts warehousing and sales business rules; S202: Obtaining a passenger vehicle parts warehousing and sales business structure rule base by structurally processing the passenger vehicle parts warehousing and sales business rules; S203: Associating the passenger vehicle parts warehousing and sales business structure rule base with the passenger vehicle parts warehousing and sales knowledge graph; S204: Obtain a business event monitoring signal, and dynamically analyze and match the business event monitoring signal and the passenger car parts warehousing and sales knowledge graph through business rule dynamic analysis to obtain an RPA dynamic script.

[0027] S204-1: Processing the business event monitoring signal through knowledge graph context extraction to obtain warehousing and sales business dynamic context data; S204-2: Processing the warehousing and sales business dynamic context data through dynamic rule matching to obtain a rule action type; S204-3: Process the rule action type through the RPA script template engine to obtain an RPA dynamic script.

[0028] In this embodiment, the processing of the passenger vehicle parts warehousing and sales event according to the RPA dynamic script to output the passenger vehicle parts warehousing and sales management abnormality event is specifically implemented by the following steps: S301: Processing the passenger vehicle parts warehousing and sales knowledge graph through RPA dynamic script parameter parsing to obtain an executable RPA dynamic script; S302: Obtain a passenger car parts warehousing and sales event, and process the passenger car parts warehousing and sales event using an executable RPA dynamic script to obtain a warehousing and sales operation log; S303: Outputting abnormal events in the storage and sales management of passenger vehicle parts according to the storage and sales operation log.

[0029] S303-1: Preset warehouse sales abnormal pattern library; S303-2: Processing the warehousing sales operation log and the warehousing sales abnormality pattern library through log streaming analysis to obtain structured warehousing sales abnormality events; S303-3: Processing the structured warehousing and sales abnormal events through knowledge graph association query to obtain passenger car parts warehousing and sales management abnormal events.

[0030] In this embodiment, the passenger vehicle parts warehousing and sales management invoicing information obtained by closed-loop exception processing of the passenger vehicle parts warehousing and sales management exception event is specifically implemented by the following steps: S401: Preset exception type and root cause mapping rules; S402: Outputting an abnormal event handling solution instruction according to the abnormality type and root cause mapping rule; S403: Perform RPA repair according to the abnormal event handling solution instruction to obtain an RPA repair script; S404: Outputting passenger vehicle parts warehousing and sales management invoicing information according to the RPA repair script.

[0031] Example 2 A warehouse sales management automation system integrating knowledge graph and cross-system includes a graph construction module, a business rule processing module, a warehouse sales management process module, and a closed-loop exception processing module; The graph construction module is used to obtain cross-system warehousing and sales data and construct a passenger car parts warehousing and sales knowledge graph based on the cross-system warehousing and sales data; The business rule processing module is used to generate an RPA dynamic script by processing the passenger car parts warehousing and sales knowledge graph through dynamic rule modeling; The warehousing and sales management process module is used to process passenger car parts warehousing and sales events according to RPA dynamic scripts and output passenger car parts warehousing and sales management abnormal events; The closed-loop exception processing module is used to obtain passenger vehicle parts warehousing and sales management invoicing information by processing the passenger vehicle parts warehousing and sales management exception event through closed-loop exception processing.

[0032] Example 3 The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0033] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0034] The program code included in the computer-readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF or the like, or any suitable combination thereof. The computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or a combination thereof, and the programming language includes an object-oriented programming language such as Java, Smalltalk, C++, and also includes a conventional procedural programming language such as "C" language or similar programming language. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, utilizing an Internet service provider to connect through the Internet).

[0035] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A warehouse sales management automation method integrating knowledge graph and cross-system, characterized by: include: S1: Obtain cross-system warehousing and sales data, and construct a passenger vehicle parts warehousing and sales knowledge graph based on the cross-system warehousing and sales data; S2: Generate an RPA dynamic script by processing the passenger car parts warehousing and sales knowledge graph through dynamic rule modeling; S3: Processing the passenger car parts warehousing and sales event according to the RPA dynamic script to output a passenger car parts warehousing and sales management abnormality event; S4: Obtaining passenger vehicle parts warehousing and sales management invoicing information by processing the passenger vehicle parts warehousing and sales management abnormal event through closed-loop exception processing.

2. The warehouse sales management automation method according to claim 1, characterized in that: The construction process of the passenger car parts warehousing and sales knowledge graph in step S1 is as follows: S101: Processing the cross-system warehouse sales data through cross-system field mapping alignment to obtain preliminary cross-system warehouse sales alignment data; S102: Processing the cross-system warehousing and sales preliminary alignment data through data consistency verification to obtain cross-system warehousing and sales alignment data; S103: Processing the cross-system warehousing and sales alignment data through entity resolution to obtain a passenger vehicle parts warehousing and sales knowledge graph.

3. The warehouse sales management automation method according to claim 1, characterized in that: The process of dynamic rule modeling in step S2 is as follows: S201: Preset passenger car parts warehousing and sales business rules; S202: Obtaining a passenger vehicle parts warehousing and sales business structure rule base by structurally processing the passenger vehicle parts warehousing and sales business rules; S203: Associating the passenger vehicle parts warehousing and sales business structure rule base with the passenger vehicle parts warehousing and sales knowledge graph; S204: Obtain a business event monitoring signal, and dynamically analyze and match the business event monitoring signal and the passenger car parts warehousing and sales knowledge graph through business rule dynamic analysis to obtain an RPA dynamic script.

4. The warehouse sales management automation method according to claim 3, characterized in that: The generation process of the RPA dynamic script in step S204 is as follows: S204-1: Processing the business event monitoring signal through knowledge graph context extraction to obtain warehousing and sales business dynamic context data; S204-2: Processing the warehousing and sales business dynamic context data through dynamic rule matching to obtain a rule action type; S204-3: Process the rule action type through the RPA script template engine to obtain an RPA dynamic script.

5. The warehouse sales management automation method according to claim 1, characterized in that: The process of RPA dynamic script processing in step S3 is as follows: S301: Processing the passenger vehicle parts warehousing and sales knowledge graph through RPA dynamic script parameter parsing to obtain an executable RPA dynamic script; S302: Obtain a passenger car parts warehousing and sales event, and process the passenger car parts warehousing and sales event using an executable RPA dynamic script to obtain a warehousing and sales operation log; S303: Outputting abnormal events in the storage and sales management of passenger vehicle parts according to the storage and sales operation log.

6. The warehouse sales management automation method according to claim 5, characterized in that: The output process of abnormal events in the storage and sales management of passenger vehicle parts in step S303 is as follows: S303-1: Preset warehouse sales abnormal pattern library; S303-2: Processing the warehousing sales operation log and the warehousing sales abnormality pattern library through log streaming analysis to obtain structured warehousing sales abnormality events; S303-3: Processing the structured warehousing and sales abnormal events through knowledge graph association query to obtain passenger car parts warehousing and sales management abnormal events.

7. The warehouse sales management automation method according to claim 1, characterized in that: The process of closed-loop exception handling in step S4 is as follows: S401: Preset exception type and root cause mapping rules; S402: Outputting an abnormal event handling solution instruction according to the abnormality type and root cause mapping rule; S403: Perform RPA repair according to the abnormal event handling solution instruction to obtain an RPA repair script; S404: Outputting passenger vehicle parts warehousing and sales management invoicing information according to the RPA repair script.

8. A warehouse sales management automation system integrating knowledge graph and cross-system, wherein the system is applied to the warehouse sales management automation method according to any one of claims 1 to 7, characterized in that: It includes graph construction module, business rule processing module, warehouse sales management process module, and closed-loop exception handling module; The graph construction module is used to obtain cross-system warehousing and sales data and construct a passenger car parts warehousing and sales knowledge graph based on the cross-system warehousing and sales data; The business rule processing module is used to generate an RPA dynamic script by processing the passenger car parts warehousing and sales knowledge graph through dynamic rule modeling; The warehousing and sales management process module is used to process passenger car parts warehousing and sales events according to RPA dynamic scripts and output passenger car parts warehousing and sales management abnormal events; The closed-loop exception processing module is used to obtain passenger vehicle parts warehousing and sales management invoicing information by processing the passenger vehicle parts warehousing and sales management exception event through closed-loop exception processing.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the warehouse sales management automation method as described in any one of claims 1-7 is implemented.

10. A storage medium containing computer-executable instructions, characterized in that: The computer executable instructions, when executed by a computer processor, are used to execute the warehouse sales management automation method as described in any one of claims 1-7.

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