Fault-tolerant optimization system and method for table in supply chain

Abstract

The invention relates to a fault-tolerant optimization system for a supply chain middle station, and the system monitors a plurality of types of information through an exception capturing module, and automatically captures and marks exceptions; further, dynamic rule verification is generated through a multi-dimensional dynamic verification module in combination with a rule base and machine learning; and finally, the fault-tolerant optimization module carries out data restoration, rapid rollback and exception notification. The problem that a traditional system cannot deal with dynamic changes of complex scenes and cannot provide real-time fault-tolerant capability is solved. And the positive effects of improving the data accuracy and integrity, ensuring the continuity and accuracy of each link of the supply chain, reducing the manual intervention cost and improving the robustness and stability of the system are achieved.

Description

Technical Field

[0001] The present application relates to the field of software development, and in particular to a supply chain fault-tolerant optimization system, method, electronic device and readable storage medium. Background Art

[0002] In the field of modern supply chain management, given the increasing complexity of business processes and the increasing frequency of cross-platform data interactions, the system is often prone to data anomalies, transmission delays, operational errors, etc., resulting in process interruptions or result deviations, which in turn have an adverse impact on the overall supply chain efficiency and service quality. For example, in order management, logistics tracking, inventory scheduling and other links, once encountering abnormal situations such as interface failures, missing data or format errors, manual intervention is generally required to correct them, which not only consumes a lot of time, but also leads to a significant increase in operating costs.

[0003] Traditional fault-tolerant processing systems mostly use fixed rule verification and relatively simple exception capture strategies, which are difficult to adapt to the dynamic changes of complex scenarios and cannot provide cross-platform, multi-dimensional real-time fault-tolerant capabilities. This puts more stringent requirements on the stability of the system.

[0004] At present, no effective solution has been proposed for the problem of poor reliability of supply chain management systems in related technologies. Summary of the invention

[0005] The embodiments of the present application provide a fault-tolerant optimization system, method, electronic device and storage medium for a supply chain middle station to at least solve the problem of poor reliability of the supply chain management system in the related art.

[0006] In a first aspect, an embodiment of the present application provides a fault-tolerant optimization system for a supply chain middle station, the system comprising: an exception capture module, a dynamic verification module and a fault-tolerant optimization module, wherein:

[0007] The abnormality capture module is used to monitor the operation data of the supply chain platform, and capture and mark abnormal data from the operation data;

[0008] The dynamic verification module is used to verify the abnormal data based on a preset verification rule base and a machine learning model, and output a verification result;

[0009] The fault-tolerant optimization module is used to select a fault-tolerant optimization strategy corresponding to the verification result and instruct the response unit to execute the fault-tolerant optimization strategy.

[0010] In some of the embodiments, the dynamic verification module includes: an integrity verification module, a consistency verification module and a timing verification module.

[0011] In some embodiments, the integrity verification module is used to obtain a field template corresponding to the abnormal data in the preset verification rule library, compare each field of the abnormal data with the preset field template, and output a verification result of whether the abnormal data meets the integrity requirements;

[0012] The consistency check module is used to compare and analyze the upstream and downstream data associated with the abnormal data, and obtain a verification result of whether the abnormal data meets the consistency from the preset verification rule library according to the comparison and analysis results;

[0013] The timing verification module is used to obtain the business operation records associated with the abnormal data from the preset verification rule library, and determine the standard time series and the actual time series of the business operation records, and output the verification result by checking whether the standard time series and the actual series are consistent.

[0014] In some embodiments, the system further comprises a rule base construction module, wherein the rule base construction module is used to:

[0015] The preset verification rule base is constructed based on historical business data and manual operation instructions.

[0016] Among them, through the machine learning model, feature extraction and analysis are performed on the abnormal data collected in real time to obtain the logical correlation information and time series features therein;

[0017] And, dynamically updating a preset verification rule base according to the logical association information and the timing characteristics.

[0018] In some embodiments, the fault-tolerant optimization module includes: a data recovery module, a rollback module and a notification module, wherein:

[0019] The data recovery module is used to repair the abnormal data according to a preset repair rule and historical data related to the abnormal data;

[0020] The rollback module is used to obtain the business process related to the abnormal data and roll back the state of the business process to the state before the abnormal data appeared;

[0021] The notification module is used to generate an exception notification according to the exception type corresponding to the abnormal data, and send the exception notification to an associated user.

[0022] In some embodiments, the rollback module includes a detection trigger module and an execution module, wherein:

[0023] The detection trigger module is used to receive the verification result from the dynamic verification module;

[0024] When the verification result indicates that a specific exception occurs in the business process, a rollback operation is triggered and a rollback instruction is fed back to the execution module, wherein the specific exception is an exception that the data recovery module determines cannot be resolved by data repair;

[0025] The execution module is used to gradually undo all operations and data changes that have occurred from the current abnormal node to the rollback node in the reverse order of the business process, and after the data rollback operation is completed, the status information related to the business process in the supply chain center will be updated.

[0026] In some embodiments, the exception capture module includes: a monitoring module and an identification marking module, wherein:

[0027] The monitoring module is used to monitor the log information, interface data flow and business operation records of the supply chain platform in real time to obtain the data to be identified;

[0028] The identification and marking module is used to analyze and identify the data to be identified, determine whether there is abnormal data therein, and if so, add a mark to the abnormal data according to a preset abnormal identification standard.

[0029] In a second aspect, an embodiment of the present application provides a fault-tolerant optimization method for a supply chain middle station, the method comprising:

[0030] Monitor the operation data of the supply chain platform through an exception capture module, and capture and mark abnormal data from the operation data;

[0031] Through the dynamic verification module, based on the preset verification rule library and machine learning model, the abnormal data is verified and the verification result is output;

[0032] A fault-tolerant optimization module is used to select a fault-tolerant optimization strategy corresponding to the verification result, and a response unit is instructed to execute the fault-tolerant optimization strategy.

[0033] In a third aspect, an embodiment of the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect above when executing the computer program.

[0034] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect above.

[0035] Compared with related technologies, the fault-tolerant optimization system of the supply chain middle station provided in the embodiment of the present application automatically captures and marks exceptions by monitoring various information through the exception capture module; further, a dynamic rule verification is generated through a multi-dimensional dynamic verification module combined with a rule base and machine learning; finally, the fault-tolerant optimization module performs data repair, rapid rollback and exception notification. It solves the problem that traditional systems cannot cope with dynamic changes in complex scenarios and provide real-time fault tolerance. It has the positive effect of improving data accuracy and integrity, ensuring the continuity and accuracy of each link in the supply chain, reducing the cost of manual intervention, and improving the robustness and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0037] Figure 1 It is a structural block diagram of a supply chain middle-end fault-tolerant optimization system according to an embodiment of the present application;

[0038] Figure 2 is a flow chart of a fault-tolerant optimization method for a supply chain middle station according to an embodiment of the present application;

[0039] Figure 3 It is a data flow diagram of a fault-tolerant optimization method of a supply chain middle station according to an embodiment of the present application;

[0040] Figure 4 It is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0042] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0043] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0044] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation, and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0045] In modern supply chain management, with the complexity of business processes and the frequent occurrence of cross-platform data interaction, the system is prone to process interruptions or result deviations due to data anomalies, transmission delays, operational errors and other problems, affecting the overall supply chain efficiency and service quality. For example, in order management, logistics tracking, inventory scheduling and other links, when abnormal situations occur due to interface failures, missing data or format errors, manual intervention is usually required to correct them, which is time-consuming and increases operating costs.

[0046] Traditional fault-tolerant processing systems mostly use fixed rule verification and simple exception capture strategies, which cannot cope with the dynamic changes of complex scenarios, and it is difficult to provide cross-platform, multi-dimensional real-time fault-tolerant capabilities. This not only puts higher requirements on the stability of the system, but also requires a fault-tolerant optimization system with intelligent, multi-dimensional, and dynamic verification capabilities to adapt to the needs of the modern supply chain middle platform.

[0047] In view of this, an embodiment of the present application provides a fault-tolerant optimization system for the supply chain middle platform, which improves the robustness and stability of the supply chain system while ensuring data integrity and accuracy through automatic capture of anomalies, multi-dimensional dynamic verification strategies and efficient fault-tolerant mechanisms.

[0048] Figure 1 is a structural block diagram of a supply chain middle-end fault-tolerant optimization system according to an embodiment of the present application, such as Figure 1 As shown, the system includes: an exception capture module 10, a dynamic verification module 20 and a fault tolerance optimization module 30, wherein:

[0049] The exception capture module 10 is used to obtain relevant data from the supply chain platform, capture and mark abnormal data;

[0050] The abnormal capture module 10 includes: a monitoring module 101 and an identification marking module 102. Specifically, the monitoring module 101 is used to monitor the log information, interface data flow and business operation records of the supply chain platform in real time to obtain the data to be identified;

[0051] For log information, the monitoring module 101 is connected to the log management system of the supply chain middle station to obtain newly generated log entries in real time.

[0052] Furthermore, for the interface data flow, the monitoring module 101 deploys a data specific monitoring tool at the interface. This type of monitoring tool can capture the transmission details of the interface data, including the flow direction, transmission rate, data format and other aspects of the data.

[0053] In addition, for business operation records, the monitoring module 101 is integrated with the business operation management system of the supply chain platform, which can obtain detailed information on various business operations performed by users on the platform in real time, including the time of the operation, the type of operation, the objects involved in the operation, etc.

[0054] The identification and marking module 102 is used to analyze and identify the data to be identified, determine whether there is abnormal data therein, and if so, add a mark to the abnormal data according to a preset abnormal identification standard. Add a specific mark symbol or label to the original record where the abnormal data is located. For example, for date data with an incorrect format, a "*" symbol may be added next to it, and "incorrect format" may be noted.

[0055] Optionally, the identification and marking module 102 records the detailed information of the abnormal data into a special abnormal database. This database will contain information such as the original value of the abnormal data, the location where it appears, the type of abnormality, the time of identification, etc. These records help relevant personnel quickly understand the full picture of the abnormality so as to take effective treatment measures.

[0056] The dynamic verification module 20 is used to verify the abnormal data based on the preset verification rule base and the machine learning model, and output the verification result;

[0057] The dynamic verification module 20 includes: an integrity verification module 201 , a consistency verification module 202 , a timing verification module 203 , and a rule base construction module 204 .

[0058] Specifically, before the module is run, it is necessary to first build a preset verification rule library, which organizes and stores various verification rules so that the corresponding rules can be quickly and accurately called to check the data during the data verification process. The rule library is the basis of the multi-dimensional dynamic verification module 20, which covers data verification requirements in different dimensions and business scenarios.

[0059] In this embodiment, the content of the rule base is derived from an in-depth analysis of supply chain business and a summary of a large amount of historical data. By combing through the problems that occurred in previous business processes and the characteristics of the data, universal and representative verification rules are extracted.

[0060] Specifically, a preset verification rule library is built based on historical business data and manual operation instructions; and, through a machine learning model, feature extraction and analysis are performed on abnormal data collected in real time to obtain logical correlation information and timing characteristics in the data, and the preset verification rule library is dynamically updated based on the logical correlation information and timing characteristics.

[0061] Those skilled in the art can know that the input business data is feature extracted and analyzed through a machine learning model. The model identifies various patterns and relationships in the data, such as the logical association between upstream and downstream data, the time series characteristics of business operations, etc. Based on these analysis results, the model will combine the existing verification rule library to generate new dynamic verification rules. Then, based on the continuously updated business data and actual verification results, the verification rules are automatically learned and adjusted. Compared with traditional static rules, in this embodiment, the Yongying machine learning model can better adapt to the dynamic changes and complex scenarios of the business.

[0062] The integrity check module 201 is used to obtain a field template corresponding to the abnormal data in a preset check rule library, compare each field of the abnormal data with the preset field template, and output a check result of whether the abnormal data meets the integrity requirements;

[0063] It can be understood that the main purpose of data integrity verification is to ensure the integrity of the data, that is, each field in the data should exist and have a reasonable value; the absence of any field may cause the business process to fail to proceed normally or produce erroneous results.

[0064] In actual detection, each field in the data will be checked one by one. It can be compared with the pre-defined field templates in the preset verification rule library, or combined with business logic to determine whether each field should exist. If a field is not detected, or its value is empty and does not meet the requirements allowed by business logic, it is determined to be missing.

[0065] The consistency check module 202 is used to compare and analyze the upstream and downstream data associated with the abnormal data, and obtain the verification result of whether the abnormal data meets the consistency from the preset verification rule library according to the comparison and analysis results;

[0066] It should be noted that the matching of upstream and downstream data affects the smoothness of the business process and the accuracy of the results. In this embodiment, the logic consistency check is intended to ensure that the logical relationship between the upstream and downstream data is correct.

[0067] Specifically, the rule base stores the logical relationship rules that should be satisfied between upstream and downstream business data; during the detection process, the related upstream and downstream data are compared and analyzed. For example, between order management and inventory management, it will check whether the product quantity in the order matches the available supply of the corresponding product in the inventory. If the order quantity is greater than the available supply in the inventory, it is judged as logical inconsistency.

[0068] The time sequence verification module 203 is used to obtain the business operation records associated with the abnormal data from the preset verification rule library, and determine the standard time sequence and the actual time sequence of the business operation records, and output the verification result by checking whether the standard time sequence and the actual sequence are consistent.

[0069] It should be noted that in the supply chain business, different business operations have their specific sequence, and reversing the sequence may lead to confusion and errors in the business process. In this embodiment, the time series verification module is used to ensure that the business operation sequence meets expectations.

[0070] Specifically, first determine the standard time sequence of business operations, such as the normal sequence of order creation, order review, inventory allocation, and delivery. Further, by analyzing the timestamps of actual business operations, determine whether the actual operation sequence is consistent with the standard sequence. If the operation sequence is found to be reversed, it is determined that the time sequence verification has failed.

[0071] It can be understood that by using the above dynamic verification module, abnormal data can be automatically corrected by pre-configuring multiple verification rules. It can correct abnormalities in a timely manner, enhance system stability, ensure data accuracy, and provide reliable support for the supply chain. In addition, since there is no need for human participation and it is dynamically updated through machine learning models, it can also improve business process efficiency.

[0072] The fault-tolerant optimization module 30 is used to select a fault-tolerant optimization strategy corresponding to the verification result and instruct the response unit to execute the fault-tolerant optimization strategy. The fault-tolerant optimization module 30 includes: a data recovery module 301, a rollback module 302 and a notification module 303, wherein:

[0073] The data recovery module 301 is used to repair the abnormal data according to preset rules and historical data related to the abnormal data;

[0074] When the verification result shows that the data is abnormal, if the abnormal data meets the repair condition, the fault-tolerant optimization module 30 starts the data repair mechanism.

[0075] Specifically, outliers are repaired through pre-set rules and historical data. This type of data recovery can be achieved through automated programs or human experience and knowledge, based on the analysis and summary of a large number of business scenarios and data patterns, which covers various possible abnormal situations and corresponding repair strategies.

[0076] The rollback module 302 is used to obtain the business process related to the abnormal data and roll back the state of the business process to the state before the abnormal data appeared;

[0077] It should be noted that, in some cases, the abnormal data may have affected the business process, causing the business process to be unable to continue in a normal logical order. At this time, the fault-tolerant optimization module 30 will perform a fast rollback operation. This operation is intended to roll back the state of the affected business process to a reasonable stage so that the system can start processing related business again from a stable state.

[0078] Specifically, the detection trigger module 3021 is used to receive the verification result from the dynamic verification module 20;

[0079] Among them, this module organizes and analyzes the abnormal data and extracts key information related to the business process status. Through real-time updating and analysis of these data, the status monitoring submodule can accurately grasp the current status of the business process and provide data support for subsequent rollback decisions.

[0080] When the verification result indicates that a specific exception occurs in the business process, a rollback operation is triggered and a reply instruction is fed back to the execution module, wherein the specific exception is an exception that the data recovery module 301 determines cannot be resolved by data repair;

[0081] The execution module 3022 is used to gradually undo all operations and data changes that have occurred from the current abnormal node to the rollback node in the reverse order of the business process; and after the data rollback operation is completed, the status information related to the business process in the supply chain will be updated.

[0082] Specifically, when the detection trigger module outputs the rollback decision formula, the data recovery submodule starts working. The data related to the business process is restored to the state corresponding to the rollback node. Optionally, it includes the recovery operation of order data, logistics data, inventory data, etc.

[0083] For example, for order data, the generated shipping records may be deleted, and the original confirmation information and customer information of the order may be restored. For logistics data, the issued shipping instructions are revoked and the logistics status is reset to the unshipped status. For inventory data, the inventory quantity is adjusted to restore it to the inventory allocation corresponding to the rollback node.

[0084] The notification module 303 is used to generate an exception notification according to the exception type corresponding to the exception data, and send the exception notification to the associated user.

[0085] Optionally, in addition to repairing abnormal data and rolling back business processes in this system, the fault-tolerant optimization module 30 also has the function of abnormal notification. Through the notification module 303, the relevant information is notified to the relevant responsible persons involved, including but not limited to order management personnel, logistics operation personnel, inventory management personnel, etc.

[0086] Optionally, the notification content not only includes the details of the abnormal data, such as the abnormal type, location, and possible business links affected, but also generates detailed repair suggestions. These repair suggestions can be based on the system's analysis of the abnormal situation and existing processing experience, and can provide clear guidance to the relevant responsible persons, helping them to quickly locate the problem and take effective solutions, thereby improving the response speed and processing efficiency of the entire supply chain system to abnormal situations.

[0087] The following are specific application examples using this system:

[0088] 1) Capture abnormal scenarios:

[0089] Data loss: The SKU field is missing in a certain order information;

[0090] Format error: The delivery time format does not match the system's expectations (e.g. "2024 / 11 / 20" is mistakenly written as "20-11-2024");

[0091] Logical conflict: The order status shows that it has been shipped, but the logistics tracking information is empty.

[0092] Validation Rule Examples

[0093] Verification Dimension Verification Rule Processing Strategy Data Integrity Verification Field Integrity Verification Fill in Missing Fields (such as default values) Time Logic Verification Order creation time is earlier than payment time Rollback and Retry Process Relevance Verification Whether the SKU exists in the current warehouse inventory list Trigger replenishment logic or adjust order status

[0094] Comparison of Implementation Effects

[0095] The test data set contains 2000 abnormal data, and the verification and repair effects are as follows:

[0096] Exception Type No Optimization System Apply Optimization System Repair Success Rate Data Loss 30％ 95％ +65％ Format Error 50％ 98％ +48％ Logic Conflict 20％ 90％ +70％

[0097] The evaluation data in this embodiment is collected from the laboratory, with a total of 2,000 test data, including 3 common anomalies, and the final repair success rate after the application optimization system is obtained.

[0098] 2) Capture abnormal scenarios

[0099] Data delay: When synchronizing data between the order management system (OMS) and the warehouse management system (WMS), order status updates are not reflected in the WMS in a timely manner, resulting in the warehouse failing to ship on time.

[0100] Data conflict: OMS shows that the order has been canceled, but WMS has generated a picking list, resulting in a logical contradiction.

[0101] Validation rule example:

[0102]

[0103]

[0104] Comparison of the achieved results:

[0105] The test data set contains 1000 abnormal data, and the verification and repair effects are as follows:

[0106] Exception Type No Optimization System Apply Optimization System Repair Success Rate Data Delay 40％ 96％ +56％ Data Conflict 35％ 94％ +59％

[0107] The evaluation data in this embodiment is collected from the laboratory, with a total of 1,000 test data, simulating the order synchronization anomaly between OMS and WMS to test the repair effect. It includes 2 types of anomalies, and finally obtains the repair success rate after the application optimization system.

[0108] Through the above system, the exception capture module 10 monitors various information to automatically capture and mark exceptions; further, the multi-dimensional dynamic verification module combines the rule base and machine learning to generate dynamic rule verification; finally, the fault-tolerant optimization module performs data repair, rapid rollback and exception notification. It solves the problem that traditional systems cannot cope with dynamic changes in complex scenarios and provide real-time fault tolerance. It has the positive effect of improving data accuracy and integrity, ensuring the continuity and accuracy of each link in the supply chain, reducing the cost of manual intervention, and improving the robustness and stability of the system.

[0109] In addition, the core logic and architecture of the system provided by the present application can be applied to other scenarios in the field. It is understood that under the inventive concept of the present system, those skilled in the art can make adaptive adjustments to the present system and apply it to other scenarios in the field.

[0110] On the other hand, the embodiment of the present application also provides a fault-tolerant optimization method for a supply chain middle station. Figure 2 is a flow chart of a fault-tolerant optimization method for a supply chain platform according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps:

[0111] S201, monitor the operation data of the supply chain middle platform through the exception capture module, and capture and mark abnormal data from the operation data;

[0112] S202, verifying abnormal data through a dynamic verification module based on a preset verification rule library and a machine learning model, and outputting a verification result;

[0113] S203: Select a fault-tolerant optimization strategy corresponding to the verification result through the fault-tolerant optimization module, and instruct the response unit to execute the fault-tolerant optimization strategy.

[0114] also, Figure 3 is a data flow diagram of a fault-tolerant optimization method for a supply chain middle station according to an embodiment of the present application, such as Figure 3As shown in the figure, the supply chain middle station first receives upstream and downstream data and captures abnormal data through the monitoring module. Furthermore, the preset verification rule library is called to perform multi-dimensional dynamic verification of the data, and the appropriate fault tolerance method is selected according to the verification results, such as data repair, fast rollback or abnormal notification. After completing the fault tolerance processing, the data returns to normal and returns to the normal process to continue to flow. This data processing process ensures the accuracy of the data and the smooth operation of the supply chain, effectively responds to various possible problems, and improves the stability and reliability of the entire supply chain system.

[0115] Through the above steps S201 to 203, the exception capture module 10 monitors various information to automatically capture and mark exceptions; further, the multi-dimensional dynamic verification module combines the rule base and machine learning to generate dynamic rule verification; finally, the fault-tolerant optimization module performs data repair, rapid rollback and exception notification. This solves the problem that traditional systems cannot cope with dynamic changes in complex scenarios and provide real-time fault tolerance. It has the positive effect of improving data accuracy and integrity, ensuring the continuity and accuracy of each link in the supply chain, reducing the cost of manual intervention, and improving the robustness and stability of the system.

[0116] This embodiment further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0117] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0118] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0119] S1, monitoring the operation data of the supply chain middle station, and capturing and marking abnormal data from the operation data;

[0120] S2, based on the preset verification rule library and machine learning model, verifies the abnormal data and outputs the verification results;

[0121] S3, selecting a fault-tolerant optimization strategy corresponding to the verification result, and instructing the response unit to execute the fault-tolerant optimization strategy.

[0122] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0123] In one embodiment, Figure 4 is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application, such as Figure 4As shown, an electronic device is provided, which may be a server, and its internal structure diagram may be as shown in Figure 4 As shown. The electronic device includes a processor, a network interface, an internal memory and a non-volatile memory connected through an internal bus, wherein the non-volatile memory stores an operating system, a computer program and a database. The processor is used to provide computing and control capabilities, the network interface is used to communicate with an external terminal through a network connection, the internal memory is used to provide an environment for the operation of the operating system, the computer program is executed by the processor to implement a fault-tolerant optimization method for a supply chain middle station, and the database is used to store data.

[0124] Those skilled in the art will understand that Figure 4 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. Specifically, the electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0125] In addition, in combination with the fault-tolerant optimization method of the supply chain middle station in the above embodiment, the embodiment of the present application can provide a storage medium for implementation. The storage medium stores a computer program; when the computer program is executed by the processor, any one of the fault-tolerant optimization methods of the supply chain middle station in the above embodiment is implemented.

[0126] In one embodiment, a computer program product is provided, including a computer program, characterized in that when the computer program is executed by a processor, it implements any one of the fault-tolerant optimization methods for a supply chain middle station in the above-mentioned embodiments.

[0127] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0128] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0129] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A fault-tolerant optimization system for a supply chain platform, characterized in that: The system includes: an exception capture module, a dynamic verification module and a fault tolerance optimization module, wherein: The abnormality capture module is used to monitor the operation data of the supply chain platform, and capture and mark abnormal data from the operation data; The dynamic verification module is used to verify the abnormal data based on a preset verification rule base and a machine learning model, and output a verification result; The fault-tolerant optimization module is used to select a fault-tolerant optimization strategy corresponding to the verification result and instruct the response unit to execute the fault-tolerant optimization strategy.

2. The system according to claim 1, characterized in that The dynamic verification module includes: an integrity verification module, a consistency verification module and a timing verification module.

3. The system according to claim 2, characterized in that: The integrity verification module is used to obtain a field template corresponding to the abnormal data in the preset verification rule library, compare each field of the abnormal data with the preset field template, and output a verification result of whether the abnormal data meets the integrity requirements; The consistency check module is used to compare and analyze the upstream and downstream data associated with the abnormal data, and obtain a verification result of whether the abnormal data meets the consistency from the preset verification rule library according to the comparison and analysis results; The timing verification module is used to obtain the business operation records associated with the abnormal data from the preset verification rule library, and determine the standard time series and the actual time series of the business operation records, and output the verification result by checking whether the standard time series and the actual series are consistent.

4. The system according to claim 3, characterized in that The system further comprises a rule base construction module, wherein the rule base construction module is used to: The preset verification rule base is constructed based on historical business data and manual operation instructions, Among them, through the machine learning model, feature extraction and analysis are performed on the abnormal data collected in real time to obtain the logical correlation information and time series features therein; And, dynamically updating a preset verification rule base according to the logical association information and the timing characteristics.

5. The system according to claim 1, characterized in that The fault-tolerant optimization module includes: a data recovery module, a rollback module and a notification module, wherein: The data recovery module is used to repair the abnormal data according to a preset repair rule and historical data related to the abnormal data; The rollback module is used to obtain the business process related to the abnormal data and roll back the state of the business process to the state before the abnormal data appeared; The notification module is used to generate an exception notification according to the exception type corresponding to the abnormal data, and send the exception notification to an associated user.

6. The system according to claim 5, characterized in that The rollback module includes a detection trigger module and an execution module, wherein: The detection trigger module is used to receive the verification result from the dynamic verification module; When the verification result indicates that a specific exception occurs in the business process, a rollback operation is triggered and a rollback instruction is fed back to the execution module, wherein the specific exception is an exception that the data recovery module determines cannot be resolved by data repair; The execution module is used to gradually undo all operations and data changes that have occurred from the current abnormal node to the rollback node in the reverse order of the business process, and after the data rollback operation is completed, the status information related to the business process in the supply chain center will be updated.

7. The system according to claim 1, characterized in that The abnormal capture module includes: a monitoring module and an identification mark module, wherein: The monitoring module is used to monitor the log information, interface data flow and business operation records of the supply chain platform in real time to obtain the data to be identified; The identification and marking module is used to analyze and identify the data to be identified, determine whether there is abnormal data therein, and if so, add a mark to the abnormal data according to a preset abnormal identification standard.

8. A fault-tolerant optimization method for a supply chain platform, characterized in that: The method comprises: Monitor the operation data of the supply chain platform through an exception capture module, and capture and mark abnormal data from the operation data; Through the dynamic verification module, based on the preset verification rule library and machine learning model, the abnormal data is verified and the verification result is output; A fault-tolerant optimization module is used to select a fault-tolerant optimization strategy corresponding to the verification result, and a response unit is instructed to execute the fault-tolerant optimization strategy.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to claim 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to claim 8 is implemented.

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