A data exchange log management method, device and medium of a service management system

Abstract

The application discloses a data exchange log management method of a service management system, and steps include: S1. obtaining a log scope based on service data, establishing a specified scope according to the log scope, and obtaining a regularized log scope list by combining multiple scopes; S2. defining a log rule market and a service tag library with log rules and service tags; S3. unifying the service data log format to obtain a log storage model; S4. processing the plug-in logic of log collection under the log scope based on the log storage model to define a data log collection plug-in program; and S5. using the data log collection plug-in program to structure the collection of logs in the log storage model and store the logs into a real-time analysis type storage medium asynchronously and efficiently to define a data log storage program. The application can automatically analyze and structure log content, realize efficient storage of logs in all channels, and improve the convenience and efficiency of log management.

Description

Technical Field

[0001] This invention relates to the field of business data management technology, specifically to a data exchange log management method, device, and storage medium for a business management system. Background Technology

[0002] With the development of big data and cloud computing technologies, construction companies are gradually transforming towards intelligent and digital transformation. In the process of enterprise digital transformation, coordinating the construction of digital systems is a key link. In the process of building digital systems, achieving interconnection and interoperability of data from various digital systems is an important guarantee for achieving refined management and improving business efficiency.

[0003] As construction companies expand their projects and innovate their businesses, the amount of business data in their digital systems is growing explosively. The data exchange between systems is also constantly expanding and being adjusted. If logs are not managed uniformly or if there is no unified storage and traceability mechanism during the data exchange process, each business system will need to store massive amounts of transmitted data separately and will be unable to centrally and globally analyze data sharing and migration, thereby increasing the accuracy of system data exchange and the burden on operation and maintenance.

[0004] Existing business management systems often employ data exchange log storage technology. By collecting data exchange flows in a rule-based manner, efficiently storing data, setting data access permissions, and preventing data leakage, it is possible to perform algorithmic monitoring on business data accumulated between systems. By formulating rules through business standards, the rules constrain the scope of data ownership and control the scope of data access, thereby enabling efficient management of exchanged data and ensuring data compliance, accuracy, and security.

[0005] In construction companies, the exchange of data between various digital systems is massive and complex. While API (Application Programming Interface) and MQ (Message Queuing) technologies can achieve massive data exchange between systems, APIs primarily focus on the data exchange itself and do not provide direct data storage functionality. Their control and storage of process data logs are relatively weak, so APIs may not meet the needs of refined data exchange management. Message Queuing (MQ) technology, while efficiently handling data exchange between systems and supporting log storage, has a short data storage time, increasing the risk of untraceable historical data and affecting the normal use of a unified data exchange log management platform. Furthermore, while mainstream distributed log collection technologies can efficiently achieve log collection, storage, and analysis, they are relatively complex, resource-intensive, and difficult to incorporate enterprise business attributes or personalized data related to digital system construction during log collection.

[0006] Therefore, this application proposes a data exchange log management method for a business management system to solve the above-mentioned technical problems. Summary of the Invention

[0007] The main objective of this invention is to provide a data exchange log management method for a business management system, so as to solve the technical problems mentioned in the background art.

[0008] The present invention solves the above-mentioned technical problems by adopting the following technical solutions:

[0009] A method for managing data exchange logs in a business management system, comprising the following steps:

[0010] S1. Obtain log scopes based on business data, establish specified scopes according to log scopes, and combine multiple sets of scopes to obtain a rule-based log scope list;

[0011] S2. Based on the log scope scenario, define a log rule mart and a business tag library with log rules and business tags;

[0012] S3. Based on business data, obtain personalized log audit dimensions for enterprises, unify the business data log format, and obtain a log storage model;

[0013] S4. Based on the log storage model, process the plugin logic for log collection under the log scope to define the data log collection plugin program;

[0014] S5. Use the data log collection plugin to collect logs in a structured manner within the log storage model and store them asynchronously and efficiently to a real-time analytical storage medium to define the data log storage program;

[0015] S6. Define standardized audit reports or visualization metrics based on the enterprise audit log dimensions.

[0016] Preferably, the scope specified in step S1 includes intra-service, inter-microservice, cross-system API interaction, message queue, and cached log.

[0017] Preferably, the specific operation process of step S2 includes:

[0018] S21. Based on the log scope scenario, define the corresponding log interception rules to obtain a log rule mart with log interception rules;

[0019] S22. For log interception rules, define four interaction types, including synchronous, asynchronous, publish, and subscribe, based on the log interaction scenarios within business data;

[0020] S23. Obtain the business tag library by marking the log business tags or custom tags that match the log interception rules.

[0021] Preferably, the log interception rules in step S21 include:

[0022] Generic interception rules applicable to pre-matching, post-matching, and arbitrary position matching of interactive features;

[0023] Specify interception rules that match complete interaction characteristics.

[0024] Preferably, the plugin logic for log collection in step S4 includes:

[0025] S41. Define global log annotations within the application service to collect client Restful interface call logs;

[0026] S42. In the interaction between microservices, define a microservice gateway log interceptor to collect RPC logs;

[0027] S43. In the context of inter-system API interactions, define an API gateway log interceptor to collect HTTP logs;

[0028] S45. Under message queue interaction, define message production and subscription log interceptors to collect asynchronous message logs;

[0029] S46. Under cache storage interaction, define a cache listener to collect cache logs.

[0030] Preferably, the data log storage process of the data log storage program in step S5 includes:

[0031] S51. Based on the log storage model obtained in step S3, use the data log collection plugin program to collect logs in a structured manner;

[0032] S52. Obtain the business tags and custom tags corresponding to the log rules from the collected log cache, and use them to mark the structured logs and assign the logs relevant business characteristics of the construction enterprise.

[0033] S53. Asynchronously store logs in real-time analytical storage media.

[0034] Preferably, the specific operation process for asynchronously storing logs in step S53 includes:

[0035] S531. Design an asynchronous thread pool to store standardized business logs to a real-time search engine or real-time analysis database to adapt to lightweight application service environments.

[0036] S532. Integrates message queues to store standardized business logs to a real-time search engine or real-time analysis database to adapt to complex distributed application environments.

[0037] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0038] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0039] As can be seen from the above technical solution, the present invention provides a data exchange log management method for a business management system. Compared with the prior art, the present invention has the following advantages:

[0040] 1. This invention analyzes the massive business data under the digital system of construction enterprises, builds a data exchange log management model that combines API and MQ, defines data log management interceptors applicable to various scopes, and can centrally collect logs under log rules, store them in a customized structure, and perform multi-dimensional audit analysis operations under enterprise business characteristics. Moreover, the structure is concise and the management is efficient.

[0041] 2. This invention, by deploying log interceptors under various scopes within a digital system, can intercept internal and external interactive logs, thereby achieving the effect of comprehensively capturing system activity records and ultimately ensuring data integrity and traceability.

[0042] 3. By setting up a rule-based log scope list and adopting a data exchange log management model that combines API and message queue (MQ), this invention can automatically parse and structure log content, achieve efficient storage of logs across all channels, and improve the convenience and efficiency of log management.

[0043] 4. By defining data log management interceptors applicable to various scopes and combining interceptors, message queues, and real-time storage search technologies, this invention can centrally collect logs, customize structured storage, and perform multi-dimensional audit analysis based on enterprise business characteristics, which facilitates the continuous optimization and construction of digital systems.

[0044] It should be understood that the descriptions in this section are not intended to identify key or essential features of embodiments of the invention, nor are they intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Of course, implementing any product of the invention does not necessarily require achieving all of the advantages described above simultaneously. Attached Figure Description

[0045] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0046] Figure 1 This is a schematic diagram of the overall process of the method of the present invention;

[0047] Figure 2 This is a schematic diagram illustrating the process of the method of the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] For details in the embodiments, please refer to Figures 1 to 2 .

[0050] Based on the business scenarios of construction projects in construction companies, analysis of massive amounts of business data reveals that the management of business data log exchange is essentially the management of log collection, storage, and analysis.

[0051] Therefore, as Figure 1 and Figure 2 As shown in the figure, this invention proposes a data exchange log management method for a business management system. The solution mainly includes formulating log collection rules and attribute aggregation design, log storage design, and log analysis design required for data log management, wherein:

[0052] The design of log collection rules and attribute aggregation mainly includes analyzing the sources of log generation, such as within application services, between microservices, cross-system API interactions, message queues, etc., and establishing a rule-based log scope list; sorting out the enterprise's business characteristics, the technical features of business systems, and the enterprise's personalized log audit dimensions, and defining the log rule mart and business tag library.

[0053] Log storage design mainly includes defining system-wide log annotations, interceptors, APIs, and message queues to collect and store log data.

[0054] Log analysis design mainly includes defining standardized audit reports or visualization metrics, which supports enterprises in deeply analyzing log data from multiple key dimensions, providing valuable reference for the continuous optimization and construction of enterprise digital systems.

[0055] The specific prediction steps of this method are as follows:

[0056] S1. Establish a rule-based log scope list. Based on the log scope derived from the analysis of massive business data, establish scopes such as service-intra-service, microservice-to-service, cross-system API interaction, message queue, cached logs, and custom scopes. These are used to cluster various technical application scenarios such as enterprise monolithic applications, microservice applications, heterogeneous systems, and hybrid new and old applications.

[0057] S2. Define log rules and business tags, sort out the enterprise's business characteristics, business system technical features and enterprise-specific log audit dimensions, and define the log rule mart and business tag library;

[0058] Specifically, the operational procedures for this step include:

[0059] S21. Based on different scope scenarios, define two types of log interception rules: one is a flexible and widely applicable generic interception rule that is suitable for pre-matching, post-matching, and arbitrary position matching of interaction features; the other is a more precise and specific specified interception rule for matching interaction features.

[0060] in:

[0061] S211. Generic interception rules mainly define the request address, program path, interface method, resource location, and other related content of the feature matching data log. Pre-matching rules define the prefix features of the above content, while post-matching rules define the suffix features of the above content. Arbitrary position rules define features that include certain content from the above content, such as:

[0062] Suppose P is a collection of information such as request address, program path, interface method, and resource location;

[0063] Pre-matching rule: The pre-matching rule can be viewed as a function f prefix (x), where x∈P, this function returns the prefix features of x;

[0064] Post-matching rule: The post-matching rule can be viewed as a function f suffix (x), where x∈P, this function returns the suffix feature of x;

[0065] Arbitrary position rule: The arbitrary position rule can be viewed as a function f anywher (x, y), where x∈P and y is the content feature to be included. This function determines whether x contains y.

[0066] S212. Specifying interception rules mainly defines the characteristics of request addresses, program paths, interface methods, resource locations, and other related content that completely match data logs. These are rules specific to a particular application scenario, including:

[0067] interception rule set Where S specific It includes features such as request address, program path, interface method, and resource location that perfectly match the data log;

[0068] Pre-matching rules:

[0069] Post-matching rules:

[0070] Arbitrary position rule:

[0071]

[0072] Specify the blocking rule: S specific ={x∈P|x completely matches the specified feature};

[0073] The above provides a formal method for describing and understanding generic interception rules and specified interception rules. In practical applications, the specific implementation of these functions and sets will depend on the specific log data and matching logic.

[0074] S22. Based on the analysis of massive log interaction scenarios, define four interaction types: synchronous, asynchronous, publish, and subscribe.

[0075] S23. To meet log analysis requirements, mark logs that match the rules with business tags or custom tags, etc.

[0076] S3. Define a standardized log storage model. Based on the analysis of massive amounts of business data and the enterprise's personalized log audit dimensions, unify the diverse business data log formats into a standardized log storage model.

[0077] Specifically, the operational procedures for this step include:

[0078] S31. Based on the company's own business needs and security requirements, determine the dimensions for data log auditing. These dimensions include timestamps, source systems, and data volumes. By clarifying these dimensions, companies can collect and analyze log data more effectively.

[0079] S32. Analyze massive amounts of business data to understand the data's source, structure, content, and characteristics. This process involves data cleaning, preprocessing, and integration.

[0080] S33. Develop a standardized log storage model. Based on the analysis results of log auditing dimensions and massive business data, define a log format that includes log naming methods, encoding methods, key information, and other content.

[0081] S4. Define a data log collection plugin program, which uses log annotations, interceptors, APIs and MQ message queues to handle the plugin logic for log collection under the log scope.

[0082] Specifically, the operational procedures for this step include:

[0083] S41. Define global log annotations within the application service to collect client Restful interface call logs;

[0084] S42. In the interaction between microservices, define a microservice gateway log interceptor to collect RPC logs (such as OpenFeign RPC, Dubbo RPC, etc.);

[0085] S43. In the case of inter-system API interaction, define an API gateway log interceptor to collect HTTP logs (such as HTTP connection client interactions such as OKHTTP, HTTPClient, etc.);

[0086] S45. In message queue interaction, define message production and subscription log interceptors to collect asynchronous message logs (such as interactions with RocketMQ, Kafka, RabbitMQ, etc.);

[0087] S46. In cache storage interaction, define a cache listener to collect cache logs (such as Redis storage);

[0088] The log collection logic under the above methods includes:

[0089] (1) Automatic configuration or manual adjustment of initialization parameters for the log plugin: Let P be the set of initialization parameters, including enabling collection rules, standardized format output, storage path, etc.

[0090] (2) Listening mechanism start: Set L(P) to receive initialization parameter P and start listening;

[0091] (3) Log event triggering and response: Set the function R(E,P) to represent data log information, which is used for the plugin to respond and collect data log information when log event E occurs;

[0092] (4) Data cleaning and processing: Set the function C(D,P) to represent the cleaned and processed data, which is used to clean and process the data according to the data processing rules in the initialization parameter P, where D is the automatically captured data log information;

[0093] (5) Output to storage medium: Let S be the storage medium. The function O(C(D,P),S,P) represents outputting the processed data to the storage medium S. The initialization parameter P (such as storage path, etc.) is also taken into consideration.

[0094] Therefore, in summary, firstly, the listening mechanism L(P) is started according to the initialization parameter P, then the log event E is triggered. Once triggered, the plugin responds and collects data log information R(E,P). Then, the data is cleaned and processed C(D,P) according to the initialization parameter P. Finally, the processed data is output to the storage medium S, with O(C(D,P),S,P).

[0095] At this point, by setting up a list of regular log scopes and adopting a data exchange log management model that combines API and message queue (MQ), log content can be automatically parsed and structured, enabling efficient storage of logs across all channels and improving the convenience and efficiency of log management.

[0096] Furthermore, through log interceptors under various scopes, it is possible to intercept internal and external interaction logs of the digital system, thereby achieving the effect of comprehensively capturing system activity records and ultimately ensuring the integrity and traceability of the data.

[0097] It should also be noted that the Application Programming Interface (API) technology mentioned here enables interaction and data sharing between systems through standardized interface protocols that are independent of technology; message queue technology is a more efficient data exchange and sharing technology that manages the data exchange process through message queues. Both are existing technologies, and their specific details will not be elaborated here.

[0098] S5. Define a data log storage program that collects logs in a structured manner and stores them asynchronously and efficiently to a real-time analytical storage medium;

[0099] Specifically, the operational procedures for this step include:

[0100] S51. Logs collected in a structured manner according to the standardized log storage model defined in S3;

[0101] S52. Retrieve business tags and custom tags corresponding to log rules from the cache, which are used to mark the structured logs and assign the logs relevant business characteristics of construction companies;

[0102] S53. Asynchronous and efficient log storage, specifically including:

[0103] (1) Design an asynchronous thread pool to store the standardized logs of the business to a real-time search engine or a real-time analysis database to adapt to the lightweight application service environment.

[0104] (2) Form a message queue (such as Kafka) to store the standardized logs of the business to a real-time search engine or real-time analysis database to adapt to complex distributed application environments;

[0105] The distributed log collection technology used here is an excellent technical solution in the field of log management and analysis, which can efficiently realize data collection, transformation, transmission, storage, search and analysis functions;

[0106] S6. Define data log statistics views and define standardized audit reports or visualization metrics based on the dimensions of enterprise audit logs.

[0107] In summary, by analyzing the massive business data under the digital system of construction enterprises, a data exchange log management model combining API and MQ was built. Data log management interceptors applicable to various scopes were defined, which can centrally collect logs under log rules, store them in customized structures, and perform multi-dimensional audit analysis operations under enterprise business characteristics. Moreover, the structure is concise and the management is efficient.

[0108] Therefore, by automatically parsing and structuring log content according to the above steps, the management effect of efficient log storage across all channels and intuitive and personalized log analysis can be achieved. By defining data log management interceptors applicable to various scopes and combining interceptors, message queues, and real-time storage search technologies, logs can be collected centrally, stored in customized structures, and subjected to multi-dimensional audit analysis based on enterprise business characteristics, which can facilitate the continuous optimization and construction of digital systems.

[0109] Furthermore, this method analyzes the large amount of data exchanged between the digital systems of construction companies to identify the clustering patterns of data exchange. Based on this, rules are developed to form a set of methods that can better adapt to changes in enterprise business and facilitate more refined data exchange log management.

[0110] In a specific implementation process, a combination of API, MQ, and ELK solutions was used to analyze and verify log data across scopes within an enterprise's digital systems. The experiment selected over 10,000 log entries from four different scopes, and the overall data is shown in the table below:

[0111]

[0112] The sample data was automatically and centrally collected, structured, and then output to generate a customized log audit checklist for enterprise analysis and decision-making. The results are shown in the table below:

[0113]

[0114]

[0115] Based on the selected sample data, the data exchange log management method achieved the expected results.

[0116] The following table illustrates examples of the specific data used in actual calculations:

[0117] Sample data from regular application services (partial examples):

[0118] 60572 2024-12-02 17:08:22 Company A's A8 Project 60570 2024-12-02 17:08:21 Company A's A7 Project 60569 2024-12-02 17:08:21 Company A's A6 Project 60568 2024-12-02 16:56:25 Company A's A5 Project 60567 2024-12-02 16:53:47 Company A's A4 Project 60566 2024-12-02 16:41:32 Company A's A3 Project 60565 2024-12-02 16:41:32 Company A's Project A2 60564 2024-12-02 16:41:32 Company A's Project A1

[0119] Sample data within the application service after processing using this method (partial example):

[0120]

[0121]

[0122] Sample data for inter-service communication in a typical microservice architecture (partial examples):

[0123]

[0124]

[0125] Sample data of microservice inter-services processed by this method (partial example):

[0126]

[0127]

[0128]

[0129] Sample data of typical cross-system API interaction logs (partial examples):

[0130] 1588 2025-01-06 14:37:22 C8 Engineering Technology Disclosure 1590 2025-01-06 14:37:14 C7 Engineering Technology Disclosure 1603 2025-01-06 14:37:14 C6 Engineering Technology Disclosure 1605 2025-01-06 14:37:14 C5 Engineering Technology Disclosure 1606 2025-01-06 14:37:07 C4 Engineering Technology Disclosure 1615 2025-01-06 14:36:44 C3 Engineering Technology Disclosure 1705 2025-01-06 11:41:40 C2 Engineering Technology Disclosure 1706 2025-01-06 11:37:34 C1 Engineering Technical Disclosure

[0131] Sample data of cross-system API interaction logs processed by this method (partial example):

[0132]

[0133]

[0134] Sample data of a regular message queue log (partial example):

[0135]

[0136]

[0137] Sample message queue log data processed by this method (partial example):

[0138]

[0139] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0140] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0141] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the data exchange log management method of any of the business management systems described above.

[0142] It is understood that the system provided in the embodiments of the present invention corresponds to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant content can be referred to the corresponding parts of the above method.

[0143] This application also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, communication interface, and memory communicate with each other via the communication bus.

[0144] Memory, used to store computer programs;

[0145] The processor, when executing programs stored in memory, implements the data exchange log management method of the aforementioned business management system.

[0146] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect Standard (PCI) bus or an Extended Industry Standard Architecture (EIS) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc.

[0147] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0148] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0149] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0150] It should also be noted that electronic devices include terminal devices, which can also be called terminals, user equipment, mobile stations, mobile terminals, etc. Terminal devices can be mobile phones, smart TVs, wearable devices, tablets, computers with wireless transceiver capabilities, virtual reality terminal devices, augmented reality terminal devices, wireless terminals in industrial control, wireless terminals in autonomous driving, wireless terminals in remote surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, and so on. The embodiments of this application do not limit the specific technologies or device forms used in the terminal devices.

[0151] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium, or a semiconductor medium (e.g., solid-state drive SSD), etc.

[0152] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0153] Furthermore, it should be noted that if any directional indication (such as up, down, left, right, front, back, etc.) is involved in the embodiments of the present invention, the directional indication is only used to explain the relative positional relationship and movement of each component in a specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0154] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the meaning of "and / or" throughout the text includes three parallel solutions; for example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, in the embodiments of this invention, "multiple" refers to two or more. Moreover, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

Claims

1. A data exchange log management method for a business management system, characterized in that, The steps include: S1. Obtain log scopes based on business data, establish specified scopes according to log scopes, and combine multiple sets of scopes to obtain a rule-based log scope list; S2. Based on the log scope scenario, define a log rule mart and a business tag library with log rules and business tags; S3. Based on business data, obtain personalized log audit dimensions for enterprises, unify the business data log format, and obtain a log storage model; S4. Based on the log storage model, process the plugin logic for log collection under the log scope to define the data log collection plugin program; S5. Use the data log collection plugin to collect logs in a structured manner within the log storage model and store them asynchronously and efficiently to a real-time analytical storage medium to define the data log storage program; The specific operation process of step S2 includes: S21. Based on the log scope scenario, define the corresponding log interception rules to obtain a log rule mart with log interception rules; S22. For log interception rules, define four interaction types, including synchronous, asynchronous, publish, and subscribe, based on the log interaction scenarios within business data; S23. Obtain the business tag library by marking the log business tags or custom tags that match the log interception rules; The log interception rules in step S21 include: Generic interception rules applicable to pre-matching, post-matching, and arbitrary position matching of interactive features; Specify interception rules that match complete interaction characteristics; The data log storage process of the data log storage program in step S5 includes: S51. Based on the log storage model obtained in step S3, use the data log collection plugin program to collect logs in a structured manner; S52. Obtain the business tags and custom tags corresponding to the log rules from the collected log cache, and use them to mark the structured logs and assign the logs relevant business characteristics of the construction enterprise. S53. Asynchronously store logs in real-time analytical storage media.

2. The data exchange log management method for a business management system as described in claim 1, characterized in that, The scope specified in step S1 includes within a service, between microservices, cross-system API interactions, message queues, and cached logs.

3. The data exchange log management method for a business management system as described in claim 1, characterized in that, The plugin logic for log collection in step S4 includes: S41. Define global logging annotations within the application service to collect client RESTful API call logs; S42. In the interaction between microservices, define a microservice gateway log interceptor to collect RPC logs; S43. In the context of inter-system API interactions, define an API gateway log interceptor to collect HTTP logs; S45. Under message queue interaction, define message production and subscription log interceptors to collect asynchronous message logs; S46. Under cache storage interaction, define a cache listener to collect cache logs.

4. The data exchange log management method for a business management system as described in claim 1, characterized in that, The specific operation process for asynchronously storing logs in step S53 includes: S531. Design an asynchronous thread pool to store standardized business logs to a real-time search engine or real-time analysis database to adapt to lightweight application service environments. S532. Integrates message queues to store standardized business logs to a real-time search engine or real-time analysis database to adapt to complex distributed application environments.

5. The data exchange log management method for a business management system as described in claim 1, characterized in that, It also includes the following steps: Define standardized audit reports or visualization metrics based on the enterprise audit log dimensions.

6. A computer-readable storage medium, characterized in that, The device stores a computer program that, when executed by a processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 5.

7. A computer device, characterized in that, It includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 5.

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