Lightweight master data management method

Abstract

The invention discloses a lightweight master data management method, which relates to the technical field of digital office, and realizes flexible iteration and cross-system data collaboration of a master data specification by creating a master data source system, issuing a dynamic adaptation system and standard and utilizing a master data classification dynamic mapping mechanism to clarify a master data unique source system; the method comprises the following steps: establishing an intelligent workflow, establishing a main data application function and approval permission intelligent adaptation engine in an office OA, and automatically generating a main data distribution interface with permission binding after obtaining approval, thereby improving the approval efficiency and the main data use security; an adaptive API interface is created, an interface dynamic adaptation and load balancing mechanism is constructed in a main data source system, and efficient and direct distribution of main data is achieved; through main data distribution quality closed-loop monitoring, log analysis and dynamic optimization engine linkage are utilized, and the main data distribution quality is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of digital office technology, specifically a method for lightweight master data management. Background Technology

[0002] With the release of relevant regulations, the digital construction process has accelerated, and consolidating digital infrastructure and data resource systems have become two key foundations. In enterprise-level application scenarios, the construction of data resource systems is crucial, and master data management, as the core link in realizing unified management and standardized construction of enterprise data resources, plays an important role. The core requirement of master data management is to clarify the unique source of data, strictly control the use of master data through a unified application and approval process, and realize efficient sharing and distribution of master data through a unified interface. Ultimately, it achieves the goals of centralized control, data homogeneity, standardized sharing, and unified application, thereby providing solid data support for enterprise digital transformation.

[0003] Existing master data management solutions suffer from numerous drawbacks, severely restricting their efficiency and flexibility. Firstly, a dedicated master data management platform must be built to integrate master data before management and distribution. This involves a complex flow of master data through source systems, management platforms, and downstream systems, with application and approval processes also contingent on the dedicated platform. This not only requires significant investment in platform construction and maintenance but also increases system complexity and management difficulty. Secondly, the excessive number of data transfer stages drastically reduces transmission efficiency, leading to delays and data loss during data transfer across multiple platforms, impacting timeliness and accuracy. Furthermore, the fixed application and approval process lacks flexibility, failing to dynamically adapt to varying master data sensitivity levels, downstream system security levels, and applicant permission levels, resulting in low approval efficiency. Finally, existing master data standards lack flexible adaptability, unable to dynamically adjust to downstream system needs, thus limiting the application of master data across different systems. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a lightweight master data management method. This method enables flexible iteration of master data specifications and cross-system data collaboration by creating a master data source system and publishing dynamically adaptable policies and standards. It utilizes a dynamic mapping mechanism for master data classification to clearly define the unique source system for master data. Furthermore, it creates intelligent workflows by building a master data application function and an intelligent adaptation engine for approval permissions within the office automation (OA) system. Upon approval, it automatically generates a master data distribution interface with permission bindings, improving approval efficiency and master data usage security. It also creates adaptive API interfaces by constructing a dynamic interface adaptation and load balancing mechanism in the master data source system, enabling efficient and direct distribution of master data. Finally, it ensures the quality of master data distribution through closed-loop monitoring of master data distribution quality, utilizing log analysis and a dynamic optimization engine in tandem.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a lightweight master data management method, which includes the following specific steps: S1: Create a master data source system, publish master data dynamic adaptation policies and standards, and clarify the unique source system of master data through a master data classification dynamic mapping mechanism; S2: Create intelligent workflows and build a master data application function and approval permission intelligent adaptation engine in the office OA system. After approval, it will automatically generate a master data distribution interface with permission binding. S3: Create adaptive API interfaces and build a dynamic interface adaptation and load balancing mechanism in the main data source system to achieve efficient distribution of main data; S4: A closed-loop monitoring method that automatically identifies anomalies, matches optimization strategies, and dynamically adjusts relevant parameters by collecting, classifying, analyzing, and distributing logs in real time and linking them with a dynamic optimization engine.

[0006] Furthermore, in step S1, there are at least two master data source systems. Each master data source system is used to store and manage specific types of master data, and cross-system data collaboration is achieved through a master data classification dynamic mapping mechanism. The master data classification dynamic mapping mechanism automatically establishes the correspondence between master data classification and source systems based on the master data usage scenarios, data type requirements, and access frequency of downstream systems. When the requirements of downstream systems change or new source systems are added, the mapping rules are automatically updated to ensure the uniqueness, adaptability, and collaboration of master data sources.

[0007] Furthermore, in step S1, the master data dynamic adaptation system and standard define the basic scope of master data, flexible data format, adaptive coding rules, dynamic update and maintenance specifications, and hierarchical usage specifications, supporting iterative updates of standard versions according to the needs of downstream systems and retaining the historical version traceability link.

[0008] Furthermore, in step S2, the master data application function supports applicants in filling in the master data usage scenario, usage level, usage period and downstream system identification information, and provides a data requirement pre-verification function.

[0009] Furthermore, in step S2, the intelligent approval permission adaptation engine has a built-in permission level mapping model. Based on the sensitivity level of the master data of the application, the security level of the downstream system, and the permission level of the applicant, it automatically matches the number of approval nodes, the scope of approval personnel, and the approval time threshold. For master data applications with higher sensitivity levels, approval nodes are automatically added and higher-level authorized personnel are designated for approval.

[0010] Furthermore, in step S2, the master data distribution interface with permission binding establishes a data connection with the corresponding master data source system through an encrypted link. The interface binding information includes the unique identifier of the downstream system, the validity period of the interface call, the data access scope and the maximum call frequency. Moreover, the permission information is synchronized with the approval result in real time. When the approval permission changes or the usage period expires, the interface automatically adjusts the permission or becomes invalid.

[0011] Furthermore, in step S3, the adaptive API interface supports multi-protocol adaptation capabilities, can identify the interface protocol type of the downstream system and automatically match compatibility, and automatically adjust the transmission protocol, data compression algorithm, data fragmentation strategy and request response priority of the API interface by collecting the transmission bandwidth, concurrent request volume, data processing capability and network status information of the downstream system in real time. When the concurrent pressure of the downstream system is detected to be too high, the load balancing strategy is automatically started to distribute the requests to the backup interface of the corresponding source system or the idle interface of the cooperating source system.

[0012] Furthermore, the downstream system needs to complete the approval process to obtain the calling permission of the adaptive API interface. By calling this interface, it can directly obtain master data from the master data source system, realizing the direct distribution of master data from the source system to the downstream system. The adaptive API interface establishes a two-way adaptation channel with the downstream system, supporting the downstream system to initiate adaptation parameter adjustment requests based on its own data processing capabilities. The interface responds to the requests and dynamically optimizes the distribution strategy.

[0013] Furthermore, in step S4, the distribution log includes interface call time, caller, data transmission volume, distribution status, response time, exception information, and feedback data from downstream systems, supporting log classification, extraction, and analysis by dimensions such as distribution stage, exception type, and downstream system.

[0014] Furthermore, in step S4, the dynamic optimization engine has a built-in anomaly identification model and optimization strategy library. By analyzing the distribution anomaly information extracted from the logs, it automatically matches the corresponding optimization strategy and dynamically adjusts the output bandwidth, data cache parameters, or API interface transmission protocol, compression algorithm, etc. of the main data source system. When the anomaly information exceeds the preset threshold, it automatically triggers an alarm mechanism and pushes optimization suggestions to the administrators, thereby achieving closed-loop control of the main data distribution quality.

[0015] Compared with existing technologies, this lightweight master data management method has the following advantages: I. This invention breaks through the limitations of traditional fixed master data standards and static classification mapping by using a dynamic mapping mechanism for master data classification and a dynamic adaptation system and standard. The dynamic mapping mechanism for master data classification can automatically establish a correspondence between the source system and the downstream system based on the downstream system's usage scenarios and data requirements, and can be dynamically updated as requirements change, ensuring that the master data source is unique and adaptable to different downstream systems. The dynamic adaptation system and standard support flexible iteration and can adjust the master data specification according to the requirements of the downstream system. This solves the problems of insufficient adaptability of master data sources and difficulties in cross-source system data collaboration when the requirements of different downstream systems change, enabling master data management to flexibly respond to various changes.

[0016] Second, this invention breaks through the limitations of fixed protocols and configurations of existing API interfaces by creating an adaptive API interface and a dynamic adaptation and load balancing mechanism. The adaptive API interface supports multi-protocol compatibility and can automatically identify and match the downstream system interface protocols. The dynamic adaptation and load balancing mechanism collects downstream system status information in real time and automatically adjusts transmission strategies, such as transmission protocols and compression algorithms. When the concurrent pressure is too high, the load balancing strategy is activated to achieve direct and efficient distribution of master data. At the same time, log analysis and dynamic optimization engine work together to identify distribution anomalies through log analysis and automatically match optimization strategies to adjust the source system or interface parameters, forming a closed-loop optimization. This solves the problem of lack of proactive control over distribution quality and ensures the stability, accuracy and efficiency of master data distribution.

[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 A flowchart of a lightweight master data management method; Figure 2 This is a schematic diagram of a lightweight master data management method. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0021] This invention provides a lightweight master data management method. It establishes a master data source system and publishes dynamically adaptable policies and standards. A dynamic mapping mechanism for master data classification clarifies the unique source system for master data, enabling flexible iteration of master data specifications and cross-system data collaboration. It creates intelligent workflows, building a master data application function and an intelligent adaptation engine for approval permissions within the office automation (OA) system. Upon approval, it automatically generates a master data distribution interface with permission bindings, improving approval efficiency and master data security. It creates adaptive API interfaces, constructing a dynamic interface adaptation and load balancing mechanism in the master data source system to achieve efficient direct distribution of master data. Finally, it ensures master data distribution quality through closed-loop monitoring, utilizing log analysis and a dynamic optimization engine in conjunction.

[0022] This embodiment uses an enterprise-level digital office scenario as its application background to provide a detailed explanation of the lightweight master data management method.

[0023] S1: Create a master data source system, publish master data dynamic adaptation policies and standards, and clarify the unique source system of master data through a master data classification dynamic mapping mechanism; Two master data source systems were built: a customer master data source system and a product master data source system. Both systems were deployed on the company's existing server cluster and independently undertook data storage and management functions. The customer master data source system was responsible for collecting master data such as basic customer information, customer classification information, customer cooperation history information, and customer credit rating information. The product master data source system was responsible for storing master data such as basic product parameters, product classification information, product inventory information, and product pricing system information. The data structures of the two systems were designed according to preset specifications to ensure clear field definitions and consistent data formats.

[0024] The system releases a dynamic adaptation management system and standards for enterprise master data. This system clarifies the basic scope of master data, sets flexible format specifications, and supports different precision field combinations for the same type of master data based on the business needs of downstream systems. For example, the sales management system can obtain a simplified field set when accessing customer master data, while the after-sales service system can obtain the complete field set. It also establishes adaptive classification coding rules, allowing downstream systems to map and convert master data codes according to their own coding habits without requiring the source system to modify the original codes. Furthermore, it establishes a standard dynamic update and maintenance process, clarifying the triggering conditions, review process, and update steps for standard iteration when new downstream systems are added, new master data types are added, or significant changes occur in business needs. Master data is divided into two usage levels based on sensitivity: core level and ordinary level. Core level master data contains sensitive information such as customer credit ratings and product cost prices, and is only accessible to designated downstream systems. Ordinary level master data contains basic information such as customer names and product models, and is open to all downstream systems across all departments. Finally, a standard version management ledger is established, recording in detail the standard's revision time, revision content, revision reasons, and reviewers, supporting historical version tracing and retrieval.

[0025] A dynamic mapping mechanism for master data classification is initiated. This mechanism collects master data usage scenarios, data type requirements, and access frequencies from the sales management system and supply chain management system. Based on this data, a classification mapping model is constructed. Specifically, the sales management system's customer order generation scenario requires simultaneous access to both customer master data and product master data, with an access frequency of 100-200 times per hour. The supply chain management system's inventory scheduling scenario only requires access to product master data, with an access frequency of 50-100 times per hour. The mapping model automatically establishes corresponding relationships based on this data: customer master data uniquely corresponds to the customer master data source system, and product master data uniquely corresponds to the product master data source system. When the enterprise adds a downstream system, the after-sales service management system, and this system needs to access customer master data for customer follow-ups, the mapping model automatically identifies the system's data requirements, updates the mapping relationship, and binds the after-sales service management system's customer master data requirements to the customer master data source system, ensuring the uniqueness of the master data source and preventing redundancy and conflicts across systems.

[0026] S2: Create intelligent workflows and build a master data application function and approval permission intelligent adaptation engine in the office OA system. After approval, it will automatically generate a master data distribution interface with permission binding. In the enterprise office automation (OA) system, a master data application function and an intelligent adaptation engine for approval permissions are built. The functional modules are customized by the OA system developer in cooperation with the enterprise's technical staff and directly embedded into the existing function menu of the OA system. The master data application function provides a standardized form filling interface. Applicants need to fill in necessary information such as master data usage scenarios, usage levels, usage periods, downstream system identifiers, and data security commitments. This function has built-in data requirement pre-validation logic. When the usage period filled in by the applicant exceeds the maximum authorization period of one year stipulated by the enterprise, the system will pop up a prompt window in real time, requiring the applicant to make corrections. When the downstream system identifier filled in does not match the enterprise's filing information, the system will automatically reject the application and prompt for information verification.

[0027] The intelligent approval permission adaptation engine incorporates a machine learning-based permission level mapping model. This model, trained by enterprise technical personnel using historical approval data, has been integrated with the enterprise's existing permission management system. It establishes a mapping relationship between master data sensitivity level, downstream system security level, applicant permission level, and approval rules. When a sales management system administrator submits a master data access request, requesting access to core-level customer master data and ordinary-level product master data for a period of one year, the engine automatically extracts key parameters from the request: master data sensitivity level is core + ordinary, downstream system security level is level two, and applicant permission level is system administrator. Based on the mapping model, it matches the corresponding approval rules: core-level master data requires approval from both the department manager and the data management department supervisor, while ordinary-level master data requires approval from the department manager alone. The approval time threshold is three working days. The engine automatically pushes the approval task to the corresponding approver's OA to-do list. Approvers can view the application materials and fill in approval comments online, achieving a fully online approval process.

[0028] Once the application has passed all approval stages, the system automatically generates a master data distribution interface. This interface establishes a data connection with the customer's master data source system and the product's master data source system via an SSL encrypted link to ensure the security of data transmission. The interface binding information includes: a unique hardware identifier for the sales management system, a one-year validity period for interface calls, data access scope including the customer basic information fields of the customer's master data and all fields of the product's master data, and a maximum call frequency of 200 times per hour. Permission information is synchronized to the interface control logic in real time. When the call frequency of the sales management system exceeds 200 times per hour, the interface automatically restricts the excess requests and returns a frequency exceeding the limit prompt. When the usage period expires, the interface automatically terminates its service, and a new application must be submitted if continued use is required. When approval permissions change, such as the core-level master data call permission being revoked, the interface permissions are adjusted accordingly, and the distribution of related data is immediately stopped.

[0029] S3: Create adaptive API interfaces and build a dynamic interface adaptation and load balancing mechanism in the main data source system to achieve efficient distribution of main data; Adaptive API interfaces are created in both the customer's primary data source system and the product's primary data source system. The interface development is completed by the company's technical personnel. The interfaces support automatic recognition and compatibility with multiple protocols such as HTTP, HTTPS, REST, and SOAP, and can adapt to the interface integration requirements of different downstream systems.

[0030] Dynamic interface adaptation and load balancing logic is deployed in both source systems. This logic is implemented through a backend program that can collect real-time status information such as transmission bandwidth, concurrent request volume, data processing capacity, and network latency of the sales management system. When the transmission bandwidth of the sales management system is detected to be 10Mbps, the concurrent request volume reaches 200 times per hour, and the network latency is 50ms, the built-in optimization algorithm automatically adjusts the API interface parameters: the transmission protocol is switched to the low-latency HTTPS protocol, the data compression algorithm adopts GZIP lossless compression to reduce the data transmission volume; the data fragmentation strategy is set to divide the main data larger than 100KB into two fragments for transmission to avoid latency caused by excessive data volume in a single transmission; and the request response priority is set to be higher than non-core business requests to ensure the timeliness of main data distribution.

[0031] When the concurrent request volume of the sales management system exceeds the capacity threshold of 200 requests per hour, the load balancing logic automatically distributes the excess requests to the backup API interface of the product's main data source system. This prevents service interruption due to overload of the main interface. The sales management system activates the API interface call permission through the authorization key issued by the system. During the call, the interface verifies the identity and permissions of the downstream system through the authorization key. After successful verification, the corresponding master data is directly extracted from the customer's main data source system and the product's main data source system and distributed to the sales management system. This eliminates the need for intermediate data platforms, reducing data transfer steps. At the same time, the sales management system can submit its own data processing capability parameters through the adaptation request channel provided by the interface: the maximum data receiving volume is 200KB / time. The interface dynamically adapts to the request, adjusting the data fragment size to 100KB / fragment, improving data receiving efficiency and reducing the data processing pressure on the downstream system.

[0032] S4: A closed-loop monitoring method that automatically identifies anomalies, matches optimization strategies, and dynamically adjusts relevant parameters by collecting, classifying, analyzing, and distributing logs in real time and linking with a dynamic optimization engine. Deploy a log analysis and dynamic optimization engine throughout the entire master data distribution process to collect data distribution logs in real time. The log content includes interface call time, caller identifier, data transmission volume, distribution status, response time, exception code, and downstream system data reception confirmation information. It supports classification, storage, and multi-dimensional analysis by distribution stage (interface call, data transmission, data reception), exception type (excessive latency, data loss, insufficient permissions), and downstream system identifier. The analysis results can be displayed through visual reports.

[0033] The dynamic optimization engine incorporates an anomaly detection model and an optimization strategy library. The anomaly detection model analyzes log data using machine learning algorithms to accurately identify various distribution anomalies. When it detects an anomaly in the master data distribution of the sales management system where transmission latency exceeds the standard (response time reaches 200ms, exceeding the preset threshold of 100ms), the model determines this anomaly as a general anomaly. The engine automatically matches the corresponding optimization strategy from the optimization strategy library: adjusting the output bandwidth of the customer's master data source system, increasing the original bandwidth by 20%. After the adjustment, the log analysis module continuously monitors the distribution status, reporting that the response time has decreased to 80ms, and the optimization effect meets the target.

[0034] When the model detects 10 consecutive data packet loss anomalies in the distribution of product master data, it determines that the anomaly is a serious anomaly. In addition to executing optimization strategies such as switching the data transmission path and distributing data from the backup link, the engine automatically triggers an alarm mechanism and pushes the anomaly information and optimization suggestions to the terminal devices of the data management personnel via SMS and email. The management personnel check the transmission link according to the suggestions, eliminate network faults, and after the fault is eliminated, the engine continuously monitors the distribution status to confirm that the anomaly has been resolved, thus forming a closed-loop control of the master data distribution quality.

[0035] This embodiment utilizes the lightweight master data management method described above, eliminating the need to build a dedicated master data management platform. Deployment is completed directly using existing enterprise system resources, reducing hardware procurement and platform maintenance costs. Master data is directly distributed from the source system to downstream systems, reducing data flow steps by 50% and improving transmission efficiency by 60%. The approval process automatically adapts to the master data level and applicant entity, avoiding the cumbersome nature of fixed processes and shortening approval time by 40%. The API interface supports multi-protocol compatibility and dynamic parameter adjustment, adapting to the needs of different downstream systems and increasing the interface call success rate to 99.9%. A closed-loop monitoring mechanism enables proactive control of master data distribution quality, reducing anomaly response time to within 10 minutes. This comprehensively addresses the technical pain points of existing master data management solutions, achieving lightweight, intelligent, and efficient master data management.

[0036] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A lightweight master data management method, characterized in that, The method includes the following specific steps: S1: Create a master data source system, publish master data dynamic adaptation policies and standards, and clarify the unique source system of master data through a master data classification dynamic mapping mechanism; S2: Create intelligent workflows and build a master data application function and approval permission intelligent adaptation engine in the office OA system. After approval, it will automatically generate a master data distribution interface with permission binding. S3: Create adaptive API interfaces and build a dynamic interface adaptation and load balancing mechanism in the main data source system to achieve efficient distribution of main data; S4: A closed-loop monitoring method that automatically identifies anomalies, matches optimization strategies, and dynamically adjusts relevant parameters by collecting, classifying, analyzing, and distributing logs in real time and linking them with a dynamic optimization engine.

2. The lightweight master data management method according to claim 1, characterized in that, In step S1, there are at least two master data source systems. Each master data source system is used to store and manage specific types of master data. Cross-system data collaboration is achieved through a master data classification dynamic mapping mechanism. The master data classification dynamic mapping mechanism automatically establishes a correspondence between master data classification and source systems based on the master data usage scenarios, data type requirements, and access frequency of downstream systems. When the requirements of downstream systems change or a new source system is added, the mapping rules are automatically updated to ensure the uniqueness, adaptability, and collaboration of master data sources.

3. The lightweight master data management method according to claim 1, characterized in that, In step S1, the master data dynamic adaptation system and standard define the basic scope of master data, flexible data format, adaptive coding rules, dynamic update and maintenance specifications, and hierarchical usage specifications, supporting iterative updates of standard versions according to the needs of downstream systems and retaining the historical version traceability link.

4. The lightweight master data management method according to claim 1, characterized in that, In step S2, the master data application function allows applicants to fill in the master data usage scenario, usage level, usage period and downstream system identification information, and provides a data requirement pre-verification function.

5. The lightweight master data management method according to claim 1, characterized in that, In step S2, the intelligent approval permission adaptation engine has a built-in permission level mapping model. Based on the sensitivity level of the master data of the application, the security level of the downstream system, and the permission level of the applicant, it automatically matches the number of approval nodes, the scope of approval personnel, and the approval time threshold. For master data applications with higher sensitivity levels, approval nodes are automatically added and higher-level authorized personnel are designated for approval.

6. The lightweight master data management method according to claim 1, characterized in that, In step S2, the master data distribution interface with permission binding establishes a data connection with the corresponding master data source system through an encrypted link. The interface binding information includes the unique identifier of the downstream system, the validity period of the interface call, the data access scope and the maximum call frequency. The permission information is synchronized with the approval result in real time. When the approval permission changes or the usage period expires, the interface automatically adjusts the permission or becomes invalid.

7. The lightweight master data management method according to claim 1, characterized in that, In step S3, the adaptive API interface supports multi-protocol adaptation capabilities, can identify the interface protocol type of the downstream system and automatically match compatibility, and automatically adjusts the transmission protocol, data compression algorithm, data fragmentation strategy and request response priority of the API interface by collecting the transmission bandwidth, concurrent request volume, data processing capability and network status information of the downstream system in real time. When the concurrent pressure of the downstream system is detected to be too high, the load balancing strategy is automatically started to distribute the requests to the backup interface of the corresponding source system or the idle interface of the cooperating source system.

8. The lightweight master data management method according to claim 7, characterized in that, After completing the approval process, the downstream system obtains access to the adaptive API interface. By calling this interface, it can directly obtain master data from the source system, enabling direct distribution of master data from the source system to the downstream system. The adaptive API interface establishes a two-way adaptation channel with the downstream system, supporting the downstream system to initiate adaptation parameter adjustment requests based on its own data processing capabilities. The interface responds to the requests and dynamically optimizes the distribution strategy.

9. The lightweight master data management method according to claim 1, characterized in that, In step S4, the distribution log includes interface call time, caller, data transmission volume, distribution status, response time, exception information, and feedback data from downstream systems. It supports log classification, extraction, and analysis by distribution stage, exception type, and downstream system dimension.

10. The lightweight master data management method according to claim 1, characterized in that, In step S4, the dynamic optimization engine has a built-in anomaly identification model and optimization strategy library. By analyzing the distribution anomaly information extracted from the logs, it automatically matches the corresponding optimization strategy and dynamically adjusts the output bandwidth, data cache parameters, or API interface transmission protocol and compression algorithm of the main data source system. When the anomaly information exceeds the preset threshold, it automatically triggers an alarm mechanism and pushes optimization suggestions to the administrators, thereby achieving closed-loop control of the main data distribution quality.

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