A method for real-time monitoring of master data distribution

Abstract

The application discloses a master data distribution real-time monitoring method, which comprises the following steps: defining a master data object used across systems uniformly and establishing a unique master data identifier; collecting a master data change behavior in an eventized manner and forming a distribution monitoring starting point; synchronizing the master data change in an orderly manner to a related business system through a differentiated distribution rule; and implementing real-time state collection, result quantization determination and abnormality identification in the distribution process, combining a whole-process record and a strategy feedback optimization mechanism to realize a master data distribution real-time monitoring method for realizing consistent, controllable, monitorable and traceable distribution management of master data among multiple business systems. The method is characterized in that: step one is to define a master data distribution monitoring range and a monitoring object standard; step two is to collect a master data change behavior and generate a distribution monitoring event; step three is to formulate a master data distribution rule and to disassemble a distribution task; and step four is to collect a real-time state of a master data distribution execution process.

Description

Technical Field

[0001] This invention relates to a real-time monitoring method for master data distribution, which is an enterprise-level master data distribution and monitoring control method belonging to the field of enterprise informatization and data governance technology. Specifically, it relates to a method that unifies the master data objects used across systems and establishes a unique master data identifier, collects master data change behavior as an event to form a distribution monitoring starting point, synchronizes master data changes to relevant business systems in an orderly manner through differentiated distribution rules, and implements real-time status collection, result quantification and anomaly identification during the distribution process. Combined with a full-process recording and strategy feedback optimization mechanism, this method achieves consistent, controllable, monitorable and traceable distribution governance of master data across multiple business systems. Background Technology

[0002] With the continuous advancement of enterprise informatization and digitalization, basic data such as organization, personnel, materials, and equipment are widely used in multiple business systems, including financial management, materials management, production scheduling, and safety control. To ensure data consistency between systems, existing technologies typically use master data management platforms, combined with interface synchronization or message push, to distribute master data change information to relevant business systems, achieving cross-system sharing of basic data. However, in current mainstream technologies, the master data distribution process usually only focuses on whether data has been pushed, lacking a unified, real-time monitoring and judgment mechanism for the distribution process. Whether master data distribution is successful, and whether there are delays or anomalies, largely relies on manual inspection, log analysis, or passive feedback from the business system side, making it difficult to perceive the distribution status in a timely manner. When distribution fails or some systems fail to synchronize, problems are often not detected immediately, easily leading to inconsistencies in master data between business systems, affecting the stability of business operations and the accuracy of management decisions. In addition, existing master data distribution mechanisms mostly adopt fixed distribution rules, lacking differentiated processing for different types of master data, making it difficult to reasonably set distribution priorities and methods based on the importance and real-time requirements of master data such as organization, personnel, materials, or equipment in the business. When multiple types of master data change simultaneously, the distribution order is difficult to coordinate, which can easily lead to delays in the updates of critical business systems.

[0003] Publication number CN113111046A discloses a data governance system based on master data, including: a master data business management module for generating corresponding instantiated business functions; a data model management module for providing data templates for different industry categories; a data quality management module for management activities such as identification, measurement, monitoring, and early warning of data quality problems throughout the data lifecycle; a data integration management module for managing data receiving and distribution middleware and standardizing data; a basic support management module for ensuring data operation security and facilitating hierarchical management; a workflow module for automating, intelligentizing, and integrating data management; a data cleaning module for cleaning fragmented, duplicate, and incomplete data after integration according to certain cleaning rules to ensure the uniqueness, accuracy, completeness, consistency, and validity of the data; and a scheduled task processing module for handling non-real-time data calculations. Although the aforementioned data governance system involves the distribution management and logging of master data, it only focuses on the post-event recording and result querying of the master data distribution process. It does not abstract master data change behavior into independently monitorable distribution events, nor does it collect and quantify the status of the distribution task execution process in real time. It cannot realize the real-time calculation, unified judgment, and proactive alarm of the completion of master data distribution and abnormal status. Therefore, it is difficult to detect distribution failures or abnormal situations in a timely manner, and the controllability and monitorability of the master data distribution process are obviously insufficient. Summary of the Invention

[0004] To improve the above situation, the present invention provides a real-time monitoring method for master data distribution. This method provides a way to uniformly define master data objects used across systems and establish unique master data identifiers. It collects master data change behavior as events and forms a distribution monitoring starting point. It synchronizes master data changes to relevant business systems in an orderly manner through differentiated distribution rules. During the distribution process, it implements real-time status collection, result quantification and anomaly identification. Combined with a full-process recording and strategy feedback optimization mechanism, this method achieves consistent, controllable, monitorable and traceable distribution governance of master data across multiple business systems.

[0005] The real-time monitoring method for master data distribution according to the present invention is implemented as follows: The real-time monitoring method for master data distribution according to the present invention is characterized by comprising the following steps: Step 1: Define the scope of master data distribution monitoring and the standards for monitoring objects. The specific implementation method of step one is as follows: Define and standardize the master data objects that need to be included in unified distribution and unified governance within the enterprise. By uniformly identifying basic data such as organization, personnel, materials, equipment, customers, and projects, these are identified as the core objects for real-time monitoring of master data distribution. A unified distribution monitoring standard is established for each type of master data object. Preferably, the master data object is a data object that is repeatedly used in multiple business systems within the enterprise and serves as the basis for business operation and management analysis. Preferably, in this step, the definition of master data monitoring objects is based primarily on cross-system usage frequency and business dependency. Only basic data objects referenced by two or more business systems are included in the master data distribution monitoring scope, thus avoiding the erroneous inclusion of local configuration data used only within a single system into the master data monitoring system. Preferably, during the master data object determination process, this method performs unified mapping and alignment of the name, code, and attribute fields of the same business object in different systems to form a unique master data object identifier. This enables the master data to be accurately identified and associated during subsequent distribution and monitoring. Preferably, this method, while clearly defining the master data distribution monitoring objects, establishes differentiated monitoring models for different types of master data based on state changes and anomalies during the master data distribution process. These monitoring models include sets of distribution states and anomaly judgment rules set separately for different master data types. This allows organization-type master data, personnel-type master data, material-type master data, and equipment-type master data to employ different state definitions and anomaly judgment rules during the distribution monitoring process, thereby improving the granularity of distribution monitoring. Preferably, during the monitoring scope setting phase, this method simultaneously determines the scope of business systems involved in master data distribution, designating financial systems, materials systems, production systems, safety management systems, and integrated management systems as the main target systems for master data distribution. This ensures that subsequent monitoring processes can fully cover the main flow paths of master data within the group. Step Two: Collection and Distribution of Master Data Change Events and Generation of Monitoring Events. Step two is implemented as follows: Real-time collection of master data additions, modifications, activations, deactivations, mergings, or splits occurring in the master data management platform. Through unified awareness of master data operations, each master data change triggers a corresponding distribution monitoring process, generating a master data distribution monitoring event that corresponds one-to-one with the change. The distribution monitoring process refers to the continuous tracking and recording of the execution status of master data distribution tasks after a master data change event is triggered. Preferably, during the master data change behavior collection process, this method synchronously collects the unique identifier of the master data object, the change type, the change occurrence time, and the data version information before and after the change, and encapsulates the above information in a structured manner so that each master data distribution monitoring event has complete contextual information. Preferably, this method records the triggering source of master data changes when generating master data distribution monitoring events. This is used to distinguish master data change behaviors caused by different business systems or different management roles, thereby providing a basis for subsequent distribution strategies and monitoring analysis. Preferably, to avoid duplicate distribution and monitoring, this method merges consecutive master data changes occurring within a short period. Under preset merging conditions, multiple changes are consolidated into a single distribution monitoring event, thereby reducing system load and improving distribution efficiency. The preset merging conditions include identical master data objects and a change interval less than a preset time threshold. Step 3: Formulating master data distribution rules and breaking down distribution tasks. Step three is implemented as follows: Based on the master data object type, change type, and business system usage relationship, the master data distribution rules are parsed and matched to determine the target system scope and corresponding distribution method for master data distribution. Furthermore, a single master data change event is broken down into multiple independently executable and monitorable master data distribution tasks. Preferably, this method sets differentiated distribution strategies for different types of master data when formulating master data distribution rules. This differentiates organizational master data, personnel master data, material master data, and equipment master data in terms of distribution target systems, distribution priorities, and distribution frequencies. This satisfies the differentiated timeliness requirements of different business systems for master data. The differentiated distribution strategy includes at least differentiated configuration of distribution target systems to limit the scope of target business systems that need to be synchronized for different types of master data, ensuring that only systems with business dependencies on that type of master data participate in the distribution process. It further includes differentiated settings for distribution priorities to determine the processing order in the distribution queue based on the degree of impact of master data on business operations when multiple types of master data change simultaneously. It also includes differentiated control of distribution frequency to control the frequency or triggering conditions for distribution based on the different real-time requirements of different types of master data, prioritizing immediate distribution for high-real-time master data and periodic or batch distribution for low-real-time master data. Preferably, during the task decomposition process, this method assigns a unique task identifier to each distribution task and establishes a correlation between the task identifier and the corresponding master data distribution monitoring event, so that subsequent distribution status collection and anomaly localization can accurately correspond to the specific target system. Preferably, during the distribution rule matching phase, this method supports setting priority distribution strategies for critical business systems, ensuring that master data, after changes, is prioritized for synchronization to systems with a significant impact on business continuity, thereby guaranteeing the stability of core business operations. Step 4: Real-time status acquisition during the master data distribution execution process. Step four is implemented as follows: Execute the master data distribution task according to the distribution rules, and collect the running status of each distribution task in real time during the distribution process, so that the status changes of master data during cross-system distribution can be continuously perceived and recorded. Preferably, during the master data distribution process, this method progressively collects the start status of the distribution task, the data sending status, the target system response status, and the data reception confirmation result, so that the master data distribution process forms a continuous state change trajectory. Preferably, this method employs an event-triggered mechanism during the status acquisition process, recording status information in real time when the status of the distributed task changes, thereby avoiding the monitoring delay caused by polling and improving the real-time performance of master data distribution monitoring. Preferably, during the status acquisition phase, this method synchronously records abnormal information generated during the distribution process, including interface call anomalies, data verification failures, and target system response timeouts, providing basic data for subsequent anomaly identification. Step 5: Aggregation of master data distribution status and determination of distribution results. Step five is implemented as follows: During the execution of multiple distribution tasks corresponding to the same master data distribution monitoring event, the real-time status information collected by each distribution task is summarized and aggregated to form the overall distribution status of the master data change event. Based on this, the master data distribution result is quantitatively determined. Preferably, during the distribution status aggregation process, this method statistically analyzes the total number of target systems participating in the master data distribution event and the number of target systems that have actually completed distribution, and calculates the master data distribution completion rate based on the statistical results, which serves as a quantitative basis for determining the distribution result. Preferably, the master data distribution completion degree D is calculated using the following formula: D = Ns / Nt, where Ns represents the number of target systems that successfully completed master data distribution within a preset time window, and Nt represents the total number of target systems participating in the master data distribution event. When the master data distribution completion degree D is greater than or equal to a preset completion degree threshold θ, the master data distribution event is determined to be in a successful distribution state. When the master data distribution completion degree D is less than the completion degree threshold θ, the master data distribution event is determined to be in an incomplete state or a partially failed state, thereby achieving a calculable determination of the master data distribution result. Preferably, during the distribution result determination process, this method simultaneously considers the distribution time and determines whether the master data distribution completion time exceeds a preset time threshold, so that the distribution result determination takes into account both distribution completeness and timeliness. Step Six: Identification and Alarm Handling of Master Data Distribution Anomalies Step six is ​​implemented by identifying and quantifying anomalies that occur during master data distribution, and triggering corresponding anomaly alarm handling procedures based on the anomaly analysis results, so that master data distribution anomalies can be detected and handled in a timely manner. Preferably, in the process of identifying distribution anomalies, this method comprehensively evaluates the anomalies in distribution time, the number of distribution failures, and the severity of the anomalies that occur during master data distribution, and constructs a master data distribution anomaly score to quantitatively characterize the level of anomalies in the master data distribution process. Preferably, the master data distribution anomaly score E is calculated using the following formula: E = α·T + β·F + γ·S, where T represents the normalized value of master data distribution time, F represents the number of failed master data distribution tasks, S represents the anomaly severity coefficient, and α, β, and γ are preset weight coefficients used to adjust the influence weight of different anomaly factors in the anomaly score. When the master data distribution anomaly score E is greater than or equal to the preset anomaly threshold ε, the current master data distribution event is determined to enter an anomaly alarm state, and the corresponding alarm processing procedure is triggered. When the anomaly score E is less than the anomaly threshold ε, the current master data distribution event is determined to be in an acceptable state. Preferably, after an anomaly alarm is triggered, this method associates the anomaly scoring result with the corresponding master data object, target system, and distribution task information, so that the anomaly alarm information can accurately reflect the location and scope of the anomaly, thereby assisting operation and maintenance personnel in quickly locating the problem and taking corrective measures. Step 7: Record the entire process of master data distribution and establish traceability relationships. Step seven is implemented by uniformly storing monitoring event information, distribution task information, status change information, and distribution result information generated during the master data distribution process, and establishing the association between master data objects and the distribution process, so that the entire master data distribution process has a complete historical record. Preferably, during the storage of distribution records, this method organizes and indexes the distribution records according to the master data object type and time order, enabling subsequent queries to quickly locate the distribution status of a specific master data object within a specified time range. Preferably, by establishing a traceable relationship in the master data distribution process, managers can trace the distribution path of master data across different business systems, providing a basis for auditing, verification, and accountability. Preferably, during the traceability relationship construction process, this method retains key node information of master data distribution, giving the master data distribution process a clear timeline structure. Step 8: Optimize the master data distribution strategy based on monitoring results. Step eight is implemented by periodically analyzing historical master data distribution monitoring data, statistically analyzing the distribution success rate, anomaly frequency, and distribution time for different master data objects, and adjusting the master data distribution strategy based on the analysis results. Preferably, this method dynamically optimizes the distribution rules based on historical distribution anomalies, adjusting the distribution method or priority on target systems with frequent anomalies, thereby reducing the impact of anomalies on the overall distribution system. Preferably, by continuously optimizing the master data distribution strategy, the stability and timeliness of the master data distribution process are continuously improved, forming a sustainable and evolving real-time monitoring mechanism for master data distribution. Beneficial effects

[0006] I. By treating master data change behavior as an event and implementing real-time status collection, completion calculation, and anomaly scoring during the distribution task execution process, the master data distribution results can be uniformly judged in the form of quantitative indicators, avoiding reliance on manual confirmation or post-event investigation, and effectively improving the transparency, controllability, and operational reliability of the master data distribution process.

[0007] Second, by establishing differentiated distribution strategies and monitoring models for different types of master data, and combining the recording and traceability of the entire distribution process, we can analyze and provide feedback on historical distribution success rates, anomaly frequency, and distribution time. This allows the distribution strategy to be continuously optimized according to the business operation status, gradually reducing the probability of anomalies and improving the overall distribution stability and timeliness. Attached Figure Description

[0008] Figure 1 This is a schematic diagram illustrating the relationship between master data distribution monitoring events and distribution tasks in a real-time master data distribution monitoring method according to the present invention. Figure 2 This is a schematic diagram of the master data distribution status monitoring and result determination logic of a real-time master data distribution monitoring method according to the present invention; Figure 3 This is a schematic diagram illustrating the differentiated distribution strategies for different types of master data in the real-time monitoring method for master data distribution according to the present invention. Figure 4 This is a schematic diagram illustrating the record and traceability relationship of the entire master data distribution process in the real-time monitoring method for master data distribution according to the present invention. Detailed Implementation Example 1

[0009] The real-time monitoring method for master data distribution according to the present invention is implemented as follows: The real-time monitoring method for master data distribution according to the present invention includes the following steps: Step 1: Define the scope of master data distribution monitoring and the standards for monitoring objects. The specific implementation method for step one is as follows: Define and standardize the master data objects that need to be included in unified distribution and unified governance within the enterprise. By uniformly identifying basic data such as organization, personnel, materials, equipment, customers, and projects, these objects are identified as the core objects for real-time monitoring of master data distribution. A unified distribution monitoring standard is established for each type of master data object. Preferably, the master data object is a data object that is repeatedly used in multiple business systems within the enterprise and serves as the basis for business operation and management analysis. Preferably, in this step, the definition of master data monitoring objects is based primarily on cross-system usage frequency and business dependency. Only basic data objects referenced by two or more business systems are included in the master data distribution monitoring scope, thereby avoiding the erroneous inclusion of local configuration data used only within a single system into the master data monitoring system. Preferably, during the master data object determination process, this method performs unified mapping and alignment of the name, code, and attribute fields of the same business object in different systems to form a unique master data object identifier. This enables the master data to be accurately identified and associated during subsequent distribution and monitoring. Preferably, this method, while clearly defining the master data distribution monitoring objects, establishes differentiated monitoring models for different types of master data based on state changes and anomalies during the master data distribution process. These monitoring models include sets of distribution states and anomaly judgment rules set separately for different master data types. This allows organization-type master data, personnel-type master data, material-type master data, and equipment-type master data to employ different state definitions and anomaly judgment rules during the distribution monitoring process, thereby improving the granularity of distribution monitoring. Preferably, during the monitoring scope setting phase, this method simultaneously determines the scope of business systems involved in master data distribution, designating financial systems, materials systems, production systems, safety management systems, and integrated management systems as the main target systems for master data distribution. This ensures that subsequent monitoring processes can fully cover the main flow paths of master data within the group. Step Two: Collection and Distribution of Master Data Change Events and Generation of Monitoring Events. Step two is implemented as follows: Real-time collection of master data additions, modifications, activations, deactivations, mergings, or splits occurring in the master data management platform. Through unified awareness of master data operations, each master data change triggers a corresponding distribution monitoring process, generating a master data distribution monitoring event that corresponds one-to-one with the change. The distribution monitoring process refers to the continuous tracking and recording of the execution status of master data distribution tasks after a master data change event is triggered. Preferably, during the master data change behavior collection process, this method synchronously collects the unique identifier of the master data object, the change type, the change occurrence time, and the data version information before and after the change, and encapsulates the above information in a structured manner so that each master data distribution monitoring event has complete contextual information. Preferably, this method records the triggering source of master data changes when generating master data distribution monitoring events. This is used to distinguish master data change behaviors caused by different business systems or different management roles, thereby providing a basis for subsequent distribution strategies and monitoring analysis. Preferably, to avoid duplicate distribution and monitoring, this method merges consecutive master data changes occurring within a short period. Under preset merging conditions, multiple changes are consolidated into a single distribution monitoring event, thereby reducing system load and improving distribution efficiency. The preset merging conditions include identical master data objects and a change interval less than a preset time threshold. Step 3: Formulating master data distribution rules and breaking down distribution tasks. Step three is implemented as follows: Based on the master data object type, change type, and business system usage relationship, the master data distribution rules are parsed and matched to determine the target system scope and corresponding distribution method for master data distribution. Furthermore, a single master data change event is broken down into multiple independently executable and monitorable master data distribution tasks. Preferably, this method sets differentiated distribution strategies for different types of master data when formulating master data distribution rules. This differentiates organizational master data, personnel master data, material master data, and equipment master data in terms of distribution target systems, distribution priorities, and distribution frequencies. This satisfies the differentiated timeliness requirements of different business systems for master data. The differentiated distribution strategy includes at least differentiated configuration of distribution target systems to limit the scope of target business systems that need to be synchronized for different types of master data, ensuring that only systems with business dependencies on that type of master data participate in the distribution process. It further includes differentiated settings for distribution priorities to determine the processing order in the distribution queue based on the degree of impact of master data on business operations when multiple types of master data change simultaneously. It also includes differentiated control of distribution frequency to control the frequency or triggering conditions for distribution based on the different real-time requirements of different types of master data, prioritizing immediate distribution for high-real-time master data and periodic or batch distribution for low-real-time master data. Preferably, during the task decomposition process, this method assigns a unique task identifier to each distribution task and establishes a correlation between the task identifier and the corresponding master data distribution monitoring event, so that subsequent distribution status collection and anomaly localization can accurately correspond to the specific target system. Preferably, during the distribution rule matching phase, this method supports setting priority distribution strategies for critical business systems, ensuring that master data, after changes, is prioritized for synchronization to systems with a significant impact on business continuity, thereby guaranteeing the stability of core business operations. Step 4: Real-time status acquisition during the master data distribution execution process. Step four is implemented as follows: Execute the master data distribution task according to the distribution rules, and collect the running status of each distribution task in real time during the distribution process, so that the status changes of master data during cross-system distribution can be continuously perceived and recorded. Preferably, during the master data distribution process, this method progressively collects the start status of the distribution task, the data sending status, the target system response status, and the data reception confirmation result, so that the master data distribution process forms a continuous state change trajectory. Preferably, this method employs an event-triggered mechanism during the status acquisition process, recording status information in real time when the status of the distributed task changes, thereby avoiding the monitoring delay caused by polling and improving the real-time performance of master data distribution monitoring. Preferably, during the status acquisition phase, this method synchronously records abnormal information generated during the distribution process, including interface call anomalies, data verification failures, and target system response timeouts, providing basic data for subsequent anomaly identification. Step 5: Aggregation of master data distribution status and determination of distribution results. Step five is implemented as follows: During the execution of multiple distribution tasks corresponding to the same master data distribution monitoring event, the real-time status information collected by each distribution task is summarized and aggregated to form the overall distribution status of the master data change event. Based on this, the master data distribution result is quantitatively determined. Preferably, during the distribution status aggregation process, this method statistically analyzes the total number of target systems participating in the master data distribution event and the number of target systems that have actually completed distribution, and calculates the master data distribution completion rate based on the statistical results, which serves as a quantitative basis for determining the distribution result. Preferably, the master data distribution completion degree D is calculated using the following formula: D = Ns / Nt, where Ns represents the number of target systems that successfully completed master data distribution within a preset time window, and Nt represents the total number of target systems participating in the master data distribution event. When the master data distribution completion degree D is greater than or equal to a preset completion degree threshold θ, the master data distribution event is determined to be in a successful distribution state. When the master data distribution completion degree D is less than the completion degree threshold θ, the master data distribution event is determined to be in an incomplete state or a partially failed state, thereby achieving a calculable determination of the master data distribution result. Preferably, during the distribution result determination process, this method simultaneously considers the distribution time and determines whether the master data distribution completion time exceeds a preset time threshold, so that the distribution result determination takes into account both distribution completeness and timeliness. Step Six: Identification and Alarm Handling of Master Data Distribution Anomalies Step six is ​​implemented by identifying and quantifying anomalies that occur during master data distribution, and triggering corresponding anomaly alarm handling procedures based on the anomaly analysis results, so that master data distribution anomalies can be detected and handled in a timely manner. Preferably, in the process of identifying distribution anomalies, this method comprehensively evaluates the anomalies in distribution time, the number of distribution failures, and the severity of the anomalies that occur during master data distribution, and constructs a master data distribution anomaly score to quantitatively characterize the level of anomalies in the master data distribution process. Preferably, the master data distribution anomaly score E is calculated using the following formula: E = α·T + β·F + γ·S, where T represents the normalized value of master data distribution time, F represents the number of failed master data distribution tasks, S represents the anomaly severity coefficient, and α, β, and γ are preset weight coefficients used to adjust the influence weight of different anomaly factors in the anomaly score. When the master data distribution anomaly score E is greater than or equal to the preset anomaly threshold ε, the current master data distribution event is determined to enter an anomaly alarm state, and the corresponding alarm processing procedure is triggered. When the anomaly score E is less than the anomaly threshold ε, the current master data distribution event is determined to be in an acceptable state. Preferably, after an anomaly alarm is triggered, this method associates the anomaly scoring result with the corresponding master data object, target system, and distribution task information, so that the anomaly alarm information can accurately reflect the location and scope of the anomaly, thereby assisting operation and maintenance personnel in quickly locating the problem and taking corrective measures. Step 7: Record the entire process of master data distribution and establish traceability relationships. Step seven is implemented by uniformly storing monitoring event information, distribution task information, status change information, and distribution result information generated during the master data distribution process, and establishing the association between master data objects and the distribution process, so that the entire master data distribution process has a complete historical record. Preferably, during the storage of distribution records, this method organizes and indexes the distribution records according to the master data object type and time order, enabling subsequent queries to quickly locate the distribution status of a specific master data object within a specified time range. Preferably, by establishing a traceable relationship in the master data distribution process, managers can trace the distribution path of master data across different business systems, providing a basis for auditing, verification, and accountability. Preferably, during the traceability relationship construction process, this method retains key node information of master data distribution, giving the master data distribution process a clear timeline structure. Step 8: Optimize the master data distribution strategy based on monitoring results. Step eight is implemented by periodically analyzing historical master data distribution monitoring data, statistically analyzing the distribution success rate, anomaly frequency, and distribution time for different master data objects, and adjusting the master data distribution strategy based on the analysis results. Preferably, this method dynamically optimizes the distribution rules based on historical distribution anomalies, adjusting the distribution method or priority on target systems with frequent anomalies, thereby reducing the impact of anomalies on the overall distribution system. Preferably, by continuously optimizing the master data distribution strategy, the stability and timeliness of the master data distribution process are continuously improved, forming a sustainable and evolving real-time monitoring mechanism for master data distribution. Preferably, this method dynamically optimizes the distribution rules based on historical distribution anomalies, adjusting the distribution method or priority on target systems with frequent anomalies, thereby reducing the impact of anomalies on the overall distribution system. Preferably, by continuously optimizing the master data distribution strategy, the stability and timeliness of the master data distribution process are continuously improved, forming a sustainable and evolving real-time monitoring mechanism for master data distribution; The design of uniformly identifying basic data such as organizations, personnel, materials, equipment, customers, and projects, and determining them as the core objects of real-time monitoring of master data distribution, and establishing a unified distribution monitoring standard for each type of master data object, can avoid different business systems using inconsistent definitions and processing methods for the same basic data, thus ensuring the consistency and authority of master data distribution and monitoring from the source. The design, which uses cross-system usage frequency and business dependency as the main criteria and only includes basic data objects referenced by two or more business systems in the master data distribution monitoring scope, can effectively avoid mistakenly including local configuration data used only within a single system in the monitoring system, thereby reducing monitoring complexity and reducing invalid distribution. The design of uniformly mapping and aligning the name, code, and attribute fields of the same business object in different systems to form a unique master data object identifier can ensure that the master data is accurately identified and associated in the subsequent distribution and monitoring process, avoiding distribution errors and monitoring distortions caused by inconsistent coding. The design of collecting changes to master data in real time, such as adding, modifying, enabling, disabling, merging, or splitting, and generating master data distribution monitoring events corresponding to these changes, can abstract master data changes into monitorable and traceable event objects, providing a unified starting point for subsequent distribution status collection and anomaly analysis. The design of setting differentiated distribution strategies for different types of master data and breaking down a master data change event into multiple independently executable and independently monitored master data distribution tasks can meet the differentiated needs of different business systems for the timeliness and priority of master data, while improving the flexibility and controllability of the master data distribution process. The design of calculating the completion rate of master data distribution and constructing a master data distribution anomaly score to quantitatively determine the distribution results and anomaly status can realize the transformation of master data distribution results from qualitative judgment to calculable and comparable technical judgment, thereby improving the objectivity and automation level of distribution monitoring. The goal is to achieve consistent, controllable, monitorable, and traceable distribution governance of master data across multiple business systems by uniformly defining master data objects used across systems and establishing unique master data identifiers, collecting master data change behavior as an event and forming a distribution monitoring starting point, orderly synchronizing master data changes to relevant business systems through differentiated distribution rules, implementing real-time status collection, result quantification and anomaly identification during the distribution process, and combining full-process recording and strategy feedback optimization mechanisms.

[0010] Other similar embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art that are not disclosed herein.

[0011] The above embodiments are preferred embodiments of the present invention. Due to space limitations, the applicant has not used other embodiments, but this is not intended to limit the scope of the present invention. Any person skilled in the art can make some modifications without departing from the scope of the present invention; that is, all equivalent modifications made in accordance with the present invention should be covered by the scope of the present invention.

Claims

1. A method for real-time monitoring of master data distribution, characterized in that, Includes the following steps: Step 1: Define the scope and standards for master data distribution monitoring; Step 2: Collect master data change behavior and generate distribution monitoring events; Step 3: Formulate master data distribution rules and break down distribution tasks; Step 4: Collect real-time status of master data distribution execution process; Step 5: Aggregate master data distribution status and determine distribution results; Step 6: Identify master data distribution anomalies and handle alarms; Step 7: Record the entire master data distribution process and build traceability relationships; Step 8: Optimize master data distribution strategy based on monitoring results.

2. The real-time monitoring method for master data distribution according to claim 1, characterized in that... The implementation method of step one is as follows: Define and standardize the master data objects that need to be included in the unified distribution and unified governance within the enterprise. By uniformly identifying basic data such as organization, personnel, materials, equipment, customers and projects, these are identified as the core objects of real-time monitoring of master data distribution. A unified distribution monitoring standard is established for each type of master data object. The master data object is a data object that is repeatedly used in multiple business systems within the enterprise and serves as the basis for business operation and management analysis. In this step, the definition of master data monitoring objects is based on the frequency of cross-system use and the degree of business dependence. Only basic data objects that are referenced by two or more business systems at the same time are included in the scope of master data distribution monitoring to avoid mistakenly including local configuration data used only within a single system into the master data monitoring system.

3. The real-time monitoring method for master data distribution according to claim 2, characterized in that... In step one, during the master data object determination process, this method uniformly maps and aligns the names, codes, and attribute fields of the same business object in different systems to form a unique master data object identifier. This allows the master data to be accurately identified and associated during subsequent distribution and monitoring. While clarifying the master data distribution monitoring object, this method establishes differentiated monitoring models for different types of master data based on state changes and anomalies during the master data distribution process. The monitoring model includes a distribution state set and anomaly judgment rule set set for different master data types, enabling different state definitions and anomaly judgment rules to be used for organizational master data, personnel master data, material master data, and equipment master data during the distribution monitoring process, thereby improving the granularity of distribution monitoring. In the monitoring scope setting stage, this method simultaneously determines the scope of business systems involved in master data distribution, taking the financial system, material system, production system, safety management system, and comprehensive management system as the main target systems for master data distribution, so that the subsequent monitoring process can fully cover the main flow path of master data within the group.

4. The real-time monitoring method for master data distribution according to claim 1, characterized in that... The second step involves real-time collection of master data additions, modifications, activations, deactivations, mergings, or splits occurring in the master data management platform. By uniformly sensing master data operations, each master data change triggers a corresponding distribution monitoring process, generating a master data distribution monitoring event that corresponds one-to-one with the change. This distribution monitoring process continuously tracks and records the execution status of master data distribution tasks after a master data change event is triggered. During the master data change collection process, this method synchronously collects the unique identifier of the master data object, the change type, the change occurrence time, and the data version information before and after the change, and then processes this information... This method employs structured encapsulation to ensure that each master data distribution monitoring event has complete contextual information. When generating a master data distribution monitoring event, it records the triggering source of the master data change to distinguish master data change behaviors caused by different business systems or different management roles. This provides a basis for subsequent distribution strategies and monitoring analysis. To avoid duplicate distribution and monitoring, this method merges consecutive master data change behaviors that occur within a short period of time. Under the condition of meeting preset merging conditions, multiple changes are integrated into a single distribution monitoring event, thereby reducing system load and improving distribution efficiency. The preset merging conditions include that the master data objects are the same and the time interval between changes is less than a preset time threshold.

5. The real-time monitoring method for master data distribution according to claim 1, characterized in that... The implementation method of step three is as follows: Based on the master data object type, change type, and business system usage relationship, the master data distribution rules are parsed and matched to determine the target system scope and corresponding distribution method for master data distribution. Furthermore, a single master data change event is broken down into multiple independently executable and monitorable master data distribution tasks. When formulating master data distribution rules, this method sets differentiated distribution strategies for different types of master data, distinguishing between organizational, personnel, material, and equipment master data in terms of target systems, distribution priority, and distribution frequency. This satisfies the differentiated timeliness requirements of different business systems for master data. The differentiated distribution strategy includes at least differentiated configuration of target systems to limit the scope of target business systems that need to synchronize different types of master data, ensuring that only systems with business dependencies on that type of master data participate in the distribution process. It further includes... The method employs differentiated priority settings to determine the processing order of master data in the distribution queue based on their varying impact on business operations when multiple types of master data change simultaneously. It also includes differentiated control of distribution frequency, controlling the frequency or triggering conditions for different types of master data based on their varying real-time requirements. This ensures that high-real-time master data is distributed immediately, while low-real-time master data is distributed periodically or in batches. During the task decomposition process, this method assigns a unique task identifier to each distribution task and establishes a correlation between this identifier and the corresponding master data distribution monitoring event. This allows subsequent distribution status collection and anomaly localization to accurately pinpoint specific target systems. In the distribution rule matching phase, this method supports setting priority distribution strategies for critical business systems, ensuring that master data changes are prioritized for synchronization to systems with a greater impact on business continuity, thereby guaranteeing the stability of core business operations.

6. The real-time monitoring method for master data distribution according to claim 1, characterized in that... The implementation method of step four is as follows: The master data distribution task is executed according to the distribution rules, and the running status of each distribution task is collected in real time during the distribution execution process. This allows the status changes of master data during cross-system distribution to be continuously perceived and recorded. During the master data distribution execution process, this method gradually collects the start status, data sending status, target system response status, and data reception confirmation results of the distribution task, so that the master data distribution process forms a continuous status change trajectory. This method adopts an event triggering mechanism during the status collection process, and records the status information in real time when the status of the distribution task changes, thereby avoiding the monitoring delay caused by the polling method and improving the real-time performance of master data distribution monitoring. During the status collection phase, this method synchronously records the abnormal information generated during the distribution process, including interface call abnormalities, data verification failures, and target system response timeouts, providing basic data for subsequent abnormal identification.

7. The real-time monitoring method for master data distribution according to claim 1, characterized in that... The implementation method of step five is as follows: During the execution of multiple distribution tasks corresponding to the same master data distribution monitoring event, the real-time status information collected by each distribution task is summarized and aggregated to form the overall distribution status of the master data change event. Based on this, the master data distribution result is quantitatively determined. During the distribution status aggregation process, this method counts the total number of target systems participating in the master data distribution event and the number of target systems that actually completed distribution, and calculates the master data distribution completion degree based on the statistical results, which is used as the quantitative basis for determining the distribution result. The master data distribution completion degree D is calculated according to the following formula: D = Ns / Nt, where Ns represents the number of target systems that successfully completed master data distribution within the preset time window, and Nt represents the total number of target systems participating in the master data distribution event. When the master data distribution completion degree D is greater than or equal to the preset completion degree threshold θ, the master data distribution event is determined to be in a distribution success state. When the master data distribution completion degree D is less than the completion degree threshold θ, the distribution status is determined to be successful. When the master data distribution event is incomplete or partially failed, the method determines that the master data distribution result is calculable. In the process of determining the distribution result, the method also combines the distribution time to determine whether the master data distribution completion time exceeds the preset time threshold, so that the distribution result determination takes into account both distribution integrity and distribution timeliness.

8. The real-time monitoring method for master data distribution according to claim 1, characterized in that... The implementation method of step six is ​​as follows: Identify and quantify the abnormal situations generated during the master data distribution process, and trigger the corresponding abnormal alarm handling process based on the abnormal analysis results. This ensures that master data distribution abnormalities can be detected and handled in a timely manner. During the distribution abnormality identification process, this method comprehensively evaluates the abnormality in distribution time, the number of distribution failures, and the severity of the abnormality that occur during the master data distribution process, and constructs a master data distribution abnormality score to quantify the level of abnormality in the master data distribution process. The master data distribution abnormality score E is calculated according to the following formula: E = α·T + β·F + γ·S, where T represents the normalized value of master data distribution time, F represents the number of failed master data distribution tasks, S represents the abnormality severity coefficient, and α, β, and γ are preset weight coefficients used to adjust the influence weight of different abnormal factors in the abnormality score. When the master data distribution abnormality score E is greater than or equal to the preset abnormality threshold ε, the current master data distribution event is determined to enter the abnormal alarm state, and the corresponding alarm handling process is triggered. When the value is less than the aforementioned anomaly threshold ε, the current master data distribution event is determined to be in an acceptable state. After the anomaly alarm is triggered, this method associates the anomaly score result with the corresponding master data object, target system, and distribution task information, so that the anomaly alarm information can accurately reflect the location and scope of the anomaly, thereby assisting operation and maintenance personnel in quickly locating the problem and taking handling measures.

9. A real-time monitoring method for master data distribution according to claim 1, characterized in that... The implementation method of step seven is as follows: Monitoring event information, distribution task information, status change information, and distribution result information generated during the master data distribution process are uniformly stored, and the association between master data objects and the distribution process is established, so that the entire master data distribution process has a complete historical record. During the storage of distribution records, this method organizes and indexes the distribution records according to the master data object type and time order, enabling subsequent queries to quickly locate the distribution status of a specific master data object within a specified time range. By establishing a traceable relationship for the master data distribution process, managers can trace the distribution path of master data in different business systems, providing a basis for auditing, verification, and responsibility determination. During the construction of the traceability relationship, this method retains key node information of master data distribution, giving the master data distribution process a clear timeline structure.

10. A real-time monitoring method for master data distribution according to claim 1, characterized in that... The implementation method of step eight is as follows: periodically analyze the historical master data distribution monitoring data, statistically analyze the distribution success rate, frequency of anomalies, and distribution time of different master data objects, and adjust the master data distribution strategy based on the analysis results. This method dynamically optimizes the distribution rules according to the historical distribution anomalies, so as to adjust the distribution method or distribution priority on the target system with frequent anomalies, thereby reducing the impact of anomalies on the overall distribution system. By continuously optimizing the master data distribution strategy, the stability and timeliness of the master data distribution process are continuously improved, forming a sustainable and evolving real-time monitoring mechanism for master data distribution.

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