Personnel data unified management system
Through the designer's unified data management system, the data synchronization method combined with Kafka and timing tasks is used to solve the problems of data isolation and high maintenance costs of business systems, data quality improvement and business collaboration are achieved, and the officialness and reliability of data sources are ensured.
Patent Information
- Application Number
- CN202411766981.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-04
- Publication Date
- 2025-03-14
AI Technical Summary
Currently, the modules of each business system are designed independently, and data connection is difficult, which leads to high data maintenance costs and poor data quality in organization personnel, and the data sources are not unique, making it difficult to determine the official data source, affecting the quality of business system docking.
Design a unified personnel data management system, including incremental synchronization module, data query module, system data maintenance module and data push module. Through the data synchronization method combined with Kafka and timing tasks, the integration and standardization of multi-source heterogeneous data is realized, and the interface is unified to serve various business systems, reduce maintenance costs and improve data quality.
It effectively solves the problems of business system data isolation, high maintenance costs and poor data quality, realizes data interaction and business collaboration between various business systems, reduces the maintenance costs of overall organization personnel data, and makes the data source more official and reliable.
Smart Images
Figure CN119624381A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of microservice technology, and in particular to a unified personnel data management system. Background Art
[0002] At present, each business system module is designed independently, data connection is difficult, and the direction and strength of information construction are difficult to concentrate. The demand for the unification of institutional personnel data and business services is more urgent, mainly for the following reasons: 1. In order to respond to unified requirements and achieve business collaboration between different systems, building a unified institutional personnel database is the basis.
[0003] 2. The accuracy, legitimacy and authority of current business system data are difficult to guarantee. Each independently developed business system has different data sources, making it difficult to guarantee data quality.
[0004] 3. High maintenance costs for institutional personnel and administrators. Institutional personnel data is maintained at multiple locations, resulting in high maintenance costs. With the deepening of informatization, the problem of data redundancy in business systems has become prominent.
[0005] 4. The official data source is not unique, and it is difficult for the business system to determine the source of the organization and personnel data. For a certain group company, the current official standard organization and personnel data mainly comes from the labor and health department and the personnel department of the group company. The personnel department data is mainly for the cadre group, and the labor and health department data is mainly for the employee group. These two data sources have problems such as different maintenance personnel, inconsistent data structure and data content, and old and missing data, which makes it impossible to guarantee the quality of business system docking.
[0006] To solve the above problems, technical personnel in this field urgently need to build a unified institutional personnel master data service platform, study the integration rules of multi-source heterogeneous data, and achieve data standardization and content unification; and serve various business systems through a unified interface, reduce maintenance costs, improve the quality of institutional personnel data, and promote business collaboration between systems. Summary of the invention
[0007] In order to overcome the deficiencies in the background technology, the present invention discloses a unified personnel data management system.
[0008] In order to achieve the above-mentioned invention object, the present invention adopts the following technical scheme: A unified personnel data management system comprises an incremental synchronization module, a data query module, a system data maintenance module and a data push module; the incremental synchronization module is used to receive incremental data from an attendance system and a personnel system, and maintain the business data of this system according to the incremental data; the data query module is used to provide query services for three sets of data, namely attendance data, personnel data and business data, for the business system; the system data maintenance module is used for the business system to actively maintain the business data of this system; the data push module is used to push incremental data to the business system.
[0009] Preferably, the operation method of the incremental synchronization module comprises the following steps: Step S11: receiving incremental data; the system receives the incremental data pushed by the personnel system through Kafka; at the same time, the system also receives the incremental data pushed by the attendance system and saves it in the database of this system; Step S12: Processing incremental data from the personnel system; using Kafka to call the incremental processing program to process the incremental data from the personnel system; Step S13: batch processing of the attendance system incremental data; batch processing of the attendance system incremental data stored in the database of this system through a scheduled task; Step S14: determine the incremental data type; determine whether the incremental data is a newly added node: if it is a newly added node, generate a new node; if it is not a newly added node, proceed to the next step; Step S15: Determine whether it is a modified node; for data of non-newly added nodes, check whether it is a modified node: if it is a modified node, query whether the relevant node exists: if not, generate a new node; if it exists, update the data of the relevant node; if it is not a modified node, proceed to the next step; Step S16: Determine whether it is a deleted node; for a node that is neither newly added nor modified, determine whether it is a deleted node: if it is a deleted node, perform an operation of deleting the relevant node data; Step S17: Refresh the cache and encapsulate the data; after completing the node generation, update or deletion operation, the system refreshes the cache; encapsulates the processed incremental data; Step S18: Push incremental data; push the packaged incremental data to each business system to complete the incremental synchronization process.
[0010] Preferably, the operation method of the data query module comprises the following steps: Step S21: Check the memory cache: When the business system initiates a data request, first check whether the data in the memory cache is available and has not expired; Step S22: Read the memory cache data: If there is valid data in the memory cache: read the data directly from the memory; encapsulate the read data into a response result and return it to the business system; Step S23: request to lock and query the Redis cache; if the data in the memory cache has expired or does not exist: request to lock to prevent other concurrent requests from updating the data at the same time; read the data from the Redis cache; Step S24: Update memory cache and release lock; When data is successfully read from the Redis cache, the data in the memory cache is updated and the lock is released so that other concurrent requests can access the latest data. Step S25: Encapsulate the query results; encapsulate the data read from the Redis cache; Step S26: Check the size of the response body; check whether the size of the encapsulated response body exceeds a preset threshold; Step S27: compress the response body; if the response body is too large, perform compression to reduce bandwidth consumption during network transmission; Step S28: Return the response result; return the compressed response result to the business system that issued the request.
[0011] Preferably, the expiration time of the memory cache is 20 seconds.
[0012] Preferably, the operation method of the system data maintenance module comprises the following steps: Step S31: triggering data maintenance; the business system calls the service of this system to formally trigger the data maintenance process; Step S32: modify the relationship node data; according to business requirements, the system makes necessary modifications to the relationship node data; Step S33: Refresh the cache; after the modification is completed, the system immediately refreshes the cache; Step S34: Pushing updated data; after completing the cache refresh, the system pushes the updated data to each related business system; Step S35: End the data maintenance process; when all updated data are successfully pushed to each business system, the data maintenance process ends.
[0013] Preferably, the operation method of the data push module comprises the following steps: Step S41: Start data push; the system starts to execute the data push process; Step S42: Obtain a push path set; the system obtains a set of all registered push paths; these paths are specific interface paths that are pre-written by the business system and registered with the system, and are used to receive incremental data; Step S43: Save incremental data; the system saves the incremental data that needs to be pushed; these incremental data are usually generated by the addition or update of business data, ensuring that they can be accurately pushed to various business systems later; Step S44: Determine the incremental data situation; the system enters a detection cycle lasting half an hour to determine whether there is new incremental data to be pushed during this period; if there is new incremental data: save these new incremental data; reset the timer and wait for the next half an hour to make another determination; if there is no new incremental data: proceed to the next step; Step S45: Encapsulate and push data; when no new incremental data is detected within half an hour, the system encapsulates all previously saved data that needs to be pushed; executes the push operation, and pushes the encapsulated incremental data to the push path specified in each business system; Step S46: data push is completed; after the push operation is completed, this round of data push process ends.
[0014] Due to the adoption of the above-mentioned technical solution, the present invention has the following beneficial effects: The present invention discloses a unified personnel data management system, which effectively solves the problems of current business system data isolation, high maintenance cost, poor data quality, etc., and provides strong support for the information construction of enterprises; compared with the traditional mode in which each business system maintains its own institutional personnel data, this system breaks down the barriers between various business systems, making data interaction between various business systems possible, which is conducive to business collaboration between various business systems, and at the same time, reduces the maintenance cost of the overall institutional personnel data, and the source of institutional personnel data is more official and reliable.
[0015] The present invention further uses a data synchronization method that combines Kafka with scheduled tasks, which not only ensures the timely update of business data, but also ensures the stability of the business system. Kafka enables the update of the personnel system to be pushed to the business system in a timely manner; the scheduled task uses the idea of batch push, which reduces the number of interactions with the business system, allowing the business system to consume incremental data during a period of time with less load, ensuring the stability of the system.
[0016] The present invention further adopts a combination of memory cache and redis cache to shorten the average response time and improve the response rate; the response body is compressed to reduce the occupation of network bandwidth.
[0017] The present invention can provide three sets of data: business data, attendance data and personnel data, to meet the needs of different business systems. The data organization personnel nodes and relationship nodes are stored separately, so that the system function has a certain scalability. Adding a set of relationship nodes as needed is a new set of organizational data. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] Figure 1 is a system block diagram of the present invention; Figure 2 It is a data structure diagram of the business data node; Figure 3 This is the incremental synchronization process flow chart; Figure 4 It is a flowchart of data query process; Figure 5 Maintain process flow chart for system data; Figure 6 This is a flowchart of the data push process. DETAILED DESCRIPTION
[0019] The present invention can be explained in detail through the following embodiments. The purpose of disclosing the present invention is to protect all technical improvements within the scope of the present invention. In the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "front", "back", "left", "right", etc. indicating directions or positional relationships, they only correspond to the drawings of the present application for the convenience of describing the present invention, and do not indicate or imply that the device or element referred to must have a specific direction.
[0020] For explanation of terminology: Full data: all the data in the database.
[0021] Incremental data: data that is newly added or updated relative to the full data.
[0022] Incremental synchronization: During the data synchronization process, only the data that has changed since the last synchronization is synchronized, rather than fully synchronizing the entire data set.
[0023] Full organization name: The organization name is formed by concatenating the names of its parent organization and its own organization in a hierarchical relationship.
[0024] Node: Nodes are divided into organization nodes, personnel nodes, and relationship nodes. Organization nodes store the basic information of an organization; personnel nodes store the basic information of a person; relationship nodes store the corresponding relationship between a person and an organization, or between organizations.
[0025] Business system: a system that requires this system to provide institutional and personnel data services.
[0026] Local data: data in the system's own database.
[0027] Kafka: A high-throughput distributed publish-subscribe messaging system. The human resources system pushes incremental data to this system through Kafka.
[0028] Personnel system: that is, the organizational personnel system, whose data is mainly aimed at the cadre group.
[0029] Attendance system: also known as the labor and health system, its data is mainly for the employee group.
[0030] Hit: The target data can be found in the memory cache or redis cache without querying the database.
[0031] Embodiment 1, in combination with Figures 1 to 6, that is, the explanation of the above terms, a unified personnel data management system, including an incremental synchronization module, a data query module, a system data maintenance module and a data push module; the incremental synchronization module is used to receive incremental data from the attendance system and the personnel system, and maintain the business data of this system based on the incremental data; the data query module is used to provide query services for three sets of data, namely attendance data, personnel data and business data, for the business system; the system data maintenance module is used for the business system to actively maintain the business data of this system; the data push module is used to push incremental data to the business system.
[0032] For incremental synchronization modules, such as Figure 3 As shown in the figure, the entire flow chart shows that data is pushed from the personnel system and the attendance system to the database of this system, the incremental data pushed by the personnel system is processed by calling the incremental data processing program through Kafka, and the incremental data pushed by the attendance system is processed in batches through scheduled tasks.
[0033] Specifically, by combining the Kafka message queue with the XXL-JOB scheduled task framework, efficient incremental data synchronization is achieved based on the different characteristics of personnel data and attendance data. Kafka is used to process incremental data of the personnel system in real time to ensure the timeliness of information updates; while XXL-JOB is responsible for periodically processing incremental attendance data when the system load is low to reduce the impact on system performance. All incremental data will be saved as historical records before formal consumption, which enhances data security and recovery capabilities.
[0034] According to different types of operations, such as adding, modifying, and deleting, the node data is processed accordingly and the cache is refreshed. The processed incremental data is packaged and pushed to each business system to ensure data consistency and synchronization.
[0035] The operation method of the incremental synchronization module includes the following steps: Step S11: receiving incremental data The system receives incremental data pushed from the human resources system via Kafka.
[0036] At the same time, the system also receives the incremental data pushed by the attendance system and saves it in the database of this system.
[0037] Step S12: Processing incremental data of the personnel system Use Kafka to call the incremental processing program to process the incremental data from the personnel system.
[0038] Step S13: Batch processing of attendance system incremental data The incremental data of the attendance system stored in the database of this system is batch processed through scheduled tasks.
[0039] Step S14: Determine the incremental data type Determine whether the incremental data is a newly added node: If it is a new node, a new node is generated.
[0040] If this is not a new node, go to the next step.
[0041] Step S15: Determine whether it is a modified node For data of non-newly added nodes, check whether it is a modified node: If you are modifying a node, check whether the relevant node exists: If it does not exist, a new node is created.
[0042] If it exists, update the data of the relevant node.
[0043] If the node is not being modified, proceed to the next step.
[0044] Step S16: Determine whether to delete the node For nodes that are neither newly added nor modified, determine whether they are deleted nodes: If the node is deleted, the operation of deleting the related node data is performed.
[0045] Step S17: Refresh cache and encapsulate data After completing the node generation, update, or deletion operation, the system refreshes the cache.
[0046] Encapsulate the processed incremental data.
[0047] Step S18: Push incremental data Finally, the packaged incremental data is pushed to each business system to complete the incremental synchronization process.
[0048] This process ensures that incremental data can be correctly processed and synchronized to each business system that needs to be updated.
[0049] For data query modules, such as Figure 4 As shown in the figure, the whole process describes the processing of business system request data. First, the business system sends a data request to this system, and this system tries to read data from the memory. If the memory cache is not empty, that is, it has not expired, the query result is directly encapsulated and returned to the business system; if the memory cache is expired, a lock is requested and data is read from the Redis cache. The lock is released after refreshing the memory cache. The expiration time of the memory cache is 20 seconds. Finally, the query result is encapsulated and returned to the business system. During the whole process, if the size of the response body exceeds the set value, it will be compressed to improve transmission efficiency.
[0050] Specifically, in order to optimize data query efficiency and reduce database pressure, the system uses a data loading process from the relational database to the Redis cache, which ensures that the agency personnel data is stored in Redis in the form of efficient key-value pairs. In this process, the key uses a predefined string format, and the value contains complete agency personnel information. At the same time, a temporary cache with a validity period of 20 seconds is built in the memory, and the saved data is the same as the redis cache.
[0051] For the processing of incremental data and database updates, the system introduces a delayed refresh mechanism. Specifically, when new incremental data or database changes are detected, Redis will trigger the delayed update process. The mechanism is set to automatically perform a cache refresh operation if no new incremental data appears within 5 consecutive minutes; conversely, if new incremental data is received during this period, the timer is reset and the update operation is delayed until the conditions are met again. This strategy effectively balances the contradiction between resource consumption caused by cache refresh and data consistency requirements, achieves efficient response and batch processing of data changes, and thus optimizes system performance and ensures data accuracy and real-time performance. The memory cache is not refreshed actively, and the validity period of the memory cache is set to 20 seconds. The cache refresh operation will only be triggered when the memory cache has expired and a new request arrives. This mechanism aims to maximize the consistency between the memory cache and the Redis cache, while improving the efficiency and response speed of the system by reducing the number of unnecessary refreshes, and saving memory space when there are no requests for a long time.
[0052] For request processing in high-concurrency scenarios, the system adopts the following strategy: When multiple requests arrive at the same time, first try to access the memory cache. If the memory cache is found to be empty (i.e. expired), the system will lock the first request, and the remaining requests will enter a waiting state. The first request is responsible for retrieving data from the Redis cache and updating the contents of the memory cache on this basis. After completion, the lock is released to allow subsequent requests to continue. If the memory cache is still valid (i.e. not empty), the request is not locked, and each request can directly query the required data from the memory cache, achieving traffic peak reduction.
[0053] This mechanism abandons the traditional method of directly querying the database and instead adopts a data access method that combines memory cache and Redis cache, which significantly improves query efficiency. According to actual test results, query efficiency has increased by about 6 to 7 times. In addition, in order to further optimize network transmission performance, when the response body size exceeds 2KB, the system automatically enables gzip compression technology to compress the response body, effectively reducing the bandwidth resources required for the response and improving the efficiency of data transmission. In this way, not only the response speed of the system is greatly improved, but also the network burden is effectively reduced, improving the user experience.
[0054] The operation method of the data query module includes the following steps: Step S21: Check the memory cache; When the business system initiates a data request, it first checks whether the data in the memory cache is available and has not expired.
[0055] Step S22: Read the memory cache data; If there is valid data in the memory cache: Read data directly from memory.
[0056] The read data is encapsulated into a response result and returned to the business system.
[0057] Step S23: Request to lock and query Redis cache; If the data in the memory cache has expired or does not exist: Request a lock to prevent other concurrent requests from updating data at the same time.
[0058] Read data from the Redis cache.
[0059] Step S24: Update memory cache and release lock; When data is successfully read from the Redis cache: update the data in the memory cache.
[0060] Release the lock so that other concurrent requests can access the latest data.
[0061] Step S25: encapsulate the query results; Encapsulates data read from the Redis cache.
[0062] Step S26: Check the response body size; Check whether the size of the encapsulated response body exceeds the preset threshold.
[0063] Step S27: compress the response body; If the response body is too large, compression is performed to reduce bandwidth consumption during network transmission.
[0064] Step S28: Return the response result; Finally, the compressed response result is returned to the business system that issued the request.
[0065] Through the above steps, it is ensured that each data request of the business system can be processed efficiently, the speed of data query is improved, and resource utilization is optimized, ensuring high performance and low latency of the system. This process design can effectively manage concurrent requests while maintaining the freshness and consistency of data.
[0066] For system data maintenance modules, such as Figure 5 As shown in the figure, after receiving the maintenance request of the business system, the system will verify and parse the request to ensure the legitimacy and validity of the request. Next, according to the parsed request content, the system locates and locks the corresponding data node to prevent concurrent conflicts during the data update process. Then, the system performs specific maintenance operations, such as adding new organization or personnel information, updating existing records, or deleting data that is no longer valid.
[0067] After the maintenance operation is completed, the system refreshes the cache to ensure that the latest data can be quickly accessed and the data consistency is maintained. If changes are involved in relationship nodes, the system will also update these relationship nodes synchronously to ensure that the relationship between institutions and personnel is correct.
[0068] The system returns a response to the business system to inform it of the results of the maintenance operation. In this way, the business system can actively and efficiently maintain its business data in the system, ensuring the accuracy and timeliness of the data, while reducing the impact on other modules and improving the stability and reliability of the overall system.
[0069] The operation method of the system data maintenance module includes the following steps: Step S31: triggering data maintenance; The business system calls the services of this system, formally triggering the data maintenance process.
[0070] Step S32: modifying relationship node data; According to business needs, this system makes necessary modifications to relationship node data.
[0071] During this process, ensure that all modifications follow established rules to maintain data accuracy and consistency.
[0072] Step S33: Refresh the cache; After the modification is completed, the system refreshes the cache immediately.
[0073] This step ensures that the latest data can be accessed quickly, reducing latency.
[0074] Step S34: Pushing updated data; After completing the cache refresh, the system pushes the updated data to each related business system.
[0075] Ensure that all business systems involved can obtain the latest data in a timely manner and maintain data synchronization.
[0076] Step S35: End the data maintenance process; When all updated data is successfully pushed to each business system, the data maintenance process is officially completed.
[0077] Through the above steps, the entire process not only ensures the real-time and consistency of data, but also improves the coordination efficiency between different business systems. Each step is closely linked to provide an efficient and reliable data maintenance mechanism.
[0078] For data push modules, such as Figure 6 As shown, before consuming incremental data, the business system needs to write a specific interface in advance and register the interface path to this system. When this system needs to push data, it first obtains the interface path set and then encapsulates the incremental data to be pushed. The addition and update of business data will generate incremental data. In order to prevent frequent operations on business data over a period of time, which will lead to repeated pushing of incremental data to the business system and cause the stability of the business system to decrease, a delayed push strategy is adopted. If there is no new incremental data to be pushed within half an hour, the push operation will be executed. On the contrary, if new incremental data arrives, this incremental data will be saved and the push operation will be delayed for another half an hour to achieve the purpose of batch push.
[0079] The operation method of the data push module includes the following steps: Step S41: start data push; The system starts executing the data push process.
[0080] Step S42: Obtain a push path set; The system obtains a set of all registered push paths. These paths are specific interface paths that are pre-written by the business system and registered with this system for receiving incremental data.
[0081] Step S43: save incremental data; The system saves the incremental data that needs to be pushed. This incremental data is usually generated by the addition or update of business data, ensuring that it can be accurately pushed to various business systems later.
[0082] Step S44: determine the incremental data situation; The system enters a detection cycle lasting half an hour to determine whether there is any new incremental data that needs to be pushed during this period.
[0083] If there is new incremental data: Save these new incremental data.
[0084] Reset the timer and wait for the next half hour to make another judgment.
[0085] If there is no new incremental data: Go to the next step.
[0086] Step S45: Encapsulate and push data; When no new incremental data is detected within half an hour, the system will package all previously saved data that needs to be pushed.
[0087] Execute the push operation to push the packaged incremental data to the specified push path in each business system.
[0088] Step S46: completing data push; After the push operation is completed, this round of data push process ends.
[0089] Through the above steps, the system can efficiently manage and push incremental data, ensuring that each business system can receive the latest data updates in a timely manner, while avoiding the waste of resources caused by frequent pushes. This process design takes into account the uncertainty and time interval of incremental data generation, and provides a flexible and efficient solution.
[0090] The parts of the present invention that are not described in detail are prior art. It is obvious to those skilled in the art that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention; therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive, and it is intended that all changes that fall within the meaning and scope of equivalent elements are included in the present invention.
Claims
1. A unified personnel data management system, characterized by: It includes incremental synchronization module, data query module, system data maintenance module and data push module; the incremental synchronization module is used to receive incremental data from the attendance system and personnel system, and maintain the business data of this system based on the incremental data; The data query module is used to provide query services for three sets of data: attendance data, personnel data and business data for the business system; the system data maintenance module is used for the business system to actively maintain the business data of the system; the data push module is used to push incremental data to the business system.
2. The personnel data unified management system according to claim 1, characterized in that: The operation method of the incremental synchronization module includes the following steps: Step S11: receiving incremental data; the system receives the incremental data pushed by the personnel system through Kafka; at the same time, the system also receives the incremental data pushed by the attendance system and saves it in the database of this system; Step S12: Processing incremental data from the personnel system; using Kafka to call the incremental processing program to process the incremental data from the personnel system; Step S13: batch processing of the attendance system incremental data; batch processing of the attendance system incremental data stored in the database of this system through a scheduled task; Step S14: determine the incremental data type; determine whether the incremental data is a newly added node: if it is a newly added node, generate a new node; if it is not a newly added node, proceed to the next step; Step S15: Determine whether it is a modified node; for data of non-newly added nodes, check whether it is a modified node: if it is a modified node, check whether the relevant node exists: if not, generate a new node; If it exists, update the data of the relevant node; if it is not a modified node, proceed to the next step; Step S16: Determine whether it is a deleted node; for a node that is neither newly added nor modified, determine whether it is a deleted node: if it is a deleted node, perform an operation of deleting the relevant node data; Step S17: Refresh the cache and encapsulate the data; After completing the node generation, update or deletion operation, the system refreshes the cache; Encapsulate the processed incremental data; Step S18: Push incremental data; push the packaged incremental data to each business system to complete the incremental synchronization process.
3. The personnel data unified management system according to claim 1, characterized in that: The operation method of the data query module includes the following steps: Step S21: Check the memory cache: When the business system initiates a data request, first check whether the data in the memory cache is available and has not expired; Step S22: Read the memory cache data: If there is valid data in the memory cache: read the data directly from the memory; encapsulate the read data into a response result and return it to the business system; Step S23: request to lock and query the Redis cache; if the data in the memory cache has expired or does not exist: request to lock to prevent other concurrent requests from updating the data at the same time; read the data from the Redis cache; Step S24: Update memory cache and release lock; When data is successfully read from the Redis cache; Update the data in the memory cache; release the lock so that other concurrent requests can access the latest data; Step S25: encapsulate the query results; Encapsulate the data read from the Redis cache; Step S26: Check the size of the response body; check whether the size of the encapsulated response body exceeds a preset threshold; Step S27: compress the response body; If the response body is too large, compression is performed to reduce bandwidth consumption during network transmission; Step S28: Return the response result; return the compressed response result to the business system that issued the request.
4. The personnel data unified management system according to claim 3, characterized in that: The expiration time of the memory cache is 20 seconds.
5. The personnel data unified management system according to claim 1, characterized in that: The operation method of the data maintenance module includes the following steps: Step S31: triggering data maintenance; the business system calls the service of this system to formally trigger the data maintenance process; Step S32: modify the relationship node data; according to business requirements, the system makes necessary modifications to the relationship node data; Step S33: Refresh the cache; After the modification is completed, the system refreshes the cache immediately; Step S34: Pushing updated data; after completing the cache refresh, the system pushes the updated data to each related business system; Step S35: End the data maintenance process; when all updated data are successfully pushed to each business system, the data maintenance process ends.
6. The personnel data unified management system according to claim 1, characterized in that: The operation method of the data push module includes the following steps: Step S41: start data push; The system starts to execute the data push process; Step S42: Obtain a push path set; the system obtains a set of all registered push paths; these paths are specific interface paths that are pre-written by the business system and registered with the system, and are used to receive incremental data; Step S43: save incremental data; The system saves the incremental data that needs to be pushed; this incremental data is usually generated by the addition or update of business data, ensuring that it can be accurately pushed to various business systems later; Step S44: Determine the incremental data situation; the system enters a detection cycle lasting half an hour to determine whether there is new incremental data to be pushed during this period; if there is new incremental data: save these new incremental data; reset the timer and wait for the next half an hour to make another determination; if there is no new incremental data: proceed to the next step; Step S45: Encapsulate and push data; when no new incremental data is detected within half an hour, the system encapsulates all previously saved data that needs to be pushed; executes the push operation, and pushes the encapsulated incremental data to the push path specified in each business system; Step S46: completing data push; After the push operation is completed, this round of data push process ends.