Efficient management personnel checking method applied to field of management business

Abstract

The invention relates to an efficient management personnel checking method applied to the field of management business, and the method comprises the steps: obtaining a management personnel information table, and obtaining a structured object through analysis; the analysis data is written into a specified theme in a RabbitMQ message queue; legality verification and filtering are carried out on the analyzed data; storing the analyzed data subjected to verification and filtering to a MySQL database cluster, and constructing a full-text retrieval index; key indexes and calculation parameters are configured, and calculation and multi-dimensional analysis of analysis data are carried out; executing data sorting and calculation logic, and writing a processed data result into a Redis cache server cluster; and carrying out visual display. The method has the advantages that the LRMX data are analyzed, a message retry mechanism is added to RabbitMQ so that data verification and filtering can be conducted more efficiently, the analysis accuracy is improved, and dynamic field expansion is supported.

Description

Technical Field

[0001] The present invention relates to the field of data analysis, and more specifically, to an efficient management personnel inventory method applied in the field of management business. Background Art

[0002] In recent years, with the rapid development of information technology, especially the widespread application of big data and artificial intelligence technology, the management work of enterprise managers is gradually transforming towards informatization, digitalization and intelligence.

[0003] At present, enterprises at all levels report management personnel information forms through a dedicated network in accordance with the unified requirements of the superior management department. The information forms contain detailed information about managers, such as personal basic information, education, work experience, and annual performance. However, this information is difficult for the enterprise itself to use. In actual applications, enterprises often need to build a management personnel management system separately, and re-collect, clean, integrate and analyze data manually. This process consumes a lot of time and resources, and there are problems of duplication of work, low efficiency, and the risk of data omission and information distortion. In addition, the existing management method of management personnel information forms is difficult to intuitively reflect the distribution of management personnel and management personnel gaps, and the depth of data mining is limited. Summary of the invention

[0004] The purpose of the present invention is to address the deficiencies of the prior art and to propose an efficient management personnel inventory method for use in the management business field.

[0005] In the first aspect, an efficient management personnel inventory method applied to the management business field is provided, comprising:

[0006] S1. Obtain a manager information table; the manager information table is an LRMX file;

[0007] S2. Parse the management personnel information table to obtain parsed data, where the parsed data is a structured object;

[0008] S3, writes the parsed data to the specified topic in the RabbitMQ message queue through multi-threaded concurrent technology;

[0009] S4, the application service layer subscribes to a specific topic of the RabbitMQ message queue to perform legitimacy verification and filtering on the parsed data;

[0010] S5. Use thread pool and concurrent lock technology to store the parsed data that has been verified and filtered into the MySQL database cluster;

[0011] S6. Subscribe to the RabbitMQ message queue again, import the parsed data into the ElasticSearch system, and build a full-text search index;

[0012] S7. Configure key indicators and calculation parameters according to business needs, and carry out calculation and multi-dimensional analysis of analytical data;

[0013] S8, execute data sorting and calculation logic, and write the processed data results into the Redis cache server cluster;

[0014] S9. Visualize the processed data results.

[0015] As a preferred embodiment, in S3, a dynamic thread adjustment strategy is introduced to dynamically adjust the number of threads according to the real-time load of the system; and priorities are set for threads to give priority to high-priority tasks.

[0016] Preferably, in S4, the legitimacy check includes: checking whether the data has been captured, checking the data type and parameters, and checking the uniqueness of the data.

[0017] Preferably, in S4, the use of RabbitMQ message queue introduces a message retry mechanism, and the message retry mechanism is: when an exception is encountered in processing a message, the message will be placed in a retry queue and repeatedly attempted to be processed until the message is successfully processed or the preset retry limit is reached; if the message reaches the maximum number of retries in the retry queue and still cannot be processed, the RabbitMQ message queue will automatically route the message to the dead letter queue.

[0018] Preferably, in S6, the ElasticSearch system is configured with index sharding based on the amount of data and the number of cluster nodes.

[0019] Preferably, in S7, the calculation of the parsed data includes calculating the number of job vacancies and recommending matching jobs based on the original data; and the multidimensional analysis includes analyzing the job grade structure, gender ratio, educational background distribution and age structure based on the original data.

[0020] Preferably, in S9, the calculation results are extracted from the Redis cache server cluster, the result files are exported and the data is encrypted using encryption technology, and then the encrypted result files are uploaded to the file server; the encryption technology includes SM2 asymmetric encryption and SM4 symmetric encryption.

[0021] In a second aspect, an efficient management personnel inventory system applied to the management business field is provided, which is used to execute any method described in the first aspect, including:

[0022] An acquisition module is used to acquire a management personnel information table; the management personnel information table is an LRMX file;

[0023] A parsing module, used to parse the management personnel information table to obtain parsed data, where the parsed data is a structured object;

[0024] The writing module is used to write the parsed data to the specified topic in the RabbitMQ message queue through multi-threaded concurrent technology;

[0025] The verification module is used by the application service layer to subscribe to specific topics of the RabbitMQ message queue and implement legitimacy verification and filtering on the parsed data;

[0026] The storage module is used to store the verified and filtered parsed data into the MySQL database cluster through thread pool and concurrent lock technology;

[0027] Build a module to subscribe to the RabbitMQ message queue again, import the parsed data into the ElasticSearch system, and build a full-text search index;

[0028] The calculation module is used to configure key indicators and calculation parameters according to business needs, and to carry out calculation and multi-dimensional analysis of analytical data;

[0029] The execution module is used to execute data sorting and calculation logic, and write the processed data results into the Redis cache server cluster;

[0030] The display module is used to visualize the processed data results.

[0031] According to a third aspect, a computer storage medium is provided, wherein a computer program is stored in the computer storage medium; when the computer program is executed on a computer, the computer executes any method described in the first aspect.

[0032] In a fourth aspect, an electronic device is provided, including:

[0033] Memory, used to store computer programs;

[0034] A processor is used to execute the computer program to implement any method as described in the first aspect.

[0035] The beneficial effects of the present invention are:

[0036] 1. The present invention parses LRMX data and adds a message retry mechanism to RabbitMQ to more efficiently perform data verification and filtering, thereby improving the accuracy of parsing and supporting dynamic field expansion.

[0037] 2. The present invention optimizes the shard indexing of ElasticSearch data organization, thereby improving query efficiency, especially in multi-field joint query scenarios.

[0038] 3. The present invention supports efficient batch import of large-scale data, realizes task scheduling and efficient concurrent processing through dynamic thread adjustment and thread priority setting, and improves the import speed.

[0039] 4. The present invention develops mobile applications, designs interactively configurable indicators to customize personalized displays, and ultimately displays visual data on mobile terminals, assisting business personnel in conveniently performing business analysis related to management personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A flow chart of an efficient management personnel inventory method applied to the management business field provided by an embodiment of the present invention;

[0041] Figure 2 A flowchart of data import analysis and display on a mobile terminal provided by an embodiment of the present invention;

[0042] Figure 3 A schematic diagram of the working principle of the RabbitMQ message queue provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The present invention is further described below in conjunction with embodiments. The description of the following embodiments is only used to help understand the present invention. It should be noted that for ordinary persons in the art, without departing from the principle of the present invention, the present invention can also be modified in some ways, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

[0044] Embodiment 1:

[0045] In order to solve the problems of the prior art, this application provides an efficient management personnel inventory method applied to the management business field. By parsing and extracting the management personnel information table (LRMX file), a management personnel information database is quickly constructed, and an efficient index is established to improve the query speed. This method provides a convenient tool for business personnel to carry out management personnel inventory work through the combination of data analysis and graphical output, and provides a scientific auxiliary reference basis for the selection and use of management personnel.

[0046] Specifically, Figure 1 As shown, the efficient management personnel inventory method applied to the management business field includes:

[0047] S1. Obtain a manager information table; the manager information table is an LRMX file.

[0048] S2. Parse the management personnel information table to obtain parsed data, where the parsed data is a structured object.

[0049] Specifically, this application first develops a unified and standardized structured data model based on the structure and multi-dimensional attributes of the LRMX file, and then deeply parses the LRMX file based on the DOM (Document Object Model) parsing technology, and converts it into a structured object according to the structured data model defined in this application.

[0050] For example, this application builds a detailed data information structure based on the expert knowledge base, which comprehensively covers the multi-dimensional information of managers, such as basic information of managers, job sequence, annual performance, etc. On this basis, the multi-dimensional attribute association analysis method of data assets is adopted to conduct in-depth and detailed mining and analysis of manager data.

[0051] In addition, DOM-based parsing of LRMX files is performed, specifically including: first, the entire LRMX document is read into memory, and a tree-structured document object model is constructed, which contains all elements, attributes, and text content of the LRMX document. Subsequently, each node can be accessed and operated by traversing this tree structure. It can obtain information such as the name, attributes, and text content of the node. In this way, the LRMX document can be randomly accessed, the node access speed can be improved, and the content or structure of the node can be easily modified.

[0052] S3. Write the parsed data to the specified topic in the RabbitMQ message queue through multi-threaded concurrent technology.

[0053] In S3, it is necessary to efficiently write to the RabbitMQ message queue through multi-threaded concurrency, and implement task scheduling and efficient concurrent processing through thread pools. In order to further optimize this process, the following improvement scheme is implemented, which mainly involves thread management strategy and core number definition.

[0054] Improvements to thread management strategies include:

[0055] (1) Dynamic thread adjustment: Traditional multithreading processing is often based on a fixed number of threads for concurrent processing, but this approach may not fully utilize system resources or cause resource competition when facing different loads. Therefore, a dynamic thread adjustment strategy is introduced to dynamically adjust the number of threads according to the real-time load of the system. When the system load is high, the number of threads is automatically increased to increase the processing speed; when the load is reduced, the number of threads is reduced to save resources.

[0056] (2) Thread priority setting: When processing a large number of concurrent requests, the importance and urgency of different requests may vary. Therefore, setting priorities for threads ensures that high-priority tasks are processed first, thereby improving the system's response speed and overall throughput.

[0057] Different thread calculation formulas are used to implement thread tasks of different scenarios encountered in manager information management:

[0058] For CPU-intensive computing scenarios, multithreading aims to improve the utilization of multi-core CPUs. In theory, 8 threads are the best for an 8-core CPU, and more threads are ineffective. If the number of threads is twice the number of cores, the CPU core will be fully loaded due to heavy computing tasks. Therefore, in CPU-intensive scenarios, it is more appropriate to set the number of threads = the number of CPU cores. In this article, Num t =Num c +1, where Num t is the number of threads, Num c The number of CPU cores is used to achieve optimal utilization, to prevent threads from occasionally being blocked due to memory page failures, etc., and to ensure that CPU clock cycles are not wasted.

[0059] For IO-intensive computing scenarios, the maximum number of threads is usually set to be much larger than the number of CPU cores, because the IO read and write speeds are slower than the CPU operation speeds. If the number of threads is set too small, CPU resources may not be fully utilized, resulting in waste. On the contrary, if a larger number of threads is set, when some threads are waiting for IO operations, they will not occupy CPU resources, while other threads can continue to use the CPU to perform other tasks, and the two will not interfere with each other. In this way, the number of waiting tasks in the task queue will be reduced, thereby achieving efficient use of resources. Usually set Num t =Num c / (1-ɑ), where ɑ is the blocking coefficient. The computationally intensive ɑ value is 0, and the IO intensive ɑ value is close to 1. Generally, ɑ∈[0.8,0.9].

[0060] S4. The application service layer subscribes to specific topics of the RabbitMQ message queue and performs legitimacy verification and filtering on the parsed data to ensure the accuracy and integrity of the data.

[0061] In S5, the service layer will parse the messages received from the RabbitMQ queue, verify and clean the data according to the preset rules and standards, remove duplicate, erroneous or invalid information, and thus form structured data results that meet business needs. The legitimacy verification of the parsed data includes:

[0062] (1) Security verification: The signature field sign generates a signature summary by taking all messages as input parameters, and then symmetric encryption is performed using the national encryption algorithm SM4. The backend then intercepts and verifies the data using the interceptor filter to verify whether the data has been captured.

[0063] (2) Parameter validity check: Check the data type and parameters, and issue error prompts for non-existent or illegal data.

[0064] (3) Uniqueness verification: Use distributed locks and database unique key values ​​to verify the uniqueness of data and prevent duplicate data from being stored in the database.

[0065] S5. Through thread pool and concurrent lock technology, the parsed data that has been verified and filtered is stored in the MySQL database cluster.

[0066] S6. Subscribe to the RabbitMQ message queue again, import the parsed data into the ElasticSearch system, and build a full-text search index.

[0067] S7. Configure key indicators and calculation parameters according to business needs, and carry out calculations and multi-dimensional analysis of analytical data.

[0068] S8. Execute data sorting and calculation logic, and write the processed data results into the Redis cache server cluster.

[0069] S9. Visualize the processed data results.

[0070] Embodiment 2:

[0071] Based on Example 1, Example 2 of the present application provides a more specific method for efficiently counting management personnel in the management business field, including:

[0072] S1. Obtain a manager information table; the manager information table is an LRMX file.

[0073] S2. Parse the management personnel information table to obtain parsed data, where the parsed data is a structured object.

[0074] S3. Write the parsed data to the specified topic in the RabbitMQ message queue through multi-threaded concurrent technology.

[0075] S4. The application service layer subscribes to a specific topic of the RabbitMQ message queue and performs legitimacy verification and filtering on the parsed data.

[0076] In S4, in order to improve the robustness and fault tolerance of the system, this application improves the use of RabbitMQ. Specifically, this application introduces a message retry mechanism. When the service layer encounters an exception or failure when processing data, it will put the message back into a special retry queue and try to process it again later. This process will be repeated until the message is successfully processed or the preset retry limit is reached.

[0077] At the same time, this application also configures a dead letter queue to handle messages that cannot be successfully processed. When a message cannot be processed after reaching the maximum number of retries in the retry queue, RabbitMQ will automatically route it to the dead letter queue. The service layer will regularly monitor the dead letter queue and analyze and process the messages stored in it, such as recording logs, sending alarms, or performing manual intervention.

[0078] For example, Figure 3 As shown, the RabbitMQ queue adopts a mirror mode built with 3 nodes to ensure that 100% of the data is not lost. That is, all queues will be set up as mirror queues, and the queues will be copied to each node, and the status of each node is consistent. The advantage of this is that if the machine of any node goes down, other nodes still contain the complete data of this queue, and other consumers can go to other nodes to consume data.

[0079] Figure 3 The arbitration queue is mainly used to solve some key defects of the mirror queue, such as the master-slave synchronization is not strongly consistent, and there is a risk of data loss. It adopts a master-slave architecture and implements strong consistency synchronization between master and slave data through the Raft protocol.

[0080] Figure 3 Port 5671 is the port where RabbitMQ connects to the client and uses the TLS / SSL protocol to encrypt the transmitted data. Port 5672 is the default non-secure port of the AMQP protocol, used for non-encrypted communication between the client and the RabbitMQ server. Port 15672 is the port used by the RabbitMQ management interface, which uses the HTTP protocol by default, allowing users to monitor and manage the RabbitMQ server through the Web interface.

[0081] The message processing flow is: if the consumer consumes the message, but the message processing fails, the failed message will be sent to the retry queue for retry operation; if it still fails in the retry queue and cannot be processed successfully, the message will eventually be sent to the dead letter queue. This processing flow can effectively deal with the situation of message processing failure. You can implement the retry queue by setting the TTL (Time To Live) attribute of the message. When the message fails to be consumed in the target queue, you can resend the message to the bound retry queue and set a certain TTL, that is, the retry time interval. If the message is not consumed successfully in the retry queue, it will be sent to the retry queue again until the set number of retries is reached. If the number of retries reaches the upper limit, the message will be discarded or sent to the dead letter queue. You can implement the dead letter queue by setting the queue's attributes and using DLX (Dead Letter Exchange). When the message cannot be consumed in the target queue for some reason, you can send the message to the bound dead letter queue. When declaring a queue, you can set the queue's x-dead-letter-exchange and x-dead-letter-routing-key attributes to specify the exchanger and routing key of the dead letter queue. When a message becomes a dead letter, it will be sent to the specified dead letter queue.

[0082] S5. Through thread pool and concurrent lock technology, the parsed data that has been verified and filtered is stored in the MySQL database cluster.

[0083] Specifically, after the structured data results that meet the standards are formed, the service layer will use thread pool technology and concurrent lock technology to efficiently and securely store this data in the MySQL database cluster. Through reasonable thread pool configuration and concurrent lock strategy, the service layer can make full use of system resources, improve the efficiency of data processing and storage, and ensure data consistency and security.

[0084] S6. Subscribe to the RabbitMQ message queue again, import the parsed data into the ElasticSearch system, and build a full-text search index.

[0085] When using ElasticSearch in S6 for data retrieval, indexes can be created by data type. If the default sharding settings are used when creating an index with a small amount of data, data query will take a lot of time to create, transmit, and wait for responses. In addition, when there are few cluster nodes and many shards, multiple shards of the same index will be concentrated on the same node, which will affect data writing and query performance.

[0086] This application performs index sharding on ElasticSearch based on the amount of data and the number of cluster nodes. The specific optimization measures are as follows:

[0087] Set the index business data volume of a single ElasticSearch shard to no more than vGB, and set the number of initial primary shards According to the characteristic that ElasticSearch will evenly distribute the shards to each node after the index sharding is successful, the number of primary shards that need to be placed on a node is obtained. Among them, nodeArr[] is the node array after verification, the corresponding position can be set to 1, and Num is the sharding result calculated according to the sharding strategy of this article.

[0088] X is the maximum number of shards of the same index allowed for node i. When N>X, take N=X. Finally, modify the number of available nodes in the cluster based on N. When the sum of the number of existing shards S_i and N of a node in the cluster is greater than or equal to the upper limit set by the parameter total_shards_per_node, the current node is considered unavailable, and the value of the corresponding position in the node array nodeArr[] is updated to 0. Finally, the number of available nodes in the cluster is obtained from the updated array. The number of available nodes is

[0089] The number of shards of a node is related to the number of available nodes in the cluster. This article takes into account the subsequent expansion of data and the fact that each primary shard needs to set up one or more replica shards to ensure availability. Shards (including primary shards and replica shards) are created according to three times the number of nodes. Therefore, the primary shard result can be roughly obtained based on the number of available nodes. Where R represents the number of replica shards created for each primary shard.

[0090] Taking into account the number of available nodes, the amount of business data indexed, and the degree of expansion of the indexed data, the number of nodes and the amount of business data indexed are linearly weighted and divided by an expansion coefficient, and the final number of primary shards Num = (k1×Num1+k2×Num2)×(1 / e) can be obtained. Among them, k1 and k2 are weight coefficients, k1+k2=1, and e is the expansion coefficient of the indexed data volume, e∈(0,1]. For indexes with large data expansion, the value of e can be appropriately smaller, but the value of e should satisfy that after the final number of shards is evenly distributed to each node, the allocated data of the index on the node should be less than or equal to the maximum number of shards X allowed for the same index by node i.

[0091] S7. Configure key indicators and calculation parameters according to business needs, and carry out calculations and multi-dimensional analysis of analytical data.

[0092] Specifically, S7 has built a core computing service for management personnel information, which performs standardization, data calculation and multi-dimensional analysis on data that has been initially filtered and verified. Standardization of data includes a series of operations such as converting data such as age and years of service into a unified unit of measurement (such as years), and converting personnel ranks into quantifiable values ​​or classification labels. Data calculation includes a series of operations such as calculating the number of job vacancies based on the original data and recommending matching positions. Multi-dimensional analysis analyzes the rank structure, gender ratio, educational distribution, age structure, etc. based on the original data. For example, the rank structure can be combined with factors such as organization and position to analyze the distribution characteristics of employees of different ranks, such as the proportion of senior, middle and grassroots employees, to provide numerical support for subsequent visualization.

[0093] S8. Execute data sorting and calculation logic, and write the processed data results into the Redis cache server cluster.

[0094] S9. Visualize the processed data results.

[0095] Specifically, S9 extracts the calculation results from the Redis cache, exports the results and applies encryption technology to encrypt the data, and then uploads the encrypted result file to the file server. The encryption algorithm uses a domestically controlled national secret algorithm, which includes SM2 asymmetric encryption, which is used for data encryption and data signing in the interface. It has high security strength and fast computing speed. In addition, SM4 symmetric encryption is used for file encryption, which can replace the international encryption algorithm of DES / AES. It has the same key length and packet length as the AES algorithm. The encryption algorithm is secure and complies with the specifications of information security level protection.

[0096] In addition, S9 develops a mobile app that can be used across terminals. The encrypted data analysis report is downloaded through the manager download service, and the encrypted result analysis report is securely transmitted to the mobile terminal using a dedicated encrypted USB disk medium. The built-in decryption algorithm of the mobile app automatically decrypts the file content, and displays the configured manager information content on the mobile terminal in the form of data visualization, assisting business personnel in analysis and decision-making.

[0097] like Figure 2 As shown, first import a batch of manager information from the manager table LRMX file into the manager management system. The manager management system parses, filters, and calculates the DOM node data, and then generates JSON format data. The JSON format data is pushed to the APP end through the network protocol Webdav. After receiving the relevant data, the APP end parses and displays it.

[0098] It should be noted that the parts in this embodiment that are the same or similar to those in Embodiment 1 can be referenced to each other and will not be described in detail in this application.

[0099] Embodiment 3:

[0100] Based on Examples 1 and 2, Example 3 of the present application provides an efficient management personnel inventory system applied to the management business field, including:

[0101] An acquisition module is used to acquire a management personnel information table; the management personnel information table is an LRMX file;

[0102] A parsing module, used to parse the management personnel information table to obtain parsed data, where the parsed data is a structured object;

[0103] The writing module is used to write the parsed data to the specified topic in the RabbitMQ message queue through multi-threaded concurrent technology;

[0104] The verification module is used by the application service layer to subscribe to specific topics of the RabbitMQ message queue and implement legitimacy verification and filtering on the parsed data;

[0105] The storage module is used to store the verified and filtered parsed data into the MySQL database cluster through thread pool and concurrent lock technology;

[0106] Build a module to subscribe to the RabbitMQ message queue again, import the parsed data into the ElasticSearch system, and build a full-text search index;

[0107] The calculation module is used to configure key indicators and calculation parameters according to business needs, and to carry out calculation and multi-dimensional analysis of analytical data;

[0108] The execution module is used to execute data sorting and calculation logic, and write the processed data results into the Redis cache server cluster;

[0109] The display module is used to visualize the processed data results.

[0110] Specifically, the system provided in this embodiment is a system corresponding to the method provided in Embodiments 1 and 2. Therefore, the parts in this embodiment that are the same or similar to Embodiments 1 and 2 can be referenced to each other and will not be repeated in this application.

[0111] In summary, the efficient manager inventory method applied to the manager management business field provided by this application can directly parse the data in the manager information table, batch import the data warehouse and visualize the data analysis results to support multi-dimensional analysis related to manager management, and help enterprises realize the digital transformation of manager management. This method will help improve information processing efficiency, reduce human intervention, ensure the integrity and accuracy of data, and thus provide strong support for the company's manager inventory and decision-making.

[0112] The information table data involved in this application have been obtained with full consent and authorization, and the collection, use and processing of relevant information comply with the relevant laws, regulations and standards of relevant countries and regions.

Claims

1. An efficient management personnel inventory method applied to the management business field, characterized in that: include: S1. Obtain a manager information table; the manager information table is an LRMX file; S2. Parse the management personnel information table to obtain parsed data, where the parsed data is a structured object; S3, writes the parsed data to the specified topic in the RabbitMQ message queue through multi-threaded concurrent technology; S4, the application service layer subscribes to a specific topic of the RabbitMQ message queue to perform legitimacy verification and filtering on the parsed data; S5. Use thread pool and concurrent lock technology to store the parsed data that has been verified and filtered into the MySQL database cluster; S6. Subscribe to the RabbitMQ message queue again, import the parsed data into the ElasticSearch system, and build a full-text search index; S7. Configure key indicators and calculation parameters according to business needs, and carry out calculation and multi-dimensional analysis of analytical data; S8, execute data sorting and calculation logic, and write the processed data results into the Redis cache server cluster; S9. Visualize the processed data results.

2. The efficient management personnel inventory method applied to the management business field according to claim 1 is characterized in that: In S3, a dynamic thread adjustment strategy is introduced to dynamically adjust the number of threads according to the real-time load of the system; and priorities are set for threads to give priority to high-priority tasks.

3. The efficient management personnel inventory method applied to the management business field according to claim 2 is characterized in that: In S4, the legality check includes: checking whether the data has been captured, checking the data type and parameters, and checking the uniqueness of the data.

4. The efficient management personnel inventory method applied to the management business field according to claim 3 is characterized in that: In S4, the use of RabbitMQ message queue introduces a message retry mechanism. The message retry mechanism is as follows: when an exception is encountered in message processing, the message will be placed in a retry queue and repeatedly attempted to be processed until the message is successfully processed or the preset retry limit is reached; if the message reaches the maximum number of retries in the retry queue and still cannot be processed, the RabbitMQ message queue will automatically route the message to the dead letter queue.

5. The efficient management personnel inventory method applied to the management business field according to claim 4 is characterized in that: In S6, the ElasticSearch system is indexed and partitioned based on the amount of data and the number of cluster nodes.

6. The efficient management personnel inventory method applied to the management business field according to claim 5 is characterized in that: In S7, the calculation of the parsed data includes calculating the number of job vacancies and recommending matching jobs based on the original data; and the multidimensional analysis includes analyzing the job grade structure, gender ratio, educational background distribution and age structure based on the original data.

7. The efficient management personnel inventory method applied to the management business field according to claim 6 is characterized in that: In S9, the calculation results are extracted from the Redis cache server cluster, the result files are exported and the data is encrypted using encryption technology, and then the encrypted result files are uploaded to the file server; the encryption technology includes SM2 asymmetric encryption and SM4 symmetric encryption.

8. An efficient management personnel inventory system applied to management business field, characterized in that: Used to perform the method according to any one of claims 1 to 7, comprising: An acquisition module is used to acquire a management personnel information table; the management personnel information table is an LRMX file; A parsing module, used to parse the management personnel information table to obtain parsed data, where the parsed data is a structured object; The writing module is used to write the parsed data to the specified topic in the RabbitMQ message queue through multi-threaded concurrent technology; The verification module is used by the application service layer to subscribe to specific topics of the RabbitMQ message queue and implement legitimacy verification and filtering on the parsed data; The storage module is used to store the verified and filtered parsed data into the MySQL database cluster through thread pool and concurrent lock technology; Build a module to subscribe to the RabbitMQ message queue again, import the parsed data into the ElasticSearch system, and build a full-text search index; The calculation module is used to configure key indicators and calculation parameters according to business needs, and to carry out calculation and multi-dimensional analysis of analytical data; The execution module is used to execute data sorting and calculation logic, and write the processed data results into the Redis cache server cluster; The display module is used to visualize the processed data results.

9. A computer storage medium, characterized in that: The computer storage medium stores a computer program; when the computer program is executed on a computer, the computer executes any one of the methods described in claims 1 to 7.

10. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the method according to any one of claims 1 to 7.

Images