Human resource data distributed task processing method and system

Abstract

The invention relates to the technical field of efficient human resource management, and discloses a human resource data distributed task processing method, which comprises the following steps of: 1, segmenting and distributing data, segmenting human resource data according to a set rule, and carrying out parallel processing through a plurality of nodes in a distributed system; 2, introducing an intelligent scheduling algorithm, dynamically allocating tasks according to the real-time processing capability of each node, and ensuring the load balance of the system; step 3, adopting a distributed transaction management mechanism to ensure the consistency and accuracy when the data is processed among a plurality of nodes; and 4, after processing of each node is completed, results are summarized to the central node for comprehensive analysis, and a human resource report is generated or further data mining is carried out. Compared with the prior art, the method has the advantages that the efficiency of processing human resource data can be improved, and data synchronization is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of efficient human resource management, and in particular to a method and system for distributed task processing of human resource data. Background Art

[0002] As the scale of enterprises expands, the human resources data within the enterprise grows rapidly, including employee information, attendance records, performance evaluation and salary management data. When using traditional centralized processing methods to process huge amounts of human resources data, various difficulties will be faced, such as slow processing speed, high load, and difficulty in data synchronization, which has a significant negative impact on the efficiency of human resources management. Summary of the invention

[0003] 1. Technical issues to be solved

[0004] The technical problem to be solved by the present invention is that currently when a large amount of human resource data is processed for human resource management, the processing efficiency is low and data synchronization is difficult.

[0005] (II) Technical solution

[0006] To solve the above technical problems, the present invention provides a technical solution: a method for distributed task processing of human resource data, comprising: step 1: dividing and allocating data, dividing the human resource data according to set rules, and processing them in parallel through multiple nodes in a distributed system;

[0007] The number of nodes is N, the data is divided into L segments, the node set is v (v∈{1, 2, 3, ···, N}), and the set of segmented data segments is e (e∈{1, 2, 3, ···, L});

[0008] Step 2: Introduce an intelligent scheduling algorithm to dynamically allocate tasks based on the real-time processing capabilities of each node to ensure system load balancing;

[0009] The service processing demand matrix of each node is T, which is an N-row and N-column matrix. The i-th row and j-th column represent the service processing demand from node i to node j. Only D node pairs with non-zero service processing demand are considered, and the service processing demand between each node pair is d (d∈{1, 2, 3, ···, D});

[0010] For each d, calculate the set P of all paths from source node i to destination node j i、j , and from the set of all paths P i、j Select K paths from the set to form a set of candidate paths;

[0011]

[0012] e∈E

[0013] Where Z is the maximum link utilization, h d is the demand for business processing demand d, c e is the link capacity of link e, δ e，d，p Indicates whether link e belongs to the alternative path P selected under the service processing demand d, P is the alternative path, P d is a set of candidate paths, x d，p is a two-dimensional variable. When the business processing demand is d, select P d Step 3: Use distributed transaction management mechanism to ensure the consistency and accuracy of data when it is processed among multiple nodes; Step 4: After each node completes the processing, the results are aggregated to the central node for comprehensive analysis, generating human resources reports or conducting further data mining.

[0014] As an improvement, the setting rules include department, position and time.

[0015] As an improvement, in step one, each node is responsible for processing the corresponding part of the data.

[0016] As an improvement, step three also includes a distributed lock technology, which is used to prevent data conflicts.

[0017] As an improvement, a distributed task processing system for human resources data includes: a distributed computing cluster: composed of multiple computing nodes and storage nodes, supporting horizontal expansion, and the number of nodes can be flexibly increased or decreased according to data processing needs; a task management module: responsible for the creation, allocation, monitoring and scheduling of tasks to ensure that tasks are executed according to the predetermined plan; a data interaction module: realizes data exchange and synchronization between nodes, adopts an efficient data transmission protocol, and reduces communication delays; a user interface module: the user interface allows users to submit data processing requests and view processing progress and results.

[0018] As an improvement, the user interface module further includes a log module, which is used to record access records of different users, and the access records include time data.

[0019] As an improvement, the user interface module also includes a permission module, which can be used to manage and assign permissions to different users.

[0020] As an improvement, the permission module includes a database, and a remote configuration system and an alarm system are provided on the permission module. The remote configuration system is used for management personnel to remotely operate and configure permissions, and the alarm system can set a threshold and send an alarm to the management personnel when the erroneous access reaches the threshold.

[0021] (III) Beneficial effects

[0022] Compared with the prior art, the advantages of the present invention are: segmentation and distribution of data, introduction of intelligent scheduling algorithm, adoption of distributed transaction management mechanism and generation of human resource reports. After the data is segmented, different parts of the data are synchronously processed by different nodes and finally summarized, which can improve the efficiency of human resource data processing and shorten the processing time. At the same time, different nodes are responsible for different tasks, which can reduce the system load. The distributed transaction management mechanism can ensure the consistency and accuracy of data when it is processed between multiple nodes, and can achieve efficient synchronization and consistency maintenance of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 The present invention is a method flow chart of a method and system for distributed task processing of human resource data.

[0024] Figure 2 It is a system principle diagram of a method and system for distributed task processing of human resource data of the present invention.

[0025] Figure 3 It is a system principle diagram of a permission module of a method and system for distributed task processing of human resource data of the present invention. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0027] A method for distributed task processing of human resource data comprises: step 1: dividing and allocating data, dividing the human resource data according to a set rule, and processing in parallel through multiple nodes in a distributed system; the number of nodes is N, the data is divided into L segments, the node set is v (v∈{1, 2, 3, ···, N}), and the set of divided data segments is e (e∈{1, 2, 3, ···, L}); step 2: introducing an intelligent scheduling algorithm, dynamically allocating tasks according to the real-time processing capability of each node to ensure system load balancing; the business processing demand matrix of each node is T, T is a matrix with N rows and N columns, the i-th row and j-th column represent the business processing demand from node i to node j, only D node pairs with non-zero business processing demand are considered, and the business processing demand between each node pair is d (d∈{1, 2, 3, ···, D}); for each d, calculate the set P of all paths from source node i to destination node j i、j , and from the set of all paths P i、j Select K paths from the set to form a set of candidate paths;

[0028]

[0029] e∈E

[0030] Where Z is the maximum link utilization, h d is the demand for business processing demand d, c e is the link capacity of link e, δ e，d，p Indicates whether link e belongs to the alternative path P selected under the service processing demand d, P is the alternative path, P d is a set of candidate paths, x d，p is a two-dimensional variable. When the business processing demand is d, select P d When the path P in the process is used to process data, it takes 1, otherwise it takes 0; Step 3: A distributed transaction management mechanism is adopted to ensure the consistency and accuracy of data when it is processed among multiple nodes, and also includes distributed lock technology, which is used to prevent data conflicts; Step 4: After the processing of each node is completed, the results are summarized to the central node for comprehensive analysis, and a human resources report is generated or further data mining is performed.

[0031] A distributed task processing system for human resources data includes: a distributed computing cluster: composed of multiple computing nodes and storage nodes, supporting horizontal expansion, and the number of nodes can be flexibly increased or decreased according to data processing needs; a task management module: responsible for the creation, allocation, monitoring and scheduling of tasks, ensuring that tasks are executed according to a predetermined plan; a data interaction module: realizing data exchange and synchronization between nodes, using an efficient data transmission protocol, and reducing communication delays; a user interface module: the user interface allows users to submit data processing requests, view processing progress and results, and the user interface module also includes a log module, which is used to record access records of different users, and the access records contain time data. The user interface module also includes a permission module, which can be used to manage and allocate permissions for different users.

[0032] The permission module includes a database, and a remote configuration system and an alarm system are provided on the permission module. The remote configuration system is used for management personnel to remotely operate and configure permissions. The alarm system can set a threshold, and an alarm can be issued to the management personnel when the erroneous access reaches the threshold. The threshold defaults to 3 times. When the number of erroneous accesses by the access personnel reaches 3 times, the management personnel can receive an alarm message.

[0033] In the specific implementation of the present invention, human resource data is first divided according to the rules of department, position and time, and then different parts of the data are processed in parallel through multiple nodes in the distributed system. Each node is responsible for processing a part of the data, which can improve the overall processing speed. Then, tasks are dynamically allocated according to the real-time processing capabilities of each node to ensure system load balancing and avoid the situation where some nodes are overloaded while other nodes are idle. A distributed transaction management mechanism is used in the processing process to ensure the consistency and accuracy of data when it is processed among multiple nodes. The data processed by each node can be summarized to the central node for comprehensive analysis, generate human resource reports or conduct further data mining, and provide support for managers to make decisions.

[0034] During the operation of the system, the distributed computing cluster composed of multiple computing nodes and storage nodes supports horizontal expansion, and can flexibly increase or decrease the number of nodes according to data processing requirements. The task management module can ensure that the task is executed according to the predetermined plan and is responsible for various operations of the task. The data interaction module can ensure the efficiency of data exchange and synchronization between nodes. The user interface module can manage and allocate user permissions, and record access logs when users access, allowing users to submit data processing requests and view processing progress and results.

[0035] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0036] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0037] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A method for distributed task processing of human resources data, characterized in that: include: Step 1: Split and distribute data. Split the human resources data according to the set rules and process them in parallel through multiple nodes in the distributed system. The number of nodes is N, the data is divided into L segments, the node set is v (v∈{1, 2, 3, ···, N}), and the set of segmented data segments is e (e∈{1, 2, 3, ···, L}); Step 2: Introduce an intelligent scheduling algorithm to dynamically allocate tasks based on the real-time processing capabilities of each node to ensure system load balancing; The service processing demand matrix of each node is T, which is an N-row and N-column matrix. The i-th row and j-th column represent the service processing demand from node i to node j. Only D node pairs with non-zero service processing demand are considered, and the service processing demand between each node pair is d (d∈{1, 2, 3, ···, D}); For each d, calculate the set P of all paths from source node i to destination node j i、j , and from the set of all paths P i、j Select K paths from the set to form a set of candidate paths; e∈E; Where Z is the maximum link utilization, h d is the demand for business processing demand d, c e is the link capacity of link e, δ e，d，p Indicates whether link e belongs to the alternative path P selected under the service processing demand d, P is the alternative path, P d is a set of candidate paths, x d，p is a two-dimensional variable. When the business processing demand is d, select P d When the path P in the process is used to process data, it takes 1, otherwise it takes 0; Step 3: Use a distributed transaction management mechanism to ensure the consistency and accuracy of data when it is processed across multiple nodes; Step 4: After processing at each node, the results are aggregated to the central node for comprehensive analysis, generating a human resources report or conducting further data mining.

2. A method for distributed task processing of human resources data according to claim 1, characterized in that: The setting rules include department, position and time.

3. A method for distributed task processing of human resources data according to claim 2, characterized in that: In step one, each node is responsible for processing the corresponding part of the data.

4. The method for distributed task processing of human resources data according to claim 1, characterized in that: Step three also includes distributed lock technology, which is used to prevent data conflicts.

5. A distributed task processing system for human resources data according to claim 1, characterized in that: include: Distributed computing cluster: It consists of multiple computing nodes and storage nodes, supports horizontal expansion, and can flexibly increase or decrease the number of nodes according to data processing needs; Task management module: responsible for the creation, allocation, monitoring and scheduling of tasks to ensure that tasks are executed according to the predetermined plan; Data interaction module: realizes data exchange and synchronization between nodes, adopts efficient data transmission protocol, and reduces communication delay; User interface module: The user interface allows users to submit data processing requests and view processing progress and results.

6. A distributed task processing system for human resources data according to claim 5, characterized in that: The user interface module also includes a log module, which is used to record access records of different users, and the access records contain time data.

7. A distributed task processing system for human resources data according to claim 6, characterized in that: The user interface module also includes a permission module, which can be used to manage and assign permissions to different users.

8. A distributed task processing system for human resources data according to claim 7, characterized in that: The permission module includes a database, and a remote configuration system and an alarm system are provided on the permission module. The remote configuration system is used for the management personnel to remotely operate and configure permissions, and the alarm system can set a threshold value, and can send an alarm to the management personnel when the erroneous access reaches the threshold value.

Images