Post matching degree analysis method and device and readable storage medium

Abstract

The invention provides a post matching degree analysis method and apparatus, and a readable storage medium. The method comprises the steps of obtaining an operation track log of a target employee in at least one business system; according to the operation track log of the target employee in the at least one business system, obtaining the proportion of the operation quantity of the target employee in each business system; and analyzing the post matching condition of the target employee according to the proportion of the operation quantity of the target employee in each business system and a preset theoretical post model. According to the method, the device and the readable storage medium, the problem that the evaluation of the employees is not accurate enough due to the lack of an objective and fair mode for judging the difference between the post work content of the employees and the actual work content in the current enterprise evaluation method can be solved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus and readable storage medium for job matching degree analysis. Background Technology

[0002] As businesses grow in size and management becomes more complex, assessing employee suitability for their positions, evaluating workload, and ensuring the accuracy of performance appraisals have become significant challenges for companies.

[0003] However, current corporate performance evaluation methods lack an objective and fair way to determine the difference between an employee's job description and actual work, resulting in inaccurate employee evaluations. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to address the above-mentioned shortcomings of the prior art by providing a job matching degree analysis method, device and readable storage medium, so as to solve the problem that the current enterprise evaluation method lacks an objective and fair way to determine the difference between the employee's job content and the actual job content, resulting in inaccurate evaluation of employees.

[0005] In a first aspect, the present invention provides a job matching degree analysis method, comprising:

[0006] Obtain the target employee's operation logs in at least one business system;

[0007] Based on the target employee's operation logs in at least one business system, the proportion of the target employee's operations in each business system is obtained;

[0008] Based on the proportion of the number of operations performed by the target employees in each of the business systems and the preset theoretical job model, the job matching situation of the target employees is analyzed.

[0009] Furthermore, obtaining the target employee's operation trajectory logs in at least one business system specifically includes:

[0010] Deploy a Kafka cluster;

[0011] Obtain the operation logs of the target employee in at least one business system through a Kafka cluster.

[0012] Furthermore, obtaining the proportion of the target employee's operations in each of the at least one business system based on the target employee's operation logs specifically includes:

[0013] Calculate the total number of logs of the target employee in each of the at least one business system and the total number of logs of the target employee in all the business systems within a preset time range based on the operation trajectory logs of the target employee in at least one business system.

[0014] The proportion of the target employee's operations in each business system is obtained by calculating the percentage of the target employee's total logs in each business system as a percentage of the total logs in all business systems.

[0015] Furthermore, before analyzing the job matching of the target employee based on the proportion of operations performed by the target employee in each business system and a preset theoretical job model, the method further includes:

[0016] Select one employee from all employees in the corresponding position of the target employee as the model employee;

[0017] The theoretical job model is set according to the proportion of operations performed by the model employee in each of the business systems.

[0018] Furthermore, the step of analyzing the job matching of the target employees based on the proportion of their operations in each business system and a preset theoretical job model specifically includes:

[0019] The target employees' operation log distribution is obtained by ranking the proportion of their operations in each business system, and the job matching of the target employees is analyzed based on the difference between the target employees' operation log distribution and the theoretical job model; or,

[0020] Based on the proportion of the number of operations performed by the target employee in each of the business systems and the theoretical job model, the cosine similarity between the target employee and the theoretical job model is calculated, and the job matching of the target employee is analyzed based on the cosine similarity between the target employee and the theoretical job model.

[0021] Furthermore, the step of analyzing the job matching of the target employee based on the difference between the distribution of the target employee's operation logs and the theoretical job model specifically includes:

[0022] The operation log distribution is compared with the theoretical job model. If there are operation contents in the operation log distribution that exceed the preset range, it is determined that the target employee has undertaken work beyond the theoretical job model.

[0023] The step of analyzing the job matching of the target employee based on the cosine similarity between the target employee and the theoretical job model specifically includes:

[0024] If the cosine similarity between the target employee and the theoretical job model is less than a preset threshold, it is determined that the target employee has undertaken work beyond the theoretical job model.

[0025] Furthermore, the operation trajectory logs of the at least one business system include at least two of the following: mobile network account opening logs, work order acceptance logs, code submission logs, financial reimbursement logs, travel application logs, training attendance logs, demand acceptance logs, demand release logs, broadband acceptance logs, data query logs, knowledge base retrieval logs, training information logs, and customer visit logs.

[0026] Secondly, the present invention provides a job matching degree analysis device, comprising:

[0027] The acquisition module is used to acquire the operation logs of the target employee in at least one business system;

[0028] The module is connected to the acquisition module and is used to obtain the proportion of the number of operations performed by the target employee in each of the at least one business system, based on the operation trajectory logs of the target employee in the at least one business system.

[0029] The analysis module, connected to the obtaining module, is used to analyze the job matching of the target employee based on the proportion of the number of operations performed by the target employee in each business system and a preset theoretical job model.

[0030] Thirdly, the present invention provides a job matching degree analysis device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to implement the job matching degree analysis method described in the first aspect above.

[0031] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the job matching degree analysis method described in the first aspect.

[0032] The job matching analysis method, apparatus, and readable storage medium provided by this invention first acquire the operation trajectory logs of a target employee in at least one business system; then, based on the operation trajectory logs of the target employee in the at least one business system, obtain the proportion of the target employee's operations in each business system; finally, based on the proportion of the target employee's operations in each business system and a preset theoretical job model, analyze the job matching situation of the target employee. This invention determines the proportion of an employee's operations in each business system by using the employee's operation trajectory logs within the enterprise, and compares and analyzes this proportion with a preset theoretical job model. This objectively assesses the employee's actual workload, improves the accuracy and rationality of employee evaluations, and solves the problem that current enterprise evaluation methods lack an objective and fair way to determine the difference between an employee's job description and actual work content, leading to inaccurate employee evaluations. Attached Figure Description

[0033] Figure 1 This is a flowchart of a job matching degree analysis method according to Embodiment 1 of the present invention;

[0034] Figure 2 This is a flowchart of another job matching degree analysis method according to an embodiment of the present invention;

[0035] Figure 3 This is a schematic diagram of the structure of a job matching degree analysis device according to Embodiment 2 of the present invention;

[0036] Figure 4 This is a schematic diagram of a job matching degree analysis device according to Embodiment 3 of the present invention. Detailed Implementation

[0037] To enable those skilled in the art to better understand the technical solution of the present invention, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0038] It is understood that the specific embodiments and accompanying drawings described herein are merely for explaining the invention and are not intended to limit the invention.

[0039] It is understood that, without conflict, the various embodiments and features in the embodiments of the present invention can be combined with each other.

[0040] It is understood that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, while the parts unrelated to the present invention are not shown in the drawings.

[0041] It is understood that each unit or module involved in the embodiments of the present invention may correspond to only one entity structure, or may be composed of multiple entity structures, or multiple units or modules may be integrated into one entity structure.

[0042] It is understood that, without conflict, the functions and steps marked in the flowcharts and block diagrams of this invention may occur in a different order than that marked in the accompanying drawings.

[0043] It is understood that the flowcharts and block diagrams of this invention illustrate the possible architecture, functions, and operations of systems, apparatuses, devices, and methods according to various embodiments of this invention. Each block in the flowchart or block diagram may represent a unit, module, program segment, or code, containing executable instructions for implementing the specified function. Furthermore, each block or combination of blocks in the block diagram and flowchart can be implemented using a hardware-based system to achieve the specified function, or using a combination of hardware and computer instructions.

[0044] It is understood that the units and modules involved in the embodiments of the present invention can be implemented by software or by hardware. For example, the units and modules can be located in a processor.

[0045] Application Overview

[0046] As businesses grow and management becomes more complex, assessing employee suitability for their roles, evaluating workload, and ensuring the accuracy of performance appraisals become significant challenges. Managers often struggle to identify discrepancies between an employee's theoretical job description and their actual work, which can severely impact the accuracy and validity of performance appraisals.

[0047] Existing performance appraisal methods in enterprises (i.e., evaluations of employees by managers) mainly fall into two categories: 1. Subjective scoring: evaluations are based on managerial ratings or collective voting; 2. Quantifiable indicator completion: quantifiable indicators are set for employees based on their positions, and employee performance is evaluated based on indicator completion or indicator scores. However, the first method is highly subjective. The second method fails to capture employees' actual work performance and lacks fairness.

[0048] Current corporate performance evaluation methods lack an objective and fair way to determine the discrepancy between an employee's job description and their actual work. This leads to inaccurate employee evaluations and an inability to analyze the reasons for the mismatch between employee performance and job responsibilities based on the composition of their daily work. In real-world work scenarios, employees in a particular position often handle multiple tasks, resulting in decreased performance or lower performance evaluations due to excessive workload. Existing methods struggle to objectively assess an employee's true workload, identify the root causes of performance decline, and address the issues, hindering management and failing to motivate employees.

[0049] To address the aforementioned technical problems, this application provides a job matching degree analysis method, apparatus, and readable storage medium. It determines the proportion of an employee's operations in each business system by analyzing the employee's operation logs within the enterprise, and compares this proportion with a pre-set theoretical job model. This objectively assesses the employee's actual workload, improving the accuracy and rationality of employee evaluations. It aims to at least solve the problem of current enterprise evaluation methods lacking an objective and fair way to determine the difference between an employee's job description and actual work content, leading to inaccurate employee evaluations.

[0050] After introducing the basic principles of this application, various non-limiting embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0051] Example 1:

[0052] This embodiment provides a method for analyzing job matching degree, such as Figure 1 As shown, the method includes:

[0053] Step S101: Obtain the target employee's operation log in at least one business system.

[0054] It should be noted that the operation trajectory logs of the at least one business system include at least two of the following: mobile network account opening logs, work order acceptance logs, code submission logs, financial reimbursement logs, travel application logs, training attendance logs, demand acceptance logs, demand release logs, broadband acceptance logs, data query logs, knowledge base retrieval logs, training information logs, customer visit logs, etc.

[0055] In one optional embodiment, obtaining the target employee's operation trajectory logs in at least one business system specifically includes:

[0056] Deploy a Kafka cluster;

[0057] Obtain the operation logs of the target employee in at least one business system through a Kafka cluster.

[0058] Specifically, the method for obtaining the target employee's operation trajectory logs in various business systems can be as follows: deploy a Kafka cluster, configure the relevant cluster parameters, deploy the Kafka producer module in each business system based on the log data and the corresponding business system, and obtain the target employee's operation trajectory logs in each business system through the Kafka cluster.

[0059] Step S102: Based on the operation trajectory logs of the target employee in at least one business system, obtain the proportion of the target employee's operations in each business system.

[0060] In this embodiment, in order to analyze the activity trajectory of the target employee in each business system and determine the actual work content of the target employee, the proportion of the target employee's operation in each business system is obtained based on the operation trajectory log of the target employee in each business system.

[0061] In one optional embodiment, obtaining the proportion of the target employee's operations in each of the at least one business system based on the target employee's operation logs in the business system specifically includes:

[0062] Calculate the total number of logs of the target employee in each of the at least one business system and the total number of logs of the target employee in all the business systems within a preset time range based on the operation trajectory logs of the target employee in at least one business system.

[0063] The proportion of the target employee's operations in each business system is obtained by calculating the percentage of the target employee's total logs in each business system as a percentage of the total logs in all business systems.

[0064] Specifically, the formula for calculating the total number of logs of the target employee in each of the business systems within the preset time range is as follows:

[0065]

[0066] Among them, L n The total number of logs for the target employee in the nth business system within a preset time frame. Let D be the total number of logs of the target employee P in the nth business system on day d, and D be the number of days within the preset time range.

[0067] The formula for calculating the total number of logs of the target employee across all the business systems within the preset time range is:

[0068]

[0069] Among them, L tN represents the total number of logs for the target employee across all business systems within a preset time frame, where N is the number of business systems.

[0070] The formula for calculating the proportion of operations performed by the target employee in each of the aforementioned business systems is as follows:

[0071]

[0072] Among them, LR n This represents the proportion of the target employee's operations in the nth business system.

[0073] Step S103: Analyze the job matching of the target employees based on the proportion of the number of operations performed by the target employees in each business system and the preset theoretical job model.

[0074] It should be noted that the theoretical job model is set by selecting one employee from all employees in the corresponding position of the target employee as the model employee; and by setting the theoretical job model according to the proportion of the number of operations performed by the model employee in each business system.

[0075] In an optional embodiment, the step of analyzing the job matching of the target employee based on the proportion of the number of operations performed by the target employee in each business system and a preset theoretical job model specifically includes:

[0076] The target employees' operation log distribution is obtained by ranking the proportion of their operations in each business system, and the job matching of the target employees is analyzed based on the difference between the target employees' operation log distribution and the theoretical job model; or,

[0077] Based on the proportion of the number of operations performed by the target employee in each of the business systems and the theoretical job model, the cosine similarity between the target employee and the theoretical job model is calculated, and the job matching of the target employee is analyzed based on the cosine similarity between the target employee and the theoretical job model.

[0078] Specifically, by sorting the proportion of operations performed by target employees in each business system, the distribution of their operation logs within a preset time range can be viewed. Based on the difference between this distribution and the theoretical job model, the job matching of the target employees can be analyzed. Specifically, the operation log distribution is compared with the theoretical job model. If the operation log distribution contains operations exceeding a preset range, it is determined that the target employee is undertaking work beyond the theoretical job model. For example, if the comparison indicates that the operation log distribution contains more than 40% of the operations outside the theoretical job model, it is determined that the target employee is undertaking work beyond the theoretical job model, proving that the target employee is taking on too much work beyond their assigned duties. Managers need to consider whether to adjust or optimize their job responsibilities.

[0079] Specifically, based on the proportion of operations performed by the target employee in each business system and the theoretical job model, log feature vectors of the target employee and the theoretical job model are constructed. Based on the log feature vectors of the target employee and the theoretical job model, the cosine similarity between the target employee and the theoretical job model is calculated. Based on the cosine similarity between the target employee and the theoretical job model, the job matching of the target employee is analyzed. Specifically, if the cosine similarity between the target employee and the theoretical job model is less than a preset threshold, it is determined that the target employee has undertaken work beyond the theoretical job model.

[0080] It is worth mentioning that the job matching analysis method disclosed in this invention is mainly used to analyze the activity trajectory of employees in various business systems within an enterprise and determine the actual work content of employees. It then compares and analyzes the actual work content with the employee's current theoretical job model to determine whether there is a discrepancy between the employee's actual work content and the job content, thereby achieving fair evaluation of employee performance and reasonable allocation of work content. This invention uses the employee's operation trajectory and behavior in the enterprise's internal systems to judge and reference the person-job matching degree, which can help managers to rationally formulate talent training strategies, promptly identify the reasons for the decline in employee efficiency, objectively assess the employee's workload, and ultimately achieve the effect of optimizing talent allocation and improving employee work enthusiasm.

[0081] In one specific embodiment, a method for analyzing job matching based on system behavior trajectories is provided. The steps include: establishing a personnel trajectory collection and analysis system; deploying a Kafka producer on the business system where data is to be collected; setting the database for data aggregation as the consumer; aggregating the operation log data from each business system and performing data cleaning to form a log data warehouse; based on the data warehouse, using statistical methods, obtaining the distribution of employee operation logs within a specified time range; determining the employee's work profile within a specific time period based on the operation log distribution results; and comparing the profile results with the employee's actual job position to analyze whether the employee's actual work content corresponds to the job position and whether the employee has excessive extra work leading to unfair evaluation.

[0082] like Figure 2 As shown, the specific steps of this job matching analysis method are as follows:

[0083] Step 1: Establish a personnel trajectory collection and analysis system

[0084] It should be noted that in the personnel trajectory collection and analysis system, it is necessary to determine the system scope and log type of the data to be collected. Taking the operator company as an example, the operation logs can be divided into mobile network account opening logs, work order acceptance logs, code submission logs, financial reimbursement logs, travel application logs, training attendance logs, demand acceptance logs, demand release logs, broadband acceptance logs, data query logs, knowledge base retrieval logs, training information logs, party affairs activity organization logs, customer visit logs, and other operation trajectory logs.

[0085] Step 2: Deploy the Kafka producer module on the system where data collection will take place.

[0086] Specifically, deploy a Kafka cluster, configure the relevant cluster parameters, and deploy the Kafka producer module in the business system based on log data and the corresponding business system. The Kafka cluster is responsible for receiving messages from the producer and sending them to the consumer.

[0087] Step 3: Collect and store employee log trajectory data.

[0088] Specifically, after receiving messages, the personnel trajectory collection and analysis system cleans the data, removing outliers and test data. Using the employee's unique identifier as a unique index, it merges the log data collected from various platforms into a wide table, defining D as the number of days within a preset time range. Let P be the total number of logs in the target employee C on day d in business system n. Based on the above explanation, the formula for calculating the total number of logs of the target employee in each business system within the preset time range is:

[0089]

[0090] Among them, L n The total number of logs for the target employee in the nth business system within a preset time frame;

[0091] The formula for calculating the total number of logs of the target employee across all the business systems within the preset time range is:

[0092]

[0093] Among them, L t N represents the total number of logs for the target employee across all business systems within a preset time frame, where N is the number of business systems.

[0094] The proportion of the target employee's operations in each business system is calculated by dividing the total number of logs of the target employee in each business system within the preset time range by the total number of logs of the target employee in all business systems within the preset time range. The calculation formula is as follows:

[0095]

[0096] Among them, LR n The percentage of the target employee's operations in the nth business system is used as the basis for the final data collection and storage.

[0097] Step 4: Set up the theoretical job model

[0098] Specifically, for each position, a model employee in that position is selected, and the theoretical position model Mcn is set based on the proportion of the model employee's operations in each business system.

[0099] Step 5: Personnel Log Trajectory Analysis

[0100] Specifically, an employee's job fit can be determined in two ways:

[0101] Method 1: For employee P, sort the logs by the proportion of their operations across various business systems to view the distribution of employee P's operation logs within a specified time range. Based on this distribution, the theoretical job model for employee P's corresponding position can be compared with the actual log content.

[0102] Method 2: For employee P, construct log feature vectors a and b based on the proportion of employee P's operations in each business system and the theoretical job model of employee P's corresponding position. Calculate the cosine similarity between a and b using the following formula:

[0103]

[0104] Based on cosine similarity, the theoretical job model corresponding to employee P and the actual log content can be compared.

[0105] Step 6: Analyze the differences between trajectory data and job model

[0106] Specifically, Method 1: If employee P's performance is poor, and in actual operation there is a large amount (i.e., exceeding the preset range, such as 40%) of the operational content outside the theoretical job model, it proves that the employee may be undertaking too much work beyond their job responsibilities, and the manager needs to consider whether to adjust or optimize their job content; Method 2: If employee P's performance is poor, and the cosine similarity value between employee P and the theoretical job model is significantly different (i.e., less than the preset threshold), it proves that the employee may be undertaking too much work beyond their job responsibilities, and the manager needs to consider whether to adjust or optimize their job content.

[0107] The job matching analysis method provided in this invention first obtains the operation trajectory logs of a target employee in at least one business system; then, based on the operation trajectory logs of the target employee in the at least one business system, it obtains the proportion of the target employee's operations in each business system; finally, based on the proportion of the target employee's operations in each business system and a preset theoretical job model, it analyzes the job matching situation of the target employee. This invention determines the proportion of an employee's operations in each business system by using the employee's operation trajectory logs within the enterprise, and compares and analyzes this proportion with a preset theoretical job model. This objectively assesses the employee's actual workload, improves the accuracy and rationality of employee evaluations, and solves the problem that current enterprise evaluation methods lack an objective and fair way to determine the difference between an employee's job description and actual work content, leading to inaccurate employee evaluations.

[0108] Example 2:

[0109] like Figure 3 As shown, this embodiment provides a job matching degree analysis device for performing the above-described job matching degree analysis method, including:

[0110] Module 11 is used to acquire the operation trajectory logs of the target employee in at least one business system;

[0111] Module 12, connected to module 11, is used to obtain the proportion of the number of operations performed by the target employee in each of the at least one business systems, based on the operation trajectory logs of the target employee in the at least one business system.

[0112] Analysis module 13, connected to the obtaining module 12, is used to analyze the job matching of the target employee based on the proportion of the number of operations performed by the target employee in each business system and a preset theoretical job model.

[0113] Furthermore, the acquisition module 11 specifically includes:

[0114] Deployment unit, used to deploy Kafka clusters;

[0115] The acquisition unit is used to acquire the operation trajectory logs of the target employee in at least one business system through a Kafka cluster.

[0116] Furthermore, the obtaining module 12 specifically includes:

[0117] The calculation unit is used to calculate, within a preset time range, the total number of logs of the target employee in each of the business systems, and the total number of logs of the target employee in all the business systems, based on the operation trajectory logs of the target employee in at least one business system.

[0118] The obtaining unit is used to obtain the proportion of the number of operations performed by the target employee in each business system based on the proportion of the total number of logs of the target employee in each business system to the total number of logs in all business systems.

[0119] Furthermore, the device also includes:

[0120] The selection module is used to select one employee as a model employee from all employees in the corresponding position of the target employee.

[0121] The configuration module is used to configure the theoretical job model based on the proportion of operations performed by the model employee in each business system.

[0122] Furthermore, the analysis module 13 specifically includes:

[0123] The analysis unit is used to sort the target employees' operation logs by proportion in each business system, obtain the target employees' operation log distribution, and analyze the job matching of the target employees based on the difference between the target employees' operation log distribution and the theoretical job model; or,

[0124] Based on the proportion of the number of operations performed by the target employee in each of the business systems and the theoretical job model, the cosine similarity between the target employee and the theoretical job model is calculated, and the job matching of the target employee is analyzed based on the cosine similarity between the target employee and the theoretical job model.

[0125] Furthermore, the analysis unit is used for:

[0126] The operation log distribution is compared with the theoretical job model. If there are operation contents in the operation log distribution that exceed the preset range, it is determined that the target employee has undertaken work beyond the theoretical job model.

[0127] The analysis unit is also used for:

[0128] If the cosine similarity between the target employee and the theoretical job model is less than a preset threshold, it is determined that the target employee has undertaken work beyond the theoretical job model.

[0129] Furthermore, the operation trajectory logs of the at least one business system include at least two of the following: mobile network account opening logs, work order acceptance logs, code submission logs, financial reimbursement logs, travel application logs, training attendance logs, demand acceptance logs, demand release logs, broadband acceptance logs, data query logs, knowledge base retrieval logs, training information logs, and customer visit logs.

[0130] Example 3:

[0131] refer to Figure 4 This embodiment provides a job matching degree analysis device, including a memory 21 and a processor 22. The memory 21 stores a computer program, and the processor 22 is configured to run the computer program to execute the job matching degree analysis method in Embodiment 1.

[0132] The memory 21 is connected to the processor 22. The memory 21 can be a flash memory, a read-only memory or other memory, and the processor 22 can be a central processing unit or a microcontroller.

[0133] Example 4:

[0134] This embodiment provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the job matching degree analysis method in Embodiment 1 above.

[0135] The computer-readable storage medium includes volatile or non-volatile, removable or non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, computer program modules, or other data). Computer-readable storage media include, but are not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory or other memory technologies, CD-ROM (Compact Disc Read-Only Memory), DVD or other optical disc storage, cartridges, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer.

[0136] In summary, the job matching analysis method, apparatus, and readable storage medium provided in this invention first acquire the operation trajectory logs of a target employee in at least one business system; then, based on the operation trajectory logs of the target employee in the at least one business system, obtain the proportion of the target employee's operations in each business system; finally, based on the proportion of the target employee's operations in each business system and a preset theoretical job model, analyze the job matching situation of the target employee. This invention determines the proportion of an employee's operations in each business system by using the employee's operation trajectory logs within the enterprise, and compares and analyzes this proportion with a preset theoretical job model. This objectively assesses the employee's actual workload, improves the accuracy and rationality of employee evaluations, and solves the problem that current enterprise evaluation methods lack an objective and fair way to determine the difference between an employee's job description and actual work content, leading to inaccurate employee evaluations.

[0137] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of the present invention, and the present invention is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A method for analyzing job matching degree, characterized in that, The method includes: Obtain the target employee's operation logs in at least one business system; Based on the target employee's operation logs in at least one business system, the proportion of the target employee's operations in each business system is obtained; Based on the proportion of the number of operations performed by the target employees in each of the business systems and the preset theoretical job model, the job matching situation of the target employees is analyzed.

2. The method according to claim 1, characterized in that, The acquisition of the target employee's operation logs in at least one business system specifically includes: Deploy a Kafka cluster; Obtain the operation logs of the target employee in at least one business system through a Kafka cluster.

3. The method according to claim 1, characterized in that, The step of obtaining the proportion of the target employee's operations in each of the at least one business system based on the target employee's operation logs in the system specifically includes: Calculate the total number of logs of the target employee in each of the at least one business system and the total number of logs of the target employee in all the business systems within a preset time range based on the operation trajectory logs of the target employee in at least one business system. The proportion of the target employee's operations in each business system is obtained by calculating the percentage of the target employee's total logs in each business system as a percentage of the total logs in all business systems.

4. The method according to claim 1, characterized in that, Before analyzing the job matching of the target employees based on the proportion of their operations in each business system and a preset theoretical job model, the method further includes: Select one employee from all employees in the corresponding position of the target employee as the model employee; The theoretical job model is set according to the proportion of operations performed by the model employee in each of the business systems.

5. The method according to claim 4, characterized in that, The step of analyzing the job matching of the target employees based on the proportion of their operations in each business system and a preset theoretical job model specifically includes: The target employees' operation log distribution is obtained by ranking the proportion of their operations in each business system, and the job matching of the target employees is analyzed based on the difference between the target employees' operation log distribution and the theoretical job model; or, Based on the proportion of the number of operations performed by the target employee in each of the business systems and the theoretical job model, the cosine similarity between the target employee and the theoretical job model is calculated, and the job matching of the target employee is analyzed based on the cosine similarity between the target employee and the theoretical job model.

6. The method according to claim 5, characterized in that, The step of analyzing the job matching of the target employee based on the difference between the target employee's operation log distribution and the theoretical job model specifically includes: The operation log distribution is compared with the theoretical job model. If there are operation contents in the operation log distribution that exceed the preset range, it is determined that the target employee has undertaken work beyond the theoretical job model. The step of analyzing the job matching of the target employee based on the cosine similarity between the target employee and the theoretical job model specifically includes: If the cosine similarity between the target employee and the theoretical job model is less than a preset threshold, it is determined that the target employee has undertaken work beyond the theoretical job model.

7. The method according to claim 1, characterized in that, The operation logs of at least one business system include at least two of the following: mobile network account opening logs, work order acceptance logs, code submission logs, financial reimbursement logs, travel application logs, training attendance logs, demand acceptance logs, demand release logs, broadband acceptance logs, data query logs, knowledge base retrieval logs, training information logs, and customer visit logs.

8. A job matching degree analysis device, characterized in that, include: The acquisition module is used to acquire the operation logs of the target employee in at least one business system; The module is connected to the acquisition module and is used to obtain the proportion of the number of operations performed by the target employee in each of the at least one business system, based on the operation trajectory logs of the target employee in the at least one business system. The analysis module, connected to the obtaining module, is used to analyze the job matching of the target employee based on the proportion of the number of operations performed by the target employee in each business system and a preset theoretical job model.

9. A job matching degree analysis device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to implement the job matching analysis method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the job matching degree analysis method as described in any one of claims 1-7.

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