A human resource office service management method and management system

Abstract

This invention discloses a human resources office service management method and system, relating to the field of data analysis technology. The method includes collecting employee position data, pending task data, and current task data; based on the employee position data and the task type data, performing task type analysis and processing to generate task type data; this human resources office service management method and system, by analyzing and processing employee positions and their assigned task types, task data, and corresponding task types, can quickly match tasks to employees with corresponding positions; and by analyzing the completion time of current tasks and pending tasks, it can prioritize assigning pending tasks to employees with earlier predicted completion times, thereby improving the overall task completion efficiency.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, specifically to a human resources office service management method and management system. Background Technology

[0002] Human resource office management involves implementing various human resource functions into daily office processes and practical operations to ensure the orderly, efficient, and standardized operation of HR work. For example, the existing technology with public disclosure number CN116934284A discloses a big data-based human resource data management method, which includes: collecting employee human resource data and preprocessing it to obtain a target sequence; determining the impact of each target data point in the target sequence on employee positions based on the target sequence; obtaining a first probability parameter for employee position changes based on the target data points in the target sequence; obtaining a baseline value sequence for each position level based on the target sequences of all employees at each level; obtaining a second probability parameter for employee position changes based on the baseline value sequence; and obtaining a probability parameter for retrieving employee target data based on the first and second probability parameters, thereby managing the human resource data. This existing technology improves the efficiency of human resource data retrieval by analyzing the probability of retrieving employee human resource data and adjusting the sorting of employee target data.

[0003] However, in the process of human resources office service management, different employees, i.e., human resources personnel, have different speeds in completing different work tasks. That is, when the same task is assigned to different employees, the speed at which they complete it varies. Therefore, how to allocate tasks to improve the overall task completion efficiency is also an important issue in human resources service management. Summary of the Invention

[0004] The purpose of this invention is to provide a human resources office service management method and management system to address the aforementioned shortcomings in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a human resources office service management method, comprising the following steps:

[0006] S1. Collect employee job data, pending task data, and current task data of employees;

[0007] S2. Based on the employee position data and the job responsibility task type data, perform employee responsibility task type analysis and processing to generate employee responsibility task type data;

[0008] S3. Based on the task data to be completed, the employee's current task data, and the task type and task characteristic data, perform task type analysis and processing to generate task type analysis data.

[0009] S4. Based on the data on the types of tasks the employees are responsible for and the task type analysis data, perform matching analysis between employees and tasks to be completed, and generate task-matching employee analysis data.

[0010] S5. Based on the task data to be completed, employee current task data, task type analysis data, and task type workload and task characteristic data, perform task workload analysis and processing for each task type to generate task workload analysis data.

[0011] S6. Based on the task data to be completed, employee current task data, task type analysis data, task workload analysis data, and task type employee target completion time task characteristic data, analyze and process the employee's target completion time for completing the task, and generate task employee target completion time analysis data.

[0012] S7. Based on the current task data of the employees and the task employee target completion time analysis data, calculate the completion time of the current task of the employees, sort the employees according to the order of task completion time, and generate a data sequence of predicted completion time of the tasks to be completed by the employees.

[0013] S8. Construct task allocation management data and perform human resources office service management feedback operations based on the task allocation management data.

[0014] Furthermore, S1 includes the following steps:

[0015] S11. Collect employee job data through the company's internal employee management information to generate an employee job data set. , , This represents the job data for the u-th employee. The maximum number of employees;

[0016] S12. Based on the company's current business activities, collect data on tasks to be completed and generate a set of data on tasks to be completed. , , This represents the r-th task data to be completed. Indicates the maximum number of data items to be completed;

[0017] S13. Collect the task data currently being handled by each employee and generate a set of current task data for each employee. , , This represents the current task data of the qth employee under the responsibility of the uth employee. This indicates the total number of tasks currently assigned to the employee.

[0018] Furthermore, S2 includes the following steps:

[0019] S21. Collect data on the types of tasks the job is responsible for, and generate a data set C of the types of tasks the job is responsible for.

[0020] S22. Based on the two-pointer method, search for data with identical job information in the employee job data set A and the job task type data set C, and generate the employee task type data set. , This represents the task type data of the u-th employee. Each employee can be responsible for one or more task types, meaning that one position can correspond to multiple task types (jobs).

[0021] Furthermore, S3 includes the following steps:

[0022] S31. Collect task feature data for each task type and generate a task feature data set for each task type. , , This represents the task type and task feature data for the p-th task type. This represents the total number of task types. The task feature data for each task type can be obtained by collecting historical task content data for different task types and using a pre-trained first convolutional neural network model to extract features from the historical task content data corresponding to each task type.

[0023] S32. Based on the nearest neighbor search algorithm, search for the task data set E containing the task type feature data set and the task data set B containing the tasks to be completed. Matching task type and task feature data Generate the first task type analysis data Regarding the above Collect and generate the first task type analysis data set. ,in This represents the data of the r-th task to be completed. Task feature data of the p-th task type Matching; when using the nearest neighbor search algorithm, it is necessary to first obtain the data for the task to be completed. The first convolutional neural network model is used to perform feature extraction processing to obtain the corresponding first task feature data to be completed, and then feature matching search is performed.

[0024] S33. Based on the nearest neighbor search algorithm, search the task type feature data set E for data that is similar to the employee's current task data set. Current task data of employees Matching task type and task feature data Generate analysis data for the second task type. Regarding the above Collect and generate a dataset for the second task type analysis. ,in This represents the current task data of the u-th employee and the task characteristic data of the p-th task type. Matching; similar to step S32, when searching, it is necessary to first match the employee's current task data. Feature extraction was performed using the first convolutional neural network model to obtain the corresponding feature data of the first employee's current task:

[0025] S34. Analyze the data set for the first task type. Second task type analysis dataset Collect and combine data to generate task type analysis data. =( , ).

[0026] Furthermore, S4 includes the following steps:

[0027] S41. Based on the two-pointer method, analyze the data set D of the employee's responsible task types and the first task type analysis data set. Data with the same task type is searched and processed to generate task-adapted employee analysis data. ,right Collect and generate task-adapted employee analysis data F, where the data set D contains data on the types of tasks each employee is responsible for. It consists of one or more task types, where each task type that an employee is responsible for can be represented as That is, D and D can be searched using the two-pointer method. The same subscript p and Generate tasks to adapt employee analysis data ,in This indicates that the r-th task data belongs to the p-th task type and is compatible with the u-th employee.

[0028] Furthermore, S5 includes the following steps:

[0029] S51. Collect historical task data for each task type and extract features to generate a task type workload feature data set. , , This represents the workload task characteristic data of the o-th workload level in the p-th task type. This indicates the maximum number of workload levels corresponding to the task type; a pre-trained second convolutional neural network model can be used to perform feature extraction operations on historical task data for each task type to obtain the corresponding task type workload task feature data. ;

[0030] S52. Based on the nearest neighbor search algorithm, search the task type workload task feature data set. The data in the data set B of tasks to be completed Matching task type, workload, task feature data Generate the workload analysis data for the first task. Regarding the above The first task workload analysis data set was collected. ;in This indicates that the r-th task data belongs to the p-th task type and corresponds to the o-th workload level; when using the nearest neighbor search algorithm, it is necessary to first search the task data. The second convolutional neural network model is used to perform feature extraction processing to obtain the corresponding feature data of the second task to be completed, and then feature matching search is performed.

[0031] S53. Based on the nearest neighbor search algorithm, search the task type workload task feature data set. The set of current task data of the employees mentioned above Current task data of employees Matching task type, workload, task feature data Generate workload analysis data for the second task. Regarding the above The second task workload analysis data set was collected. ;in This indicates that the task currently assigned to the qth employee by the uth employee belongs to task type p and corresponds to workload level o; similarly to S52, the current task data of employees needs to be processed before the search. Feature extraction is performed using a second convolutional neural network model to obtain the corresponding feature data of the second employee's current task.

[0032] S54. Analyze the workload data set of the first task. Second task workload analysis data set Collect and combine data to generate task workload analysis data. =( , ).

[0033] Furthermore, S6 includes the following steps:

[0034] S61. Collect historical task completion time data for different task types from employees, extract features, and generate a task feature data set for each task type and employee target completion time. , , This represents the task characteristic data of the u-th employee requiring the m-th task type to complete within the m-th task duration. This represents the maximum number of categories for the target completion time of each task type and employee. During feature extraction in this step, a pre-trained third convolutional neural network model can be used to extract features from the task data for each task type, each employee, and each target completion time category, resulting in task feature data for each task type, employee, and target completion time. The completion time for each type of objective can correspond to a time period, such as... The corresponding target completion time is 0-1 hours (excluding 1 hour). The corresponding target completion time is 1-3 hours (excluding) etc.;

[0035] S62, Based on the data of the task to be completed First task type analysis data Search for the corresponding task type, employee target completion time, and task characteristic data set. It can perform character-based searches to retrieve data matching the task to be completed. First task type analysis data The data set containing the same subscript p for the same task type, employee target completion time, and task characteristics. This determines which one should be in. Search and Matching ;

[0036] S63. The searched task type, employee target completion time, and task characteristic data set. Search for and match the corresponding task data in the middle. Matching and employee current task data Matching Generate analysis data on the completion time of the first task's employee goals. Analysis of employee goal completion time for the second task , This indicates that the r-th task to be completed by the u-th employee requires a completion time of the m-th type. This indicates that the task currently assigned to the qth employee by the uth employee requires the mth type of completion time to complete, and includes the following steps:

[0037] S631, Initialize algorithm parameters, as described in... N bird nests are randomly generated in the middle;

[0038] S632, Match the bird's nest with the corresponding The negative of the similarity is used as the fitness function;

[0039] S633. Calculate the fitness value of each nest based on the fitness function, and record the location of the nest with the smallest fitness value. Find the optimal location of the bird's nest ;

[0040] S634. Based on the Levy flight mechanism, the cuckoo searches for a new nest from each existing nest during the target completion time, records the location of the new nest, and calculates its fitness value. If the fitness value of the new nest is less than that of the original nest, the cuckoo abandons the original nest and chooses the new nest. The formula for finding a new nest using the Levy flight mechanism is as follows:

[0041] ,

[0042] in, This is the step size control coefficient. This represents the dot product. The Lévy step size follows a heavy-tailed distribution;

[0043] S635, Target completion time: Search for cuckoos flying randomly to set probability. The formula for replacing an old bird's nest upon discovering a new one is as follows:

[0044] ,

[0045] Where s is a random number , , For any two other bird nest locations.

[0046] S636. Calculate the fitness value of each nest based on the fitness function, and update the optimal nest position. Then determine whether the maximum number of iterations has been reached. If so, output the optimal location of the Bird's Nest. corresponding Generate data on the time taken for employees to complete the first task's objectives. Or, the data on the time taken to complete employee goals for the second task. Otherwise, return to S634;

[0047] S64. Analyze the data on the employee's completion time for the first task. Analysis of employee goal completion time for the second task Collect and combine data to generate task and employee target completion time analysis data. .

[0048] Furthermore, S7 includes the following steps:

[0049] S71, Based on the employee's current task data Analysis of employee goal completion time for the second task It calculates and processes the completion time of employees' current tasks, and generates predicted completion time data for employees' pending tasks. ;

[0050] S72. Predicted completion time data for the tasks to be completed by the employees. Arrange the data in chronological order to generate a predicted data sequence L of the completion time of the tasks to be completed by employees.

[0051] Furthermore, S8 includes the following steps:

[0052] S81, regarding the employee's current task data set Data on the types of tasks assigned to employees Task type analysis data Task-adapted employee analysis data F, task workload analysis data Analysis of data on the time taken for employees to complete their tasks and goals Collect and combine the data sequence L of the predicted completion time of employees' tasks to be completed to generate task allocation and management data Y;

[0053] S82. Execute human resources office service management feedback operations based on the task allocation management data Y.

[0054] A human resources office service management system includes an employee information collection module, an enterprise task information collection module, a storage device, a processor, and a feedback module.

[0055] The employee information collection module is used to collect employee job data, employee current task data, and can also collect data such as the completion time of employees' historical task data for different task types.

[0056] The enterprise task information collection module is used to collect data on tasks to be completed, data on task types for each position, task characteristic data for each task type, and historical task data for each task type.

[0057] The storage device is used to store data collected by the computer program, the employee information collection module, and the enterprise task information collection module.

[0058] The processor is used to execute the computer program to implement the human resources office service management method described above.

[0059] The feedback module is used to feed back the task allocation management data to the system administrator, such as by sending the task allocation management data to the system administrator's terminal via the Internet and / or by displaying it directly on a screen.

[0060] 1. Compared with the prior art, the present invention provides a human resources office service management method and management system, which analyzes and processes employee positions and their responsible task types, task data and corresponding task types, thereby quickly matching tasks to employees with corresponding positions.

[0061] 2. Compared with the prior art, the present invention provides a human resources office service management method and management system, which analyzes the completion time of the tasks currently being handled by employees and the tasks to be assigned, thereby prioritizing the assignment of tasks to employees with earlier predicted completion times, so as to improve the overall task completion efficiency. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0063] Figure 1 This is a flowchart illustrating the method steps provided in an embodiment of the present invention;

[0064] Figure 2 This is a system structure block diagram provided for an embodiment of the present invention. Detailed Implementation

[0065] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0066] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0067] Exemplary embodiments will be described more fully below with reference to the accompanying drawings; however, these exemplary embodiments may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will enable those skilled in the art to fully understand the scope of this disclosure.

[0068] Where there is no conflict, the various embodiments of this disclosure and the features thereof in the embodiments may be combined with each other.

[0069] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0070] The embodiments described herein can be described with reference to plan views and / or cross-sectional views using the ideal schematic diagrams of this disclosure. Therefore, the example illustrations can be modified according to manufacturing techniques and / or tolerances. Therefore, the embodiments are not limited to those shown in the drawings, but include modifications to configurations formed based on manufacturing processes. Therefore, the areas illustrated in the drawings are schematic in nature, and the shapes of the areas shown in the figures illustrate specific shapes of areas of an element, but are not intended to be limiting.

[0071] Please see Figure 1 A human resources office service management method includes the following steps:

[0072] S1. Collect employee job data, pending task data, and employee current task data, including the following steps:

[0073] S11. Collect employee job data through the company's internal employee management information to generate an employee job data set. , , This represents the job data for the u-th employee. The maximum number of employees;

[0074] S12. Based on the company's current business activities, collect data on tasks to be completed and generate a set of data on tasks to be completed. , , This represents the r-th task data to be completed. Indicates the maximum number of data items to be completed;

[0075] S13. Collect the task data currently being handled by each employee and generate a set of current task data for each employee. , , This represents the current task data of the qth employee under the responsibility of the uth employee. This indicates the total number of tasks currently assigned to the employee.

[0076] S2. Based on employee position data and job responsibility task type data, perform employee responsibility task type analysis and processing to generate employee responsibility task type data, including the following steps:

[0077] S21. Collect data on the types of tasks the job is responsible for, and generate a data set C of the types of tasks the job is responsible for.

[0078] S22. Based on the two-pointer method, search for data with identical job information in both the employee job data set A and the job-responsibility task type data set C, and generate the employee-responsibility task type data set. , This represents the task type data of the u-th employee. Each employee can be responsible for one or more task types, meaning that one position can correspond to multiple task types (jobs).

[0079] S3. Based on the data of tasks to be completed, the data of employees' current tasks, and the task type and task characteristic data, perform task type analysis and processing to generate task type analysis data, including the following steps:

[0080] S31. Collect task feature data for each task type and generate a task feature data set for each task type. , , This represents the task type and task feature data for the p-th task type. This represents the total number of task types. The task feature data for each task type can be obtained by collecting historical task content data for different task types and using a pre-trained first convolutional neural network model to extract features from the historical task content data corresponding to each task type.

[0081] S32. Based on the nearest neighbor search algorithm, search for the task type and task feature data set E that are similar to the task data set B of the task data set to be completed. Matching task type and task feature data Generate the first task type analysis data ,right Collect and generate the first task type analysis data set. ,in This represents the data of the r-th task to be completed. Task feature data of the p-th task type Matching; when using the nearest neighbor search algorithm, it is necessary to first obtain the data for the task to be completed. The first convolutional neural network model is used to perform feature extraction processing to obtain the corresponding first task feature data to be completed, and then feature matching search is performed.

[0082] S33. Based on the nearest neighbor search algorithm, search the task type and task feature data set E for data that matches the employee's current task data set. Current task data of employees Matching task type and task feature data Generate analysis data for the second task type. ,right Collect and generate a dataset for the second task type analysis. ,in This represents the current task data of the u-th employee and the task characteristic data of the p-th task type. Matching; similar to step S32, when searching, it is necessary to first match the employee's current task data. Feature extraction was performed using the first convolutional neural network model to obtain the corresponding feature data of the first employee's current task:

[0083] S34. Analyze the data set for the first task type. Second task type analysis dataset Collect and combine data to generate task type analysis data. =( , ).

[0084] S4. Based on employee task type data and task type analysis data, perform matching analysis between employees and tasks to be completed to generate task-matched employee analysis data, including the following steps:

[0085] S41. Based on the two-pointer method, analyze the data set D of employee-responsible task types and the first task type analysis data set. Data with the same task type is searched and processed to generate task-adapted employee analysis data. ,right Collect and generate task-adapted employee analysis data F, where the data set D contains data on the types of tasks each employee is responsible for. It consists of one or more task types, where each task type that an employee is responsible for can be represented as That is, D and D can be searched using the two-pointer method. The same subscript p and Generate tasks to adapt employee analysis data ,in This indicates that the r-th task data belongs to the p-th task type and is compatible with the u-th employee.

[0086] S5. Based on the data of tasks to be completed, employee current task data, task type analysis data, and task type workload and task characteristic data, perform task workload analysis and processing for each task type to generate task workload analysis data, including the following steps:

[0087] S51. Collect historical task data for each task type and extract features to generate a task type workload feature data set. , , This represents the workload task characteristic data of the o-th workload level in the p-th task type. This indicates the maximum number of workload levels corresponding to the task type; a pre-trained second convolutional neural network model can be used to perform feature extraction operations on historical task data for each task type to obtain the corresponding task type workload task feature data. ;

[0088] S52. Based on the nearest neighbor search algorithm, search for a dataset containing task type, workload, and task characteristics. The data set of tasks to be completed in set B Matching task type, workload, task feature data Generate the workload analysis data for the first task. ,right The first task workload analysis data set was collected. ;in This indicates that the r-th task data belongs to the p-th task type and corresponds to the o-th workload level; when using the nearest neighbor search algorithm, it is necessary to first search the task data. The second convolutional neural network model is used to perform feature extraction processing to obtain the corresponding feature data of the second task to be completed, and then feature matching search is performed.

[0089] S53. Based on the nearest neighbor search algorithm, search for a dataset of task types, workload, and task characteristics. Data set of current tasks of employees Current task data of employees Matching task type, workload, task feature data Generate workload analysis data for the second task. ,right The second task workload analysis data set was collected. ;in This indicates that the task currently assigned to the qth employee by the uth employee belongs to task type p and corresponds to workload level o; similarly to S52, the current task data of employees needs to be processed before the search. Feature extraction is performed using a second convolutional neural network model to obtain the corresponding feature data of the second employee's current task.

[0090] S54. Analysis of the workload data set for the first task Second task workload analysis data set Collect and combine data to generate task workload analysis data. =( , ).

[0091] S6. Based on the data of tasks to be completed, employee current task data, task type analysis data, task workload analysis data, and task type employee target completion time task characteristic data, analyze and process the employee's target completion time for completing tasks to generate task employee target completion time analysis data, including the following steps:

[0092] S61. Collect historical task completion time data for different task types from employees, extract features, and generate a task feature data set for each task type and employee target completion time. , , This represents the task characteristic data of the u-th employee requiring the m-th task type to complete within the m-th task duration. This represents the maximum number of categories for the target completion time of each task type and employee. During feature extraction in this step, a pre-trained third convolutional neural network model can be used to extract features from the task data for each task type, each employee, and each target completion time category, resulting in task feature data for each task type, employee, and target completion time. The completion time for each type of objective can correspond to a time period, such as... The corresponding target completion time is 0-1 hours (excluding 1 hour). The corresponding target completion time is 1-3 hours (excluding) etc.;

[0093] S62, Based on data of tasks to be completed First task type analysis data Search for the corresponding task type, employee target completion time, and task characteristic data set. It can perform character-based searches to retrieve data matching the task to be completed. First task type analysis data The data set containing the same subscript p for the same task type, employee target completion time, and task characteristics. This determines which one should be in. Search and Matching ;

[0094] S63. The searched task type, employee target completion time, and task characteristic data set. Search for and match the corresponding task data in the middle. Matching and employee current task data Matching Generate analysis data on the completion time of the first task's employee goals. Analysis of employee goal completion time for the second task , This indicates that the r-th task to be completed by the u-th employee requires a completion time of the m-th type. This indicates that the task currently assigned to the qth employee by the uth employee requires the mth type of completion time to complete, and includes the following steps:

[0095] S631. Initialize algorithm parameters, in N bird nests are randomly generated in the middle;

[0096] S632, Match the bird's nest with the corresponding The negative of the similarity is used as the fitness function;

[0097] S633. Calculate the fitness value of each nest based on the fitness function, and record the location of the nest with the smallest fitness value. Find the optimal location of the bird's nest ;

[0098] S634. Based on the Levy flight mechanism, the cuckoo searches for a new nest from each existing nest during the target completion time, records the location of the new nest, and calculates its fitness value. If the fitness value of the new nest is less than that of the original nest, the cuckoo abandons the original nest and chooses the new nest. The formula for finding a new nest using the Levy flight mechanism is as follows:

[0099] ,

[0100] in, This is the step size control coefficient. This represents the dot product. The Lévy step size follows a heavy-tailed distribution;

[0101] S635, Target completion time: Search for cuckoos flying randomly to set probability. The formula for replacing an old bird's nest upon discovering a new one is as follows:

[0102] ,

[0103] Where s is a random number , , For any two other bird nest locations.

[0104] S636. Calculate the fitness value of each nest based on the fitness function, and update the optimal nest position. Then determine whether the maximum number of iterations has been reached. If so, output the optimal location of the Bird's Nest. corresponding Generate data on the time taken for employees to complete the first task's objectives. Or, the data on the time taken to complete employee goals for the second task. Otherwise, return to S634;

[0105] S64. Data analysis on the time taken for employees to complete the first task's objectives. Analysis of employee goal completion time for the second task Collect and combine data to generate task and employee target completion time analysis data. .

[0106] S7. Based on the current task data and the task target completion time analysis data of employees, calculate and process the completion time of employees to complete their current tasks, sort employees according to the order of task completion time, and generate a data sequence of predicted completion times for employees' tasks, including the following steps:

[0107] S71, Based on employee's current task data Analysis of data on the time taken for employees to complete their tasks and goals Collect data on employee completed current tasks and pending tasks. The completion time calculation and processing generates predicted completion time data for employees' pending tasks. ;

[0108] S72. Predicted data on the completion time of employees' pending tasks Arrange the tasks in chronological order to generate a sequence L of predicted completion times for employee tasks. This allows system administrators to easily use the predicted completion times of employee tasks in sequence L. Based on the corresponding employee order, tasks are preferentially assigned to employees who are earlier in the L sequence so that tasks can be completed as quickly as possible, thereby improving task completion efficiency.

[0109] S8. Construct task allocation management data and perform human resources office service management feedback operations based on the task allocation management data, including the following steps:

[0110] S81, Data set of current tasks for employees Data on the types of tasks assigned to employees Task type analysis data Task-adapted employee analysis data F, task workload analysis data Analysis of data on the time taken for employees to complete their tasks and goals Collect and combine the data sequence L of the predicted completion time of employees' tasks to be completed to generate task allocation and management data Y;

[0111] S82. Perform human resources office service management feedback operations based on task allocation management data Y.

[0112] Please see Figure 2The present invention also provides a human resources office service management system, including an employee information collection module, an enterprise task information collection module, a storage device, a processor, and a feedback module;

[0113] The employee information collection module is used to collect employee job data, employee current task data, and can also collect data such as the completion time of employees' historical task data for different task types.

[0114] The enterprise task information collection module is used to collect data on tasks to be completed, data on the types of tasks a job is responsible for, data on the characteristics of each task type, and historical task data for each task type.

[0115] The storage device is used to store data collected by the computer program, the employee information collection module, and the enterprise task information collection module;

[0116] The processor is used to execute computer programs to implement a human resources office service management method.

[0117] The feedback module is used to provide task assignment management data back to system administrators, such as by sending the data to their terminals via the internet and / or by displaying it directly on a screen.

[0118] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for managing human resource office services, characterized by: Includes the following steps: S1. Collect employee job data, pending task data, and current task data of employees; S2. Based on the employee position data and the job responsibility task type data, perform employee responsibility task type analysis and processing to generate employee responsibility task type data; S3. Based on the task data to be completed, the employee's current task data, and the task type and task characteristic data, perform task type analysis processing to generate task type analysis data, which includes first task type analysis data and second task type analysis data. S4. Based on the data on the types of tasks the employees are responsible for and the task type analysis data, perform matching analysis between employees and tasks to be completed, and generate task-matching employee analysis data. S5. Based on the task data to be completed, employee current task data, task type analysis data, and task type workload and task characteristic data, perform task workload analysis and processing for each task type to generate task workload analysis data. S6. Based on the task data to be completed, employee current task data, task type analysis data, task workload analysis data, and task type employee target completion time task characteristic data, analyze and process the employee's target completion time for completing the task to generate task employee target completion time analysis data, including the following steps: S61. Collect historical task completion time data for different task types from employees, extract features, and generate a task feature data set for each task type and employee target completion time. , , This represents the task characteristic data of the u-th employee requiring the m-th task type to complete within the m-th task duration. This indicates the maximum number of corresponding task types and employee target completion time categories; S62, Based on data of tasks to be completed First task type analysis data Search for the corresponding task type, employee target completion time, and task characteristic data set. ; S63. The searched task type, employee target completion time, and task characteristic data set. Search for and match the corresponding task data in the middle. Matching and employee current task data Matching Generate analysis data on the completion time of the first task's employee goals. Analysis of employee goal completion time for the second task This includes the following steps: S631, Initialize algorithm parameters, as described in... N bird nests are randomly generated in the middle; S632, Match the bird's nest with the corresponding The negative of the similarity is used as the fitness function; S633. Calculate the fitness value of each nest based on the fitness function, and record the location of the nest with the smallest fitness value. Find the optimal location of the bird's nest ; S634. Based on the Levy flight mechanism, the cuckoo starts from each nest to find a new nest during the target completion time search, records the location of the new nest, and calculates the fitness value of the new nest. If the fitness value of the new nest is less than that of the original nest, the cuckoo abandons the original nest and chooses a new nest during the target completion time search. S635, Target completion time: Search for cuckoos flying randomly to set probability. The discovery of a new bird's nest rendered the original one obsolete. S636. Calculate the fitness value of each nest based on the fitness function, and update the optimal nest position. Then determine whether the maximum number of iterations has been reached. If so, output the optimal location of the Bird's Nest. corresponding Generate data on the time taken for employees to complete the first task's objectives. Or, the data on the time taken to complete employee goals for the second task. Otherwise, return to S634; S64. Analyze the data on the employee's completion time for the first task. Analysis of employee goal completion time for the second task Collect and combine data to generate task and employee target completion time analysis data. ; S7. Based on the current task data of the employees and the task target completion time analysis data, calculate the completion time of the current task of the employees, sort the employees according to the order of task completion time, and generate a data sequence of predicted completion time of the tasks to be completed by the employees. S8. Construct task allocation management data and perform human resources office service management feedback operations based on the task allocation management data.

2. The human resources office service management method according to claim 1, characterized in that: S1 includes the following steps: S11. Collect employee job data and generate an employee job data set. , , This represents the job data for the u-th employee. The maximum number of employees; S12. Collect data on tasks to be completed and generate a data set of tasks to be completed. , , This represents the r-th task data to be completed. Indicates the maximum number of data items to be completed; S13. Collect the task data currently being handled by each employee and generate a set of current task data for each employee. , , This represents the current task data of the qth employee under the responsibility of the uth employee. This indicates the total number of tasks currently assigned to the employee.

3. The human resources office service management method according to claim 2, characterized in that: S2 includes the following steps: S21. Collect data on the types of tasks the job is responsible for, and generate a data set C of the types of tasks the job is responsible for. S22. Based on the two-pointer method, search for data with identical job information in the employee job data set A and the job task type data set C, and generate the employee task type data set. , This represents the task type data for the u-th employee.

4. The human resources office service management method according to claim 3, characterized in that: S3 includes the following steps: S31. Collect task feature data for each task type and generate a task feature data set for each task type. , , This represents the task type and task feature data for the p-th task type. Indicates the total number of task types; S32. Based on the nearest neighbor search algorithm, search for the task data set E containing the task type feature data set and the task data set B containing the tasks to be completed. Matching task type and task feature data Generate the first task type analysis data Regarding the above Collect and generate the first task type analysis data set. ; S33. Based on the nearest neighbor search algorithm, search the task type feature data set E for data that is similar to the employee's current task data set. Current task data of employees Matching task type and task feature data Generate analysis data for the second task type. Regarding the above Collect and generate a dataset for the second task type analysis. : S34. Analyze the data set for the first task type. Second task type analysis dataset Collect and combine data to generate task type analysis data. = ( , ).

5. A human resources office service management method according to claim 4, characterized in that: S4 includes the following steps: S41. Based on the two-pointer method, analyze the data set D of the employee's responsible task types and the first task type analysis data set. Data with the same task type is searched and processed to generate task-adapted employee analysis data. ,right Collect and generate task-adapted employee analysis data F.

6. The human resources office service management method according to claim 5, characterized in that: S5 includes the following steps: S51. Collect historical task data for each task type and extract features to generate a task type workload feature data set. , , This represents the workload task characteristic data of the o-th workload level in the p-th task type. This indicates the maximum number of workload levels corresponding to the task type. S52. Based on the nearest neighbor search algorithm, search the task type workload feature data set. The data in the data set B of tasks to be completed Matching task type, workload, task feature data Generate the workload analysis data for the first task. Regarding the above The first task workload analysis data set was collected. ; S53. Based on the nearest neighbor search algorithm, search the task type workload task feature data set. The set of current task data of the employees mentioned above Current task data of employees Matching task type, workload, task feature data Generate workload analysis data for the second task. Regarding the above The second task workload analysis data set was collected. ; S54. Analyze the workload data set of the first task. Second task workload analysis data set Collect and combine data to generate task workload analysis data. = ( , ).

7. A human resources office service management method according to claim 6, characterized in that: S7 includes the following steps: S71, Based on the employee's current task data Analysis of employee goal completion time for the second task It calculates and processes the completion time of employees' current tasks, and generates predicted completion time data for employees' pending tasks. ; S72. Predicted completion time data for the tasks to be completed by the employees. Arrange the data in chronological order to generate a predicted data sequence L of the completion time of the tasks to be completed by employees.

8. The human resources office service management method according to claim 7, characterized in that: S8 includes the following steps: S81, regarding the employee's current task data set Data on the types of tasks assigned to employees Task type analysis data Task-adapted employee analysis data F, task workload analysis data Analysis of data on the time taken for employees to complete their tasks and goals Collect and combine the data sequence L of the predicted completion time of employees' tasks to be completed to generate task allocation and management data Y; S82. Execute human resources office service management feedback operations based on the task allocation management data Y.

9. A human resources office service management system, used to execute the human resources office service management method according to any one of claims 1-8, characterized in that: It includes an employee information collection module, an enterprise task information collection module, a storage device, a processor, and a feedback module; The employee information collection module is used to collect employee job data and employee current task data; The enterprise task information collection module is used to collect data on tasks to be completed, data on task types for each position, task characteristic data for each task type, and historical task data for each task type. The storage device is used to store data collected by the computer program, the employee information collection module, and the enterprise task information collection module. The processor is used to execute the computer program to implement a human resources office service management method as described in any one of claims 1-8; The feedback module is used to feed back the task allocation and management data to the system administrators.

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