CN115471192A

CN115471192A - Data processing method, device, equipment and storage medium in workload acceptance check

Info

Publication number: CN115471192A
Application number: CN202211116629.3A
Authority: CN
Inventors: 林新; 陈晶; 张在峰; 武妍格
Original assignee: Industrial and Commercial Bank of China Ltd ICBC
Current assignee: Industrial and Commercial Bank of China Ltd ICBC
Priority date: 2022-09-14
Filing date: 2022-09-14
Publication date: 2022-12-13

Abstract

The application provides a data processing method, a device, equipment and a storage medium in workload acceptance. The method relates to the field of artificial intelligence. The method comprises the following steps: the method comprises the steps that attendance data of a plurality of target employees are obtained from target attendance equipment, and workload related data in the task execution process of each target employee are obtained from a task management system; the workload related data includes: task workload; calculating the total efficiency score corresponding to each target employee according to the attendance data and the workload related data; determining the efficiency level corresponding to each target employee according to the total efficiency score corresponding to each target employee; calculating the initial acceptance workload of each target employee according to the corresponding efficiency level of each target employee; determining the target acceptance workload of each target employee according to the initial acceptance workload, the attendance data and the task workload corresponding to each target employee; and sending the target acceptance workload to the settlement system so that the settlement system settles on the basis of the target acceptance workload.

Description

Data processing method, device, equipment and storage medium in workload acceptance check

Technical Field

The present application relates to artificial intelligence technologies, and in particular, to a method, an apparatus, a device, and a storage medium for processing data in workload verification.

Background

In enterprises which carry out salary settlement by taking the workload as a standard, when carrying out salary settlement, checking and accepting the workload of target employees are needed, and salary is calculated and paid according to the target checking and accepting workload.

In the prior art, a semi-manual mode is generally adopted, and target acceptance workload is confirmed in a mode of subjectively determining reward and punishment workload according to attendance data and task workload of target employees.

The mode of semi-manually determining the target acceptance workload corresponding to the target employees lacks evaluation basis, and the acceptance efficiency and accuracy are low.

Disclosure of Invention

The application provides a data processing method, a data processing device, data processing equipment and a storage medium in workload acceptance, and aims to solve the problems that evaluation basis is lacked when a target acceptance workload corresponding to a target employee is determined in a semi-manual mode, and acceptance efficiency and accuracy are low.

In a first aspect, the present application provides a data processing method in workload verification, including:

the method comprises the steps that attendance data of a plurality of target employees are obtained from target attendance equipment, and workload related data in the task execution process of each target employee are obtained from a task management system; the workload related data comprises: task workload;

calculating the total efficiency score corresponding to each target employee according to the attendance data and the workload related data;

determining the efficiency level corresponding to each target employee according to the total efficiency score corresponding to each target employee;

calculating the initial acceptance workload of each target employee according to the efficiency level corresponding to each target employee;

determining the target acceptance workload of each target employee according to the initial acceptance workload, the attendance data and the task workload corresponding to each target employee;

and sending the target acceptance workload to a settlement system so that the settlement system can settle based on the target acceptance workload.

In a second aspect, the present application provides a data processing apparatus in workload acceptance, comprising:

the system comprises an acquisition module, a task management system and a processing module, wherein the acquisition module is used for acquiring attendance data of a plurality of target employees from target attendance equipment and acquiring workload related data of each target employee in a task execution process from the task management system; the workload related data comprises: task workload;

the first calculation module is used for calculating the total efficiency score corresponding to each target employee according to the attendance data and the workload related data;

the first determining module is used for determining the efficiency level corresponding to each target employee according to the total efficiency score corresponding to each target employee;

the second calculation module is used for calculating the initial acceptance workload of each target employee according to the efficiency level corresponding to each target employee;

the second determination module is used for determining the target acceptance workload of each target employee according to the initial acceptance workload, the attendance data and the task workload corresponding to each target employee;

and the sending module is used for sending the target acceptance workload to a settlement system so that the settlement system can settle based on the target acceptance workload.

In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory and transceiver communicatively coupled to the processor;

the memory stores computer execution instructions; the transceiver is used for receiving and transmitting data with a settlement system;

the processor executes computer-executable instructions stored by the memory to implement the method of any one of the first aspects.

In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon computer-executable instructions for implementing the method according to any one of the first aspect when executed by a processor.

According to the data processing method, device, equipment and storage medium in workload acceptance check, attendance data of a plurality of target employees are obtained from target attendance equipment, and workload related data in a task execution process of each target employee are obtained from a task management system; the workload related data comprises: task workload; calculating the total efficiency score corresponding to each target employee according to the attendance data and the workload related data; determining the efficiency level corresponding to each target employee according to the total efficiency score corresponding to each target employee; calculating the initial acceptance workload of each target employee according to the efficiency level corresponding to each target employee; determining target acceptance workload of each target employee according to the initial acceptance workload, the attendance data and the task workload corresponding to each target employee; and sending the target acceptance workload to a settlement system so that the settlement system can settle based on the target acceptance workload. The total efficiency score of the target employee is determined by the objective data, namely the attendance data of the target employee and the workload related data in the task execution process, which are acquired from the target attendance equipment and the task management system, so that the total efficiency score and the efficiency level of the target employee are determined by calculation, and a more accurate basis can be provided for calculating the initial acceptance workload corresponding to the target employee. Because the efficiency level influences the determination of the acceptance workload of the target employees, the initial acceptance workload of each target employee is calculated according to the efficiency level corresponding to each target employee, and the initial acceptance workload of the target employees can be more accurately determined. Because the target acceptance workload of the target staff is beyond the initial acceptance workload, the influence of the attendance data and the task workload is also considered, the target acceptance workload of each target staff is determined according to the initial acceptance workload, the attendance data and the task workload corresponding to each target staff, and the target acceptance workload of the target staff can be as accurate as possible and close to the actual working condition. The attendance data required by calculating the target acceptance workload of the target employee and the workload related data in the task execution process are automatically acquired from the target attendance equipment and the task management system through the computer, and the calculation process is automatically completed by the computer, so that the acceptance efficiency of the target acceptance workload corresponding to the target employee is effectively improved.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and, together with the description, serve to explain the principles of the application.

Fig. 1 is an application scenario diagram of a data processing method in workload verification provided in an embodiment of the present application;

FIG. 2 is a flowchart of a method for processing data during workload verification according to an embodiment of the present disclosure;

fig. 3 is a flowchart of a method of processing data in workload verification according to a second embodiment of the present application;

fig. 4 is a flowchart of a method of processing data in workload verification according to a third embodiment of the present application;

fig. 5 is a flowchart of a method of processing data in workload verification according to a fourth embodiment of the present application;

fig. 6 is a flowchart of a method of a data processing method in workload acceptance according to a fifth embodiment of the present application;

fig. 7 is a flowchart of a method of processing data in workload verification according to a sixth embodiment of the present application;

fig. 8 is a flowchart of a method of processing data in workload verification according to a seventh embodiment of the present application;

fig. 9 is a flowchart of a method of processing data in workload verification according to an eighth embodiment of the present application;

FIG. 10 is a flowchart of a method for processing data during workload verification according to a ninth embodiment of the present application;

fig. 11 is a schematic structural diagram of a data processing apparatus in workload verification according to a tenth embodiment of the present application;

fig. 12 is a schematic structural diagram of a data processing device in workload verification according to an eleventh embodiment of the present application.

Specific embodiments of the present application have been shown by way of example in the drawings and will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

In order to better understand the data processing method in workload verification provided by the present application, the following detailed description of the prior art is provided.

The prior art generally uses a semi-manual approach to calculate the target acceptance workload of the target employee. The method comprises the steps that attendance data of target employees are obtained through a computer, the attendance days and the overtime days in the attendance data are summed to obtain an initial acceptance workload, and then the manager adds the reward and punishment workload on the basis of the initial acceptance workload in a manual mode to obtain the target acceptance workload of the target employees.

Because the judgment of the winning and punishing workloads in the prior art is totally judged by the intuitive feeling of the manager, the mode of semi-manually determining the target acceptance workload corresponding to the target staff lacks evaluation basis and has lower acceptance efficiency and accuracy. In order to solve the problems that in the prior art, evaluation basis is lacked when the target acceptance workload corresponding to the target staff is determined, and acceptance efficiency and accuracy are low, the inventor finds through creative research that accurate evaluation basis is required to be obtained and can be obtained according to objectively recorded data. The attendance data of the target staff and the workload related data in the task execution process are objective data recorded by the system, so that the attendance data of each target staff and the workload related data in the task execution process can be acquired from the target attendance equipment and the task management system respectively, the total efficiency score corresponding to each target staff is calculated according to the attendance data and the workload related data, and the efficiency level corresponding to each target staff is determined according to the total efficiency score corresponding to each target staff. And taking the efficiency level as one of evaluation bases of target acceptance workload corresponding to the target employee. The target acceptance workload of each target employee is further determined according to the initial acceptance workload, the attendance data and the task workload corresponding to the target employee. In order to improve the acceptance efficiency of the target acceptance workload corresponding to the target employee, the attendance data and workload related data acquisition process and the calculation processes of the total efficiency score, the efficiency level and the target acceptance workload can be automatically processed by a computer.

In order to better understand the data processing method in workload acceptance provided by the present application, an application scenario of the present application is described in detail below.

Fig. 1 is an application scenario diagram of a data processing method in workload verification provided by the present application. As shown in fig. 1, includes an electronic device 1, an electronic device 2, a database 3, and a database 4. The electronic device 1 runs the data processing method in the workload verification provided by the present application. The electronic device 2 runs a settlement system thereon. Each of the

electronic devices

1 and 2 may be a personal computer, a server, a blade server, or another device having computing capabilities. The database 3 stores attendance data of the target employee collected by the target attendance device. The database 4 stores workload related data collected by the task management system for target employees in the course of performing tasks. The target attendance checking equipment and the task management system can be mutually independent and can also have data interaction. The method and the device are mainly applied to salary settlement of enterprises according to the target acceptance workload corresponding to the target employees. When the enterprise human manager carries out salary settlement on the target staff, the target acceptance workload of the target staff needs to be confirmed. The electronic equipment 1 acquires attendance data of a plurality of target employees collected by the target attendance equipment from the database 3, and acquires workload related data of each target employee in a task execution process collected by the task management system from the database 4; calculating the total efficiency score corresponding to each target employee according to the attendance data and the workload related data; determining the efficiency level corresponding to each target employee according to the total efficiency score corresponding to each target employee; calculating the initial acceptance workload of each target employee according to the efficiency level corresponding to each target employee; determining the target acceptance workload of each target employee according to the initial acceptance workload, the attendance data and the task workload corresponding to each target employee; the target acceptance workload is sent to a settlement system operating on the electronic device 2 so that the settlement system settles based on the target acceptance workload.

It can be understood that the electronic device 1 may directly communicate with the target attendance device to obtain the attendance data of the target employee. The electronic device 1 directly communicates with a device corresponding to the task management system to obtain the workload related data of each target employee in the task execution process.

The data processing method in the workload check-in process provided by the application is particularly applied to a scene of carrying out the workload check-in when outsourcing personnel pay settlement.

It should be noted that the data processing method, the data processing apparatus, the electronic device, and the storage medium in workload acceptance check provided by the present application may be applied to the technical field of artificial intelligence, and may also be applied to any field except the field of artificial intelligence, and the application field of the present application is not limited.

The data processing method in workload verification and acceptance aims to solve the technical problems in the prior art.

The following describes the technical solution of the present application and how to solve the above technical problems in detail by specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.

Example one

The embodiment provided by the application aims to solve the problems that in the prior art, evaluation basis is lacked when the target acceptance workload corresponding to the target staff is determined, and acceptance efficiency and accuracy are low. Fig. 2 is a flowchart of a method of a data processing method in workload verification according to an embodiment of the present application, and as shown in fig. 2, an execution subject of the data processing method in workload verification according to the present application is a data processing device in workload verification, where the data processing method in workload verification according to the present embodiment specifically includes the following steps:

step S101, attendance data of a plurality of target employees are obtained from the target attendance equipment, and workload related data in the task execution process of each target employee are obtained from the task management system. The workload related data includes: task workload.

The target attendance equipment is used for recording attendance data of the target staff. The attendance data refers to data related to the attendance of the target staff. Including the number of attendance days, the number of overtime days, the number of legal working days in the month, etc. And the task management system is used for managing the task execution process of the target staff. Workload related data refers to data that is relevant to the performance of a task by a target employee during the course of work. The workload related data may be different for different post employees. Illustratively, for software developers, the task workload, the number of code submission lines, the number of yield requirement items, the task overdue rate, the defect density, etc. may be included.

Optionally, attendance data of a plurality of target employees is obtained from a database corresponding to the target attendance device. And acquiring the workload related data of each target employee in the task execution process from a database corresponding to the task management system.

And S102, calculating the total efficiency score corresponding to each target employee according to the attendance data and the workload related data.

The total efficiency score is a result of carrying out multi-dimensional evaluation on the work efficiency of the target employee in a quantification mode according to an efficiency index system inside the enterprise. The performance index system may be divided into multiple dimensions, such as quality, yield, execution, and so on.

Specifically, the total efficacy score is calculated according to attendance data and workload-related data, and an efficacy index system needs to be constructed first. The performance index system may be established according to the evaluation indexes of different departments and is not specifically limited herein. And calculating to obtain the total efficiency score of each target employee according to the efficiency index system and the attendance data and workload related data of each target employee.

For example, for software developers, the performance system may be composed of three parts, namely, an execution performance index, a yield performance index and a quality performance index. The execution efficiency index can be subdivided into two-level efficiency indexes such as attendance rate, attendance load rate and the like; for the output performance index, the output performance index can be divided into secondary performance indexes such as the daily average submitted code line number, the daily average output demand item number and the like; the quality performance index can be further divided into two-level performance indexes such as defect density. Wherein, the attendance rate = attendance days/legal working days of the month; attendance load rate = (attendance days + number of shift days)/number of attendance days; defect density generally refers to the number of defects that occur per kilo-line of code. And endowing each secondary performance index with a corresponding weight value, wherein the sum of the weight values of all the secondary performance indexes is 100 percent. For each secondary performance index, corresponding score conditions are required to be given, for example, the score conditions of the attendance rate index can be given, wherein the attendance rate is more than 90 percent, and 10 points are obtained; the attendance rate is more than 80% and less than 90%, and 8 points are obtained; the attendance rate is more than 70% and less than 80%, and 6 points are obtained; the attendance rate is less than 70%, and 4 points are obtained. And calculating the score of each secondary efficiency index according to the scoring condition, the target employee attendance data and the workload related data. For example, when the attendance rate of the target employee is 95%, the target employee is given 10 points. And multiplying the score of the secondary performance index by the corresponding weight to obtain a corresponding weighted score. And summarizing and counting the weighted scores of the secondary performance indexes according to the class of the primary performance indexes to obtain the scores of the execution performance indexes, the output performance indexes and the quality performance indexes. And summarizing and counting the scores of the performance indexes of all the levels, namely summarizing and counting the scores of the execution performance indexes, the output performance indexes and the quality performance indexes to obtain a total performance score. The performance index system in the examples is shown in the following table:

the first behavior is a first-level performance index, the second behavior is a subdivided second-level performance index, and the third behavior is a weight corresponding to the second-level performance index.

And S103, determining the efficiency level corresponding to each target employee according to the total efficiency score corresponding to each target employee.

The performance level is a level division for dividing the performance of different levels.

Specifically, the performance level corresponding to the target employee is determined, and the performance level needs to be divided first. The efficiency level division is not particularly limited, and may be determined according to the actual conditions of each department of each post. And after the efficiency levels are divided, determining the efficiency level corresponding to each target employee. The specific method is not limited. For example, the lowest total performance score to be achieved for each performance level may be set, the performance level corresponding to the target employee may be determined according to the total performance score, or the performance levels of the target employee may be determined according to the total performance ranking and the ratio corresponding to each performance level by first sorting the performance levels from large to small, and then setting the corresponding ratio for each performance level.

Optionally, the method sets a corresponding proportion for each efficiency level, and determines the efficiency level of the target employee according to the total efficiency ranking of the target employee and the proportion corresponding to each efficiency level.

And step S104, calculating the initial acceptance workload of each target employee according to the efficiency level corresponding to each target employee.

The initial acceptance workload refers to the preliminarily determined acceptance workload.

Specifically, the calculation rule of the initial acceptance workload corresponding to each performance level needs to be determined first. The calculation rule of the initial acceptance workload is not specifically limited, and is determined according to actual conditions. After the calculation rule is determined, according to the calculation rule, relevant data of the target staff required to be obtained by the calculation rule, such as the number of attendance days, the task workload, the ranking in the corresponding efficiency level, the total number of the target staff, the proportion corresponding to each efficiency level and the like, are determined. And calculating the initial acceptance workload of each target employee according to the data and the calculation rule.

Illustratively, the performance level is determined to be in third gear. And the calculation formulas are initial acceptance workload = preset task workload + reward task workload. The first-gear efficiency level calculation rule is that when the monthly task workload of the target staff reaches 25, the excess part is multiplied by the first-gear reward proportion; the second gear efficiency level is calculated by multiplying the excess part by the second gear reward rate when the monthly task workload of the target staff reaches 22; the third gear performance level is calculated by multiplying the excess portion by the third gear reward rate when the monthly task work load of the target employee reaches 20. The initial acceptance workload of each target employee can be calculated by acquiring the task workload of each target employee, the affiliated performance level and the reward proportion of the corresponding performance level.

Optionally, an initial workload acceptance model corresponding to each pre-trained target employee performance level is obtained. And acquiring the total number of target employees, attendance data corresponding to each target employee, a proportion corresponding to the efficiency level and ranking in the corresponding efficiency level. And calculating the initial acceptance workload of each target employee according to the total number of the target employees, the initial workload acceptance model corresponding to each target employee, the attendance data, the proportion corresponding to the efficiency level and the ranking in the corresponding efficiency level.

And S105, determining the target acceptance workload of each target employee according to the initial acceptance workload, the attendance data and the task workload corresponding to each target employee.

Specifically, the method of determining the target acceptance workload of the target employee is not particularly limited. Illustratively, the initial acceptance workload of the target employee is 24, the number of attendance days is 22, the number of shift days is 3, the task workload is 20, and the target acceptance workload of the target employee is calculated by averaging the task workload, the number of attendance days + the number of shift days, and the initial acceptance workload. The target acceptance workload of the target employee is (24 + (22 + 3) + 20)/3 =23.

Optionally, the target acceptance workload of the application is the minimum value of the initial acceptance workload, the sum of the number of attendance days and the number of overtime days, and the task workload.

And step S106, sending the target acceptance workload to a settlement system so that the settlement system can settle based on the target acceptance workload.

Specifically, a message is sent to the settlement system, wherein the message comprises the target acceptance workload. And the settlement system receives the message, extracts the target acceptance workload, and settles the target acceptance workload according to the target acceptance workload and the price of each unit of target acceptance workload.

In the embodiment of the application, attendance data of a plurality of target employees are obtained from the target attendance equipment, and workload related data in the task execution process of each target employee is obtained from the task management system. The workload related data includes: task workload. And calculating the total efficiency score corresponding to each target employee according to the attendance checking data and the workload related data. And determining the efficiency level corresponding to each target employee according to the total efficiency score corresponding to each target employee. And calculating the initial acceptance workload of each target employee according to the efficiency level corresponding to each target employee. And determining the target acceptance workload of each target employee according to the initial acceptance workload, the attendance data and the task workload corresponding to each target employee. And sending the target acceptance workload to the settlement system so that the settlement system settles on the basis of the target acceptance workload. The total efficiency score of the target employee is determined by the objective data, namely the attendance data of the target employee and the workload related data in the task execution process, which are acquired from the target attendance equipment and the task management system, so that the total efficiency score and the efficiency level of the target employee are determined by calculation, and a more accurate basis can be provided for calculating the initial acceptance workload corresponding to the target employee. Because the efficiency level influences the determination of the acceptance workload of the target employees, the initial acceptance workload of each target employee is calculated according to the efficiency level corresponding to each target employee, and the initial acceptance workload of the target employees can be more accurately determined. Because the target acceptance workload of the target employees is beyond the initial acceptance workload, the influence of the attendance data and the task workload is also considered, so that the target acceptance workload of each target employee is determined according to the initial acceptance workload, the attendance data and the task workload corresponding to each target employee, and the target acceptance workload of the target employees can be as accurate as possible and close to the actual working condition. Because the attendance data required for calculating the target acceptance workload of the target employee and the workload related data in the task execution process are automatically acquired from the target attendance equipment and the task management system through the computer, and the calculation process is automatically completed by the computer, the acceptance efficiency of the target acceptance workload corresponding to the target employee is effectively improved.

Example two

Fig. 3 is a flowchart of a method of a data processing method in workload verification according to a second embodiment of the present disclosure, and as shown in fig. 2, on the basis of the first embodiment, the present disclosure relates to a specific implementation manner in which step S102 calculates a total performance score corresponding to each target employee according to attendance data and workload-related data. In the data processing method for workload acceptance check, when calculating the total efficiency score corresponding to each target employee according to the attendance data and the workload-related data, the specific steps include:

step S201, calculating a score corresponding to an execution efficiency index in the process of executing the task according to the attendance data.

The performance index refers to a performance index related to the execution of a job. The performance index is a first level performance index, and more second level performance indexes can be subdivided below the first level performance index. For example, the secondary performance indicator may be an attendance rate.

Specifically, the attendance data is substituted according to a secondary execution performance index calculation formula, and an actual result of the secondary execution performance index is calculated. And comparing the actual result of the secondary execution performance index with the score condition of the secondary execution performance index, thereby determining the score of each secondary execution performance index.

Illustratively, the attendance index calculation formula is attendance days/total work days. And substituting the number of the target staff attendance days and the total working day number of days in a preset time period into a formula, and calculating the attendance rate of the target staff. Comparing the attendance rate with the scoring condition, wherein the attendance rate is 87%, and the scoring condition is that the attendance rate is greater than 90%, and 10 points are obtained. Attendance >80% and <90% for 8 points. Attendance >70% and attendance <80%, giving 6 points. The attendance was <70%, 4 points were scored, and the attendance score was 8 points. The preset time period is not particularly limited, and may be one month, one quarter, one year, or the like.

Step S202, calculating the output performance index value and the quality performance index value in the task execution process according to the workload related data.

The output performance index refers to a performance index related to the working output. The output performance index is a first-level performance index, and more second-level output performance indexes can be subdivided below the first-level performance index. For example, for software developers, the secondary yield performance index may be the number of lines of submitted codes per day and the number of yield requirement items. The quality performance index refers to a performance index related to the working quality. The quality performance index is a primary performance index, under which more secondary quality performance indexes can be subdivided. For example, for a software developer, the secondary quality performance indicator may be defect density. Defect density generally refers to the number of errors that occur in each thousand lines of code.

Specifically, the actual result of the secondary output performance index is calculated by substituting the data related to the workload according to the secondary output performance index calculation formula. And comparing the actual result of the secondary output performance index with the scoring condition of the secondary output performance index, thereby determining the score of each secondary output performance index.

Illustratively, for the index of the number of code lines submitted per day, the number of the code lines submitted by the target employees in a preset time period is counted, and the number of the code lines submitted per day is calculated by dividing the number of days of a working day in the preset time period. The preset time period is not particularly limited. Which may be a month, a quarter, a year, etc. If the number of code lines submitted by the target employees in 7 months per day is calculated, counting the number of the code lines submitted by the target employees in 7 months, and dividing the counting result by the number of days of the working days in 7 months to obtain the number of the code lines submitted by the target employees in 7 months per day. Comparing the number of code lines submitted in the average day with the score condition, wherein if the number of code lines submitted in the average day is 1250 lines, the score condition is that the number of code lines submitted in the average day is more than 2000, and 10 points are obtained. The number of code lines submitted per day is >1500 and the number of code lines submitted per day is <2000, giving 8 points. The average daily submitted code line number >1000 and the average daily submitted code line number <1500, for 6 minutes, the average daily submitted code line number >500 and the average daily submitted code line number <1000, for 4 minutes. And if the average daily submitted code line number is less than 500, scoring 2, and the index score of the average daily submitted code line number of the target employee is 6.

The process of calculating the secondary quality performance index score is similar to the above-mentioned process of calculating the secondary output performance index score, and is not repeated herein.

Step S203, weights corresponding to the performance index, the throughput performance index and the quality performance index are obtained.

Wherein, each secondary performance index corresponds to a weight, and the sum of the weights of all the secondary performance indexes is 100%.

Specifically, the weight corresponding to each secondary performance index under the execution performance index, the output performance index and the quality performance index is obtained from the performance index system.

Step S204, performing weighted summation based on the values and weights corresponding to the execution performance index, the output performance index, and the quality performance index to obtain a corresponding total performance value.

In this embodiment, the total performance score includes an execution performance index weighted score, a yield performance index weighted score, and a quality performance index weighted score.

Specifically, the score of the secondary performance indicator is multiplied by the corresponding weight to obtain the weighted score of each secondary performance indicator. And adding all the secondary execution performance indexes to obtain the weighted score of the execution performance indexes. Similarly, a weighted value of the yield performance indicator and a weighted value of the quality performance indicator may be obtained. And summing the weighted scores of the three primary performance indexes, namely the execution performance index, the output performance index and the quality performance index to obtain a total performance score.

Illustratively, the performance indicators correspond to m second-level performance indicators eff _ei The corresponding weight is w _ei I is more than or equal to 1 and less than or equal to m; corresponding to n secondary output performance indexes eff under the output performance index _oj Corresponding weight is w _oj J is more than or equal to 1 and less than or equal to n; corresponding to l secondary quality efficiency indexes eff under the quality efficiency index _qk Corresponding weight is w _qk ，1≤k≤l。

Then, the overall potency Score, score _eff Can be formulated as:

in the embodiment of the application, the score corresponding to the execution efficiency index in the task execution process is calculated according to the attendance data. And calculating the output performance index value and the quality performance index value in the task execution process according to the workload related data. And acquiring weights corresponding to the execution efficiency index, the output efficiency index and the quality efficiency index respectively. And performing weighted summation based on the values and weights corresponding to the execution efficiency index, the output efficiency index and the quality efficiency index to obtain a corresponding total efficiency value. The execution efficiency index, the output efficiency index and the quality efficiency index can respectively reflect the actual working condition of the target staff from multiple dimensions, so that the corresponding index score is determined according to the efficiency index, and the actual working condition of the target staff can be more comprehensively reflected. Because the importance degree of each efficiency index is different, the actual work effect of the target employee can be more accurately reflected by the weighted score and the total efficiency score calculated based on the index weight.

EXAMPLE III

Fig. 4 is a flowchart of a method of a data processing method in workload verification according to a third embodiment of the present application, and as shown in fig. 4, on the basis of the first embodiment or the second embodiment, the present application relates to a specific implementation manner in which step S103 determines the performance level corresponding to each target employee according to the total performance score corresponding to each target employee. In the data processing method in workload acceptance check, when determining the performance level corresponding to each target employee according to the total performance score corresponding to each target employee, the specific steps include:

step S301, sorting the total efficiency scores corresponding to the target employees.

Specifically, the total performance scores corresponding to the multiple target employees are sorted from large to small. A total efficacy ranking is obtained.

And step S302, dividing the sorted total efficiency score according to a predetermined ratio corresponding to each efficiency level to determine the efficiency level corresponding to each target employee.

The proportion corresponding to the efficiency level is the proportion of the number of people corresponding to the efficiency level to the total number of the target employees.

Specifically, after sorting, the total efficacy score corresponding to each target employee corresponds to one total efficacy ranking. And multiplying the ratio corresponding to each efficiency level by the total number of the target employees to obtain the nominal number corresponding to each efficiency level. And determining the efficiency level corresponding to each target employee and the ranking in the corresponding efficiency level according to the total efficiency ranking and the nominal number corresponding to each efficiency level.

Specifically, the sum of the number of the famous people corresponding to the first two grades, the first three grades and the third grade of 823030is calculated in sequence. N is the last level of performance, and N is greater than or equal to 2. The calculation result and the number of the famous and famous people corresponding to the first level performance level form a plurality of numerical value intervals. Respectively [0, the number of the famous groups corresponding to the first level of the performance level ], [ the number of the famous groups corresponding to the first level of the performance level +1, the sum of the number of the famous groups corresponding to the first two levels of the performance level ], \ 8230 ], the sum of the number of the famous groups corresponding to the first N-1 levels of the performance level +1, and the sum of the number of the famous groups corresponding to the first N levels of the performance level ]. And comparing the total efficiency ranking of the target staff with the plurality of numerical value intervals, determining the numerical value interval in which the total efficiency ranking of the target staff is positioned, and taking the sequence number of the corresponding numerical value interval in the plurality of numerical value intervals as the efficiency level corresponding to the target staff. The ranking of the target staff in the corresponding performance level = total performance ranking-sum of the number of the famous people corresponding to the performance levels of the previous levels.

Optionally, the present application employs three performance levels.

Illustratively, the performance levels are divided into three levels. The number of famous people corresponding to the first performance level is 30, the number of famous people corresponding to the second performance level is 35, and the rest is the third performance level, so that the performance level of the target staff 30 before the total performance ranking is the first performance level. The ranking in the corresponding performance level is consistent with the overall performance ranking. The target employees with a total performance rank of 31 to 65 have a performance level of second rank, with the rank in the corresponding performance level being the total performance rank-30. The performance level of the target employee with the overall performance ranking after 65 is the third rank, and the rank in the corresponding performance level is the overall performance ranking of-65.

In the embodiment of the application, the total efficiency scores corresponding to the target employees are sorted. And dividing the sorted total efficiency score according to the predetermined proportion corresponding to each efficiency level so as to determine the efficiency level corresponding to each target employee. The total efficiency score can accurately reflect the actual work efficiency of the target employees, so that the efficiency level of each target employee can be more accurately determined by sequencing according to the total efficiency score and dividing according to the predetermined proportion corresponding to each efficiency level.

Example four

Fig. 5 is a flowchart of a method of a data processing method in workload verification according to a fourth embodiment of the present application, and as shown in fig. 5, on the basis of the third embodiment, the present application relates to a specific implementation manner in which step S104 calculates an initial verification workload of each target employee according to a performance level corresponding to each target employee. In the data processing method for workload acceptance check, when calculating the initial acceptance workload of each target employee according to the performance level corresponding to each target employee, the specific steps include:

step S401, obtaining an initial workload acceptance model corresponding to each pre-trained target employee performance level.

Specifically, an initial workload acceptance model corresponding to the efficacy level of each trained target employee is obtained.

Optionally, in this embodiment, the initial workload acceptance model is divided into three performance levels, and the formula may be expressed as formula (1):

wherein i is the ith target employee, A _1i 、A _2i 、A _3i The initial acceptance workload, W, corresponding to the first, second and third effect levels respectively _i Is the number of days of attendance, Q, of the ith target employee within a preset time period ₁ 、Q ₂ 、Q ₃ The maximum reward coefficients corresponding to the first, second and third effect levels are respectively. E _1i 、E _2i 、E _3i The rank of the target employee i in the corresponding efficiency level is shown, G is the total number of the target employees in the preset time period, and r1, r2 and r3 are the corresponding proportions of the first, second and third efficiency levels. Wherein, r1, r2, r3, Q ₁ 、Q ₂ 、Q ₃ And the model parameters are trained. The preset time period is not particularly limited, and may be one month, one quarter, one year.

Step S402, obtaining the total number of target employees, attendance data corresponding to each target employee, proportions corresponding to the effectiveness levels and ranking in the corresponding effectiveness levels.

Specifically, the total number of target employees and attendance data corresponding to each target employee in a preset time period, such as the number of attendance days, the number of overtime days, the legal working day of the month and the like, are obtained from a database; and acquiring the proportion corresponding to the performance level of the target staff and the ranking in the corresponding performance level. The ranking of the target employee in the corresponding performance level may be determined as described in step S302.

And S403, calculating the initial acceptance workload of each target employee according to the total number of the target employees, the initial workload acceptance model corresponding to each target employee, the attendance data, the proportion corresponding to the efficiency level and the ranking in the corresponding efficiency level.

Specifically, the total number of target employees, attendance data, the proportion corresponding to the efficiency level and the ranking in the corresponding efficiency level in a preset time period are substituted into the initial workload acceptance model of the target employees at the corresponding efficiency level to obtain the initial acceptance workload.

In the embodiment of the application, the initial workload acceptance model corresponding to the performance level of each target employee, which is trained in advance, is obtained. And acquiring the total number of target employees, attendance data corresponding to each target employee, a proportion corresponding to the efficiency level and ranking in the corresponding efficiency level. And calculating the initial acceptance workload of each target employee according to the total number of the target employees, the initial workload acceptance model corresponding to each target employee, the attendance data, the proportion corresponding to the efficiency level and the ranking in the corresponding efficiency level. The initial workload acceptance model is trained in advance, so that the model parameters are accurate, the initial acceptance workload of each target employee is calculated according to the total number of the target employees, the initial workload acceptance model corresponding to each target employee, attendance data, the proportion corresponding to the efficiency level and the ranking in the corresponding efficiency level, and the initial acceptance workload of the target employees can be determined more accurately.

EXAMPLE five

Fig. 6 is a flowchart of a method of a data processing method in workload acceptance check according to a fifth embodiment of the present application, and as shown in fig. 6, on the basis of the fourth embodiment, the present application relates to a specific implementation manner in which step S403 calculates an initial acceptance workload of each target employee according to a total number of the target employees, an initial workload acceptance model corresponding to each target employee, attendance data, a ratio corresponding to an efficiency level, and a rank in a corresponding efficiency level. Wherein, attendance data includes: the number of days on attendance. In the data processing method for workload acceptance, when the initial acceptance workload of each target employee is calculated according to the total number of the target employees, the initial workload acceptance model corresponding to each target employee, the attendance data, the proportion corresponding to the efficiency level and the ranking in the corresponding efficiency level, the specific steps include:

step S501, inputting the total number of target employees, the number of attendance days corresponding to each target employee, the proportion corresponding to the efficiency level and the ranking in the corresponding efficiency level into the corresponding initial workload acceptance model.

The method comprises the steps of determining the efficiency level of target employees as shown in the formula (1), and inputting the total number of the target employees, the number of attendance days corresponding to each target employee, the proportion corresponding to the efficiency level and the ranking in the corresponding efficiency level into an initial workload acceptance model of the efficiency level corresponding to the formula (1).

And step S502, calculating the initial acceptance workload of the corresponding target employee by adopting each initial workload acceptance model.

Specifically, as shown in formula (1), the initial acceptance workload of the target employee corresponding to the performance level is calculated according to the calculation formula of the obtained initial workload acceptance model corresponding to the performance level.

In this application embodiment, attendance data includes: the number of days on attendance. And inputting the total number of the target employees, the number of the attendance days corresponding to each target employee, the proportion corresponding to the efficiency level and the ranking in the corresponding efficiency level into the corresponding initial workload acceptance model. And calculating the initial acceptance workload of the corresponding target employee by adopting each initial workload acceptance model. Because different target employees correspond to different efficiency levels, the initial acceptance workload of the target employees can be more accurately calculated by adopting the initial workload acceptance model corresponding to the efficiency levels.

EXAMPLE six

Fig. 7 is a flowchart of a method of a data processing method in workload acceptance check according to a sixth embodiment of the present application, and as shown in fig. 7, on the basis of the fourth embodiment or the fifth embodiment, in the embodiment of the present application, the initial workload acceptance model further includes: the maximum reward factor. In the data processing method for workload acceptance, before calculating the initial acceptance workload of each target employee according to the total number of the target employees, the initial workload acceptance model corresponding to each target employee, attendance data, the proportion corresponding to the efficiency level and the ranking in the corresponding efficiency level, the following scheme is further included:

step S601, obtaining a training sample for training each initial workload acceptance model, wherein the training sample is a data set of historical acceptance workloads.

The data set of the historical acceptance workload refers to a historical acceptance record set of which the acceptance workload has been confirmed in the past. Each target employee has a historical acceptance record per month. The historical acceptance records may include month, department, personnel code, acceptance workload, legal work day days of the month, attendance days, overtime days, task workload.

Specifically, a set of historical acceptance records for the last year is obtained from the database. And carrying out statistical arrangement according to the month and department fields in the historical acceptance record, carrying out data cleaning on the historical acceptance record, and establishing a data set of the historical acceptance workload. And taking the data set of the historical checking workload as a training sample of each initial workload checking model.

Step S602, training the proportion and the maximum reward coefficient corresponding to the effect level in each initial workload acceptance model by adopting a training sample.

The maximum reward coefficient is a coefficient of the acceptance workload of the additional reward determined according to the performance level in consideration of the difficulty and the load of executing the task.

Specifically, an initial value of a ratio corresponding to each performance level is preset, and the total number of target employees is determined through statistics. And multiplying the proportion corresponding to each efficiency level by the total number of the target employees to obtain the nominal number corresponding to each efficiency level. And counting the sum of the acceptance workload and the sum of the attendance days of each target employee within a preset time period according to the personnel codes to obtain a statistical data set of the target employees. The statistical data set comprises personnel codes, statistics of checking and accepting workload, statistics of attendance days, statistics of overtime days and statistics of task workload. And sequencing the statistical data sets from large to small according to the statistical acceptance workload. And according to the number of the famous persons corresponding to each efficiency level, sequentially taking out the persons corresponding to the famous persons from the sorted statistical data set as target persons corresponding to the efficiency level of the file. And respectively acquiring the statistical acceptance workload and the statistical attendance days corresponding to the target staff from the statistical data set, substituting the statistical acceptance workload and the statistical attendance days into the following formula for calculation, and obtaining the maximum reward coefficient corresponding to the efficiency level.

The maximum reward factor of each performance level can be expressed as shown in formula (2):

wherein Q ₁ 、Q ₂ 、Q ₃ The performance levels of the first gear, the second gear and the third gear are respectively corresponding to the maximum reward coefficients. r1, r2, r3 are the ratios corresponding to the first gear, second gear, third gear performance levels, respectively. G is the total number of target employees. B is _i And counting the checking workload of the ith target employee in the first gear performance level within a preset time period. W _i And counting the attendance days of the ith target employee in the first gear performance level within a preset time period. B _j And counting the acceptance workload of the jth target employee in the second-gear performance level within a preset time period. W _j And counting the attendance days of the jth target employee in the second-gear performance level within a preset time period. Wherein, i is more than or equal to 1 and less than or equal to r 1G, j is more than or equal to 1 and less than or equal to r 2G, and r1+ r2+ r3=100%.

The preset time period is not particularly limited. It can be a month, a quarter or a year.

Illustratively, the total number of target employees is 100, r1=10%, r2=30%, r3=60%, and a data set of historical acceptance workloads of the last year is obtained as a training set. Firstly, the sum of the acceptance workload and the sum of the number of attendance days of each target employee in the last year are counted according to the personnel codes to obtain a statistical data set of the target employees. The statistical data set comprises personnel codes, statistics of checking and accepting workload, statistics of attendance days, statistics of overtime days and statistics of task workload. And secondly, sorting the statistical data sets from large to small according to the statistical acceptance workload. And thirdly, calculating the number of the famous persons corresponding to each efficiency level according to the proportion corresponding to each efficiency level. In this example, the number of nominations corresponding to the first performance level is the total number of target employees r1=100 r 10% = 10. Similarly, the second performance level corresponds to 30 famous people, and the third performance level corresponds to 60 famous people. And according to the number of the famous persons corresponding to each grade of the efficiency grade, sequentially taking out the persons corresponding to the famous persons from the sorted statistical data set as target persons corresponding to the efficiency grade of the grade. In this example, the staff with the total effectiveness ranking from 1 st to 10 th are taken as the target staff of the first level of effectiveness; taking employees with the 11 th to 40 th total performance ranks as target employees with the second performance level; the rest are target employees of the third-gear performance level. And aiming at each level of efficiency, obtaining the statistical acceptance workload and the statistical attendance days of all target employees corresponding to the level of efficiency, and substituting the statistical acceptance workload and the statistical attendance days into the maximum reward coefficient calculation formula corresponding to the level of efficiency to obtain the value of the maximum reward coefficient. Taking the target employee with the first-level performance level as an example, the statistics of the attendance days of the target employee corresponding to the first-level performance level are respectively as follows: 258. 256, 255, 254, 252, 251, 250, 248, 247, 246; the statistical acceptance workload of the target employee corresponding to the first-level efficiency level is respectively as follows: 297. 294, 288, 282, 279, 273, 267, 264, 258, 252. Substituting the data into a maximum reward coefficient calculation formula corresponding to the first-gear efficiency level to obtain:

accordingly, Q ₂ 、Q ₃ The calculation method is similar to that described above, and is not described in detail herein.

Step S603, if the accuracy of the acceptance workload is determined to be greater than or equal to the preset accuracy threshold, determining the initial workload acceptance model with the accuracy greater than or equal to the preset accuracy threshold as the trained initial workload acceptance model.

Specifically, the data set of the historical acceptance workload of the last year is used as the verification set according to the maximum reward coefficient of each performance level calculated in the previous step. And counting the total number of the attendance days and the total number of the checking and accepting workloads of each target employee within a preset time period. And obtaining a statistical data set of the target employee. The preset time period is not limited and may be one month, one quarter, one year, or the like. The statistical data set comprises personnel codes, statistics of acceptance workload, statistics of attendance days, statistics of shift days and task workload. And sequencing the statistical data set from large to small according to the statistical acceptance workload, taking the sequenced number as the total efficiency ranking corresponding to the target staff in a preset time period, confirming the efficiency level corresponding to the target staff and the ranking in the corresponding efficiency level according to the nominal number of the target staff corresponding to each efficiency level and the total efficiency ranking of the target staff for each target staff, substituting the maximum reward coefficient of each efficiency level, the statistical attendance days of the target staff in the statistical data set and the ranking of the target staff in the corresponding efficiency level into the initial workload acceptance model of the corresponding efficiency level, and calculating the initial acceptance workload of the target staff. And substituting the initial acceptance workload, the counted attendance days and the counted overtime days of the target employee in the statistical data set and the counted task workload into a determination formula of the target acceptance workload to obtain the target acceptance workload of the target employee within a preset time period. And comparing the calculated target acceptance workload with the statistical acceptance workload corresponding to the target employee in the statistical data set, and determining the deviation rate of the calculation result and the actual result.

The calculation formula of the deviation ratio p can be expressed as shown in formula (3):

p＝(Oc _cal -Oc _act )/Oc _act *100% type (3)

Wherein, oc is _cal The target acceptance workload, oc, corresponding to the target employee determined according to the formula _act And obtaining the corresponding statistical acceptance workload of the target employee from the statistical data set.

And when the deviation rate is smaller than a preset threshold value, marking the statistical data in the statistical data set corresponding to the target employee as data conforming to the calculation result. The threshold value range is preset as [0, 100% ]. Illustratively, it may be 10%.

According to the steps, the deviation rate corresponding to the target acceptance workload of all the target employees in the statistical data set in the preset time period is calculated, and data conforming to the calculation result are marked in the statistical data set.

And dividing the number of data which accord with the calculation result by the total number of the data of the statistical data set to obtain the accuracy. The formula can be expressed as:

accuracy = number of data fitting calculation result/total number of data of statistical data set

When the accuracy is less than the preset accuracy threshold, adjusting r1, r2 and r3 of the initial workload acceptance model, and correspondingly recalculating Q ₁ 、Q ₂ 、Q ₃ And ending the model training until the accuracy reaches a preset accuracy threshold, and determining the model as an initial workload acceptance model after the training is finished. The preset accuracy threshold is not particularly limited, and may be, for example, 90%.

In this embodiment of the present application, the initial workload acceptance model further includes: the maximum reward factor. And acquiring a training sample for training each initial workload acceptance model, wherein the training sample is a data set of historical acceptance workload. And training the proportion and the maximum reward coefficient corresponding to the efficiency level in each initial workload acceptance model by adopting a training sample. And if the accuracy of the acceptance workload is determined to be greater than or equal to the preset accuracy threshold, determining the initial workload acceptance model with the accuracy greater than or equal to the preset accuracy threshold as the trained initial workload acceptance model. Because the accuracy can objectively reflect the fitting degree of the initial workload acceptance model and the historical actual condition, the accuracy is used as the measurement standard of model training, so that the parameter training of the initial workload acceptance model is more accurate and more in line with the actual condition.

EXAMPLE seven

Fig. 8 is a flowchart of a method of a data processing method in workload verification according to a seventh embodiment of the present application, and as shown in fig. 8, on the basis of any one of the fourth to sixth embodiments of the present application, the method of data processing in workload verification according to the present application further includes the following steps:

and S701, calculating the total work amount of each target employee in a preset time period by adopting a preset total work amount estimation model.

The preset time period is not limited, and may be one month, one quarter, one year, and the like.

Specifically, the formula of the total work amount prediction model can be expressed as shown in formula (4):

y = ax z + b formula (4)

Wherein y represents the estimated total work amount, x represents the legal working day number of the month, z represents the total number of target employees, and a and b are model parameters.

Specifically, the work total estimation model firstly obtains a data set of historical acceptance workload, and accounts the acceptance workload of all target employees in the department of the current month according to the department monthly to obtain an acceptance workload statistical data set. The total checking work amount statistical data set comprises a total number of target employees, legal working days of the month and data fields of the total checking work amount. The statistical data set of the total amount of work to be checked is used as a training set, a least square method is used for training a total amount of work estimation model, values of parameters a and b can be obtained, and a formula is expressed as a formula (5):

where n is the number of training samples,

is the average of the product of the number of legal working days in the month and the total number of target employees,

the formula can be expressed as shown in formula (6):

is an average value of the total amount of statistical acceptance work, and the formula can be expressed as shown in formula (7):

wherein x is _i Number of legal working days of the month, y, for the ith training sample _i For the statistical acceptance of the total amount of work, z, of the ith training sample _i The target employee total for the ith training sample. Wherein n is more than or equal to 1, i is more than or equal to 1 and less than or equal to n.

Specifically, the total work amount of each target employee in a preset time period is predicted by using a trained total work amount prediction model. And substituting the total number of target employees per month and legal working days of the current month in a calculation formula of a total work amount estimation model within a preset time period to obtain the estimated total work amount per month, and accumulating the estimated total work amount per month to obtain the estimated total work amount of each target employee within the preset time period.

And S702, calculating the total attendance days according to the attendance data of the target employees and calculating the total task workload according to the task workload of the target employees.

Specifically, attendance data in a preset time period are obtained, and the total attendance days and the total task workload of each target employee in the preset time period are counted.

And step S703, if it is determined that the total sum of the target acceptance workloads corresponding to the target employees is greater than or equal to at least one of the total work amount, the total attendance days and the total task workload in the preset time period, adjusting the initial workload acceptance model.

Specifically, the target acceptance workload of each target employee is calculated according to the methods of the fourth embodiment, the fifth embodiment and the ninth embodiment, and the target acceptance workload of each target employee is calculatedAnd (4) carrying out workload summary statistics to obtain a target acceptance workload sum, comparing the target acceptance workload sum with the total work amount, the total attendance days and the total task workload in the preset time period obtained in the steps S701 and S702, and if the target acceptance workload sum corresponding to each target employee is determined to be more than or equal to at least one of the total work amount, the total attendance days and the total task workload in the preset time period, indicating that the target acceptance workload sum exceeds an upper limit threshold value. Readjusting r1, r2, r3 parameters of the initial workload acceptance model and recalculating Q ₁ 、Q ₂ 、Q ₃ Until the initial workload acceptance model meets both the requirement of accuracy rate described in the sixth embodiment and the requirement of the verification rule of the embodiment of the present application.

In the embodiment of the application, the total work amount of each target employee in the preset time period is calculated by adopting a preset total work amount estimation model. And calculating the total attendance days according to the attendance data of each target employee and calculating the total task workload according to the task workload of each target employee. And if the sum of the target acceptance workloads corresponding to the target employees is determined to be more than or equal to at least one of the total work amount, the total attendance days and the total task workload in the preset time period, adjusting the initial workload acceptance model. The total work amount, the total attendance days and the total task workload in the preset time period can be used as the upper limit threshold of the target acceptance workload, so that the target acceptance workload sum is compared and verified with the total work amount, the total attendance days and the total task workload in the preset time period, and the target acceptance workload can be more accurate. The target acceptance workload model takes the minimum value from the initial acceptance workload, the sum of the number of days of attendance and the number of days of overtime and the task workload, so that the initial acceptance workload model is adjusted by adjusting the target acceptance workload. When the sum of the target acceptance workload exceeds the upper limit threshold, parameters of each initial workload acceptance model are adjusted, so that the initial workload acceptance model is more accurate.

Example eight

Fig. 9 is a flowchart of a method of a data processing method in workload verification provided in an eighth embodiment of the present application, and as shown in fig. 9, on the basis of the seventh embodiment, the method of data processing in workload verification provided in the embodiment of the present application further includes the following steps:

and step S801, acquiring the number of recruiting employees in each preset time period.

The preset time period is not particularly limited, and may be one month, one quarter, or one year.

Specifically, the number of recruiters in each preset time period in the current year history is obtained from the database. And if the current month is 1 month or 2 months, acquiring the number of recruiters in each preset time period in the history of the previous year.

Optionally, the preset time period in the embodiment of the present application is one month.

And S802, determining the estimated number of the recruitment employees in the future preset time period according to the number of the recruitment employees in each preset time period in the history.

Specifically, the change condition of the number of the recruitment employees in each preset time period in history is calculated, namely the number of the recruitment employees in the next preset time period in history-the number of the recruitment employees in the previous preset time period in history. Illustratively, if the change of the number of the recruiters is found in 4 months, namely the number of the recruiters is found in 3 months, and the change number of the recruiters is found in 4 months.

Specifically, the number of the changed persons of the recruiters in each preset time period is calculated, the average number is calculated, and the average number of the changed persons of the historical recruiters is obtained. The formula can be expressed as shown in formula (8):

wherein, the first and the second end of the pipe are connected with each other,

the average number of the changed persons for the historical recruiters is m, wherein m is m historical preset time periods, and X is _i And changing the number of recruiters in the ith historical preset time period. Wherein i is more than or equal to 1 and less than or equal to m, and m is more than or equal to 2.

Specifically, the average change number of the historical recruiters is added with the number of the recent recruiters in the historical preset time period, so as to obtain the estimated number of the recruiters in the current preset time period.

Illustratively, the last historical preset time period is 4 months, and the number of recruiters in 4 months is X _off The average number of the changed historical recruiters is

Then

And S803, calculating the estimated total work amount in the future preset time period according to the estimated number of the recruiting staff in the future preset time period and a preset total work amount estimation model.

Specifically, the estimated number of recruiters in the future preset time period is obtained according to the steps, the estimated number of recruiters and the legal working day number in the future preset time period are substituted into the trained total work amount estimation model, and the estimated total work amount in the future preset time period is calculated.

And S804, determining the number of recruiters to be pushed according to the estimated total work amount in the future preset time period.

Specifically, since the successful recruitment of employees is a probabilistic event, the success rate of recruitment is obtained according to prior experience. And obtaining the number of the recruitment employees to be pushed according to the estimated total work amount and the recruitment success rate. The formula is expressed as:

the number of recruiting staff to be pushed = estimated total work amount/recruitment success rate

Specifically, the estimated work amount and the recruitment success rate are substituted into the calculation formula to obtain the number of the recruitment staff to be pushed.

And S805, pushing the number of the recruiters to be pushed to the recruitment equipment.

Specifically, a pushing message is sent to the recruitment device, the pushing message comprises the information of the number of the recruitment staff to be pushed, and the recruitment staff number to be pushed determined in the previous step is pushed to the recruitment device.

In the embodiment of the application, the number of recruiting employees in each preset time period in history is obtained. And determining the estimated number of the recruitment employees in the future preset time period according to the number of the recruitment employees in each historical preset time period. And calculating the estimated total work amount in the future preset time period according to the estimated number of recruiters in the future preset time period and a preset total work amount estimation model. And determining the number of recruiting staff to be pushed according to the estimated total work amount in the future preset time period. And pushing the number of the recruiting staff to be pushed to the recruiting device. The historical number of the recruiters has a reference function on the number of the recruiters in the future, so that the estimated number of the recruiters in the future preset time period can be accurately estimated according to the number of the recruiters in the historical preset time period. The preset total work amount estimation model can accurately estimate the total work amount, so that the number of the employees to be delivered can be more accurately determined according to the estimated total work amount, and the recruitment condition can be better controlled.

Example nine

Fig. 10 is a flowchart of a method of a data processing method in workload acceptance check according to a ninth embodiment of the present application, and as shown in fig. 10, on the basis of any one of the first to eighth embodiments, the embodiment of the present application relates to a specific implementation manner in which step S105 determines a target acceptance workload of each target employee according to an initial acceptance workload, attendance data, and a task workload corresponding to each target employee. In the data processing method for workload acceptance, when determining the target acceptance workload of each target employee according to the initial acceptance workload, the attendance data and the task workload corresponding to each target employee, the specific steps include:

step S901, comparing the initial acceptance workload, attendance data, and task workload corresponding to each target employee.

Specifically, the attendance days in the attendance data are added with the overtime days to obtain a total working day. And counting the total working days and the task workload in a preset time period. Wherein the preset time period is consistent with the time range of the initial acceptance workload. And comparing the initial acceptance workload corresponding to the target staff with the total working days and the task workload of the preset time period.

And S902, aiming at each target employee, determining the minimum value of the corresponding initial acceptance workload, attendance data and task workload as the target acceptance workload of the corresponding target employee.

Specifically, the minimum value among the initial acceptance workload corresponding to the target employee, the total working days of the counted preset time period and the task workload is determined as the target acceptance workload of the corresponding target employee. Target acceptance workload G _eff The calculation formula can be expressed as shown in formula (9):

G _eff ＝min(W+V，B _eff t) formula (9)

Wherein W is the number of attendance days in the preset time period, V is the number of overtime days in the preset time period, B _eff And T is the task workload in a preset time period. Wherein the preset time period is consistent with the time range of the initial acceptance workload.

In the embodiment of the application, the initial acceptance workload, the attendance data and the task workload corresponding to each target employee are compared. And aiming at each target employee, determining the minimum value of the corresponding initial acceptance workload, the attendance data and the task workload as the target acceptance workload of the corresponding target employee. Because the acceptance rule needs to simultaneously meet the requirements of the initial acceptance workload, the attendance data and the task workload, the intersection of the three is taken, namely the target acceptance workload obtained by the minimum value is more accurate.

Example ten

Fig. 11 is a virtual configuration diagram of a data processing apparatus in workload verification according to a tenth embodiment of the present invention, and as shown in fig. 11, a data processing apparatus 100 in workload verification according to an embodiment of the present invention is provided. The device comprises: the system comprises an acquisition module 101, a first calculation module 102, a first determination module 103, a second calculation module 104, a second determination module 105 and a sending module 106.

The obtaining module 101 is configured to obtain attendance data of a plurality of target employees from the target attendance device, and obtain data related to workload of each target employee in a task execution process from the task management system. The workload related data includes: task workload.

The first calculating module 102 is configured to calculate a total efficiency score corresponding to each target employee according to the attendance data and the workload-related data.

The first determining module 103 is configured to determine an efficiency level corresponding to each target employee according to the total efficiency score corresponding to each target employee.

The second calculating module 104 is configured to calculate an initial acceptance workload of each target employee according to the performance level corresponding to each target employee.

And the second determining module 105 is configured to determine the target acceptance workload of each target employee according to the initial acceptance workload, the attendance data, and the task workload corresponding to each target employee.

And the sending module 106 is configured to send the target acceptance workload to the settlement system, so that the settlement system performs settlement based on the target acceptance workload.

Optionally, the first calculating module 102 is specifically configured to calculate, according to the attendance data, a score corresponding to an execution performance index in the process of executing the task when calculating the total performance score corresponding to each target employee according to the attendance data and the workload related data. And calculating the output performance index value and the quality performance index value in the task execution process according to the workload related data. And acquiring weights corresponding to the execution efficiency index, the output efficiency index and the quality efficiency index respectively. Based on the execution performance index, the output performance index and the score and the weight corresponding to the quality performance index, the weighted sum is carried out to obtain the corresponding total performance score.

Optionally, the first determining module 103 is specifically configured to sort the total efficiency scores corresponding to the multiple target employees when determining the efficiency level corresponding to each target employee according to the total efficiency score corresponding to each target employee. And dividing the sorted total efficiency score according to the predetermined proportion corresponding to each efficiency level so as to determine the efficiency level corresponding to each target employee.

Optionally, the second calculating module 104 is specifically configured to obtain an initial workload acceptance model corresponding to each pre-trained performance level of each target employee when calculating the initial acceptance workload of each target employee according to the performance level corresponding to each target employee. And acquiring the total number of target employees, attendance data corresponding to each target employee, a proportion corresponding to the efficiency level and ranking in the corresponding efficiency level. And calculating the initial acceptance workload of each target employee according to the total number of the target employees, the initial workload acceptance model corresponding to each target employee, the attendance data, the proportion corresponding to the efficiency level and the ranking in the corresponding efficiency level.

Optionally, the second calculating module 104 is specifically configured to input the total number of the target employees, the number of attendance days corresponding to each target employee, the proportion corresponding to the performance level, and the ranking in the corresponding performance level into the corresponding initial workload acceptance model when calculating the initial acceptance workload of each target employee according to the initial workload acceptance model, the attendance data, the proportion corresponding to the performance level, and the ranking in the corresponding performance level corresponding to each target employee. And calculating the initial acceptance workload of the corresponding target employee by adopting each initial workload acceptance model.

Optionally, the data processing apparatus in workload acceptance check provided by the present application further includes a model training module.

And the model training module is specifically used for acquiring training samples for training each initial workload acceptance model before calculating the initial acceptance workload of each target employee according to the total number of the target employees, the initial workload acceptance models corresponding to each target employee, the attendance data, the proportion corresponding to the efficiency levels and the ranks in the corresponding efficiency levels, wherein the training samples are data sets of historical acceptance workloads. And training the proportion and the maximum reward coefficient corresponding to the efficiency level in each initial workload acceptance model by adopting the training samples. And if the accuracy of the checking workload is determined to be greater than or equal to the preset accuracy threshold, determining the initial workload checking model with the accuracy greater than or equal to the preset accuracy threshold as the trained initial workload checking model.

Optionally, the data processing apparatus in workload verification and acceptance provided by the present application further includes a verification parameter adjustment module.

And the checking and parameter adjusting module is used for calculating the total work amount of each target employee in a preset time period by adopting a preset total work amount estimation model. And calculating the total attendance days according to the attendance data of each target employee and calculating the total task workload according to the task workload of each target employee. And if the sum of the target acceptance workloads corresponding to the target employees is determined to be more than or equal to at least one of the total work amount, the total attendance days and the total task workload in the preset time period, adjusting the initial workload acceptance model.

Optionally, the data processing apparatus in workload acceptance provided by the present application further includes a third calculation module and a pushing module.

And the third calculation module is used for acquiring the number of the recruiters in each preset time period. And determining the estimated number of the recruitment employees in the future preset time period according to the number of the recruitment employees in each historical preset time period. And calculating the estimated total work amount in the future preset time period according to the estimated number of recruiters in the future preset time period and a preset total work amount estimation model. And determining the number of recruiting staff to be pushed according to the estimated total work amount in the future preset time period.

And the pushing module is used for pushing the number of the recruiters to be pushed to the recruitment equipment.

Optionally, the second determining module 105 is specifically configured to compare the initial acceptance workload, the attendance data, and the task workload corresponding to each target employee when determining the target acceptance workload of each target employee according to the initial acceptance workload, the attendance data, and the task workload corresponding to each target employee. And aiming at each target employee, determining the minimum value of the corresponding initial acceptance workload, the attendance data and the task workload as the target acceptance workload of the corresponding target employee.

EXAMPLE eleven

Fig. 12 is an entity structure diagram of an electronic device for data processing in workload verification according to an eleventh embodiment of the present application, and as shown in fig. 12, an electronic device 110 according to an embodiment of the present application includes: a processor 111, and a memory 112 and a transceiver 113 communicatively coupled to the processor 111.

The memory 112 stores computer-executable instructions. A transceiver 113 for transceiving data with the settlement system.

The processor 111 executes computer executable instructions stored by the memory 112 to implement the method as described in any of the first through ninth embodiments.

The embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, the method according to any one of the first to ninth embodiments is implemented.

The embodiments of the present application further provide a computer program product, which includes a computer program, and when the computer program is executed by a processor, the method provided in any one of the embodiments of the present application is implemented.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of modules is merely a division of logical functions, and an actual implementation may have another division, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form.

Modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

In addition, functional modules in the embodiments of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware form, and can also be realized in a form of hardware and a software functional module.

Program code for implementing the methods of the present application may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable compliance detection device such that the program codes, when executed by the processor or controller, cause the functions/acts specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

Further, while operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the application. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. A method of processing data in workload verification, the method comprising:

2. The method of claim 1, wherein calculating a total performance score for each target employee based on the attendance data and the workload-related data comprises:

calculating a score corresponding to an execution efficiency index in the task execution process according to the attendance data;

calculating output performance index values and quality performance index values in the task execution process according to the workload related data;

acquiring weights corresponding to the execution efficiency index, the output efficiency index and the quality efficiency index respectively;

based on the execution performance index, the output performance index and the score and the weight corresponding to the quality performance index, the weighted sum is carried out to obtain the corresponding total performance score.

3. The method of claim 1, wherein determining the performance level for each target employee based on the total performance score for each target employee comprises:

sorting the total efficiency scores corresponding to the target employees;

and dividing the sorted total efficiency score according to a predetermined ratio corresponding to each efficiency level to determine the efficiency level corresponding to each target employee.

4. The method of claim 3, wherein calculating the initial acceptance workload of each target employee based on the performance level corresponding to each target employee comprises:

acquiring an initial workload acceptance model corresponding to each pre-trained target employee efficiency level;

acquiring the total number of target employees, attendance data corresponding to each target employee, a proportion corresponding to the efficiency level and ranking in the corresponding efficiency level;

and calculating the initial acceptance workload of each target employee according to the total number of the target employees, the initial workload acceptance model corresponding to each target employee, the attendance data, the proportion corresponding to the efficiency level and the ranking in the corresponding efficiency level.

5. The method of claim 4, wherein the attendance data comprises: the number of attendance days;

the step of calculating the initial acceptance workload of each target employee according to the initial workload acceptance model corresponding to each target employee, the attendance data, the proportion corresponding to the efficiency level and the rank in the corresponding efficiency level comprises the following steps:

inputting the total number of the target employees, the number of attendance days corresponding to each target employee, the proportion corresponding to the efficiency level and the ranking in the corresponding efficiency level into a corresponding initial workload acceptance model;

and calculating the initial acceptance workload of the corresponding target employee by adopting each initial workload acceptance model.

6. The method of claim 4, wherein the initial workload acceptance model further comprises: a maximum reward factor;

before calculating the initial acceptance workload of each target employee according to the total number of the target employees, the initial workload acceptance model corresponding to each target employee, the attendance data, the proportion corresponding to the efficiency level and the ranking in the corresponding efficiency level, the method further comprises the following steps:

acquiring a training sample for training each initial workload acceptance model, wherein the training sample is a data set of historical acceptance workload;

training the proportion and the maximum reward coefficient corresponding to the effect level in each initial workload acceptance model by adopting a training sample;

and if the accuracy of the acceptance workload is determined to be greater than or equal to the preset accuracy threshold, determining the initial workload acceptance model with the accuracy greater than or equal to the preset accuracy threshold as the trained initial workload acceptance model.

7. The method of claim 4, further comprising:

calculating the total work amount of each target employee in a preset time period by adopting a preset total work amount estimation model;

calculating the total attendance days according to the attendance data of each target employee and calculating the total task workload according to the task workload of each target employee;

and if the total sum of the target acceptance workload corresponding to each target employee is determined to be more than or equal to at least one of the total work amount, the total attendance days and the total task workload in the preset time period, adjusting each initial workload acceptance model.

8. The method of claim 7, further comprising:

acquiring the number of recruiters in each preset time period in history;

determining the estimated number of the recruitment employees in the future preset time period according to the number of the recruitment employees in each historical preset time period;

calculating the estimated total work amount in the future preset time period according to the estimated number of recruiting staff in the future preset time period and a preset total work amount estimation model;

determining the number of recruiting staff to be pushed according to the estimated total work amount in a future preset time period;

and pushing the number of the recruitment employees to be pushed to the recruitment equipment.

9. The method of any one of claims 1-8, wherein determining the target acceptance workload of each target employee based on the initial acceptance workload, the attendance data, and the task workload for each target employee comprises:

comparing the initial acceptance workload, the attendance data and the task workload corresponding to each target employee;

and aiming at each target employee, determining the minimum value of the corresponding initial acceptance workload, the attendance data and the task workload as the target acceptance workload of the corresponding target employee.

10. A data processing apparatus in workload verification, the apparatus comprising:

the acquisition module is used for acquiring attendance data of a plurality of target employees from the target attendance equipment and acquiring workload related data of each target employee in the task execution process from the task management system; the workload related data comprises: task workload;

11. An electronic device, comprising: a processor, and a memory and transceiver communicatively coupled to the processor;

the memory stores computer-executable instructions; the transceiver is used for receiving and transmitting data with a settlement system;

the processor executes computer-executable instructions stored by the memory to implement the method of any of claims 1-9.

12. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, perform the method of any one of claims 1-9.