Salary data statistical method and device, equipment and storage medium

Abstract

The invention provides a salary data statistical method and device, equipment and a storage medium, and the method comprises the steps: obtaining a target latitude selected for salary analysis, the target latitude comprising any post system or business department in an enterprise; obtaining salary data, wherein the salary data comprises the property of each salary of each employee, and the post system identifier and the business department identifier to which each employee belongs; according to the property of each emolument of each employee, an average emolument structure under the target latitude is determined, and the emolument structure comprises a target ratio of a fixed emolument to a total emolument; a target ratio range corresponding to the target latitude is determined according to a preset mapping relation between latitude and salary, and the mapping relation comprises a reasonable ratio range of fixed salary in total salary under each dimension; and if the target ratio is not within the target ratio range, outputting a prompt that the salary distribution is unreasonable. Through analysis based on the salary data, unreasonable salary distribution can be found in time, and the situation of unreasonable salary distribution is avoided.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to a method, device, equipment, and storage medium for statistical analysis of salary data. Background Art

[0002] In large group enterprises, the salary management systems of each business unit, department, and job series usually vary, especially in terms of salary analysis criteria and frameworks. This inconsistent salary management framework leads to information asymmetry in salary decision-making, making it difficult for the group level to comprehensively grasp the salary distribution of each unit, thus affecting the rationality and fairness of the overall salary.

[0003] Due to the inconsistent salary management standards of each department and job series, the group level faces many difficulties in salary analysis. The salary data of different business units and positions cannot be compared uniformly, it is difficult to evaluate the rationality of the salary structure, and abnormal salary distributions cannot be detected in a timely manner.

[0004] Therefore, how to integrate the group's salary data and timely detect unreasonable salary structures is the problem faced. Summary of the Invention

[0005] This application provides a method, device, equipment, and storage medium for statistical analysis of salary data to help the group level evaluate the reasonable distribution of the salary structure and timely detect unreasonable salary structures.

[0006] In a first aspect, this application provides a method for statistical analysis of salary data, which is applied to a salary integration platform. The method includes:

[0007] In response to a user's salary analysis operation, obtain the target dimension selected for the salary analysis, where the target dimension includes any job series or business unit within the enterprise;

[0008] Obtain salary data, where the salary data includes the nature of each salary payment for each employee, as well as the job series identifier and business unit identifier to which each employee belongs;

[0009] Determine the average salary structure under the target dimension according to the nature of each salary payment for each employee. The salary structure includes the target ratio of fixed salary to total salary;

[0010] Determine the target ratio range corresponding to the target dimension according to the preset mapping relationship between the dimension and the salary, where the mapping relationship includes the reasonable ratio range of fixed salary to total salary under each dimension;

[0011] If the target ratio is not within the target ratio range, output a prompt indicating that the salary distribution is unreasonable.

[0012] Optionally, if the target dimension is the target job series, determining the average salary structure under the target dimension according to the nature of each employee's salary per item includes:

[0013] Determine the total salary and total fixed salary of the target job series according to the nature of each employee's salary per item and the job series identifier to which each employee belongs;

[0014] Divide the total fixed salary by the total salary to determine the target ratio.

[0015] Optionally, if the target dimension is the target business unit, determining the average salary structure under the target dimension according to the nature of each employee's salary per item includes:

[0016] Determine the total salary and total fixed salary of the target business unit according to the nature of each employee's salary per item and the business unit identifier to which each employee belongs;

[0017] Divide the total fixed salary by the total salary to determine the target ratio.

[0018] Optionally, the method further includes:

[0019] For the target business unit, determine the total bonus amount according to the nature of each employee's salary per item;

[0020] Obtain financial report data and determine the total profit amount from the financial report data;

[0021] If the proportion of the total bonus amount to the total profit amount exceeds the preset proportion, output a prompt indicating that the salary distribution is unreasonable.

[0022] Optionally, the method further includes:

[0023] For the target business unit, obtain the employee comprehensive score ranking data;

[0024] According to the employee comprehensive score ranking data, determine the total bonus amount of the employees in the preset percentage from the bottom in the score ranking;

[0025] If the percentage obtained by dividing the total bonus amount by the total profit amount is greater than the preset percentage, output a prompt indicating that the salary distribution is unreasonable.

[0026] Optionally, the method further includes:

[0027] For the target business unit, predict the amount to be paid in the next salary payment cycle according to the total salary;

[0028] If the amount to be paid is greater than the cash flow of the business unit, output a prompt message indicating insufficient funds.

[0029] Second aspect, the present application provides a statistical device for salary data, the device comprising:

[0030] A first acquisition module, configured to acquire a target dimension selected for the salary analysis in response to a salary analysis operation of a user, the target dimension including a job series or a business unit;

[0031] A second acquisition module, configured to acquire salary data, the salary data including the nature of each salary of each employee, and the job series identifier and business unit identifier to which each employee belongs;

[0032] A first determination module, configured to determine an average salary structure under the target dimension according to the nature of each salary of each employee, the salary structure including a target ratio of fixed salary to total salary;

[0033] A second determination module, configured to determine a salary target range for the target dimension according to a mapping relationship between a preset dimension and a normal salary range;

[0034] An output module, configured to output a prompt that the salary distribution is unreasonable if the target ratio is not within the salary target range.

[0035] Third aspect, the present application provides an electronic device, comprising: a memory, a processor;

[0036] The memory stores computer-executable instructions;

[0037] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the method according to any one of the first aspect.

[0038] Fourth aspect, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the method according to any one of the first aspect.

[0039] Fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it is the method according to any one of the first aspect.

[0040] The statistical method, device, equipment and storage medium for salary data provided by this application. The method includes: in response to the salary analysis operation of the user, obtaining the target dimension selected for salary analysis, where the target dimension includes any job series or business division within the enterprise; obtaining salary data, where the salary data includes the nature of each salary payment of each employee, as well as the job series identifier and business division identifier to which each employee belongs; determining the average salary structure under the target dimension according to the nature of each salary payment of each employee, and the salary structure includes the target ratio of fixed salary to total salary; determining the target ratio range corresponding to the target dimension according to the preset mapping relationship between the dimension and the salary, where the mapping relationship includes the reasonable ratio range of fixed salary to total salary under each dimension; if the target ratio is not within the target ratio range, then output a prompt indicating that the salary distribution is unreasonable. Through data-based analysis, salary analysis no longer relies on subjective judgment, ensuring that the salary structure of each job series or business division conforms to the salary policy and standards of the group, and avoiding the situation of unreasonable salary distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings herein are incorporated into the specification and form a part of this specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application.

[0042] Figure 1 It is a schematic flowchart of Embodiment 1 of the statistical method for salary data provided by this application;

[0043] Figure 2 It is a schematic flowchart of Embodiment 2 of the statistical method for salary data provided by this application;

[0044] Figure 3 It is a schematic flowchart of Embodiment 3 of the statistical method for salary data provided by this application;

[0045] Figure 4 It is a schematic diagram of the data processing route of the statistical method for salary data provided by this application;

[0046] Figure 5 It is a schematic structural diagram of a statistical device for salary data provided by this application;

[0047] Figure 6 It is a schematic structural diagram of the electronic equipment provided by this application.

[0048] Through the above-mentioned accompanying drawings, the clear embodiments of this application have been shown, and there will be more detailed descriptions hereinafter. These accompanying drawings and the textual descriptions are not intended to limit the scope of the concept of this application in any way, but to illustrate the concept of this application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] Exemplary embodiments will be described in detail herein, and examples thereof are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of apparatuses and methods consistent with some aspects of the present application as detailed in the appended claims.

[0050] Inside large enterprises, due to the inconsistent salary management standards among different departments and job series, the group level faces many difficulties in conducting salary analysis. Salary data of different business units and positions cannot be uniformly compared, it is difficult to evaluate the rationality of the salary structure, and abnormal salary distributions cannot be detected in a timely manner.

[0051] The present application aims to solve the problem of how to build a unified salary analysis model at the group level and the solution of salary analysis ideas in the case of inconsistent internal analysis architectures within each organizational system in large group enterprises. By accessing human resources project bonuses, salaries, dividend data, and financial operation data on the salary integration platform, users can select different dimensions for analysis on the salary integration platform. When analyzing by job series dimension, the salary distribution of each job series within the group can be intuitively seen; when analyzing by business unit dimension, whether the salaries of each business unit are reasonable can be intuitively seen, thus supporting the management in making strategic adjustments at the macro level.

[0052] The execution subject of the present application is a salary management platform or an electronic device on which the salary management platform is deployed.

[0053] The following uses specific embodiments to describe in detail the technical solution of the present application and how the technical solution of the present application solves the above technical problems. These several specific embodiments below can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0054] Figure 1 It is a schematic flowchart of the first embodiment of a statistical method for salary data provided by the present application, as Figure 1 shown, the method includes:

[0055] S101. In response to a user's salary analysis operation, obtain the target dimension selected for salary analysis, where the target dimension includes any job series or business unit within the enterprise.

[0056] Design a user interface on the salary management platform, where users can select the target dimension for salary analysis on this platform, such as "job series" or "business unit". After the user makes a selection, the target dimension is transmitted to the backend for processing.

[0057] The platform receives the selected target dimension by the user and obtains relevant salary data by calling the data interface. Each salary paid by the salary payment system records the nature of the salary.

[0058] S102. Obtain salary data, where the salary data includes the nature of each salary payment for each employee, as well as the job series identifier and business unit identifier to which each employee belongs.

[0059] According to the target dimension (job series or business unit), query the salary information of all employees under the target dimension, including the specific amount and nature of each salary payment (such as fixed salary, variable salary, bonus, subsidy, etc.). Store the query results in memory in the form of structured data for subsequent processing.

[0060] S103. Determine the average salary structure under the target dimension according to the nature of each salary payment for each employee. The salary structure includes the target ratio of fixed salary to total salary.

[0061] Classify the salary data according to the nature of each salary payment for each employee. The classification categories include fixed salary (basic salary) and variable salary (bonus, performance, subsidy, etc.).

[0062] For each employee under the target dimension, calculate the ratio of their fixed salary to their total salary respectively. Then, calculate the average of the fixed salary ratios of all employees as one item in the salary structure.

[0063] In one implementation, if the target dimension is the target job series, determine the total fixed salary and total salary of the target job series according to the nature of each salary payment for each employee and the job series identifier to which each employee belongs; divide the total fixed salary by the total salary to determine the target ratio.

[0064] In one implementation, if the target dimension is the target business unit, determine the total salary and total fixed salary of the target business unit according to the nature of each salary payment for each employee and the business unit identifier to which each employee belongs; divide the total fixed salary by the total salary to determine the target ratio.

[0065] S104. Determine the target ratio range corresponding to the target dimension according to the preset mapping relationship between the dimension and the salary, where the mapping relationship includes the reasonable ratio range of the fixed salary to the total salary under each dimension.

[0066] The platform has preset the reasonable ratio range of fixed salary to total salary under each dimension. For example, through job series classification, the variable salary ratio of marketing and service personnel is relatively high, and the salary structure is mainly based on bonuses. If the ratio of fixed salary to total salary is less than 30%, it is abnormal, and at the same time, if it is higher than 80%, it is also abnormal. Therefore, the reasonable ratio range of fixed salary to total salary for marketing and service personnel is 30% - 80%; the fixed salary of management personnel is relatively high, mainly based on salary. If the ratio of fixed salary to total salary is less than 80%, it is abnormal. Therefore, the reasonable ratio range of fixed salary to total salary is 80% - 100%. Another example is that through business unit classification, as an administrative secondary department, if the ratio of fixed salary to total salary of the financial headquarters is less than 80%, it is abnormal; as a secondary department with business attributes, if the ratio of fixed salary to total salary of the overseas operation headquarters is less than 30%, it is abnormal, and at the same time, if it is higher than 80%, it is also abnormal.

[0067] The above preset mapping relationship can be modified by the administrator user.

[0068] S105. If the target ratio is not within the target ratio range, then output a prompt indicating that the salary distribution is unreasonable.

[0069] Compare the average fixed salary ratio calculated in step S103 with the target salary range obtained in step S104. If it exceeds the target ratio range, it is determined that the salary distribution is unreasonable, and the user is prompted.

[0070] If the target ratio is within the target ratio range, the specific ratio can be output, and it can also be output in the form of a chart, allowing the user to view it more intuitively.

[0071] This embodiment provides a method for statistical analysis of salary data. The method includes: in response to the user's salary analysis operation, obtaining the target dimension selected for salary analysis, where the target dimension includes any job series or business unit within the enterprise; obtaining salary data, where the salary data includes the nature of each salary of each employee, as well as the job series identifier and business unit identifier to which each employee belongs; determining the average salary structure under the target dimension according to the nature of each salary of each employee, where the salary structure includes the target ratio of fixed salary to total salary; determining the target ratio range corresponding to the target dimension according to the preset mapping relationship between the dimension and the salary, where the mapping relationship includes the reasonable ratio range of fixed salary to total salary under each dimension; if the target ratio is not within the target ratio range, then output a prompt indicating that the salary distribution is unreasonable. Through data-based analysis, salary analysis no longer relies on subjective judgment, ensuring that the salary structure of each job series or business unit complies with the group's salary policy and standards, and avoiding unreasonable salary distribution.

[0072] Based on the above embodiment, salary analysis can also be performed from other perspectives within the business unit. Figure 2Schematic diagram of the second embodiment of a method for statistically analyzing salary data provided by this application, as shown in Figure 2 shown. For the target business unit, the following analysis steps are included:

[0073] S201. Determine the total bonus amount according to the nature of each salary of each employee.

[0074] In this step, the user selects a target business unit for analysis, sums up the bonuses of all employees under the target business unit, and obtains the total bonus amount.

[0075] S202. Obtain financial report data and determine the total profit amount from the financial report data.

[0076] In this step, obtain the total profit amount of the target business unit from the financial report data.

[0077] S203. If the proportion of the total bonus amount to the total profit amount exceeds the preset proportion, output a prompt indicating that the salary distribution is unreasonable.

[0078] The preset proportions for different business units are also different and are set by the management personnel.

[0079] This embodiment mainly analyzes the proportion of the bonus of the business unit to the profit and analyzes whether the overall bonus proportion is too high.

[0080] In addition, the management personnel of the business unit can conduct salary analysis on the internal personnel of the business unit. Figure 3 Schematic diagram of the third embodiment of a method for statistically analyzing salary data provided by this application, as shown in Figure 3 shown. For the target business unit to be analyzed, the following steps are included:

[0081] S301. Obtain the comprehensive score ranking data of employees.

[0082] In this step, the bonus of the employees in each business unit is related to the ranking of the comprehensive score. Normally, the bonuses of some employees with higher rankings should be higher than those of employees with lower rankings.

[0083] S302. Determine the total bonus amount of the employees in the preset percentage from the bottom up according to the comprehensive score ranking data of employees.

[0084] In this step, obtain the bonuses of the employees with the preset percentage of the lowest rankings and accumulate them to obtain the total bonus amount.

[0085] S303. If the percentage obtained by dividing the total bonus amount by the total profit amount is greater than the preset percentage, output a prompt indicating that the salary distribution is unreasonable.

[0086] Exemplarily, if 30% of the bonuses are distributed to the users with the last 30% of the comprehensive scores, the distribution is unreasonable.

[0087] Through the method of this embodiment, the salary analysis object can be flexibly changed, the rationality of the overall bonus distribution of the business unit can be evaluated, the workload of personnel can be reduced, and the work efficiency can be improved.

[0088] Salary analysis can be carried out in multiple analysis directions and displayed on the same visualization platform, providing a more comprehensive perspective for the group leadership.

[0089] The data can be centrally displayed according to the following dimensions:

[0090] 1) Analyze according to the "business unit" dimension; that is, analyze according to each industrial cluster under the group, such as the "excavator business unit", "crane business unit", etc. When analyzing, key operating indicators such as "sales volume" and "total profit" need to be obtained synchronously.

[0091] 2) Analyze according to the "job series" dimension; that is, conduct analogical analysis according to each job series under the group, such as the "R & D job series", "finance job series", "marketing job series", etc.

[0092] 3) Analyze according to the "salary composition" dimension; that is, according to the group salary structure, such as "basic salary", "sporadic bonus", "year-end bonus", "equity incentive", "profit sharing incentive (multiple policies)", etc.

[0093] By comparing from different angles, setting standards, and identifying anomalies:

[0094] 1) Comparative analysis from the perspective of salary structure

[0095] The group / business unit / secondary department can view the comparison of fixed salary (salary, year-end bonus, five social insurances and one housing fund) and variable salary (bonus) of their own unit, and set different standards according to the attributes of the business unit and the different functional departments to analyze whether it is reasonable. For example: As an administrative secondary department, if the proportion of fixed salary in the total salary of the financial headquarters is less than 80%, it is abnormal; as a secondary department with business attributes, if the proportion of fixed salary in the total salary of the overseas operation headquarters is less than 30%, it is abnormal, and at the same time, if it is higher than 80%, it is also abnormal.

[0096] The group / business unit can view the comparison of fixed salary and variable salary of different job series and analyze whether it is reasonable. For example: The proportion of variable salary of marketing service personnel is relatively high, and the salary structure is mainly based on bonuses. If the proportion of fixed salary in the total salary is less than 30%, it is abnormal, and at the same time, if it is higher than 80%, it is also abnormal; for management personnel, the fixed salary is relatively high, mainly based on salary. If the proportion of fixed salary in the total salary is less than 80%, it is abnormal.

[0097] 2) Comparative analysis of sales volume, profit and salary

[0098] Group level: Mainly analyze the proportion of bonuses in the incremental profit to see if the overall bonus intensity is too high. For a manufacturing enterprise group, if the proportion of bonuses in the incremental profit at the overall level exceeds 25%, it is abnormal and an automatic identification warning will be issued.

[0099] Business unit / secondary department level: Mainly analyze the proportion of bonuses in sales and pre-tax profit to see if the bonus intensity among departments is reasonable. For business units / secondary departments that do not engage in sales or production, the proportion of bonuses in the total department should not exceed 5%, otherwise it is unreasonable and the platform will automatically identify it; for business units / secondary departments that engage in sales or production, if the deviation between the proportion of department sales / pre-tax profit and the proportion of bonuses exceeds 20%, it will be identified as unreasonable.

[0100] 3) Specific bonus comparison analysis for individuals

[0101] The group / business unit has set up a bonus ranking table and a personal 360 comprehensive score respectively. The corresponding leaders can view the bonus situation of the personnel in their units and analyze whether it matches the actual performance. For the employees in the last 30% of the personal 360 comprehensive score, if the bonus proportion is in the top 30%, it is identified as an unreasonable distribution.

[0102] As the basis for internal bonus adjustment, for those with relatively low overall bonuses but good performance, the department leader can allocate more of the department's reserved bonuses to them.

[0103] Centralized display of the actual paid salary and the predicted amount to be paid:

[0104] 1) According to the various dimensions and perspectives mentioned above, integrate the predicted amounts to be paid according to the group incentive policy, and switch and compare the display according to the actual paid amount and the amount to be paid, which can enable senior leaders to comprehensively grasp the current and future salary situations.

[0105] 2) Integrate the company's cash flow indicators into the platform. If the cash flow of the business unit cannot cover the amount to be paid, promptly notify the senior leaders to resolve the cash flow problem as soon as possible. At the same time, it can also be used as the basis for whether the bonus can be paid. In one implementation, the amount to be paid can be predicted based on the historical change trend of the total salary for the next fund payment cycle.

[0106] The overall idea of salary analysis is;

[0107] 1) In the salary payment section of the human resources field, classify the salary according to unified standards; this will involve incentive payment-related systems such as the human resources system, shared system, and fund system, with unified standards and unified master data.

[0108] 2) Classify the employees' positions; the positions of general employees will include "Product Manager", "Financial Specialist", etc. The employees' positions can be classified in combination with the internal system of the enterprise; this mainly involves the human resources system.

[0109] 3) Classify the assessment subjects; conduct "business unit" classification in combination with the group's industrial layout, generally each assessment subject in the group's monthly operation analysis meeting; and organize and code the "assessment subjects" and map them to the group's administrative structure (i.e., the human resources organizational structure).

[0110] 4) The business assessment indicators are integrated through the assessment system and matched to the human resources organizational structure through the mapping relationship in 3).

[0111] 5) When each salary is paid (except for wages, personal dimension wages are generally confidential information), clearly mark the nature of the salary, which award it belongs to, and which category it belongs to in the salary composition; which department and which job series the employee receiving the salary belongs to (i.e., the employee master data information).

[0112] 6) Classify the unified salary payment data of each human resources department according to the "job series" to which it belongs.

[0113] 7) Combining "5)" and "6)" can realize the classification of all salary data according to the "job series", and can also realize the hierarchical summary according to the "department / business unit"; combining "4)" can display "salary" and "business indicators" at the "business unit level" at the same time.

[0114] Based on the above analysis ideas, it is necessary to conduct docking and integration from a data perspective. Figure 4 The data processing route schematic diagram of the salary data statistical method provided for this application is as Figure 4 shown, including the following steps:

[0115] ① Through the interfaces provided by human resources on the integration platform, access the human resources project bonuses, five social insurances and one housing fund, and salary dividend data, and store them in the project bonus table (employee number, organization, bonus type, amount, year and month, etc.) and the salary dividend table (organization, salary, dividend, year and month, etc.) respectively; obtain the operation analysis data of business units, subsidiaries, etc. through the interfaces provided by the integration platform by integrating and storing data from different systems (bw), and store them in the operation analysis table (organization, operation analysis and other financial indicator data, year and month, etc.).

[0116] ② Calculate the data according to the financial operation data provided by the business according to the organization year and month and the rules related to the group's operation policy, and input it into the incentive system for storage and processing.

[0117] ③ Process the data in the project bonus table through a scheduled task, convert it into specific award data, and accumulate it according to the month. Calculate the individual annual cumulative reward according to the employee number and year and month, and store the statistical data in the individual actual incentive table. Calculate the organizational annual cumulative incentive data according to the organization and year and month to which the personnel belong, and store the statistical data in the individual actual incentive table and the organizational actual incentive table.

[0118] ④Based on the reported data and the rule data, perform incentive calculations, and store the calculation results in the individual result tables and organizational result tables for each award. Subsequently, through the scheduled tasks of the system, run the data, summarize all the calculated award data, store all the award data accumulated monthly and annually for individuals in the individual predicted incentive result table, and store all the award data accumulated monthly and annually for organizations in the organizational predicted incentive result table.

[0119] ⑤Through the scheduled tasks of the incentive system, query the group personnel data according to the employee number, and obtain information such as the corresponding positions, job series, and organizations of the personnel. Perform conversions through the job series conversion relationship table provided by the human resources department.

[0120] Process the personnel data in the individual actual incentive table and the individual predicted incentive result table, and mark the job series; mark the organization for the personnel according to the latest organizational data; perform cumulative processing of the actual and predicted incentives shared by the personnel, and store the results in the individual predicted incentive result table after obtaining the results.

[0121] ⑥Query data at the group level. The bar chart data is returned after being processed from the data in tables such as the organizational predicted incentive result table and the organizational actual incentive table;

[0122] The actual and predicted tree chart data at the group level is returned through two sets of interfaces for actual and predicted provided by the system, and the front end can switch arbitrarily;

[0123] The group-level interface supports drilling down to query the business analysis and incentive data of all business units.

[0124] ⑦Display data at the dimensions of business unit job series, awards, company, etc., which can be queried and displayed through the data processed previously;

[0125] Sub-organizations such as subsidiaries affiliated to the business unit organization support downward query at the business unit level.

[0126] Through the above design method, the following beneficial effects can be brought:

[0127] 1) Analyze from the "business unit" dimension; combined with key business indicators such as "sales volume" and "total profit", etc.; it can be directly seen whether the salary of each "business unit" is proportional to the "profit" / "sales volume", thus supporting the management to make strategic adjustments at the macro level.

[0128] 2) Analyze from the "job series" dimension; it can be directly seen the salary distribution of each job series within the group. Further combined with the powers and functions of each job series, it can be directly seen whether the salary settings of each job series are reasonable.

[0129] 3) Analyze according to the "compensation composition" dimension; it is possible to intuitively feel whether the compensation composition is reasonable through methods such as the proportion of the compensation structure or the proportion of the incentive policy, and then guide the reform and adjustment of the compensation system.

[0130] 4) The salary data can be refined according to the "employee" dimension for this technical solution, and then the data can be summarized layer by layer through the unified human resources organizational structure. It can not only support the flexible drilling down of data during analysis and the arbitrary switching of each analysis angle, but also support the flexible integration of data when the organizational structure is adjusted or changed, thereby greatly improving the scalability.

[0131] Figure 5 The structure diagram of a statistical device for salary data provided by this application is as Figure 5 shown. The statistical device 40 for salary data provided in this embodiment includes:

[0132] The first acquisition module 401 is configured to acquire the target dimension selected for the salary analysis in response to the user's salary analysis operation, and the target dimension includes the job series or business unit;

[0133] The second acquisition module 402 is configured to acquire salary data, where the salary data includes the nature of each salary of each employee, and the job series identifier and business unit identifier to which each employee belongs;

[0134] The first determination module 403 is configured to determine the average salary structure under the target dimension according to the nature of each salary of each employee, and the salary structure includes the target ratio of the fixed salary to the total salary;

[0135] The second determination module 404 is configured to determine the salary target range of the target dimension according to the mapping relationship between the preset dimension and the normal salary range;

[0136] The output module 405 is configured to output a prompt that the salary distribution is unreasonable if the target ratio is not within the salary target range.

[0137] Optionally, if the target dimension is the target job series, the first determination module 403 is specifically configured to:

[0138] Determine the total salary and total fixed salary of the target job series according to the nature of each salary of each employee and the job series identifier to which each employee belongs;

[0139] Divide the total fixed salary by the total salary to determine the target ratio.

[0140] Optionally, if the target dimension is the target business unit, the first determination module 403 is specifically configured to:

[0141] Determine the total compensation and total fixed compensation of the target business unit according to the nature of each employee's compensation per item and the business unit identifier to which each employee belongs;

[0142] Divide the total fixed compensation by the total compensation to determine the target ratio.

[0143] Optionally:

[0144] The first determination module 403 is further configured to determine the total bonus amount for the target business unit according to the nature of each employee's compensation per item;

[0145] The second acquisition module 402 is further configured to acquire financial report data and determine the total profit amount from the financial report data;

[0146] The output module 405 is further configured to output a prompt that the salary distribution is unreasonable if the proportion of the total bonus amount to the total profit amount exceeds a preset proportion.

[0147] Optionally:

[0148] The second acquisition module 402 is further configured to acquire employee comprehensive score ranking data for the target business unit;

[0149] The first determination module 403 is further configured to determine the total bonus amount of the employees in the preset percentage from the bottom up in the score ranking according to the employee comprehensive score ranking data;

[0150] The output module 405 is further configured to output a prompt that the salary distribution is unreasonable if the percentage obtained by dividing the total bonus amount by the total profit amount is greater than the preset percentage.

[0151] The salary data statistical device provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effect are similar, which will not be elaborated here in this embodiment.

[0152] Figure 6 It is a schematic structural diagram of an electronic device provided in this application. As Figure 6 shown, the electronic device 50 provided in this embodiment includes: at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. Among them, the processor 501, the memory 502, and the communication component 503 are connected through a bus 504.

[0153] In the specific implementation process, at least one processor 501 executes the computer execution instructions stored in the memory 502, so that at least one processor 501 executes the above method.

[0154] For the specific implementation process of the processor 501, reference can be made to the above method embodiments. Their implementation principles and technical effects are similar, and thus will not be elaborated herein.

[0155] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU for short), or other general-purpose processors, digital signal processors (DSP for short), application specific integrated circuits (ASIC for short), etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the method disclosed in combination with the invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.

[0156] The memory may include a high-speed random access memory (RAM), and may also include a non-volatile memory (NVM), such as at least one disk memory.

[0157] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

[0158] This application also provides a computer program product, including a computer program which, when executed by a processor, implements the above method.

[0159] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above method.

[0160] The above-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disk. The readable storage medium may be any available medium accessible by a general-purpose or special-purpose computer.

[0161] An exemplary readable storage medium is coupled to the processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium may also be an integral part of the processor. The processor and the readable storage medium may be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium may also exist as discrete components in a device.

[0162] The division of units is merely a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed couplings or direct couplings or communication connections between each other may be indirect couplings or communication connections through some interfaces, devices, or units, and may be in electrical, mechanical, or other forms.

[0163] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0164] In addition, in each embodiment of the present invention, the functional units may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0165] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, and other various media that can store program codes.

[0166] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When this program is executed, it executes the steps including the above method embodiments; and the aforementioned storage medium includes: ROMs, RAMs, magnetic disks, or optical discs, and other various media that can store program codes.

[0167] Finally, it should be noted that: After considering the specification and practicing the invention disclosed herein, those skilled in the art will readily think of other implementation manners of the present invention. The present invention is intended to cover any variations, uses, or adaptive changes of the present invention. These variations, uses, or adaptive changes follow the general principles of the present invention and include the common general knowledge or conventional technical means in the technical field of the present invention that are not disclosed in the present invention. It is not limited to the exact structure described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A statistical method for salary data, characterized in that: Applied to a payroll integration platform, the method includes: In response to the user's salary analysis operation, obtaining a target dimension selected for the salary analysis, wherein the target dimension includes any position system or business unit within the enterprise; Obtaining salary data, including the nature of each salary of each employee, and the position series identifier and business unit identifier of each employee; Determine the average salary structure under the target latitude according to the nature of each salary of each employee, and the salary structure includes the target ratio of fixed salary to total salary; Determine the target ratio range corresponding to the target latitude according to the preset mapping relationship between latitude and salary, wherein the mapping relationship includes the reasonable ratio range of fixed salary to total salary under each dimension; If the target ratio is not within the target ratio range, a prompt indicating that the salary distribution is unreasonable is output.

2. The method according to claim 1, characterized in that: If the target dimension is a target position system, the average salary structure under the target dimension is determined according to the nature of each salary of each employee, including: Determine the total salary and total fixed salary of the target job series according to the nature of each salary of each employee and the job series identifier of each employee; The target ratio is determined by dividing the total fixed salary by the total salary.

3. The method according to claim 1, characterized in that: If the target dimension is a target business unit, the average salary structure under the target dimension is determined based on the nature of each salary of each employee, including: Determine the total salary and total fixed salary of the target business unit based on the nature of each salary of each employee and the business unit identifier of each employee; The target ratio is determined by dividing the total fixed salary by the total salary.

4. The method according to claim 3, characterized in that The method further comprises: For the target division, determine the total bonus value based on the nature of each salary for each employee; Obtaining financial report data and determining total profits from the financial report data; If the proportion of the total bonus value to the total profit exceeds the preset proportion, a prompt indicating that the salary distribution is unreasonable will be output.

5. The method according to claim 4, characterized in that The method further comprises: For the target business unit, obtain the comprehensive score ranking data of employees; Determine the total bonus amount of employees with a preset percentage in the ranking from the back according to the comprehensive ranking data of the employees; If the percentage of the total bonus to the total profit is greater than the preset percentage, a prompt indicating that the salary distribution is unreasonable is output.

6. The method according to any one of claims 3 to 5, characterized in that: The method further comprises: For the target business unit, based on the total salary, predict the amount to be paid out in the next fund payment cycle; If the amount to be paid is greater than the cash flow of the business unit, a prompt message indicating insufficient funds is output.

7. A statistical device for salary data, characterized in that: The device comprises: A first acquisition module is used to acquire a target dimension of the salary analysis selection in response to a user's salary analysis operation, wherein the target dimension includes a job series or a business unit; The second acquisition module is used to acquire salary data, wherein the salary data includes the nature of each salary of each employee, and the position identification and business unit identification of each employee; A first determination module is used to determine an average salary structure under the target latitude according to the nature of each salary of each employee, wherein the salary structure includes a target ratio of fixed salary to total salary; A second determination module is used to determine the target salary range of the target latitude according to the mapping relationship between the preset latitude and the normal salary range; The output module is used to output a prompt indicating that the salary distribution is unreasonable if the target ratio is not within the salary target range.

8. An electronic device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

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