A human resources information processing system and method

Abstract

The present invention relates to a human resource information processing system and method, wherein the system comprises a data query module, a data classification module, an information processing module and an information application module, wherein the data query module is configured to query a human resource database according to an employee's personal identity identifier to obtain the employee's related human resource information; the data classification module is configured to classify the human resource information into interpersonal information and personal information according to the number of employee personal identity identifiers involved in the human resource information; the information processing module is configured to process the interpersonal information and personal information according to the configured indicators and processing strategies to obtain corresponding indicator data; the information application module is configured to process the multiple indicator data according to preset application conditions to obtain corresponding application information. The present invention expands the scope of data collection and improves the scope and ability of information processing.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a human resources information processing system and method. Background Art

[0002] Human resources refers to a series of management activities such as selection, use, training, assessment, rewards and punishments of staff members in order to achieve certain goals. With the development of computer technology and the popularization of the Internet, digital office has gradually penetrated into all aspects of daily operations of enterprises, and more and more enterprises use human resource management systems to assist in completing some human resource management activities.

[0003] The Chinese invention patent application with the publication number CN111401841A and the invention name "A human resource management system and its method" discloses a human resource management system, which includes HR basic application module, talent recruitment module, training and development module, attendance and holiday module, salary and welfare module, performance appraisal module, employee self-service full participation module and leadership self-service decision analysis module; the HR basic application module includes employee archive unit, labor relations unit, organizational structure unit, personnel affairs unit and reminder warning unit. Collect and store various personal information of enterprise employees, and set corresponding functional calculation logic according to employee personal information and in various functional modules, such as performance appraisal module, salary and welfare module, training and development module, attendance and holiday module, etc., and perform automatic calculation according to the set logic according to employee personal information, such as calculating the employee's salary according to the employee's attendance and the company's salary system, determining the required training according to the employee's position and recording its completion, etc. The leadership self-service decision-making analysis module includes a human map unit, a subordinate information unit, a cost analysis unit, an abnormal alarm unit, and a transaction approval unit; when it is determined that employees need to be picked up by shuttle bus, the employees who are entitled to use the shuttle bus and the shuttle bus routes are determined based on the geographical distribution of the company's employees, and the required costs are analyzed. When a sudden failure occurs in the shuttle bus, early warning notifications can be sent to the affected employees in a timely manner, and employees can also submit applications for not being able to ride through the system. The Chinese patent application with publication number CN108022083A and invention name "Enterprise Human Resource Management System" also discloses a human resource management system that records and tracks employee training progress, adjusts employee training plans according to circumstances, and performs skill certification based on training results. There are other human resource management systems with similar solutions, which will not be repeated here.

[0004] The existing human resource management system is mainly a system that collects data related to human resources (including personal data of employees) and implements certain basic functions (such as attendance, salary calculation, etc.). While collecting data, it also has functions such as report generation, simple data analysis, and information sharing. However, the functions of the existing human resource management system are not powerful enough to become the core infrastructure of the company's human resource management and a beneficial tool for improving the company's management efficiency. Whether in terms of human resource information collection, human resource information processing, or providing solutions to human resource problems, there is an urgent need for improvement. Summary of the invention

[0005] In view of the technical problems existing in the prior art, the present invention proposes a human resource information processing system and method for timely discovering problems existing in human resource management and providing solutions.

[0006] In order to solve the above technical problems, the present invention provides a human resources information processing system, which includes a data query module, a data classification module, an information processing module and an information application module. The data query module is used to query the human resources database according to the personal identity of the employee to obtain the human resources information related to the employee; the data classification module is connected to the data query module and is configured to classify the human resources information of the employee into interpersonal information and personal information; the information processing module is connected to the data classification module and is configured to process the interpersonal information and personal information according to the configured indicators and processing strategies to obtain corresponding indicator data; the information application module is connected to the information processing module and is configured to process the multiple indicator data according to preset application conditions to obtain corresponding application information.

[0007] According to another aspect of the present invention, there is provided a method for processing human resources information, which includes the following steps: querying a human resources database according to an employee's personal identity to obtain human resources information related to the employee; classifying the human resources information related to the employee into interpersonal information and personal information; reading configured indicators and processing strategies to process the interpersonal information and personal information to obtain corresponding indicator data; and processing a variety of indicator data according to preset application conditions to obtain corresponding application information.

[0008] The present invention expands the scope of data collection and improves the scope and ability of processing human resource information. It can automatically obtain problems existing in human resource management and provide solutions, saving a lot of manual work such as statistics, analysis, and decision-making for human resource management personnel, and reducing dependence on human resource specialists' skills, work attitudes, etc. In addition, the system provided by the present invention has good compatibility and flexible expansion. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The preferred embodiments of the present invention will be further described in detail below with reference to the accompanying drawings, wherein:

[0010] Figure 1 is a principle block diagram of a human resources information processing system provided according to an embodiment of the present invention;

[0011] Figure 2 is a principle block diagram of an information processing module provided according to an embodiment of the present invention;

[0012] Figure 3 is a principle block diagram of an information application module provided according to an embodiment of the present invention;

[0013] Figure 4 is a principle block diagram of a human resources information processing system provided according to another embodiment of the present invention;

[0014] Figure 5 is an overall principle block diagram of a human resources system provided according to another embodiment of the present invention;

[0015] Figure 6 is a principle block diagram of a human resources information processing system provided according to an embodiment of the present invention;

[0016] Figure 7 is a schematic diagram of the structure connection of a human resources information processing system according to an application embodiment of the present invention;

[0017] Figure 8 is a flow chart of a method for processing human resources information provided according to an embodiment of the present invention;

[0018] Fig. 9 is a flow chart of a method for obtaining indicator data provided according to an embodiment of the present invention; and

[0019] Fig.10 It is a further processing flow chart of generating application information according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] In the following detailed description, reference may be made to the various specification drawings that are part of the present application and are used to illustrate specific embodiments of the present application. In the accompanying drawings, similar reference numerals describe substantially similar components in different figures. The various specific embodiments of the present application are described below in sufficient detail so that a person of ordinary skill in the art with relevant knowledge and skills in the art can implement the technical solutions of the present application. It should be understood that other embodiments may also be used or structural, logical or electrical changes may be made to the embodiments of the present application.

[0022] Due to the limitation of the types of data collected by the existing human resource management system, the data processing function completed by the existing human resource management system is only the low-end and basic data processing in human resources, such as calculating salary based on attendance data, calculating performance data based on attendance and task completion, etc., and some human resources such as arranging or adjusting employees to reasonable positions, improving employee work efficiency, finding problems and obtaining solutions based on employee performance in tasks and task completion, etc., still rely on the subjective judgment of enterprise managers. Usually, enterprise managers match team members for employees, or manually investigate and find the reasons to adjust the team's personnel composition, task allocation, etc. when employee conflicts, team or individual performance are poor. Therefore, in the prior art, since the collection of data and the processing of information are limited to the low-end and basic data level, the powerful data processing capabilities of information technology are not brought into play, resulting in a lot of more valuable information still relying on manual acquisition. In response to these problems, the present invention proposes a new generation of human resource information processing system and method.

[0023] Figure 1 1 is a principle block diagram of a human resource information processing system according to an embodiment of the present invention. The human resource information processing system and the human resource system 10 in this embodiment are two interconnected systems, and the human resource system 10 includes various modules, such as an interview module 101, an attendance module 102, a training module 103, a performance module 104, a salary and benefits module 105, a geographic location module 106, and a task module 107. In particular, these modules in the human resource system 10 have data collection functions while having corresponding logic processing functions, and store the collected data and information in the human resource database 20.

[0024] For example, the interview module 101 includes an information collection unit, an interview evaluation unit, and a team assignment unit. When an employee is interviewed by an interviewer before entering the company, the job seeker fills out a resume through the information collection unit, thereby collecting the resume information filled out by the job seeker. After the interview is passed, the interviewer adds various initial tags to the employee through the interview evaluation unit, and assigns the employee to the target team through the team assignment unit. The initial tags added by the interviewer to the employee include work number, age, work location, position, initial skill tags, etc. The interview module 101 collects basic employee information, interview evaluation information, and assigned team information, and stores all this information in the human resources database 20.

[0025] The attendance module 102 is a functional module for recording and managing the attendance information of employees' daily work, overtime, vacation, etc., which records the daily attendance data, overtime hours, vacation usage, etc. of employees. The on-the-job management unit in the attendance module 102 records the attendance of employees' daily work, including daily working hours and off-duty hours; the shift management unit in the attendance module 102 records the overtime arrangement and overtime hours of employees; and the vacation management unit in the attendance module 102 records the accumulated available vacation days and the vacation days used by employees. These attendance information are stored in the human resources database 20 and are transferred to the performance module 104 as part of the basic performance of employees for analysis and settlement of employee work performance.

[0026] The training module 103 is a functional module for employee training and skills certification management. According to the employee's initial skill label, the course management unit gives priority to arranging employees to learn related skills that they did not have before to expand their knowledge reserves; after the employee completes the training of a certain skill, the result assessment unit in the training module 103 will automatically assemble an assessment paper to check the employee's training and learning effect, where the assessment questions include selection, judgment, practical operation, writing, gamification test, etc.; after the employee passes the assessment, the skill label unit in the training module 103 will give the employee a new skill label according to the training project the employee participated in; the employee's skill label information will be passed to the performance module as the employee's basic performance for analysis and settlement of the employee's work performance. All relevant data of the aforementioned various units are stored in the human resources database 20.

[0027] The performance module 104 is a module for comprehensive application of employee personal information and performance information. The performance settlement unit records the employee's work completion efficiency and work completion quality on a monthly basis, integrates, analyzes, and evaluates the employee's work performance, and calculates the employee's monthly original performance data; the indicator management unit in the performance module 104 compares the employee's performance expected at the beginning of the month with the employee's actual original performance data for the month, and gives the employee's performance rating; the performance supplement unit in the performance module 104 will independently record some independent performance events shown by the employee in the month, and score these independent performance events and weight them on the employee's performance rating. The data obtained by the above-mentioned units are also stored in the human resources database 20.

[0028] The salary and benefits module 105 receives the employee performance information transmitted by the performance module 104, and also receives other basic information of employees transmitted by the interview module 101, the attendance module 102, and the training module 103. After comprehensively analyzing all the information of the employees, the basic salary, performance bonus and benefits of the employees are evaluated and managed, providing a direct reference for managers to distribute employee salaries and benefits, and storing these data in the human resources database 20.

[0029] The geographic location module 106 is used to record the geographic location of each employee at work, such as the city and building, the floor of the building, and the seat number. For example, the manager enters the information based on the employee's actual seat when the employee joins the company or changes seats. The employee region unit in the geographic location module 106 records the city and building where the employee works, the employee floor unit in the geographic location module 106 records the floor of the building where the employee works, and the employee seat unit in the geographic location module 106 records the employee's seat number, so that the geographic location of each employee can be located.

[0030] The task module 107 is used to record the basic information of the projects or tasks in which the employees participate, the responsibilities / roles of the employees in the projects or tasks, the total progress information of the projects, the progress information of the tasks of the employees, and all the communication records of the employees in the process of the progress of the projects or tasks. Among them, some information is entered by the management personnel, for example, when the employees join the specific project group, the information includes the basic information of the projects or tasks, the specific project number in which the employees participate, the responsibilities / roles of the employees when they join the specific project group, etc. Some information is entered by the employees themselves, including the progress of their tasks, the communication records of active communication and passive communication, etc. The task module 107 can calculate the total progress information of the projects according to the task progress information of each employee and the basic information of the projects. All these information are stored in the human resources database 20.

[0031] The human resource information processing system provided in this embodiment includes a data query module 1a, a data classification module 2a, an information processing module 3a and an information application module 4a, wherein the data query module 1a queries the human resource database 20 according to the personal identity of the employee to obtain the human resource information related to the employee.

[0032] The data classification module 2a is connected to the data query module 1a. After the data query module 1a obtains the human resource information of each employee, the human resource information is classified into interpersonal information and personal information. In one embodiment, each module of the aforementioned human resource system sets a label for the information collected. For example, the interview module 101, the attendance module 102, the training module 103, the performance module 104 and the salary and welfare module 105 set a personal information label for the collected information, while the geographic location module 106 and the task module 107 set an interpersonal information label for the information collected. The data classification module 2a classifies an employee information into personal information or interpersonal information through information labels. Personal information is, for example, various interview information, employee attendance information, welfare information and training information of employees. Interpersonal information is, for example, geographic location information and task-related information.

[0033] The information processing module 3a is connected to the data classification module 2a, and processes the interpersonal information and personal information according to the configured indicators and processing strategies to obtain corresponding indicator data. Figure 2As shown, the information processing module 3a includes an information quantification unit 31a, a basic indicator data unit 32a and a derived indicator data unit 33a. Among them, the information quantification unit 31a quantifies the interpersonal information and personal information according to the indicators and their processing strategy requirements to obtain corresponding interpersonal data and personal data. For example, codes are set for various information, such as data related to interview information starts with the letter A, different representative letters are set for different company departments and teams, and the roles or positions in the team are represented by two digits. For example, the team of the mechanical workshop is represented by the letter M, 01 represents an intern mechanical engineer, and 02 represents a mechanical engineer; the team of the electrical workshop is represented by the letter E, 01 represents an intern electrical engineer, and 02 represents an electrical engineer; the administrative department is represented by the letter A, 01 represents the general manager... 10 represents the secretary, etc. For some personal information, it can be represented by the corresponding English word, for example, gender is represented by the English word Sex, and the letters M and F represent two genders respectively. Attendance data starts with the letter B, and different letters or numbers are used to represent different attendance data. The interpersonal data includes employee geographic location-related data and employee task-related data, wherein the employee geographic location-related data includes the employee's current and historical work location number, which represents the city, building, floor and seat number of the employee, for example, the location number BBC1830 represents Beijing, Beichen Building, 18th floor, 30th seat; WHADS0806 represents Wuhan, Hanhua Building, 8th floor, 6th seat. The employee task-related data includes basic data of the project or task in which the employee participates, the employee's responsibilities / roles, the total project progress data, the employee's task progress data, and the communication record data in the progress of the project or task, etc. For example, the project starts with the English project, and the suffix of multiple digits represents the project sequence number. The progress data includes the progress plan data extracted from the project plan, such as the tasks to be completed at the scheduled time; the corresponding relationship between the project and the employee is established according to the project in which the employee participates, and the active communication employee, the employee receiving the communication, the communication time, the content, etc. are extracted from each communication record information. After being processed by the information quantization unit 31a, various information is processed into the form of character strings, information bars, etc., so as to facilitate the next step of data processing.

[0034] The basic indicator data unit 32a is connected to the information quantification unit 31a, and is configured to process the interpersonal data and personal data according to the interpersonal basic indicators and personal basic indicators and their processing strategies to obtain interpersonal basic indicator data and personal basic indicator data.

[0035] The personal basic indicators include, for example, age, gender, skill label, salary, attendance rate, training courses, performance scores, etc. The age, gender, training courses, etc. can be directly obtained from the quantified personal data, while the skill label, attendance rate, etc. need to be obtained through certain logical operations. For example, a score is set for each training course, and the original training result score is calculated with the individual's education, major, experience, etc. as weights to obtain the final skill score, and then the employee's skill label is determined based on the score range.

[0036] The interpersonal basic indicator is, for example, the geographical activity range of an employee. According to the employee's current work position number, one or more associated employee groups of activity range levels are determined for each employee according to the distance from the work position, and the associated employee groups of different activity range levels are used as the indicator data of the geographical activity range of each employee. For example, according to the employee's current work position number, the eight employees around him are determined as his associated employee group, that is, his geographical activity range. It is also possible to take multiple employees directly adjacent to him as the first-level geographical activity range, such as employees adjacent to the left and right, front and back of the employee's seat as the first-level geographical activity range. Depending on the specific location of the employee and the layout of the employee's location distribution, the number of employees in the first-level geographical activity range may be different.

[0037] The interpersonal basic indicator can also be the scope of employee business activities. When calculating the indicator data, first determine the identity identification and responsibilities / roles of other employees in the same project or the same task based on the basic data of the project or task in which the employee participates; generate a project employee matrix table based on the identity identification and responsibilities / roles of the employees in the same project and / or the same task, and the employee matrix table serves as the employee's business activity scope. Similar to the geographical scope of employee activities, multiple different levels of business activity scopes are determined from the employee matrix table based on the close or distant business relationship with the employee. For example, personnel who have a direct business relationship with the employee are determined as the first-level business activity scope, for example, the leaders of each task group are the first-level business activity scope of the project leader; the members of each task group are the second-level business activity scope of the project leader.

[0038] The interpersonal basic index can also be the total progress of the project. When calculating the index data, the time and the task information to be completed in the project progress index data are read and compared with the currently completed task information. For example, according to the progress expectation, 6 tasks should be completed at the current time point, but the number of currently completed tasks is 5, so 5 / 6=83%, thereby obtaining the current progress percentage value of the project, which is used as the total progress index data of the project.

[0039] The interpersonal basic indicators can also be task progress and employee task progress. The employee task progress percentage value is calculated based on the current completion data of the employee task and the expected completion target, and used as the employee task progress indicator data; the average value of the task progress indicator data of all employees in a task is used as the task progress indicator data.

[0040] The interpersonal basic index also includes a project complaint index. From the basic data of the project or task, the total number of complaints, the tasks and employees corresponding to each complaint, and the number of times the employee has been complained about are counted. These data are used as project complaint index data.

[0041] When the interpersonal basic indicator is a communication indicator, the relevant indicators include the total number of exchanges, the number of exchanges initiated / accepted by each employee, the calculation of the duration of each exchange, and the number of employees and the number of employees participating in each exchange.

[0042] The derived indicator data unit 33a is connected to the information quantification unit 31a and the basic indicator data unit 32a, and performs deep processing on the interpersonal basic indicator data and the personal basic indicator data according to the derived indicators and their processing strategies to obtain corresponding derived indicator data.

[0043] Among them, the derived indicators include average employee work performance, employee age distribution and employee skill labels. In order to obtain the above-mentioned derived indicator data, the performance data, age and skill label data of each employee in the associated employee group of the target employee are obtained from the personal basic indicator data. The geographical activity range of each employee, that is, the employee's associated employee group, is obtained from the interpersonal basic indicator data, and the average work performance of the associated employee group is calculated based on the performance data of each employee in the associated employee group; the age distribution and age mean of the associated employee group are statistically calculated based on the age of each employee in the associated employee group; the skill label overlap rate is calculated based on the skill label data of each employee in the associated employee group, such as the percentage of the number of identical or similar skill labels to the total number of skill labels in the associated employee group.

[0044] The derived indicators also include employee communication type, employee cooperation and employee communication scope. First, calculate the proportion of employee-initiated communication times to the total communication times; then, determine the communication type determined by the proportion threshold for the employee based on the proportion of employee communication times and the threshold. For example, when the proportion of an employee's communication times to the total communication times is less than 30%, the employee is determined to be a passive communication type, and when the proportion of his communication times to the total communication times is greater than or equal to 30%, he is determined to be an active communication type. Calculate the ratio or difference between the current progress percentage value of each employee's task and the planned progress percentage value of the task; compare the ratio or difference with the threshold. When the ratio or difference is greater than or equal to the threshold, and the number of complaints from the employee is greater than the threshold, it is determined that the indicator value of the employee's cooperation situation is a problem, otherwise it is determined that there is no problem. Determine the scope of employee communication based on the employees and the number of employees participating in the communication.

[0045] Derivative indicators also include project progress health indicators, project quality health indicators and project overall health indicators. Among them, the current progress percentage value of the project is compared with the planned progress percentage value of the project. When the ratio or difference between the current progress percentage value of the project and the planned progress percentage value of the project is greater than the threshold, for example, the ratio is less than 80%, or the difference is greater than 20%, the project progress health indicator value is determined to be unhealthy. If the ratio is greater than 80%, or the difference is less than 20%, the project progress health indicator value is determined to be healthy. The specific ratio threshold and difference threshold can be set manually as a parameter. For example, managers with authority can set the ratio threshold or difference threshold according to multiple factors such as the specific business environment of the team and the team's own business processing level to ensure that it meets the company's own operating environment and characteristics. Later, the parameter is also adjusted in stages to improve the matching degree with actual business requirements. When the project progress health indicator value is unhealthy, several tasks with slow progress and corresponding employees are determined from the project. Compare the number of project complaints with the threshold, for example, set the complaint threshold to any value between 1 and 5. When the number of project complaints is greater than the threshold, determine that the project quality health index value is unhealthy, and determine the complained tasks and corresponding employees. When it is determined that the project progress health index value of a project is unhealthy and the project quality health index value is also unhealthy, determine that the overall health index value of the project is unhealthy.

[0046] In another embodiment, the information processing module 3a further includes a model training unit 34a, which is configured to train a corresponding machine learning model for each derived indicator; correspondingly, the derived indicator data unit 33a uses the machine learning model to use the interpersonal basic indicator data and personal basic indicator data of the employee as the input of the model to obtain the corresponding derived indicator data. In this embodiment, the interpersonal basic indicator data and personal basic indicator data can be used as training data to train the corresponding machine learning models according to the needs of various derived indicators, such as a machine learning model M1 for obtaining the average work performance of employees, a machine learning model M2 for obtaining the age distribution of employees, a machine learning model M3 for obtaining the skill labels of employees, a machine learning model M4 for obtaining the communication type of employees, a machine learning model M5 for obtaining the cooperation status of employees, a machine learning model M6 for obtaining the communication scope of employees, a machine learning model M7 for obtaining the health indicator of project progress, a machine learning model M8 for obtaining the health indicator of project quality, and a machine learning model M9 for obtaining the overall health indicator of the project, etc. Select supervised or unsupervised machine learning models based on specific actual conditions. For example, the aforementioned machine learning model M7 for obtaining project progress health indicators, the machine learning model M8 for obtaining project quality health indicators, and the machine learning model M9 for obtaining project overall health indicators can be classification models, which determine whether the project progress is healthy, whether the project quality is healthy, and whether the project is healthy overall through input data. The machine learning model M4 for obtaining employee communication types can also use a classification model, which determines which type of employee communication type is based on input data, and so on.

[0047] The information application module 4a is connected to the information processing module 3a, and processes the various indicator data according to the preset application conditions to obtain corresponding application information. Figure 3As shown, the information application module 4a includes an employee portrait unit 41a and an application information generation unit 42a. The employee portrait unit 41a integrates the interpersonal basic indicator data, personal basic indicator data and derived indicator data to obtain employee portrait data. The application information generation unit 42a generates corresponding application information based on application conditions and corresponding processing strategies. Among them, in order to display the employee portrait, a display module 5a is also included, which is connected to the employee portrait unit 41a and the application information generation unit 42a, and processes the employee portrait data according to the set format, such as graphics, charts, images, etc., so that the employee portrait can be displayed in the set format. For example, when the bar chart display mode is set, different colors are used to represent different indicators, and the dark color of each indicator color represents the indicator data of the employee, and the light color represents the normal or average data of the indicator. As a comprehensive "analysis chart", employee portrait data can enable managers to understand the work performance and working environment of employees in the enterprise in an image and clear manner. While displaying the portrait data of each employee, the application information generated based on the employee portrait can also be marked next to the portrait, so that managers can get suggestions on how to intervene and manage employees after understanding their work performance and work environment in the company.

[0048] Among them, the application conditions used in the application information generation unit 42a include judging whether an indicator data or a combination of multiple indicator data is data with negative effects. The data with negative effects are, for example: the average work performance of the associated employee group is less than the average work performance of all employees, the difference between the age of the employee and the age mean of the associated employee group is greater than the threshold, the skill label overlap rate of the associated employee group is greater than the threshold, the employee communication type is a specified communication type (such as passive / active communication type), the indicator value of the employee cooperation situation is a problem, the project progress health indicator value is unhealthy, the project quality health indicator value is unhealthy, and the overall health indicator value of the project is unhealthy, etc. When an indicator data or a combination of multiple indicator data is data with negative effects, the application information generation unit 42a reads the corresponding application information template from the application information template library, and modifies the original application information on the template according to the current specific data, so as to generate application information that conforms to the current situation. The application information is, for example, a suggestion for converting indicator data or a combination of multiple indicator data with negative effects into positive effect data. In one embodiment, different application information templates store positive suggestions for different negative effect indicator data, such as "pay attention to the environment around employees", "change seats", "change skill structure", "increase training", "intervene in work attitude", "guide social interaction", "change roles", "follow up tasks", "conference communication", "provide role models", "arrange assistance personnel" or "follow up individually", etc.

[0049] For example, when the average work performance of the associated employee group is less than the average work performance of all employees, the application information generation unit 42a traces back to obtain the original indicator data of the indicator data, such as the performance data of each employee in the group, the task completion data of each employee, etc., so as to determine the employees and reasons that cause the average work performance of the associated employee group to be less than the average work performance of all employees. For example, the employees in this group are generally passive communication types, the training is not completed or the training effect is not good, the work completion efficiency is low, the work completion quality is poor, the skill mastery is single, the attendance or independent performance events are few, etc. The application information generation unit 42a determines the corresponding employees and reasons by tracing the above data, and then determines the application information that converts the indicator data of negative effects or the combination of multiple indicator data into positive effects one by one according to the reasons. For example, when the number of absences of the employee obtained by the attendance data is greater than the threshold, the application information of "intervention work attitude-improvement attendance" or "individual follow-up" is generated. For another example, when the skill tag repetition rate of the employee's group is too high, the skill tags with high repetition rate are determined, and employees in other groups that can match the skill tags of the employee's group are queried, thereby generating two application information of "changing skill structure" and "changing seats-xxx". When the reason is that a certain employee lacks skills, the application information of "arranging training-adding XX course training" is generated according to the missing skills, and employees who have mastered the skills are queried to generate "arranging assistance personnel-xxx" and other application information.

[0050] For another example, the application information generation unit 42a calculates the difference between the employee's age and the mean age of the employee group associated with it, and compares it with the threshold. When the difference between the employee's age and the mean age of the employee group associated with it is greater than or equal to the threshold, such as the mean age of the associated employee group is 25 years old, and one of the employees is 30 or 40 years old, it is obvious that the age gap is too large. The large age gap will cause the employee to have problems integrating into the surrounding environment. The application information generation unit 42a either gives the information of "Guide social interaction - eliminate age barriers" or finds a matching employee group for the employee according to each employee group, and generates the application information of "Change seats - xxx". The application information generation unit 42a can also extract the three worst performers in a group, analyze the reasons for the poor performance, generate corresponding information, and recommend managers to follow up.

[0051] For another example, when the overall health index of the project, the health index of the project progress, or the health index data of the project quality is unhealthy, the application information generation unit 42a traces back to obtain the basic index number of this index data. For example, when the health index of the project progress is unhealthy, the task progress and the corresponding employee are queried according to the task that determines the project progress, so as to determine the employee who causes the slow progress of the task; then the current portrait data of the employee is queried to determine the index data below the threshold or average value, such as the employee's geographical activity range, the employee's business activity range, the employee's cooperation situation and the employee's communication range, the employee's skill label, and other index data, and the application information generation unit 42a packages these to generate subsidiary information, and at the same time generates the application schedule of "meeting and communication" as a suggestion and a warning prompt information of slow project progress. When the manager receives such a warning prompt information, the corresponding suggestion and the subsidiary information are queried, so that the relevant manager and the project members meet and communicate, find and analyze the reasons why the project progress is slower than expected, and intervene accordingly to the reasons, such as adjusting the project process, changing the relevant settings of the project, interviewing the employees, understanding the reasons that the employees personally think and the employees' improvement ideas, and adjusting and improving with the employees, and arranging the employees to retrain if necessary, etc.

[0052] The various application information generated by the aforementioned application information generation unit 42a can be displayed through the display module 5a. For example, when the information processing system processes regularly or in real time to obtain one or more pieces of application information, it is stored in a table, and a reminder message is sent to remind the manager that a new application message has been generated. Alternatively, when the manager queries the employee portrait data, the processing system can generate the corresponding various application information based on the indicator data constituting the employee portrait, and display it together with the portrait data.

[0053] In another embodiment, Figure 3As shown by the dotted line in , the information application module 4a also includes a parameter modification unit 43a, which includes a parameter extraction unit 431a, a parameter value generation unit 432a, a modification unit 433a and a modification notification unit 434a, wherein the parameter extraction unit 431a is connected to the application information generation unit 42a, and is used to extract the modifiable parameter items from the generated application information, and obtain the original parameter value corresponding to the parameter item from the human resources database. For example, when the application information is "arrange training-add course C training", the parameter item "training course" in the training module is determined from it, and the course A to be trained recently is obtained by reading the training data of the employee. The parameter value generation unit 432a is connected to the parameter extraction unit 431a, and by comparing the original parameter value "course A" and the adjustment information "course C" of the parameter item in the application information and the adjustment rule of the parameter item, such as the rule that the employee training course needs to be trained step by step and cannot be skipped, the employee's recent training course arrangement is modified to "course A-course B-course C". Check whether the parameter item has sufficient modification authority. If the modification authority of the parameter is set to allow automatic modification, the generated new parameter value "Course A-Course B-Course C" is sent to the modification unit 433a. The modification unit 433a modifies the parameter item "Training Course" in the training module, and changes the original parameter value "Course A" to "Course A-Course B-Course C", and then generates a modification notification and sends it to a designated location, such as the trainer's mailbox, the human resources director's mailbox, the employee's mailbox, etc., or records it at a certain location on the display interface to remind relevant personnel of the changes. If the modification authority of the parameter is set to not be automatically modified, the parameter value generation unit 432a generates a parameter modification notification, which includes at least the original parameter value of the parameter and the new parameter value recommended for modification, and will be sent to a designated location, such as the trainer's mailbox, the human resources director's mailbox, or recorded at a certain location on the display interface to remind relevant personnel of the changes.

[0054] In another embodiment, the parameter modification unit 43a mentioned above can be a unit in the corresponding module of the original human resource system 10. The application information generation unit 42a generates parameter adjustment information while generating the application information and sends it to the corresponding module in the human resource management system 10, and the parameter modification unit in the corresponding module modifies it. For example, when generating the application information of "arrange training-add XX course training", the parameter adjustment information of "arrange XX course training" is generated to be sent to the training module 103 in the human resource management system 10. When the training module 103 receives the message, it queries the internally recorded training plan. If there is currently an XX course, the employee is added to the list of trainees corresponding to the plan; if there is no XX course in the training plan, the XX course is added to the training plan, and the employee is added to the corresponding list of trainees. Of course, the training module 103 needs to have the corresponding authority when performing relevant operations. For example, adding trainees to existing training courses does not require authority restrictions, but the creation of new training courses needs to be confirmed by relevant personnel. For example, when the training module 103 adds an XX course, a confirmation notice is sent to the management personnel. After the management personnel confirms the change, the XX course is added to the effective training plan. For example, the training module 103 adds a new XX course according to the "arrange XX course training" message received from the application processing system, adds a "pending approval" label to the XX course, and sends a confirmation notice to the manager. After receiving the notice, the manager finds through query that the number of employees currently trained in the course is too small, and thus does not approve it. Every time the training module 103 receives the "arrange XX course training" message from the application processing system, it adds employees to the trainees of the XX course until the manager feels that the current number of trainees is sufficient, and then sends a message confirming the XX course. After receiving the confirmation message of the XX course, the training module 103 changes the label of the XX course from "pending approval" to "approved", and adds it to the current training plan list. Of course, the training module 103 can also count the number of trainees of the "pending approval" course by itself, and only send a confirmation notice of the XX course to the manager when the number of trainees is sufficient.

[0055] Similarly, the parameter adjustment information generated by the application information generation unit 42a may be adjustment information for salary and benefits, adjustment information for employee tasks, etc. The corresponding salary and benefits module 104 and task module 107 may be adjusted accordingly according to the authority. The adjustment principle is as described above for the training module 103 and will not be repeated here.

[0056] like Figure 46 is a principle block diagram of a human resources information processing system according to another embodiment of the present invention. Since the present invention collects a large amount of various data and enables the system to replace the work of existing human resources management personnel and obtain various application information through the collected data, in this embodiment, the information processing system also includes a parameter configuration module 6a, which is used to configure and modify various indicators and obtain processing strategies for indicator data, establish associations between indicator data, and ultimately obtain application information types, conditions and processing strategies.

[0057] In the aforementioned embodiment, the human resources information processing system provided by the present invention can be used as a supplement to the existing human resources system. When implemented, it is separated from the existing human resources system and the two are connected through an interface. By collecting, classifying, pre-processing and further processing various data generated in the existing human resources system, more practical guiding application information can be obtained. In another embodiment, Figure 5 As shown, the human resources information processing system 30b of the present invention, the database 20b, and the basic human resources system 10b constitute a complete human resources system. The information processing system 30b and the basic human resources system 10b respectively have corresponding interfaces, which are used by personnel with corresponding permissions. As a system integrating data collection and low-end processing functions, the basic human resources system 10b has usage permissions for ordinary employees, such as interviewers, attendance recorders, training instructors, ordinary human resources specialists, etc. Employees use the basic human resources system 10b through the low-authority interface 100b, such as recording various types of raw data. The basic human resources system 10b processes and processes the raw data according to the set functions to obtain corresponding primary information, such as interview result records, attendance records, performance scores, salaries and benefits, employee task assignments, job roles, etc., which can be obtained by ordinary employees through the low-authority interface 100b. The primary information recorded and generated by the basic human resources system 10b can be stored in the database 20b and sent to the information processing system 30b as needed. The information processing system 30b is an information processing system with high usage permissions, such as Figure 6As shown, the information processing system 30b includes a data collection module 31b, which includes a personal information collection unit 311b, a task information collection unit 312b and a geographic location collection unit 313b. Correspondingly, the human resources database 20b includes a personal information library 21b and an interpersonal information library 22b. The personal information collection unit 311b stores the personal information collected from the basic human resources system 10b in the personal information library 21b, and the task information collection unit 312b and the geographic location collection unit 313b store the interpersonal information of employees collected from the basic human resources system 10b in the interpersonal information library 22b. The data query module 32b in the information processing system 30b queries the human resources database 20b to obtain all the human resources information of the employees, and the data classification module 33b classifies the query data. After multiple levels of data processing by the information processing module 34b and the information application module 35b, decision-making and guiding application information is obtained, which can be viewed by managers with high usage authority through the high authority interface 300b. Various data and information generated by the information processing system 30b are also stored in the human resources database 20b, such as the employee portrait database. The employee's personal portrait obtained by the information application module 35b can be visually displayed to the management personnel through the high-authority interface 300b, and the indicator data is processed according to various application conditions to obtain decision-making and guiding information corresponding to the employee, and displayed to the management personnel, so that the management personnel can not only understand the employee in an all-round way through the personal portrait, including the employee's business skills, work environment, work performance, etc., but also obtain specific suggestions for improving the employee's performance and improving work efficiency, thereby saving a lot of statistical, analytical, and decision-making work for human resource management personnel, and reducing the company's management personnel's dependence on the skills and work attitudes of human resource specialists. To a certain extent, the human resource information processing system replaces the work of human resource specialists. During the operation of the system, decision makers only need to set relevant indicators, and maintenance personnel need to maintain the correct operation of the system, so as to obtain decision-making opinions that could only be obtained by senior human resource specialists.

[0058] In an application embodiment, the job seeker first fills in his resume information through a low-authority interface. The interviewer screens the online resume and arranges an interview. After the interview is passed, the interviewer will give the proposed employee an initial label and assign him to the target team. After the employee joins the company, the attendance module receives the basic information of the employee transmitted by the interview module, manages and records the attendance, overtime and vacation of the employee's daily work. The training module receives the initial skill label information of the employee transmitted by the interview module, arranges the learning plan of the employee according to the initial skill label of the employee, and conducts assessment and certification and issuance of new skill labels for the employee. When the monthly performance appraisal is carried out, the performance module will receive the information transmitted by the interview module, the attendance module and the training module, complete the performance settlement of the employee, and give the performance rating of the employee. The salary and benefits module receives the employee performance information transmitted by the performance module, and at the same time receives the basic information of other employees transmitted by the interview module, the attendance module and the training module. After a comprehensive analysis of all the information of the employee, the basic salary, performance bonus and benefits of the employee are evaluated and managed, providing a direct reference for the management personnel to issue employee salaries and benefits. At the same time, when an employee joins the company (the same applies when an employee changes positions), the manager enters the city, floor, and seat number information of the employee through the low-authority interface 100b, and the information processing system 30b can analyze and infer the employee's interpersonal relationship and social activity scope in the workplace based on the above data. When an employee joins a specific project, the manager enters the specific project number and division of labor arrangement, and the employee records the situation of cooperating with partners to complete the task during the work process. The information processing system 30b can analyze and infer the employee's social activity scope and cooperative interaction with team members during the project process based on the above data. The information processing system 30b obtains employee portrait data through multiple processing, integration and analysis of the employee's personal information and interpersonal information, and reveals the correlation between the employee's work environment, personal situation and work results from the portrait data, forming a comprehensive "analysis chart" of the employee's work performance and work environment in the enterprise. The authorized manager can view the employee portrait through the high-authority interface 300b, and can view the corresponding application information generated.

[0059] The present invention can organically combine the human resource information processing system provided by the present invention with the existing human resource system through modular design, with good compatibility and scalability. The information finally generated can be displayed in a visual, modular and dynamic manner, which is convenient for managers to interact and consult, thereby facilitating managers to carry out specific team management work.

[0060] Figure 7: is a schematic diagram of the structure connection of a human resources information processing system according to an application embodiment of the present invention. In the present invention, the human resources information processing system includes a server and a terminal, the terminal can be a computer 10c and a personal terminal 11c, and the server is, for example, one or more servers 2c. The server is connected to the terminal via a network, and the computers 10c can be connected via an internal network. Among them, various modules of the human resources information processing system can be distributed in different servers 2c, computer hosts 10c and personal terminals 11c. The server also includes a database 3c, which is used as a human resources database to store various information and data.

[0061] In another aspect, the present invention also provides a method for processing human resources information, such as Figure 8 As shown, Figure 8 The following is a flow chart of a method for processing human resources information according to an embodiment of the present invention. The method comprises the following steps:

[0062] Step S1, querying a human resources database according to the employee's personal identity to obtain human resources information related to the employee. Figure 1 Human Resources Database 20 or Figure 5-6 The various modules in the human resources system 10 have data collection functions, which collect various employee information related to the employee from the employee interview and store it in the human resources database 20. Figure 6 The dedicated data collection module collects the employee's geographical location information, the work information the employee participates in, and the employee's personal information and stores them in the human resources database 20. In order to facilitate query, when storing the collected information, in one embodiment, Figure 6 As shown, the geographical location information of the employee and the work information in which the employee participates are stored in the interpersonal information library 22b in the human resources database, and the personal information of the employee is stored in the personal information library 21b in the human resources database. Alternatively, tags are set for the collected data, for example, interpersonal information tags are set for the geographical location information of the employee and the work information in which the employee participates, and personal information tags are set for the personal information of the employee;

[0063] Step S2, classify the human resource information related to the employee into interpersonal information and personal information. For example, classify the information obtained from the interpersonal information library 22b into interpersonal information, and classify the information obtained from the personal information library 21b into personal information. Alternatively, classify the obtained information into interpersonal information or personal information according to the label of the information.

[0064] Step S3, read the configured indicators and processing strategies to process the interpersonal information and personal information to obtain corresponding indicator data. The configured indicators include multiple interpersonal basic indicators, multiple personal basic indicators and multiple derived indicators; the corresponding process of obtaining indicator data is as follows: Fig. 9 As shown, including:

[0065] Step S31, according to the interpersonal basic indicators and personal basic indicators and their corresponding processing strategies, the interpersonal information and personal information are quantified to obtain corresponding interpersonal data and personal data. Figure 2 The information quantization unit 31a processes various information into character strings, information bars, etc., so as to facilitate the next step of data processing.

[0066] Step S32, read the preset interpersonal basic indicators and personal basic indicators, and process the interpersonal data and personal data according to the corresponding processing strategies to obtain the interpersonal basic indicator data and personal basic indicator data of each employee. Depending on the indicators, the processing strategies are also different. For example, personal basic indicator data such as age, gender, and training courses experienced can be directly obtained from the quantified personal data, while personal basic indicator data such as skill labels and attendance rate need to be obtained through certain logical operations. Interpersonal basic indicators need to be calculated according to certain rules, such as the geographical activity range of employees, the business activity range of employees, the total progress of the project, etc. For details, please refer to the description of the basic indicator data unit 32a in the aforementioned system, which will not be repeated here.

[0067] Step S33, read the preset derived indicators, and process the interpersonal basic indicator data and personal basic indicator data of each employee according to the corresponding processing strategy to obtain the derived indicator data of each employee. The derived indicators are, for example, average work performance of employees, employee age distribution and employee skill labels, employee communication types, employee cooperation and employee communication scope, project progress health indicators, project quality health indicators and project overall health indicators, etc. For details, please refer to the description of the derived indicator data unit 33a in the aforementioned system, which will not be repeated here.

[0068] Step S4, processing a variety of indicator data according to preset application conditions to obtain corresponding application information. For a specific application, the interpersonal basic indicator data, personal basic indicator data and derived indicator data of each employee are used to obtain employee portrait data, and can be graphically displayed in the interface. The application conditions include, for example, determining whether an indicator data or a combination of multiple indicator data is data with negative effects. When there is data with negative effects, the corresponding application information template can be read from the application information template library, and the original application information on the template can be modified according to the current specific data, so as to generate application information that conforms to the current situation. The application information is, for example, a suggestion for converting indicator data with negative effects or a combination of multiple indicator data into positive effect data. For details, please refer to the description of the information application module 4a in the aforementioned system, which will not be repeated here.

[0069] Fig.10 FIG. 1 is a flowchart of further processing of generating application information according to an embodiment of the present invention. In this embodiment, after generating the application information, the following steps are also included:

[0070] Step S41, extracting modifiable human resource parameter items from the application information;

[0071] Step S42, generating a new parameter value according to the adjustment information of the parameter item in the application information and the adjustment rule of the parameter item;

[0072] Step S43, determine whether the modification authority is granted for the parameter item. If so, modify the parameter value of the parameter item in step S44, and generate a notification of parameter modification and send it to the designated location. If the modification authority is not granted, generate a parameter modification notification and send it to the designated location in step S45, and the parameter modification notification includes at least the original parameter value of the parameter and the recommended new parameter value.

[0073] With the changes in market competition and technological innovation faced by enterprises, more and more work needs to be completed in a team mode. According to research, individuals with different emotional processing abilities (also known as emotional intelligence) as a component unit of a team not only determine the team emotional intelligence that exists as a team state, but also determine the team's work performance, thereby affecting the overall competitiveness and performance of the enterprise. As existing studies have shown, even short-term positive interactions between employees of an enterprise can have a quantifiable positive impact on the employees' work attitude, learning behavior, and physical and mental health, thereby improving the overall performance and competitiveness of the enterprise or organization. The present invention can analyze the geographical activity range, social activity range, interpersonal relationship, and cooperative interaction of team members in the workplace through the collected employee personal information and interpersonal information, so as to detect the employee's emotions and generate specific suggestions that can improve the employee's work efficiency, improve the working environment, adjust the employee's emotions, etc., so as to give full play to the employee's potential, improve the emotional intelligence of individuals and teams, and improve the overall competitiveness and performance of the enterprise.

[0074] The above embodiments are only used to illustrate the present invention, but not to limit the present invention. Ordinary technicians in the relevant technical field can make various changes and modifications without departing from the scope of the present invention. Therefore, all equivalent technical solutions should also fall within the scope of the present invention.

Claims

1. A human resources information processing system, comprising: a data query module configured to query a human resources database based on an employee's personal identification to obtain human resources information related to the employee; a data classification module, connected to the data query module and configured to classify the human resource information of the employee into interpersonal information and personal information; An information processing module, which is connected to the data classification module and is configured to process the interpersonal information and personal information according to the configured indicators and processing strategies to obtain corresponding indicator data; as well as An information application module, which is connected to the information processing module and is configured to process a variety of indicator data according to preset application conditions to obtain corresponding application information; It further includes a parameter configuration module for configuring indicators and processing strategies for obtaining indicator data, and / or for configuring application conditions and processing strategies; The parameter configuration module further includes: A basic indicator configuration unit, used to configure interpersonal basic indicators and personal basic indicators, and to configure a processing strategy for obtaining interpersonal basic indicator data and personal basic indicator data; A derived indicator configuration unit, used to configure the derived indicator and obtain a processing strategy for derived indicator data; and An application condition configuration unit, used to configure application conditions and obtain a processing strategy for corresponding application information; The information processing module includes: An information quantification unit, configured to quantify the interpersonal information and personal information according to the interpersonal basic indicators and personal basic indicators and their processing strategies to obtain corresponding interpersonal data and personal data; a basic indicator data unit connected to the information quantification unit and configured to process the interpersonal data and the personal data according to the interpersonal basic indicators and the personal basic indicators and their processing strategies to obtain interpersonal basic indicator data and personal basic indicator data; and The derived indicator data unit is connected to the information quantification unit and the basic indicator data unit, and is configured to process the interpersonal basic indicator data and the personal basic indicator data according to the derived indicators and their processing strategies to obtain the derived indicator data.

2. The system according to claim 1, further comprising a data collection module configured to collect various employee information related to the employee starting from the employee interview and store the information in a human resources database.

3. The system of claim 2, wherein the data collection module comprises: a geographic location collection unit configured to collect geographic location information of employees; A task information collection unit is configured to collect work information in which an employee participates; wherein the geographic location information at least includes location layout information of the employee's work place; the work information includes one or more of basic information of projects and / or tasks in which the employee participates, project and / or task progress record information, employee's responsibilities / roles in projects or tasks, and personnel communication record information during the progress of the project or task.

4. The system according to claim 3, wherein the data collection module further comprises a personal information collection unit configured to collect personal information of employees, wherein the personal information comprises one or more of personal identity information, interview information, training information, attendance information, performance information, and salary and benefits information.

5. The system according to claim 4, wherein the human resources database includes an interpersonal information database and a personal information database, wherein: The geographical location information of the employees collected by the geographical location collection unit and the work information of the employees collected by the task information collection unit are stored in the interpersonal information database, and the personal information of the employees collected by the personal information collection unit is stored in the personal information database; Correspondingly, the data classification module classifies the information read from the interpersonal information database as interpersonal information, and classifies the information read from the personal information database as personal information.

6. The system according to claim 4, wherein the data collection module further comprises a label unit, which is connected to the geographic location collection unit, the task information collection unit and the personal information collection unit, and is configured to set an interpersonal information label for the information collected by the geographic location collection unit and the task information collection unit, and set a personal information label for the information collected by the personal information collection unit; Correspondingly, the data classification module classifies the information obtained from the human resources database into interpersonal information and personal information according to the labels of the information.

7. The system according to claim 1, wherein the information processing module further comprises a model training unit, which is configured to train a corresponding machine learning model corresponding to one or more derived indicators; correspondingly, the derived indicator data unit utilizes the machine learning model to take the employee's interpersonal basic indicator data and personal basic indicator data as the input of the model to obtain the corresponding derived indicator data.

8. The system according to claim 1 or 7, wherein the information application module comprises: An employee portrait unit, configured to obtain the employee's portrait data based on the employee's interpersonal basic indicator data, personal basic indicator data and derived indicator data; and The application information generating unit is configured to generate corresponding application information according to the employee's portrait data or the employee's corresponding indicator data, based on application conditions and corresponding processing strategies.

9. The system according to claim 8 further comprises an application information template library, which is configured to provide application information templates corresponding to the various application conditions; correspondingly, the application information generation unit determines the corresponding application information template according to the application conditions satisfied by the employee portrait data or the corresponding indicator data of the employee, and modifies the corresponding data in the application information template based on the employee portrait data or the corresponding indicator data of the employee to generate application information.

10. The system according to claim 8 further comprises a display module, which is configured to process the employee portrait data and / or application information into corresponding graphics or charts for display or output according to preset visualization requirements.

11. The system according to any one of claims 9-10, wherein the information application module further comprises a parameter modification unit, which comprises: A parameter extraction unit, configured to extract modifiable parameter items from the application information, and obtain original parameter values ​​corresponding to the parameter items from the human resources database; a parameter value generating unit connected to the parameter extracting unit and configured to generate a new parameter value based on the original parameter value and the adjustment information of the parameter item in the application information and the adjustment rule of the parameter item; A modification unit connected to the parameter value generation unit, which modifies the parameter value of the parameter item when a new parameter value of the parameter item with modification authority is obtained; as well as A modification notification unit is connected to the parameter value generating unit and the modification unit, and generates a modification notification and sends it to a designated location when the modification unit has modified the parameter value of the parameter item with modification authority; or generates a parameter modification notification and sends it to a designated location when the parameter item has no modification authority, and the parameter modification notification includes at least the original parameter value of the parameter item and the new parameter value recommended for modification.

12. A method for processing human resources information, comprising: Query the human resources database based on the employee's personal identification to obtain the human resources information related to the employee; Categorize employee-related human resource information into interpersonal information and personal information; Read the configured indicators and processing strategies to process the interpersonal information and personal information to obtain corresponding indicator data; as well as Process various indicator data according to preset application conditions to obtain corresponding application information; The configured indicators include multiple interpersonal basic indicators, multiple personal basic indicators and multiple derived indicators; the corresponding process of obtaining indicator data includes: According to the interpersonal basic indicators and personal basic indicators and their corresponding processing strategies, the interpersonal information and personal information are quantified to obtain corresponding interpersonal data and personal data; Reading preset interpersonal basic indicators and personal basic indicators, and processing the interpersonal data and personal data according to corresponding processing strategies to obtain interpersonal basic indicator data and personal basic indicator data of each employee; and Read the preset derived indicators, and process each employee's interpersonal basic indicator data and personal basic indicator data according to the corresponding processing strategy to obtain each employee's derived indicator data.

13. The method according to claim 12, further comprising a data collection step, configured to collect various employee information related to the employee starting from the employee interview and store it in a human resources database; wherein, The employee information includes the employee's geographic location information, the work information the employee participates in, and the employee's personal information.

14. The method according to claim 13, wherein the data collection further comprises: Store the geographical location information of employees and the work information they participate in into the interpersonal information base in the human resources database; as well as Store employees' personal information in the personal information repository in the human resources database; Further, the information obtained from the interpersonal information database is classified as interpersonal information, and the information obtained from the personal information database is classified as personal information.

15. The method according to claim 13, wherein the data collection further comprises: Set interpersonal information tags for employees' geographic location information and work information they participate in; as well as Set up personal information labels for employees’ personal information; Furthermore, the acquired information is classified into interpersonal information or personal information according to the label of the information.

16. The method according to claim 13, wherein: The geographic location information includes at least the location layout information of the employee's work place; the work information includes one or more of the basic information of the projects and / or tasks in which the employee participates, project and / or task progress record information, the employee's responsibilities / roles in the project or task, and personnel communication record information during the progress of the project or task.

17. The method according to claim 12, further comprising: Utilize one or more trained derivative indicator machine learning models, and use employees' interpersonal basic indicator data and personal basic indicator data as model input to obtain corresponding derivative indicator data.

18. The method according to claim 12 or 17, wherein the interpersonal basic indicators include one or more of employee geographical activity scope, employee business activity scope, communication indicators, overall project progress, employee task progress, and project complaint indicators.

19. The method according to claim 18, wherein the derived indicators include: One or more of average employee performance, employee age distribution, and employee skill labels; One or more of project progress health indicators, project quality health indicators and project overall health indicators; one or more of employee communication types, employee cooperation and employee communication scope.

20. The method of claim 19, further comprising: Based on each employee's interpersonal basic indicator data, personal basic indicator data and derived indicator data, employee portrait data is obtained; as well as Corresponding application information is generated according to the employee's portrait data or the employee's corresponding indicator data, based on application conditions and corresponding processing strategies.

21. The method according to claim 20, further comprising: Extracting modifiable human resource parameter items from the application information; generating a new parameter value according to the adjustment information of the parameter item in the application information and the adjustment rule of the parameter item; and Modify the parameter values ​​of the human resources parameter items in the system.

22. The method according to claim 21, further comprising: Determine whether the parameter item has modification permission, and in response to the parameter item having modification permission, modify the parameter value of the parameter item, and generate a notification of parameter modification and send it to a designated location; In response to not having the modification authority for the parameter item, a parameter modification notification is generated and sent to a designated location, wherein the parameter modification notification includes at least an original parameter value of the parameter and a new parameter value that is recommended for modification.

23. The method of claim 20, further comprising: According to preset visualization requirements, employee portrait data and / or application information are processed into corresponding graphics or charts for display or output.

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