Human resource integrated management platform system

Abstract

The invention discloses a human resource integrated management platform system which comprises a basic data standardization module which is used for establishing a data standard system in the whole field of human resource management. Standardized definition, format specification and verification rule setting are carried out on employee basic information, organization structure information, post information, salary and welfare information, attendance information and performance information, and unified collection, storage and sharing of data are achieved. The method has the beneficial effects that based on a basic data standardization module, all-field data standards are unified and automatically checked, data redundancy and errors are eliminated, cross-module data integration is smoother, and the efficiency of basic transactions such as salary accounting and attendance statistics is improved; the multi-dimensional intelligent analysis module integrates data and applies big data and a machine learning algorithm, not only generates a basic analysis report, but also can accurately predict employee demission risks, outputs decision suggestions such as retention, training optimization and the like, and assists enterprise data-driven management.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of human resource management, and specifically relates to a human resource comprehensive management platform system. BACKGROUND

[0002] In today's globalized and digitized business environment, enterprises are facing increasingly fierce market competition and complex internal management challenges. Human resources, as the most core strategic resource of enterprises, directly affect the survival and development of enterprises. The human resource comprehensive management platform system, as an important tool for enterprise human resource management, integrates personnel information, salary data, attendance records, performance evaluation, etc. scattered in various departments into a unified platform through technical means, realizes the whole life cycle management of employees from employment to retirement, and has become an indispensable part of modern enterprise operation. With the continuous expansion of the scale of enterprises and the continuous expansion of the scope of business, the traditional human resource management method gradually exposes many problems. Early human resource management relies on manual operation, which is not only low in efficiency, but also prone to data entry errors, information transmission delays and other problems, resulting in high management costs. Even if some enterprises introduce simple human resource management software, there are often defects such as single function, independent module and data incompatibility, which cannot meet the needs of enterprises for comprehensive and collaborative management of human resources.

[0003] In order to solve the above problems, various human resource comprehensive management platforms have been continuously introduced in the industry. Among them, an enterprise human resource comprehensive management platform with a patent announcement number CN119941204B discloses a resource management system determination module, a management logic chain determination module, a task management strategy determination module, a sensitive management mode setting module, a sensitive management strategy determination module, a pre-management task control module, etc. This patent mainly focuses on building an extensible resource management system and management logic chain to realize the control of pre-management tasks and the protection of sensitive data, and adopts a differential conversion mode balancing sensitivity and data value for sensitive data processing. However, the prior art still has many deficiencies; first, in the standardization processing mechanism, although the existing platform mentions part of the standardized operation, it fails to establish a unified and perfect standardization processing mechanism for each link of the whole process of human resources, and the business processes and data standards between different modules are not unified, resulting in low efficiency of basic transaction processing and difficulty in ensuring data accuracy; second, in the construction of intelligent analysis platform, the existing technology pays more attention to the simple integration and display of data, lacks deep mining and intelligent analysis capability of multi-dimensional data, and cannot provide comprehensive and accurate decision basis for enterprise management; finally, in meeting the needs of enterprise full-scene human resource management, the functional modules of the existing platform are designed for general scenarios, lack the adaptation capability for special business scenarios of enterprises, and cannot meet the individualized management needs of enterprises of different industries and different scales. SUMMARY

[0004] The purpose of the present application is to provide a human resource comprehensive management platform system, which builds a whole-process standardization processing mechanism to ensure more efficient and accurate company basic transaction processing, builds a multi-dimensional intelligent analysis platform to support multi-dimensional data integration and deep mining analysis, optimizes management decision basis, adopts modular and customizable design to meet the needs of enterprise full-scene human resource management, and improves the level of enterprise human resource management and core competitiveness.

[0005] To achieve the above purpose, the present application provides the following technical scheme: a human resource comprehensive management platform system, comprising A basic data standardization module is used to establish a data standard system in the whole field of human resource management, to standardize the definition, format specification and verification rule setting of employee basic information, organizational structure information, post information, salary and welfare information, attendance information and performance information, to realize unified collection, storage and sharing of data; A whole-process business management module is built based on the data standard system constructed by the basic data standardization module, to build a whole life cycle business management process covering employee recruitment, employment management, training development, performance management, salary accounting, attendance management, resignation management and employee relationship maintenance, to set standardized operation nodes, approval processes and business rules for each business process, to realize automatic transfer and efficient control of business processes; A multi-dimensional intelligent analysis module acquires various data in the basic data standardization module and the whole-process business management module through a data interface, constructs a multi-dimensional data model, uses big data analysis algorithm and machine learning algorithm to deeply mine and analyze the data, generates personnel structure analysis report, salary and welfare analysis report, performance analysis report, training effect analysis report and resignation risk prediction report, and displays them to users in the form of visual charts, to provide data support for human resource management decision. The personalized customization adaptation module provides modular functional components and customized configuration tools according to the personalized management needs of different industries and different scale enterprises, so that the enterprises can select the functional components and perform personalized configuration on business processes, form styles, report formats and permission settings, and realize accurate adaptation of the system to the actual management needs of the enterprises. The data security protection module adopts data encryption technology, access control technology, data backup and recovery technology and security audit technology to perform hierarchical and classified management on sensitive data in the platform, set access permissions of different roles, and simultaneously perform real-time monitoring and auditing on system operation behaviors. The system collaborative interaction module provides data interaction interfaces with internal financial systems, OA systems, ERP systems of enterprises and external recruitment websites, social security accumulation fund management systems and individual tax declaration systems, and realizes real-time synchronization and sharing of data.

[0006] As a preferred technical solution of the present application, the basic data standardization module comprises a data standard formulation unit, a data acquisition standardization unit, a data verification unit and a data update and maintenance unit.

[0007] As a preferred technical solution of the present application, the whole-process business management module comprises a recruitment management unit, an employment management unit, a training management unit, a performance management unit, a salary management unit, an attendance management unit, a separation management unit and an employee relationship management unit.

[0008] As a preferred technical solution of the present application, the multi-dimensional intelligent analysis module comprises a data integration unit, a model construction unit, an algorithm analysis unit, a result display unit and a decision suggestion unit.

[0009] As a preferred technical solution of the present application, the multi-dimensional intelligent analysis module further comprises a prediction model training unit, which trains and optimizes the prediction model through historical data, and the specific steps comprise: S1: Collect historical data of historical employee separation data, performance data, salary data and training data, clean and pretreat the historical data, and remove abnormal data and missing values; S2: Select machine learning algorithms such as logistic regression algorithm, random forest algorithm and gradient boosting tree algorithm to construct a prediction model, and divide the pretreated historical data into a training set and a test set; S3: Train the prediction model using the training set, adjust the model parameters, and make the prediction accuracy of the model on the training set reach a preset threshold; S4: using the test set to verify the trained prediction model, evaluating the prediction performance of the model accuracy, recall rate, F1 value, if the model performance does not reach the expectation, return to step S3 to adjust the parameters, if the model performance reaches the expectation, the model is used as the final prediction model; S5: collect new historical data, retrain and optimize the prediction model.

[0010] As a preferred technical solution of the application, the individualized customization adaptation module includes a functional component library, a configuration tool unit and a customization development interface unit.

[0011] As a preferred technical solution of the application, the data security protection module includes a sensitive data grading and classification unit, a data encryption unit, an access control unit, a data backup and recovery unit and a security audit unit.

[0012] As a preferred technical solution of the application, the system collaborative interaction module includes an internal system interface unit, an external system interface unit and a data synchronization management unit.

[0013] As a preferred technical solution of the application, the standardized data interaction protocol is used between the standardized data module, the whole-process business management module, the multi-dimensional intelligent analysis module, the individualized customization adaptation module, the data security protection module and the system collaborative interaction module to realize real-time data flow and function collaboration. When the data or function of any module is updated, the update information is synchronized to the related associated modules through the data synchronization mechanism.

[0014] Compared with the prior art, the application has the following advantages: Relying on the standardized data module, the data standards in all fields are unified and automatically checked, data redundancy and errors are eliminated, cross-module data integration is smoother, and the efficiency of basic transactions such as salary accounting and attendance statistics is improved; The multi-dimensional intelligent analysis module integrates data and uses big data and machine learning algorithms to not only generate basic analysis reports, but also accurately predict employee turnover risks and output retention, training optimization and other decision-making suggestions to help enterprises with data-driven management; The individualized customization adaptation module cooperates with the whole-process business management module to provide selectable functional components and visual configuration tools, supports third-party secondary development, and adapts to the needs of enterprises of different industries and sizes; The system collaborative interaction module realizes data synchronization with internal and external systems, eliminates information delay, and eliminates the need for manual switching between systems in recruitment, social security declaration and other processes, thereby improving the overall operational efficiency of the enterprise. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 It is a human resource comprehensive management platform system schematic diagram of the application. DETAILED DESCRIPTION

[0016] Referring to Figure 1 The present application provides a human resource comprehensive management platform system, comprising The basic data standardization module is used for establishing a data standard system in the whole field of human resource management, defining, format standardizing and setting verification rules of the basic information of employees, the information of organizational structure, the information of posts, the information of salary and welfare, the information of attendance, and the information of performance, realizing unified collection, storage and sharing of data, and eliminating data redundancy and inconsistency. The whole-process business management module is built based on the data standard system of the basic data standardization module, and builds a whole life cycle business management process covering employee recruitment, employment management, training development, performance management, salary accounting, attendance management, resignation management and employee relationship maintenance. Each business process is provided with standardized operation nodes, approval processes and business rules, realizing automatic transfer and efficient control of the business process. The multi-dimensional intelligent analysis module obtains various data in the basic data standardization module and the whole-process business management module through a data interface, constructs a multi-dimensional data model, uses big data analysis algorithms and machine learning algorithms to deeply mine and analyze the data, generates personnel structure analysis reports, salary and welfare analysis reports, performance analysis reports, training effect analysis reports and resignation risk prediction reports, and displays them to users in the form of visual charts, providing data support for human resource management decisions. The individualized customization adaptation module provides modular functional components and customized configuration tools according to the individualized management needs of enterprises in different industries and of different scales, so that enterprises can select functional components and perform individualized configuration on business processes, form styles, report formats and permission settings, realizing accurate adaptation of the system to actual management needs of enterprises. The data security protection module uses data encryption technology, access control technology, data backup and recovery technology and security audit technology to perform hierarchical and classified management on sensitive data in the platform, sets access permissions for different roles, simultaneously performs real-time monitoring and auditing on system operation behaviors, and ensures the security and integrity of system data. The system collaborative interaction module provides data interaction interfaces with internal financial systems, OA systems, ERP systems of enterprises, and external recruitment websites, social security and public accumulation management systems, and individual tax declaration systems, realizes real-time synchronization and sharing of data, breaks information silos, promotes collaborative operation of various systems, and improves overall operation efficiency of enterprises.

[0017] In this embodiment, preferably, the basic data standardization module includes a data standard formulation unit, a data collection standardization unit, a data verification unit, and a data update and maintenance unit; The data standard formulation unit formulates data classification standards, data coding rules, data field definitions, data format specifications, and data quality evaluation indicators for each field of human resource management by investigating industry standards, enterprise management needs, and relevant legal and regulatory requirements, and forms a unified data standard system document; The data collection standardization unit designs standardized data collection forms and data import templates according to the data standard system, and supports manual input, batch import, and interface connection data collection methods; The data verification unit performs format verification, logical verification, integrity verification, and uniqueness verification on the collected data based on data verification rules, generates error prompt information for data that fails verification, and feeds back the information; The data update and maintenance unit establishes a data update and maintenance mechanism, sets up a data update approval process and operation permissions, and records and traces the addition, modification, and deletion operations of data.

[0018] In this embodiment, preferably, the full-process business management module includes a recruitment management unit, an onboarding management unit, a training management unit, a performance management unit, a salary management unit, an attendance management unit, a separation management unit, and an employee relationship management unit; The recruitment management unit is used to publish recruitment needs, screen resumes, organize interviews, issue employment notices, realize online management of the recruitment process, and automatically synchronize the information of the employed personnel to the employee basic information database; The onboarding management unit provides online onboarding guidance for new employees, collects onboarding materials, automatically completes labor contract signing, social security fund opening, and office supply application, and enters the information of the new employees into related business modules; The training management unit formulates training plans, manages training courses, organizes training activities, records training processes, evaluates and feeds back training effects, and forms employee training archives; The performance management unit decomposes performance indicators according to enterprise strategic targets, formulates performance evaluation schemes, supports 360-degree evaluation, KPI evaluation, and OKR evaluation methods, automatically calculates performance results, and associates salary adjustment and position promotion; The performance management unit also includes a performance indicator library management subunit, which is used to establish an enterprise-level performance indicator library and manage performance indicators. The specific steps include: T1: According to the enterprise strategic targets and the business functions of each department, decompose to form performance indicators at all levels, and clearly define the name, definition, calculation method, data source, evaluation period, weight, and evaluation standard of each performance indicator; T2: Performance indicators are classified and managed according to indicator type and assessment object. Indicator types include financial indicators, operational indicators, customer indicators, and learning and growth indicators. Assessment objects include company-level indicators, department-level indicators, and job-level indicators. These are classified and stored to form a performance indicator library. T3: Establish a performance indicator maintenance mechanism, regularly evaluate and update the indicators in the performance indicator library, and add, modify or delete performance indicators according to corporate strategy adjustments, business process optimization and changes in the external environment; T4: Set access permissions for the performance indicator library. Senior executives, department managers, and human resources administrators have different permissions to view, use, and maintain the indicators. The payroll management unit automatically calculates salaries and benefits based on employee attendance data, performance data, and job information, generates payroll details, and supports integration with bank systems to achieve automatic payroll disbursement and individual income tax declaration. The attendance management unit supports fingerprint attendance, facial recognition attendance, and mobile APP attendance methods. It automatically collects attendance data, performs attendance anomaly analysis and processing, and generates attendance reports. The employee departure management unit processes departure applications, handles work handover, returns of belongings, and procedures for suspending social security and housing provident fund contributions. It also conducts exit interviews and reason analysis, and establishes files for departing employees. The Employee Relations Management Unit manages employee reward and punishment records, transfer records, and employment contract terms; handles employee grievances and suggestions; and organizes employee care activities.

[0019] In this embodiment, preferably, the multi-dimensional intelligent analysis module includes a data integration unit, a model building unit, an algorithm analysis unit, a result display unit, and a decision suggestion unit; The data integration unit uses ETL tools to extract, clean, transform, and integrate data from the basic data standardization module and the full-process business management module to form a unified data analysis dataset. The model building unit constructs a multi-dimensional data model based on the analysis themes of personnel structure analysis, salary analysis, and performance analysis, defining data dimensions such as time dimension, organization dimension, job dimension, and personnel category dimension, as well as measurement indicators such as number of people, total salary, and average performance score. The algorithm analysis unit employs big data analysis algorithms such as clustering analysis, association analysis, and regression analysis, as well as machine learning algorithms such as decision tree, neural network, and support vector machine, to perform in-depth analysis of the dataset and uncover data patterns and trends. The results display unit uses data visualization technologies such as line charts, bar charts, pie charts, heat maps, and dashboards to present the analysis results in a visual chart format, and supports interactive operations such as drill-down, filtering, and sorting. The decision suggestion unit generates management decision suggestions of retention measures and course optimization suggestions based on the analysis results, combined with industry data and enterprise management experience.

[0020] In this embodiment, preferably, the multi-dimensional intelligent analysis module further includes a prediction model training unit, which trains and optimizes the prediction model through historical data. The specific steps include: S1: Collect historical employee turnover data, performance data, salary data, and training data. Clean and preprocess the historical data to remove abnormal data and missing values. S2: Select machine learning algorithms such as logistic regression algorithm, random forest algorithm, and gradient boosting tree algorithm to build a prediction model. Divide the preprocessed historical data into training set and test set. S3: Train the prediction model using the training set and adjust the model parameters to make the prediction accuracy of the model on the training set reach the preset threshold. S4: Verify the trained prediction model using the test set and evaluate the prediction performance of the model accuracy, recall rate, and F1 value. If the model performance does not meet expectations, return to step S3 to adjust the parameters. If the model performance meets expectations, use the model as the final prediction model. S5: Collect new historical data and retrain and optimize the prediction model.

[0021] In this embodiment, preferably, the individualized customization adaptation module includes a function component library, a configuration tool unit, and a customization development interface unit. The function component library includes independent function components of recruitment management component, training management component, performance management component, salary management component, and attendance management component. Each function component has a standardized interface and configurable parameters. The configuration tool unit provides visual form design tools, process design tools, report design tools, and permission configuration tools for enterprise users to design business form fields and layouts, set business process nodes and approval personnel, define report dimensions and indicators, and assign user roles and permissions through drag-and-drop and check operations. The customization development interface unit provides standardized development interfaces and development documents to support enterprise or third-party development teams to conduct secondary development based on the interfaces to achieve customized development of specific functions.

[0022] In this embodiment, preferably, the data security protection module includes a sensitive data grading and classification unit, a data encryption unit, an access control unit, a data backup and recovery unit, and a security audit unit. The sensitive data grading and classification unit sorts platform data, classifies data into high-sensitive data, medium-sensitive data and low-sensitive data according to sensitivity, high-sensitive data includes ID number, bank card number and salary details, medium-sensitive data includes employee performance results and training records, and low-sensitive data includes employee name and department information, and different levels of data adopt different security protection strategies; The data encryption unit adopts symmetric encryption algorithm of AES algorithm and asymmetric encryption algorithm of RSA algorithm to encrypt sensitive data in the transmission process and the storage process; The access control unit sets user roles of human resource administrator, department manager, ordinary employee and system administrator based on the RBAC model, and allocates operation permission and data access permission to each role; The data backup and recovery unit adopts a combination of timed backup and real-time backup to backup platform data, and stores backup data in multiple different physical locations to quickly recover data in case of system failure or data loss; The security audit unit records system operation behaviors of user login, data query, data modification and approval operation in real time, generates audit logs containing operation time, operator, operation content and operation result, and supports query, statistics and analysis of audit logs.

[0023] In the embodiment, preferably, the system cooperative interaction module includes an internal system interface unit, an external system interface unit and a data synchronization management unit; The internal system interface unit provides RESTAPI and SOAPAPI standardized interfaces with enterprise internal financial system, OA system and ERP system, realizes sharing and synchronization of employee information, salary data, attendance data and performance data among systems, synchronizes salary data to the financial system for accounting, and synchronizes OA system approval process data to the whole-process business management module to promote business processes; The external system interface unit provides docking interfaces with external recruitment websites, social security and public endowment management systems and individual tax declaration systems, realizes two-way data interaction, imports resume data from recruitment websites to the recruitment management unit, synchronizes social security and public endowment payment data to the social security and public endowment management system to complete payment declaration, and synchronizes individual tax data to the individual tax declaration system to complete individual tax declaration; The data synchronization management unit sets data synchronization frequency, synchronization mode and exception handling mechanism, data synchronization frequency includes real-time synchronization and timed synchronization, synchronization mode includes full synchronization and incremental synchronization, and sends warning information and supports retry and recovery when data synchronization is abnormal.

[0024] In the embodiment, preferably, the base data standardization module, the whole-process business management module, the multi-dimensional intelligent analysis module, the individualized customization adaptation module, the data security protection module and the system collaborative interaction module realize data real-time flow and function collaboration through standardized data interaction protocol, and when data or function of any module is updated, the update information is synchronized to the related associated modules through the data synchronization mechanism.

Claims

1. A human resource integrated management platform system, characterized in that: Comprising The basic data standardization module is used for establishing a data standard system of human resource management in all fields, standardizing definition, format specification and check rule setting of employee basic information, organizational structure information, post information, salary and welfare information, attendance information and performance information, and realizing unified collection, storage and sharing of data; The whole-process business management module is built based on the data standard system of the basic data standardization module, covers the whole life cycle business management process of employee recruitment, employment management, training development, performance management, salary accounting, attendance management, separation management and employee relationship maintenance, sets standardized operation nodes, approval processes and business rules for each business process, and realizes automatic transfer and efficient control of the business process; The multi-dimensional intelligent analysis module obtains various data in the basic data standardization module and the whole-process business management module through a data interface, constructs a multi-dimensional data model, uses big data analysis algorithms and machine learning algorithms to deeply mine and analyze the data, generates personnel structure analysis reports, salary and welfare analysis reports, performance analysis reports, training effect analysis reports and separation risk prediction reports, and displays them to users in the form of visual charts, and provides data support for human resource management decision-making; The personalized customization adaptation module provides modular function components and customized configuration tools according to the personalized management needs of enterprises in different industries and of different scales, so that enterprises can select function components and perform personalized configuration on business processes, form styles, report formats and permission settings, and realize accurate adaptation of the system to actual management needs of enterprises; The data security protection module uses data encryption technology, access control technology, data backup and recovery technology and security audit technology to perform hierarchical and classified management on sensitive data in the platform, sets access permissions for different roles, and simultaneously performs real-time monitoring and auditing on system operation behaviors; The system collaborative interaction module provides data interaction interfaces with internal financial systems, OA systems, ERP systems of enterprises and external recruitment websites, social security and public accumulation fund management systems and individual tax declaration systems, realizes real-time synchronization and sharing of data.

2. The human resource comprehensive management platform system according to claim 1, characterized in that: The basic data standardization module comprises a data standard formulation unit, a data collection standardization unit, a data check unit and a data update and maintenance unit.

3. The human resource integrated management platform system according to claim 1, characterized in that: The whole-process business management module comprises a recruitment management unit, an employment management unit, a training management unit, a performance management unit, a salary management unit, an attendance management unit, a separation management unit and an employee relationship management unit.

4. The human resource integrated management platform system according to claim 1, characterized in that: The multi-dimensional intelligent analysis module comprises a data integration unit, a model construction unit, an algorithm analysis unit, a result display unit and a decision suggestion unit.

5. The human resource integrated management platform system according to claim 4, characterized in that: The multi-dimensional intelligent analysis module further comprises a prediction model training unit, which trains and optimizes the prediction model through historical data, and the specific steps comprise: S1: Collect historical employee turnover data, performance data, salary data, and training data, clean and preprocess the historical data, and remove abnormal data and missing values; S2: Select machine learning algorithms such as logistic regression, random forest, and gradient boosting tree to build a prediction model, and divide the preprocessed historical data into training set and test set; S3: Train the prediction model using the training set, adjust the model parameters, and make the prediction accuracy of the model on the training set reach the preset threshold; S4: Verify the trained prediction model using the test set, evaluate the prediction performance of the model accuracy, recall rate, and F1 value, if the model performance does not meet the expectation, return to step S3 to adjust the parameters, if the model performance meets the expectation, use the model as the final prediction model; S5: Collect new historical data and retrain and optimize the prediction model.

6. The human resource integrated management platform system according to claim 1, characterized in that: The individual customization adaptation module includes a functional component library, a configuration tool unit, and a customization development interface unit.

7. The human resource integrated management platform system according to claim 1, characterized in that: The data security protection module includes a sensitive data classification unit, a data encryption unit, an access control unit, a data backup and recovery unit, and a security audit unit.

8. The human resource integrated management platform system according to claim 1, characterized in that: The system collaborative interaction module includes an internal system interface unit, an external system interface unit, and a data synchronization management unit.

9. The human resource integrated management platform system according to claim 1, characterized in that: The standardized data interaction protocol is used to realize real-time data flow and function collaboration between the basic data standardization module, the whole-process business management module, the multi-dimensional intelligent analysis module, the individual customization adaptation module, the data security protection module, and the system collaborative interaction module. When the data or function of any module is updated, the update information is synchronized to the related associated modules through the data synchronization mechanism.

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