A one-stop human resources outsourcing intelligent service platform system

Abstract

The invention discloses a one-stop human resource outsourcing intelligent service platform system, comprising a talent pool management module, an interview management module, a project management module, an attendance management module, a salary management module, an intelligent risk management module, and a notification and announcement module. The talent pool management module is used for intelligently screening and managing talent information, the interview management module is used for interview management of employees, the project management module is used for following up and managing projects, the attendance management module is used for formulating and managing attendance strategies, the salary management module is used for calculating and managing personnel salaries, the intelligent risk management module is used for intelligent risk assessment, early warning and analysis, and the notification and announcement module is used for issuing company announcements. The invention discloses a one-stop human resource outsourcing intelligent service platform system, proposes a talent retrieval and recommendation algorithm based on naive Bayes to retrieve and intelligently recommend information in the talent pool, and proposes an enterprise risk assessment algorithm based on artificial intelligence to intelligently assess, analyze and predict enterprise risks.

Description

Technical Field

[0001] The present invention relates to the fields of naive Bayes, artificial intelligence and risk management, and in particular to a one-stop human resource outsourcing intelligent service platform system. Background Art

[0002] Naive Bayes technology is a machine learning method based on probability theory, which aims to solve large-scale data classification and prediction problems. This technology uses Bayesian theorem to assume the independence of each feature, and can efficiently process and analyze data. In the one-stop human resources outsourcing intelligent service platform system, Naive Bayes technology is used to intelligently retrieve and recommend massive information in the talent pool. By analyzing the data of candidates' skills, experience, and educational background, the Naive Bayes algorithm can quickly and accurately recommend talents that meet job requirements, thereby improving recruitment efficiency and reducing the cost of employment matching.

[0003] Artificial intelligence and risk management technology is a technology that uses machine learning and data mining methods to conduct risk assessment, analysis, and prediction. It aims to solve potential risk problems in the employment process of enterprises. In the one-stop human resource outsourcing intelligent service platform system, artificial intelligence and risk management technology can identify potential risks in advance and provide early warning functions through intelligent analysis of employees' behavior patterns, work performance, and historical data, helping enterprises avoid possible risks in employment decisions, optimize human resource management, and ensure the safety and stability of the employment process.

[0004] However, the existing one-stop human resource outsourcing intelligent service platform system lacks accuracy and efficiency in talent recommendation and cannot quickly match the most suitable candidates. Secondly, the existing risk management functions are relatively limited and lack in-depth analysis and prediction of potential risks, which causes companies to face unforeseen risks in the employment process. In addition, the system is not intelligent enough in overall decision-making support and it is difficult to provide a comprehensive employment management optimization solution. Summary of the invention

[0005] The purpose of the present invention is to provide a one-stop human resources outsourcing intelligent service platform system to solve the problems of the existing one-stop human resources outsourcing intelligent service platform system proposed in the above background technology, namely, the lack of accuracy and efficiency in talent recommendation and the inability to quickly match the most suitable candidates, as well as the limited risk management function of the existing system and the lack of in-depth analysis and prediction of potential risks, which leads to the problem that enterprises face unforeseen risks in the employment process.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a one-stop human resource outsourcing intelligent service platform system, including a talent pool management module, an interview management module, a project management module, an attendance management module, a salary management module, an intelligent risk management module, and a notification and announcement module, characterized in that: the talent pool management module proposes a talent retrieval and recommendation algorithm based on naive Bayes, which is used to collect and manage the resume information of job seekers and existing employees, and intelligently recommend employees who meet the job requirements according to job requirements; the interview management module is used for online appointment of interviews, interview evaluation, interview progress tracking, and automatic sending of interview notifications and reminders, and records and manages interview evaluations, and provides interview effect analysis; the project management module includes a task allocation unit and a project progress tracking unit, the task allocation unit is used to intelligently allocate project tasks according to project requirements and employee skills, and the project progress tracking unit is used to track project progress in real time and provide progress Report, the attendance management module is used to provide two ways of clocking in, namely GPS positioning clocking in and facial recognition clocking in, and manage employees' attendance and leave. The salary management module is used to intelligently calculate salaries based on working hours and task completion, and help companies formulate salary strategies. The intelligent risk management module includes a risk identification and early warning unit and a management support decision-making unit. The risk identification and early warning unit proposes an enterprise risk assessment algorithm based on artificial intelligence to identify risk information in the process of flexible employment, and provides risk prediction and early warning functions to help companies avoid employment risks in advance. The management support decision-making unit is used to conduct a comprehensive analysis of key indicators of employees' skills, work performance, and behavior patterns, provide decision support, and help companies make scientific and reasonable decisions in employment management. The notification and announcement module is used to publish and manage company announcement information, and intelligently classify information and push announcements according to employees' roles and preferences.

[0007] Preferably, the talent pool management module proposes a talent retrieval and recommendation algorithm based on Naive Bayes, and intelligently parses and matches resumes through artificial intelligence technology. The system can automatically retrieve and recommend employees who are suitable for job requirements, ensuring the accuracy and efficiency of the recruitment process, and by collecting, storing and managing the resumes, skills and work experience information of job seekers and existing employees, ensuring that the company can efficiently manage and screen suitable talents.

[0008] Specifically, the talent search and recommendation algorithm based on naive Bayes is as follows: First, by constructing a comprehensive evaluation framework, the evaluation dimensions and indicators for talent search and recommendation are determined, including educational background, work experience, and interview performance, to ensure that the potential and suitability of talent information can be evaluated from multiple perspectives. The specific formula for determining the evaluation dimensions and indicators is expressed as follows:

[0009]

[0010] in, Expressed as The total ability gap score of individual talent information, Represented as talent information In Dimension Ratings on Represented as dimensions The target score represents the expected ability level of the talent. It is a talent serial number used to distinguish different talent information. It is represented as a serial number identifier of the evaluation dimension, which is used to distinguish different evaluation indicators. It is expressed as the total number of evaluation dimensions. Then, the evaluation indicators are divided into core factors and secondary factors. The core factors are expressed as the key indicators that determine whether the talent information is suitable for the position, and the secondary factors are expressed as auxiliary information for the suitability of the talent information. The specific formula for calculating and grouping the core factors and secondary factors is expressed as follows:

[0011]

[0012] in, Represented as talent information The average score of the core factors Represented as talent information The average score of the secondary factors, is expressed as the number of core factors, Expressed as The core factors are in the dimension The weight on Represented as talent information In the The scores on the core factors are Represented as the core factor number index, Expressed as Personal talent information is in The scores on the secondary factors reflect the performance of talent information on non-core but still important evaluation dimensions. Represented as the index of the number of secondary factors, Expressed as in dimension Top, The weight of each secondary factor reflects the relative importance of the secondary factor in the evaluation process. Secondly, after completing the calculation of the core and secondary factors, the core factors and secondary factors are combined to calculate the total value of each talent information. By integrating the various indicators of talent information, a comprehensive score is obtained. The specific calculation formula is expressed as:

[0013]

[0014] in, Expressed as The comprehensive value score of individual talent information represents the quantification of the comprehensive performance of talent information in all evaluation dimensions. It is expressed as the proportion of the relative importance of the secondary factors in the entire evaluation. It is expressed as the weight ratio of the core factors. Then, the naive Bayes algorithm is used to predict the job arrangement of talent information. By associating the characteristics of talent information with job types, an intelligent prediction model is established. The specific formula is expressed as follows:

[0015]

[0016] in, Represented as a given feature Time Category The posterior probability of the category Represented as different job types, characteristics Represented as different characteristic attributes of talent information, Represented as category The prior probability of talent information being assigned to a position type without any additional information The probability of Represented as the total number of features of talent information, It is represented as the index of the number of features of talent information. Expressed as The characteristic attributes of personal talent information, Represented as in category Next, Features The conditional probability of It is expressed as a proportional operation. Finally, the prediction results of the naive Bayes model and the comprehensive score of talent information are combined to perform talent retrieval and intelligent recommendation, which can recommend the most suitable talent information for job vacancies. The specific calculation formula for intelligent recommendation of talent information is expressed as:

[0017]

[0018] in, Expressed as the Personal talent information for job types The recommendation score of Expressed as The comprehensive value score of individual talent information represents the quantification of the comprehensive performance of talent information in all evaluation dimensions. Represented as in category Next, Features The conditional probability of Expressed as The overall algorithm comprehensively considers multiple dimensions of talent information and uses the constructed naive Bayesian intelligent prediction model to make intelligent recommendations to improve the efficiency and accuracy of talent recruitment.

[0019] Preferably, the interview management module ensures the smooth progress of the interview process by providing online interview appointment, time scheduling and interview progress tracking functions. At the same time, it intelligently arranges interviews according to the schedules of applicants and interviewers, and provides an assessment of interview effectiveness by analyzing interview evaluations, ensuring the scientificity and fairness of recruitment decisions.

[0020] Preferably, the project management module includes a task allocation unit, which intelligently allocates tasks by analyzing project requirements and employee skills to ensure that each project task is performed by appropriate employees, thereby ensuring the efficiency and effectiveness of project execution.

[0021] Preferably, the project management module includes a project progress tracking unit, which monitors the progress of the project in real time, provides progress reports and early warning information, ensures that the project proceeds as planned, and promptly discovers and resolves delays in the project advancement process.

[0022] Preferably, the attendance management module ensures the accuracy and timeliness of employee attendance records by providing GPS positioning and facial recognition clocking-in and leave management functions. The system ensures the transparency and fairness of attendance management by automatically recording attendance data and analyzing leave patterns.

[0023] Preferably, the salary management module ensures the accuracy and rationality of employee salaries through intelligent salary calculation and personalized salary plan design. The system automatically calculates salaries based on working hours and task completion, and helps the company formulate appropriate salary strategies.

[0024] Preferably, the intelligent risk management module includes a risk identification and early warning unit, which proposes an enterprise risk assessment algorithm based on artificial intelligence, conducts real-time risk identification and prediction through historical data and real-time data of the enterprise's internal and external environment, identifies risk points based on historical risk data and current conditions, and generates early warning notifications to help the enterprise take timely response measures, thereby reducing the probability of risk occurrence.

[0025] Specifically, the enterprise risk assessment algorithm based on artificial intelligence is as follows: First, through the historical data and current data collected and managed by the system, including financial data, market data, operational data, and external environment data, a risk assessment model based on a neural network is constructed. In the model, the input layer is represented by various input variables, the hidden layer captures the complex relationship between the data, and the output layer provides the result of the risk assessment. The specific formula is expressed as follows:

[0026]

[0027] in, Expressed as time The predicted value of enterprise risk at the time represents the model at time Estimate the level of risk currently faced by the enterprise at the moment, Represented as the ReLU activation function, Represents the weight matrix of the importance of each input feature to the prediction result, Expressed as time The input variable vector at the time contains all input features related to enterprise risk assessment, such as financial data, market indicators, operating conditions, and external environment data. is represented as a bias vector, It is expressed as the moment of the acquired data. By building an enterprise risk assessment model based on artificial intelligence, the potential risks of the enterprise are intelligently assessed and predicted to ensure that the system can identify and predict the risks that affect the operation of the enterprise, thereby improving the enterprise's ability to cope with competition. Then, the various potential risk factors of the enterprise are intelligently analyzed to extract the key factors that have the most significant impact on the enterprise risk. By building a tree model to automatically identify and weigh the impact of different risk factors, the contribution of each input feature to the final risk prediction is analyzed to ensure the accuracy of the assessment results. The specific formula is expressed as:

[0028]

[0029] in, Represented as a feature The importance score of the feature Contribution to the entire risk assessment model, Represented as the feature number index, Expressed as the total number of times, representing the model evaluation characteristics The time span of importance, Represented as a feature In time The information gain at the moment is used to measure the feature The contribution of adding to reduce model uncertainty is Represented as a feature In time The entropy value at each moment is reduced. By optimizing feature selection, the variables that contribute significantly to risk assessment are retained, and noise data are eliminated to ensure the simplicity and efficiency of the model. Secondly, based on intelligent analysis, the selected features are input into the constructed time series prediction model to predict future corporate risks and provide a reference for enterprises to formulate response strategies. The specific formula is expressed as follows:

[0030]

[0031] in, Expressed as time The risk prediction value at the moment is the future risk value predicted by inputting current and past risk data. Expressed as time The actual risk value at the moment, Expressed as time The actual risk value at the moment, Expressed as time The actual risk value at the moment, It is expressed as the number of moments from the current time. Expressed as the model function for risk prediction, It is represented as a set of model parameters, including weights and bias items. According to the results of the model output, the risks that the enterprise may face in different time periods are predicted. Then, based on the aforementioned risk prediction results, an early warning and response mechanism for enterprise risks is established so that appropriate measures can be taken before the risk occurs to reduce the impact of the risk. The early warning threshold is set. When the predicted risk value exceeds the threshold, the system automatically triggers an early warning. The specific formula is expressed as:

[0032]

[0033] in, Expressed as the predicted risk value Exceeding the warning threshold Under the condition of The probability of occurrence, It is expressed as the warning threshold, which is used to decide when to issue a risk warning. It is expressed as an indicator function. Finally, through online learning and incremental updating, the prediction ability of the model is continuously improved by using new data. The specific formula is expressed as:

[0034]

[0035] in, Represented as updated model parameters that determine the model's behavior and output, Represents the model parameters before updating, which is used for comparison before model updating. Denoted as the learning rate, Expressed as gradient calculation, Expressed as the loss function, Represents the new input data, Represented as new input data The corresponding real labels are continuously optimized and updated by the model to adapt to the dynamic environmental changes of the enterprise, ensuring that the algorithm can continue to provide accurate risk assessment results under different environmental and data conditions.

[0036] Preferably, the intelligent risk management module includes a management support decision unit, which provides data-driven management suggestions by comprehensively analyzing employees' skills, work performance, behavioral patterns and risk assessment results, supports the company's decision-making in flexible employment management, and ensures that the company can make reasonable employment decisions based on objective data.

[0037] Preferably, the notification and announcement module ensures that employees can obtain relevant information in a timely manner by publishing and managing company announcements and notification information. At the same time, the system intelligently classifies information and pushes announcements based on the roles and preferences of employees to ensure effective communication of information and employee participation.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. The talent pool management module proposes a talent retrieval and recommendation algorithm based on Naive Bayes. First of all, the algorithm can evaluate talent information from multiple perspectives by constructing a comprehensive evaluation framework. Specifically, the evaluation framework includes multiple evaluation dimensions and indicators such as educational background, work experience, and interview performance, so as to more comprehensively evaluate the potential and job suitability of talents. The evaluation dimensions and indicators are further divided into core factors and secondary factors. The core factors are the key to determining whether talents are suitable for their positions, while the secondary factors provide auxiliary information to help understand talent information more comprehensively. In the implementation of the algorithm, the core and secondary factors are first calculated, and then the two are combined to obtain the comprehensive value score of each talent. The comprehensive score reflects the overall performance of the talent in all evaluation dimensions, thereby providing a quantitative standard for job matching. Then, the Naive Bayes algorithm is used to predict the job arrangement of talents. By establishing a relationship model between talent characteristics and job types, the Naive Bayes algorithm can predict the most suitable job type for a talent based on the characteristics of talent information, such as educational background, professional skills, and past experience. It makes full use of the concepts of prior probability and conditional probability, and makes decisions by combining prior knowledge with new information. Finally, by combining the prediction results of the Naive Bayes model with the comprehensive score, the algorithm can recommend the most suitable talent information for job vacancies. This algorithm not only improves the accuracy of talent recommendations, but also significantly improves the efficiency of the recruitment process, and effectively reduces the workload of recruiters in resume screening and interview arrangements. Overall, the talent retrieval and recommendation algorithm based on Naive Bayes comprehensively considers the multiple dimensions of talents and uses artificial intelligence technology to make intelligent predictions on job matching, thereby achieving efficient and accurate talent recommendations in the flexible employment system and greatly optimizing the company's talent management process.

[0040] 2. The risk identification and early warning unit proposes an enterprise risk assessment algorithm based on artificial intelligence. First, the system collects and manages historical data and current data, including financial data, market data, operational data and external environment data, and builds a risk assessment model based on a neural network. The model can intelligently process these data to capture complex relationships and patterns, thereby processing various data variables in the input layer, and the hidden layer uses the ability of neural networks to capture nonlinear relationships between data. The output layer provides the results of enterprise risk assessment. The assessment model based on neural networks helps to identify and predict potential risks that affect enterprise operations, enabling enterprises to take measures in advance to respond, thereby improving their reaction speed and decision-making ability in market competition. Secondly, the algorithm also uses artificial intelligence technology to intelligently analyze various potential risk factors of the enterprise. By building a decision tree model, the algorithm can automatically identify and weigh the impact of different risk factors, and analyze the contribution of each input feature to the final risk prediction. The algorithm ensures the accuracy of the assessment results and helps In order to extract the key factors that have the greatest impact on corporate risks, in the process of optimizing and screening features, the algorithm can retain variables that contribute significantly to risk assessment, while eliminating data that may introduce noise, thereby ensuring the simplicity and efficiency of the model. By inputting the optimized features into the time series prediction model, the system can predict future risks and provide a reliable reference for enterprises to formulate long-term risk response strategies. Based on the risk prediction results, the system can establish an effective early warning and response mechanism. When the predicted risk value exceeds the set early warning threshold, the system can automatically trigger an early warning to remind enterprise managers to take necessary measures to mitigate the impact of potential risks. In addition, through online learning and incremental updates, the algorithm can use new data to continuously improve the model's predictive ability, ensure that the algorithm can adapt to dynamically changing environments and data conditions, and provide enterprises with continuous and accurate risk assessment results. The dynamic update mechanism enables the system to maintain its effectiveness and accuracy during long-term use, providing enterprises with continuous risk monitoring and management support. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The invention is further illustrated by the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0042] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0044] See also Figure 1 The present invention provides a one-stop human resource outsourcing intelligent service platform system, including a talent pool management module, an interview management module, a project management module, an attendance management module, a salary management module, an intelligent risk management module, and a notification and announcement module, characterized in that: the talent pool management module proposes a talent retrieval recommendation algorithm based on naive Bayes, which is used to collect and manage the resume information of job seekers and existing employees, and intelligently recommend employees who meet the job requirements according to job requirements; the interview management module is used for online appointment of interviews, interview evaluation, interview progress tracking, and automatic sending of interview notifications and reminders, and records and manages interview evaluations, and provides interview effect analysis; the project management module includes a task allocation unit and a project progress tracking unit, the task allocation unit is used to intelligently allocate project tasks according to project requirements and employee skills, the project progress tracking unit is used to track project progress in real time and provide progress reports, and the attendance The management module is used to provide two ways of clocking in, namely GPS positioning clocking in and facial recognition clocking in, and to manage employee clocking in and out and leave. The salary management module is used to intelligently calculate salaries based on working hours and task completion, and help companies formulate salary strategies. The intelligent risk management module includes a risk identification and early warning unit and a management support decision-making unit. The risk identification and early warning unit proposes an enterprise risk assessment algorithm based on artificial intelligence to identify risk information in the flexible employment process, and provides risk prediction and early warning functions to help companies avoid employment risks in advance. The management support decision-making unit is used to conduct a comprehensive analysis of key indicators of employees' skills, work performance, and behavior patterns, provide decision support, and help companies make scientific and reasonable decisions in employment management. The notification and announcement module is used to publish and manage company announcement information, and intelligently classify information and push announcements based on employees' roles and preferences.

[0045] See also Figure 1 Furthermore, the talent pool management module proposes a talent retrieval and recommendation algorithm based on Naive Bayes. Through artificial intelligence technology, resumes are intelligently parsed and matched. The system can automatically retrieve and recommend employees who are suitable for job requirements to ensure the accuracy and efficiency of the recruitment process. It also ensures that the company can efficiently manage and screen suitable talents by collecting, storing and managing the resumes, skills and work experience information of job seekers and existing employees.

[0046] See also Figure 1,Furthermore, the talent search and recommendation algorithm based on naive Bayes is specifically as follows: First, by constructing a comprehensive evaluation framework, the evaluation dimensions and indicators for talent search and recommendation are determined, including educational background, work experience, and interview performance, to ensure that the potential and suitability of talent information can be evaluated from multiple perspectives. The specific formula for determining the evaluation dimensions and indicators is expressed as:

[0047]

[0048] in, Expressed as The total ability gap score of individual talent information, Represented as talent information In Dimension Ratings on Represented as dimensions The target score represents the expected ability level of the talent. It is a talent serial number used to distinguish different talent information. It is represented as a serial number identifier of the evaluation dimension, which is used to distinguish different evaluation indicators. It is expressed as the total number of evaluation dimensions. Then, the evaluation indicators are divided into core factors and secondary factors. The core factors are expressed as the key indicators that determine whether the talent information is suitable for the position, and the secondary factors are expressed as auxiliary information for the suitability of the talent information. The specific formula for calculating and grouping the core factors and secondary factors is expressed as follows:

[0049]

[0050] in, Represented as talent information The average score of the core factors Represented as talent information The average score of the secondary factors, is expressed as the number of core factors, Expressed as The core factors are in the dimension The weight on Represented as talent information In the The scores on the core factors are Represented as the core factor number index, Expressed as Personal talent information is in The scores on the secondary factors reflect the performance of talent information on non-core but still important evaluation dimensions. Represented as the index of the number of secondary factors, Expressed as in dimension Top, The weight of each secondary factor reflects the relative importance of the secondary factor in the evaluation process. Secondly, after completing the calculation of the core and secondary factors, the core factors and secondary factors are combined to calculate the total value of each talent information. By integrating the various indicators of talent information, a comprehensive score is obtained. The specific calculation formula is expressed as:

[0051]

[0052] in, Expressed as The comprehensive value score of individual talent information represents the quantification of the comprehensive performance of talent information in all evaluation dimensions. It is expressed as the proportion of the relative importance of the secondary factors in the entire evaluation. It is expressed as the weight ratio of the core factors. Then, the naive Bayes algorithm is used to predict the job arrangement of talent information. By associating the characteristics of talent information with job types, an intelligent prediction model is established. The specific formula is expressed as follows:

[0053]

[0054] in, Represented as a given feature Time Category The posterior probability of the category Represented as different job types, characteristics Represented as different characteristic attributes of talent information, Represented as category The prior probability of talent information being assigned to a position type without any additional information The probability of Represented as the total number of features of talent information, It is represented as the index of the number of features of talent information. Expressed as The characteristic attributes of personal talent information, Represented as in category Next, Features The conditional probability of It is expressed as a proportional operation. Finally, the prediction results of the naive Bayes model and the comprehensive score of talent information are combined to perform talent retrieval and intelligent recommendation, which can recommend the most suitable talent information for job vacancies. The specific calculation formula for intelligent recommendation of talent information is expressed as:

[0055]

[0056] in, Expressed as the Personal talent information for job types The recommendation score of Expressed as The comprehensive value score of individual talent information represents the quantification of the comprehensive performance of talent information in all evaluation dimensions. Represented as in category Next, Features The conditional probability of Expressed as The overall algorithm comprehensively considers multiple dimensions of talent information and uses the constructed naive Bayesian intelligent prediction model to make intelligent recommendations to improve the efficiency and accuracy of talent recruitment.

[0057] See also Figure 1 ,Furthermore, the interview management module ensures the smooth progress of the ,interview process by providing online interview appointment, time scheduling and ,interview progress tracking functions. At the same time, the interview ,module intelligently arranges interviews according to the schedules of applicants and ,interviewers, and provides an evaluation of the interview effect by analyzing ,interview evaluations to ensure the scientific and fair nature of recruitment ,decision-making.

[0058] See also Figure 1 Furthermore, the project management module includes a task allocation unit, which intelligently allocates tasks by analyzing project requirements and employee skills to ensure that each project task is performed by appropriate employees, thereby ensuring the efficiency and effectiveness of project execution.

[0059] See also Figure 1 Furthermore, the project management module includes a project progress tracking unit, which monitors the progress of the project in real time, provides progress reports and early warning information, ensures that the project proceeds as planned, and promptly discovers and resolves delays in the project process.

[0060] See also Figure 1 Furthermore, the attendance management module ensures the accuracy and timeliness of employee attendance records by providing GPS positioning and facial recognition clocking-in and leave management functions. The system automatically records attendance data and analyzes leave patterns to ensure the transparency and fairness of attendance management.

[0061] See also Figure 1 Furthermore, the salary management module ensures the accuracy and rationality of employee salaries through intelligent salary calculation and personalized salary plan design. The system automatically calculates salaries based on working hours and task completion, and helps companies develop appropriate salary strategies.

[0062] See also Figure 1Furthermore, the intelligent risk management module includes a risk identification and early warning unit, which proposes an enterprise risk assessment algorithm based on artificial intelligence, conducts real-time risk identification and prediction through historical data and real-time data of the enterprise's internal and external environment, identifies risk points based on historical risk data and current conditions, and generates early warning notifications to help enterprises take timely response measures, thereby reducing the probability of risk occurrence.

[0063] See also Figure 1 ,Furthermore, the enterprise risk assessment algorithm based on artificial intelligence is as follows: First, through the historical data and current data collected and managed by the system, including financial data, market data, operational data, and external environment data, a risk assessment model based on a neural network is constructed. In the model, the input layer is represented by various input variables, the hidden layer captures the complex relationship between the data, and the output layer provides the result of the risk assessment. The specific formula is expressed as:

[0064]

[0065] in, Expressed as time The predicted value of enterprise risk at the time represents the model at time Estimate the level of risk currently faced by the enterprise at the moment, Represented as the ReLU activation function, Represents the weight matrix of the importance of each input feature to the prediction result, Expressed as time The input variable vector at the time contains all input features related to enterprise risk assessment, such as financial data, market indicators, operating conditions, and external environment data. is represented as a bias vector, It is expressed as the moment of the acquired data. By building an enterprise risk assessment model based on artificial intelligence, the potential risks of the enterprise are intelligently assessed and predicted to ensure that the system can identify and predict the risks that affect the operation of the enterprise, thereby improving the enterprise's ability to cope with competition. Then, the various potential risk factors of the enterprise are intelligently analyzed to extract the key factors that have the most significant impact on the enterprise risk. By building a tree model to automatically identify and weigh the impact of different risk factors, the contribution of each input feature to the final risk prediction is analyzed to ensure the accuracy of the assessment results. The specific formula is expressed as:

[0066]

[0067] in, Represented as a feature The importance score of the feature Contribution to the entire risk assessment model, Represented as the feature number index, Expressed as the total number of times, representing the model evaluation characteristics The time span of importance, Represented as a feature In time The information gain at the moment is used to measure the feature The contribution of adding to reduce model uncertainty is Represented as a feature In time The entropy value at each moment is reduced. By optimizing feature selection, the variables that contribute significantly to risk assessment are retained, and noise data are eliminated to ensure the simplicity and efficiency of the model. Secondly, based on intelligent analysis, the selected features are input into the constructed time series prediction model to predict future corporate risks and provide a reference for enterprises to formulate response strategies. The specific formula is expressed as follows:

[0068]

[0069] in, Expressed as time The risk prediction value at the moment is the future risk value predicted by inputting current and past risk data. Expressed as time The actual risk value at the moment, Expressed as time The actual risk value at the moment, Expressed as time The actual risk value at the moment, It is expressed as the number of moments from the current time. Expressed as the model function for risk prediction, It is represented as a set of model parameters, including weights and bias items. According to the results of the model output, the risks that the enterprise may face in different time periods are predicted. Then, based on the aforementioned risk prediction results, an early warning and response mechanism for enterprise risks is established so that appropriate measures can be taken before the risk occurs to reduce the impact of the risk. The early warning threshold is set. When the predicted risk value exceeds the threshold, the system automatically triggers an early warning. The specific formula is expressed as:

[0070]

[0071] in, Expressed as the predicted risk value Exceeding the warning threshold Under the condition of The probability of occurrence, It is expressed as the warning threshold, which is used to decide when to issue a risk warning. It is expressed as an indicator function. Finally, through online learning and incremental updating, the prediction ability of the model is continuously improved by using new data. The specific formula is expressed as:

[0072]

[0073] in, Represented as updated model parameters that determine the model's behavior and output, Represents the model parameters before updating, which is used for comparison before model updating. Denoted as the learning rate, Expressed as gradient calculation, Expressed as the loss function, Represents the new input data, Represented as new input data The corresponding real labels are continuously optimized and updated by the model to adapt to the dynamic environmental changes of the enterprise, ensuring that the algorithm can continue to provide accurate risk assessment results under different environmental and data conditions.

[0074] See also Figure 1 Furthermore, the intelligent risk management module includes a management support decision unit, which provides data-driven management suggestions by comprehensively analyzing employees' skills, work performance, behavior patterns and risk assessment results, supports the company's decision-making in flexible employment management, and ensures that the company can make reasonable employment decisions based on objective data.

[0075] See also Figure 1 Furthermore, the notification module ensures that employees can obtain relevant information in a timely manner by publishing and managing company announcements and notification information. At the same time, the system intelligently classifies information and pushes announcements based on the roles and preferences of employees to ensure effective communication of information and employee participation.

[0076] In specific use, first, the talent pool management module proposes a talent retrieval recommendation algorithm based on Naive Bayes to collect and manage the resume information of job seekers and existing employees, and intelligently recommend employees who meet the job requirements based on job requirements. Then, the interview management module is used to make online interview appointments, interview evaluations, interview progress tracking, and automatically send interview notifications and reminders, and record and manage interview evaluations, and provide interview effect analysis. Secondly, the project management module includes a task allocation unit and a project progress tracking unit. The task allocation unit is used to intelligently allocate project tasks based on project requirements and employee skills. The project progress tracking unit is used to track project progress in real time and provide progress reports. Then, the attendance management module is used to provide two ways of clocking in, namely GPS positioning clocking in and facial recognition clocking in, and to clock in and out employees. The card is used to manage attendance and leave, and the salary management module is used to intelligently calculate salaries based on working hours and task completion, and help companies formulate salary strategies. Secondly, the intelligent risk management module includes a risk identification and early warning unit and a management support decision-making unit. The risk identification and early warning unit proposes an enterprise risk assessment algorithm based on artificial intelligence to identify risk information in the process of flexible employment, and provides risk prediction and early warning functions to help companies avoid employment risks in advance. The management support decision-making unit is used to conduct a comprehensive analysis of key indicators of employees' skills, work performance, and behavior patterns, provide decision-making support, and help companies make scientific and reasonable decisions in employment management. Finally, the notification and announcement module is used to publish and manage company announcement information, and intelligently classify information and push announcements based on employees' roles and preferences.

[0077] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A one-stop human resource outsourcing intelligent service platform system, including a talent pool management module, an interview management module, a project management module, an attendance management module, a salary management module, an intelligent risk management module, and a notification module, characterized in that: The talent pool management module proposes a talent retrieval recommendation algorithm based on Naive Bayes, which is used to collect and manage the resume information of job seekers and existing employees, and intelligently recommend employees who meet the job requirements based on job requirements. The interview management module is used to make online interview appointments, interview evaluations, interview progress tracking, and automatically send interview notifications and reminders, and record and manage interview evaluations, and provide interview effect analysis. The project management module includes a task allocation unit and a project progress tracking unit. The task allocation unit is used to intelligently allocate project tasks based on project requirements and employee skills. The project progress tracking unit is used to track project progress in real time and provide progress reports. The attendance management module is used to provide two clocking-in methods, namely GPS positioning clocking-in and facial recognition clocking-in, and to perform clocking-in and clocking-in on employees. The salary management module is used to intelligently calculate salaries according to working hours and task completion, and help enterprises formulate salary strategies. The intelligent risk management module includes a risk identification and early warning unit and a management support decision unit. The risk identification and early warning unit proposes an enterprise risk assessment algorithm based on artificial intelligence to identify risk information in the flexible employment process, and provides risk prediction and early warning functions to help enterprises avoid employment risks in advance. The management support decision unit is used to conduct a comprehensive analysis of key indicators of employees' skills, work performance, and behavior patterns, provide decision support, and help enterprises make scientific and reasonable decisions in employment management. The notification and announcement module is used to publish and manage company announcement information, and intelligently classify information and push announcements according to employees' roles and preferences; The talent pool management module proposes a talent search and recommendation algorithm based on Naive Bayes. Through artificial intelligence technology, resumes are intelligently parsed and matched. The system can automatically search and recommend employees who are suitable for job requirements to ensure the accuracy and efficiency of the recruitment process. It also collects, stores and manages the resumes, skills and work experience information of job seekers and existing employees to ensure that the company can efficiently manage and screen suitable talents. First, by building a comprehensive evaluation framework, we determine the evaluation dimensions and indicators for talent search and recommendation, including educational background, work experience, and interview performance, to ensure that the potential and suitability of talent information can be evaluated from multiple perspectives. The specific formula for determining the evaluation dimensions and indicators is as follows: Among them, G i It is expressed as the total score of the ability gap of the i-th talent information, P ij It is represented by the score of talent information i on dimension j, T j It is represented as the target score of dimension j, which represents the expected ability level of the talent. i is represented as the serial number of the talent, which is used to distinguish different talent information. j is represented as the serial number of the evaluation dimension, which is used to distinguish different evaluation indicators. n is represented as the total number of evaluation dimensions. Then, the evaluation indicators are divided into core factors and secondary factors. The core factors are represented as the key indicators that determine whether the talent information is suitable for the position. The secondary factors are represented as auxiliary information for the suitability of the talent information. The specific formula for calculating and grouping the core factors and secondary factors is expressed as follows: Among them, CF i It is expressed as the average score of the core factors of talent information i, SF i is the average score of the secondary factors of talent information i, m is the number of core factors, and W kj It is expressed as the weight of the kth core factor on dimension j, P ik It is represented as the score of talent information i on the kth core factor, k is the number index of core factors, P il It is represented as the score of the i-th talent information on the l-th secondary factor, reflecting the performance of talent information on non-core but still important evaluation dimensions. l is the index of the number of secondary factors, and W lj It is expressed as the weight of the lth secondary factor on dimension j, which reflects the relative importance of the secondary factor in the evaluation process. Secondly, after completing the calculation of the core and secondary factors, the core factors and secondary factors are combined to calculate the total value of each talent information. By integrating various indicators of talent information, a comprehensive score is obtained. The specific calculation formula is expressed as: TV i =(1-α)×CF i +α×SF i Among them, TV i It is expressed as the comprehensive value score of the i-th talent information, which represents the quantification of the comprehensive performance of talent information in all evaluation dimensions. α is expressed as the relative importance of secondary factors in the entire evaluation, and 1-α is expressed as the weight ratio of core factors. Then, the naive Bayes algorithm is used to predict the job arrangement of talent information. By associating the characteristics of talent information with job types, an intelligent prediction model is established. The specific formula is expressed as follows: Among them, P(H k |E1,...,E N ) is expressed as given features E1,...,E N Time Category H k The posterior probability of class H k Represented as different job types, features E1,...,E N Represented as different characteristic attributes of talent information, P(H k ) is represented by category H k The prior probability represents the probability that talent information is assigned to position type H without any additional information. k The probability of N is the total number of features of talent information, s is the index of the number of features of talent information, E is It is represented as the characteristic attribute of the i-th talent information, P(E is |H k ) is expressed as k Next, feature E is The conditional probability of ∝ is expressed as a proportional operation. Finally, combining the prediction results of the naive Bayes model and the comprehensive score of talent information, talent retrieval and intelligent recommendation are performed to recommend the most suitable talent information for job vacancies. The specific calculation formula for intelligent recommendation of talent information is expressed as: Among them, R i It is represented as the i-th talent information for position type H k Recommended score, TV (I-i) It is expressed as the comprehensive value score of the ith talent information, which represents the quantification of the comprehensive performance of talent information in all evaluation dimensions. (I-i)s |H k ) is expressed as k Next, feature E (I-i)s The conditional probability, E (I-i)s Represented as the characteristic attribute of the ith talent information, the overall algorithm comprehensively considers multiple dimensions of talent information and uses the constructed naive Bayesian intelligent prediction model to make intelligent recommendations to improve the efficiency and accuracy of talent recruitment.

2. The one-stop human resource outsourcing intelligent service platform system according to claim 1 is characterized by: The interview management module ensures the smooth progress of the interview process by providing online interview appointment, time scheduling and interview progress tracking functions. At the same time, it intelligently arranges interviews according to the schedules of applicants and interviewers, and provides an assessment of interview results by analyzing interview evaluations to ensure the scientificity and fairness of recruitment decisions.

3. The one-stop human resource outsourcing intelligent service platform system according to claim 1 is characterized in that: The project management module includes a task allocation unit and a project progress tracking unit. The task allocation unit intelligently allocates tasks by analyzing project requirements and employee skills to ensure that each project task is performed by appropriate employees, thereby ensuring the efficiency and effectiveness of project execution; the project progress tracking unit monitors the progress of the project in real time, provides progress reports and early warning information, ensures that the project proceeds as planned, and promptly discovers and resolves delays in the project advancement process.

4. The one-stop human resource outsourcing intelligent service platform system according to claim 1 is characterized in that: The attendance management module ensures the accuracy and timeliness of employee attendance records by providing GPS positioning and facial recognition clocking-in and leave management functions. The system ensures the transparency and fairness of attendance management by automatically recording attendance data and analyzing leave patterns.

5. The one-stop human resource outsourcing intelligent service platform system according to claim 1 is characterized in that: The salary management module ensures the accuracy and rationality of employee salaries through intelligent salary calculation and personalized salary plan design. The system automatically calculates salaries based on working hours and task completion, and helps companies develop appropriate salary strategies.

6. The one-stop human resource outsourcing intelligent service platform system according to claim 1 is characterized in that: The intelligent risk management module includes a risk identification and early warning unit and a personnel risk analysis and unit. The risk identification and early warning unit proposes an enterprise risk assessment algorithm based on artificial intelligence, performs real-time risk identification and prediction on historical data and real-time data of the enterprise's internal and external environment, identifies risk points based on historical risk data and current conditions, and generates early warning notifications to help enterprises take timely response measures, thereby reducing the probability of risk occurrence; The management support decision unit provides data-driven management recommendations by comprehensively analyzing employees' skills, work performance, behavioral patterns and risk assessment results, supports enterprises' decision-making in flexible employment management, and ensures that enterprises can make reasonable employment decisions based on objective data.

7. The one-stop human resource outsourcing intelligent service platform system according to claim 6 is characterized by: First, a risk assessment model based on a neural network is constructed through the historical data and current data collected and managed by the system, including financial data, market data, operational data, and external environment data. In the model, the input layer is represented by various input variables, the hidden layer captures the complex relationship between the data, and the output layer provides the results of the risk assessment. The specific formula is expressed as: R t =f(W t ·X t +b t ) Among them, R t It is expressed as the predicted value of enterprise risk at time t, which represents the model's estimate of the risk level currently faced by the enterprise at time t. f is represented by the ReLU activation function, and W t Represents the weight matrix of the importance of each input feature to the prediction result, X t It is represented as the input variable vector at time t, which contains all the input features related to enterprise risk assessment, b t Represented as a bias vector, t represents the time when the data is obtained. By building an enterprise risk assessment model based on artificial intelligence, the potential risks of the enterprise are intelligently assessed and predicted to ensure that the system can identify and predict the risks that affect the operation of the enterprise, thereby improving the enterprise's ability to cope with competition. Then, the various potential risk factors of the enterprise are intelligently analyzed to extract the key factors that have the most significant impact on the enterprise risk. By building a tree model to automatically identify and weigh the impact of different risk factors, the contribution of each input feature to the final risk prediction is analyzed to ensure the accuracy of the assessment results. The specific formula is expressed as: Among them, I j It is represented as the importance score of feature j, which is used to measure the contribution of feature j in the entire risk assessment model. j is represented as the feature number index. T is represented as the total time, which represents the time span of the model evaluating the importance of feature j. j,t It is expressed as the information gain of feature j at time t, which is used to measure the contribution of adding feature j to reducing model uncertainty. j,t It is expressed as the reduction of the entropy value of feature j at time t. By optimizing feature selection, the variables that contribute significantly to risk assessment are retained, and noise data are eliminated to ensure the simplicity and efficiency of the model. Secondly, based on intelligent analysis, the selected features are input into the constructed time series prediction model to predict future corporate risks and provide a reference for enterprises to formulate response strategies. The specific formula is expressed as: in, It is expressed as the risk prediction value at time t+1, which is the future risk value predicted by inputting current and past risk data. Expressed as the actual risk value at time t, It is expressed as the actual risk value at time t-1, It is represented as the actual risk value at time tn, n is represented as the number of moments from the current time, g is represented as the model function of risk prediction, θ is represented as the parameter set of the model, including weights and bias terms. According to the results of the model output, the risk situation that the enterprise may face in different time periods is predicted. Then, based on the aforementioned risk prediction results, an early warning and response mechanism for enterprise risks is established so that appropriate measures can be taken before the risk occurs to reduce the impact of the risk. The early warning threshold is set. When the predicted risk value exceeds the threshold, the system automatically triggers an early warning. The specific formula is expressed as follows: Among them, P(E|R t >α) is expressed as the predicted risk value R t The probability of risk event E occurring when the warning threshold α is exceeded. α is the warning threshold, which is used to determine when to issue a risk warning. It is expressed as an indicator function. Finally, through online learning and incremental updating, the prediction ability of the model is continuously improved by using new data. The specific formula is expressed as: Among them, θ new Represented as the updated model parameters that determine the behavior and output of the model, θ old Represents the model parameters before updating, which is used for comparison before model updating. η represents the learning rate. It is represented as gradient calculation, L is the loss function, X new Represents the new input data, Y new Represented as new input data X new The corresponding real labels are continuously optimized and updated by the model to adapt to the dynamic environmental changes of the enterprise, ensuring that the algorithm can continue to provide accurate risk assessment results under different environmental and data conditions.

8. The one-stop human resource outsourcing intelligent service platform system according to claim 1 is characterized in that: The notification module ensures that employees can obtain relevant information in a timely manner by publishing and managing company announcements and notification information. At the same time, the system intelligently classifies information and pushes announcements based on the roles and preferences of employees to ensure effective communication of information and employee participation.

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