Enterprise staff health management system and method based on big data

Abstract

The invention relates to the technical field of enterprise health management, in particular to an enterprise staff health management system and method based on big data, and the system mainly comprises a data collection layer, a data storage layer, a data analysis layer, an application service layer and a function module. The cloud platform provides functions of staff health condition statistical analysis, disease risk early warning and the like, provides a scientific basis for an enterprise management layer to make health management policies, can grasp health trends of the staff in real time, is helpful for enterprises to make more reasonable decisions in the aspect of human resource management, can reduce medical cost, and can improve human resource management efficiency. Through continuous monitoring and early intervention on the health condition of the staff, occurrence and deterioration of diseases are reduced, so that the expenditure of an enterprise in the aspect of medical expenses of the staff is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise health management, and in particular to an enterprise employee health management system and method based on big data. Background Art

[0002] With the development of society, people's attention to health has increased day by day, and enterprises have gradually realized the importance of employees' health status to enterprise development. Traditional employee health management methods have many limitations. For example, the data collection channels are single and incomplete, mainly relying on regular physical examinations, and it is difficult to obtain employees' daily health data such as real-time physiological indicators, exercise, sleep, and diet information. The management efficiency is low, the data processing and analysis capabilities are limited, and it is impossible to provide personalized health management services for employees in a timely manner. And there is a lack of in-depth mining and utilization of employees' health data, making it difficult to formulate targeted health intervention measures.

[0003] The rise of big data technology provides a new way to solve these problems. Through big data technology, multi-source health data can be integrated and deeply analyzed, so as to provide more accurate and efficient health management services for enterprise employees, which helps to improve employees' health level, reduce enterprise costs, and enhance enterprise competitiveness. Summary of the Invention

[0004] In view of the problems existing in the above-mentioned prior art, the present invention is proposed.

[0005] Therefore, the technical problems to be solved by the present invention are: to grasp the health dynamics of employees in real time, provide statistical analysis of employees' health status and disease risk early warning, and provide a scientific basis for enterprises to formulate health management policies. Improve the decision-making efficiency of enterprises in health management, optimize human resource management, and reduce employees' medical costs. Through continuous health monitoring and early intervention, reduce the occurrence and deterioration of diseases, thereby reducing the enterprise's medical expense expenditure. Improve employees' health level and work efficiency, enhance the competitiveness and sustainable development ability of enterprises, and at the same time shape a responsible corporate image.

[0006] To solve the above technical problems, the present invention provides the following technical solutions. An enterprise employee health management system based on big data includes: a data collection layer, a data storage layer, a data analysis layer, an application service layer, and functional modules.

[0007] As a preferred solution of an enterprise employee health management system based on big data according to the present invention, wherein: the data collection layer includes channels for collecting employees' health data through wearable devices, health examination devices, and mobile applications;

[0008] The health data includes physiological indicators, exercise data, sleep data, and diet data; and the collected data is cleaned, transformed, and standardized.

[0009] As a preferred solution of an enterprise employee health management system based on big data according to the present invention, wherein: the data storage layer includes storing the processed data on a cloud platform by using a distributed storage technology, and at the same time establishing a data warehouse to classify, sort and file the stored data.

[0010] As a preferred solution of an enterprise employee health management system based on big data according to the present invention, wherein: the data analysis layer includes using big data analysis technology to deeply analyze the employee health data, including analyzing the employee's health status, disease risk, exercise habits and diet preferences, and providing health management suggestions for the enterprise.

[0011] As a preferred solution of an enterprise employee health management system based on big data according to the present invention, wherein: the application service layer includes providing health consultation, health assessment and health intervention services, enabling employees to obtain health management services anytime and anywhere through the cloud platform; providing decision-making support for enterprise management, including statistical analysis of employee health status, disease risk warning, and formulation of health management policies; and integrating with the human resource management system and attendance system within the enterprise to achieve data sharing and collaborative management.

[0012] As a preferred solution of an enterprise employee health management system based on big data according to the present invention, wherein: the functional modules include a health record management module, a health monitoring and warning module, a health assessment and advice module, a health intervention and management module, and a decision-making support and report module.

[0013] As a preferred solution of an enterprise employee health management system based on big data according to the present invention, wherein: the health record management module includes establishing an employee's health record, dynamically managing the employee's health record, and updating the employee's health data;

[0014] The health record includes basic information, a health examination report, a disease history and a family history;

[0015] The health monitoring and warning module includes real-time monitoring of the employee's health status through wearable devices and health examination devices;

[0016] Among them, the health status includes blood pressure, blood sugar and heart rate; when the employee's health indicators are abnormal, a warning message is issued;

[0017] The health assessment and advice module includes assessing the health status of employees based on their health records and health monitoring data, including physical condition, disease risk, exercise ability, and diet and nutrition; and providing personalized health management advice for employees according to the assessment results, including exercise plans, diet adjustments, and disease prevention.

[0018] The health intervention and management module includes providing health intervention services for employees, including health lectures, psychological counseling, and rehabilitation guidance; tracking and evaluating the health management of employees, and adjusting the health management plan.

[0019] The decision support and reporting module includes providing decision support for enterprise management, including statistical analysis of employees' health status, disease risk warning, and formulation of health management policies; and generating reports, including employees' health record reports, health monitoring reports, and health assessment reports.

[0020] Another object of the present invention is to provide a big data-based enterprise employee health management method. By integrating multi-source health data and using data mining, machine learning, and statistical analysis techniques, it deeply analyzes employees' health status, disease risks, exercise habits, and diet preferences, provides personalized health management advice and health intervention measures for employees, and at the same time provides health management decision support for enterprise management, improving the scientificity and efficiency of enterprise health management.

[0021] To solve the above technical problems, the present invention provides the following technical solutions: A big data-based enterprise employee health management method, including: The big data analysis technology includes data mining, machine learning, and statistical analysis.

[0022] The data mining includes association rule mining and clustering analysis.

[0023] Among them, association rule mining includes discovering the association relationships between data items in the dataset, finding the associations between health indicators, lifestyle habits and specific diseases and health conditions, analyzing employees' health data, determining frequently occurring item sets, and generating association rules; if it is found that employees who often consume a high-salt diet and have little exercise have a high probability of suffering from hypertension, the association rule of high-salt diet and little exercise → increased hypertension risk is obtained.

[0024] Among them, clustering analysis includes dividing employees with the same characteristics into different clusters, understanding the distribution pattern of the health status of the employee group, and discovering the group characteristics of different health conditions; clustering according to the so-called health data of employees; clustering employees according to health indicators, exercise frequency and intensity, and diet structure, and obtaining a cluster with good health status and positive lifestyle, a cluster with certain health risks but adjustable lifestyle, and a cluster with poor health status and in need of attention.

[0025] The machine learning includes decision trees and neural networks;

[0026] Among them, the decision tree includes constructing a tree - shaped structure model to classify and predict the health status of employees based on multiple features, using the historical health data and real - time monitoring data of employees; taking the historical health data and real - time monitoring data of employees as input features to construct a decision tree model; predicting the risk level of employees suffering from diabetes, and formulating physical examination plans and health management programs for employees with different risk levels according to the model results;

[0027] Among them, the neural network includes modeling complex non - linear relationships by simulating the working mode of human brain neurons, deeply mining the hidden patterns and rules in employees' health data to achieve health status assessment and disease risk prediction; inputting multi - dimensional health data of employees, the neural network automatically extracts features and performs complex calculations by learning the internal relationships in the data, including analyzing the long - term change trend of employees' physiological indicators, the complex relationship between exercise habits and sleep quality, predicting the change trend of employees' future health status and evaluating their overall health level; according to the prediction results of the neural network model, intervening in employees with health problems in advance, including providing personalized health guidance and adjusting work arrangements;

[0028] The statistical analysis includes descriptive statistics and correlation analysis;

[0029] Among them, descriptive statistics includes making a general description of employees' health data to understand the basic features of the data, including central tendency and dispersion degree; calculating the mean, median, mode and standard deviation of health indicators; calculating the distribution range of the average blood pressure value and blood sugar value of all employees in the enterprise to understand the overall health indicator level of employees; mastering the overall situation of employees' health data through descriptive statistics and finding out the outliers in the data;

[0030] Among them, correlation analysis includes determining the degree of linear relationship between different health data variables, including the correlation between exercise data and physiological indicators, and the correlation between diet data and disease risk; calculating the correlation coefficient between variables and analyzing the positive and negative correlation relationships between variables; if it is found through analysis that the weekly exercise time of employees is negatively correlated with blood pressure level, that is, the longer the exercise time, the lower the blood pressure, and the sugar intake in diet is positively correlated with blood sugar level.

[0031] A computer device includes a memory and a processor, the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of a corporate employee health management system based on big data as described above are implemented.

[0032] A computer-readable storage medium stores a computer program thereon, characterized in that when the computer program is executed by a processor, it implements the steps of an enterprise employee health management system based on big data as described above.

[0033] Advantages of the present invention: 1. The functions of statistical analysis of employees' health status and disease risk early warning provided by the cloud platform of the present invention provide a scientific basis for the enterprise management to formulate health management policies.

[0034] 2. The present invention can grasp the health dynamics of employees in real time, which helps the enterprise make more reasonable decisions in human resource management.

[0035] 3. The present invention can reduce medical costs. By continuously monitoring and early intervening in the health status of employees, the occurrence and deterioration of diseases are reduced, thereby reducing the enterprise's expenditure on employees' medical expenses.

[0036] 4. The present invention can reduce human resource costs. The absenteeism rate of healthy employees is reduced, work efficiency is improved, and the human resource costs of recruitment and training brought about by employees' sick leave and resignation are reduced. At the same time, by implementing effective health management policies, the enterprise improves employees' satisfaction and loyalty, reduces the employee turnover rate, and further saves human resource costs.

[0037] 5. Through the present cloud platform, a healthy workforce can create more value for the enterprise, improve the enterprise's production efficiency, innovation ability and market competitiveness, and promote the sustainable development of the enterprise. Healthy employees are more creative and energetic at work, can better cope with work challenges, and bring more business opportunities and economic benefits to the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0039] Figure 1 It is a module diagram of an enterprise employee health management system based on big data provided by an embodiment of the present invention.

[0040] Figure 2 It is a schematic diagram of the cloud platform structure of an enterprise employee health management system based on big data provided by an embodiment of the present invention.

[0041] Figure 3 It is a schematic diagram of the functional module structure of an enterprise employee health management system based on big data provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0042] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following provides a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0043] Example 1, referring to Figure 1 - Figure 2 , which is an embodiment of the present invention. This embodiment provides an enterprise employee health management system based on big data, including:

[0044] It should be noted that, as Figure 1 shown, it mainly includes a data collection layer, a data storage layer, a data analysis layer, an application service layer, and a function module.

[0045] Furthermore, the data collection layer: is used to collect employees' health data through various channels such as wearable devices, health examination devices, and mobile applications. The health data includes physiological indicators, exercise data, sleep data, and diet data; and performs cleaning, transformation, and standardization processing on the collected data to ensure the accuracy and consistency of the data;

[0046] The data storage layer: uses distributed storage technology to store the processed data on a cloud platform to ensure the security and reliability of the data; at the same time, a data warehouse is established to classify, organize, and archive the stored data for subsequent data analysis and management;

[0047] The data analysis layer: uses big data analysis technologies, including data mining, machine learning, and statistical analysis, to deeply analyze employees' health data to analyze employees' health conditions, disease risks, exercise habits, and diet preferences, and provide personalized health management suggestions for the enterprise;

[0048] The application service layer: is used to provide health consultation, health assessment, and health intervention services, enabling employees to obtain health management services anytime and anywhere through the cloud platform; providing decision-making support for enterprise management, including statistical analysis of employees' health conditions, disease risk early warning, and formulation of health management policies; and being able to integrate with the human resource management system and attendance system within the enterprise to achieve data sharing and collaborative management.

[0049] The function module includes the following:

[0050] The health record management module: is used to establish employees' health records, which include basic information, health examination reports, disease history, and family history; and dynamically manage employees' health records, updating employees' health data in a timely manner;

[0051] Health Monitoring and Warning Module: Real-time monitor the health status of employees through wearable devices and health examination devices. The health status includes blood pressure, blood sugar, and heart rate. When abnormal health indicators are detected for employees, warning messages are sent in a timely manner to remind employees and enterprise managers to take corresponding measures.

[0052] Health Assessment and Recommendation Module: Based on the health records and health monitoring data of employees, assess the health status of employees, including physical condition, disease risk, exercise ability, and diet nutrition. According to the assessment results, provide personalized health management recommendations for employees, such as exercise plans, diet adjustments, and disease prevention.

[0053] Health Intervention and Management Module: Provide health intervention services for employees, such as health lectures, psychological counseling, and rehabilitation guidance. Track and evaluate the health management of employees, and adjust the health management plan in a timely manner.

[0054] Decision Support and Report Module: Provide decision support for enterprise management, including statistical analysis of employees' health status, disease risk warning, and formulation of health management policies. Generate various reports, including employee health record reports, health monitoring reports, and health assessment reports, to facilitate enterprise managers' management and decision-making.

[0055] Furthermore, as Figure 2 shown, the specific structure of the pan-tilt is as follows:

[0056] Platform Architecture:

[0057] Data Acquisition Layer: Collect comprehensive health data of employees through various channels, including wearable devices (smart bracelets, smart watches), health examination devices (blood pressure monitors, blood glucose meters), and mobile applications (applications for employees to record their diet and exercise information). The data includes, but is not limited to, physiological indicators (blood pressure, blood sugar, heart rate, body temperature), exercise data (steps, exercise distance, exercise time, exercise type), sleep data (sleep time, sleep quality, sleep stages), and diet data (diet records, nutrient intake). Clean the collected data to remove incorrect, duplicate, or incomplete data; perform conversion to unify the format; and conduct standardization processing to ensure data consistency across different devices and sources for subsequent analysis.

[0058] Data Storage Layer: Use distributed storage technology (Hadoop Distributed File System) to securely store the processed data on the cloud platform. At the same time, build a data warehouse to organize and archive the data according to certain classification rules (by employee individual, by health indicator category, by time) for convenient and rapid retrieval and analysis.

[0059] Data Analysis Layer: By leveraging big data analysis technologies, data mining algorithms (association rule mining, clustering analysis), machine learning models (decision trees, neural networks), and statistical analysis methods (descriptive statistics, correlation analysis), it deeply analyzes employees' health data. It mines the potential patterns of employees' health conditions, predicts disease risks, and analyzes the characteristics of exercise habits and dietary preferences, so as to provide personalized health management suggestions for enterprises, including exercise plan recommendations and dietary adjustment plans for different employees.

[0060] Application Service Layer: It provides diverse health services for employees. Health consultation services can answer employees' health questions through online doctors and health experts; health assessment gives a comprehensive health status assessment report based on employees' health records and real-time monitoring data; health interventions include organizing health lectures, providing psychological counseling services, and rehabilitation guidance. It provides decision-making support for enterprise management. By statistically analyzing the distribution of employees' health conditions and disease risk trend data, it assists in formulating health management policies (adjusting physical examination frequencies, optimizing health benefits). And it realizes integration with other internal enterprise systems (obtaining employees' basic information and attendance data from the human resources management system, and combining with the health status to analyze the reasons for absenteeism in the attendance system), achieving data sharing and collaborative management.

[0061] Functional Modules: As Figure 3 shown;

[0062] Health Record Management Module: It creates a detailed health record for each employee, covering basic information (name, age, gender, position), previous health examination reports (results and change trends of various physical examination indicators), medical history (past illness records and treatment situations), and family history (family genetic disease information). It updates the health data in the record in real time to ensure the timeliness and accuracy of the information.

[0063] Health Monitoring and Warning Module: It uses wearable devices and health examination devices to collect employees' health indicator data in real time, sets reasonable threshold ranges, and when it detects that an employee's health indicators exceed the normal range (high blood pressure, abnormal blood sugar), it immediately sends warning messages to the employee himself / herself and enterprise managers via text messages and APP push, reminding them to take timely measures, such as seeking medical treatment and adjusting lifestyle.

[0064] Health Assessment and Recommendation Module: Based on the historical data and real-time monitoring data in the employee health records, a scientific assessment model is used to comprehensively evaluate the physical condition of employees (body mass index, body composition analysis), disease risks (probability of suffering from various diseases evaluated according to age, family history, and lifestyle factors), exercise ability (evaluated in terms of endurance, strength, and flexibility), and diet and nutrition (analyzing whether the nutrient intake is balanced and whether key nutrients are lacking). According to the evaluation results, personalized health management recommendations are generated for employees, such as suitable exercise frequency and intensity, diet structure adjustment suggestions, and disease prevention precautions.

[0065] Health Intervention and Management Module: According to the health needs and evaluation results of employees, various health intervention activities are organized and carried out. Health lectures cover popularization of health knowledge (disease prevention, health preservation) and mental health maintenance topics; psychological counseling services are provided by professional psychological counselors to offer guidance to employees in aspects such as emotion management and stress coping; rehabilitation guidance provides rehabilitation training plans and suggestions for employees who are ill or have undergone surgery. Track the process of employees' health management, regularly evaluate the intervention effects, and adjust the health management plan in a timely manner according to the actual situation.

[0066] Decision Support and Report Module: Provide multi-dimensional decision support data for the enterprise management. It includes statistical analysis of employees' health conditions (average values and abnormal rates of health indicators of employees in different age groups, genders, and departments), disease risk early warning (early warning of possible large-scale disease risk situations), and evaluation of the effects of health management policies (analysis of the improvement of employees' health indicators after implementing a certain health intervention measure). Generate various reports, such as employee health record reports (detailed records of each employee's health record information), health monitoring reports (displaying real-time and historical data of employees' health indicators), and health assessment reports (presenting the results and changing trends of various health assessments of employees), which facilitate the management to intuitively understand the health conditions of employees and provide a strong basis for management decisions.

[0067] Implementation Steps:

[0068] Requirement Analysis: Have in-depth exchanges with the enterprise management to clarify the enterprise's goals in employee health management (reducing medical costs, improving employee work efficiency), expected functions (monitoring of key health indicators of focus, specific health intervention activity requirements), and management strategies (management methods for employees with different health conditions). At the same time, communicate widely with employees to understand their needs for their own health management (convenient health consultation channels, personalized health plans) and expected service forms (whether online services are preferred or offline activities are more popular). Conduct a comprehensive investigation of the enterprise's existing health management model, including existing data collection methods, management processes, and existing problems (low data accuracy, untimely service response).

[0069] Platform Design: Based on the results of requirements analysis, design the overall architecture of the cloud platform, and determine the functions and interrelationships of each layer (data collection, storage, analysis, application services). Detail the specific implementation methods and interaction processes of each functional module. For example, how the health record management module realizes data entry and update, and what algorithm model the health assessment module adopts. Develop a technical solution, select appropriate technical frameworks (front-end development framework, back-end server technology, database management system), big data processing technologies (Hadoop, Spark), and data transmission protocols (to ensure secure and efficient data transmission). Plan the project implementation plan, clarify the tasks, time nodes, and responsible persons for each stage.

[0070] Data Collection and Integration: According to the platform design requirements, select suitable wearable devices and health examination devices to ensure the accuracy and compatibility of their data. Determine the functions and interface designs of the mobile application to facilitate employees to record diet and exercise data. Establish data collection specifications to ensure the uniformity of data formats from different sources. Clean the collected data to remove noise data (abnormal values caused by equipment failures) and duplicate data; perform conversion to unify different data formats into a format that can be processed by the platform; perform standardization processing, such as converting blood pressure data to mmHg units uniformly, and then upload the integrated data to the data storage layer of the cloud platform.

[0071] Platform Development and Testing: According to the platform design plan, organize the development team to carry out front-end and back-end development work. Front-end development focuses on the friendliness and interactivity of the user interface to ensure that employees and management can use platform functions conveniently and quickly. Back-end development realizes the core functions of data processing, storage, analysis, and service provision. During the development process, conduct unit tests to ensure the correctness of each functional module; conduct integration tests to verify the collaborative working ability of different modules; conduct system tests to simulate actual usage scenarios and detect the performance (response time, data processing speed), stability (whether there are failures during long-term operation), and security (data encryption, user permission management effectiveness) of the platform. Repair vulnerabilities and optimize functions in a timely manner according to the test results.

[0072] Platform Deployment and Training: Deploy the developed and tested cloud platform to the enterprise's server for final debugging before going live. Train enterprise managers to familiarize them with the management functions of the platform (viewing reports, formulating health management policies) and operation processes (how to set warning thresholds). Train employees to guide them on how to use the platform to obtain health services (viewing health records, making appointments for consultations) and how to upload health data through wearable devices and mobile applications. Provide training materials and operation manuals to ensure that relevant personnel can use the platform proficiently.

[0073] Platform Operation and Maintenance: After the platform is launched, establish a daily operation and maintenance mechanism. Monitor the running status of the platform, including server performance (CPU, memory, disk resource usage), network connection status, and service response. Handle the failures that occur during the platform operation in a timely manner (server downtime, data transmission interruption) to ensure the stability and availability of the platform. Regularly update the data of the platform, such as synchronizing new health knowledge and updating the parameters of the disease risk assessment model. Optimize and upgrade the functions of the platform. According to user feedback and business development needs, improve the existing functions (optimize the health assessment algorithm to improve accuracy) or add new functions (add social interaction functions to encourage employees to live a healthy life).

[0074] Expected Effects:

[0075] Improvement of Employees' Health Level and Work Efficiency: By comprehensively collecting employees' health data and conducting accurate analysis, provide personalized health management suggestions and services for employees, guide employees to improve their lifestyle (reasonable diet, moderate exercise), effectively prevent the occurrence of diseases, and thus improve the overall health level of employees. Employees in good health can maintain higher energy and concentration at work, reduce work mistakes and inefficiencies caused by physical discomfort, and thus improve work efficiency and work quality.

[0076] Reduction of Enterprise Costs: Real-time monitoring of employees' health conditions can timely detect potential health problems and intervene to avoid high medical expenses caused by the deterioration of diseases. The absenteeism rate of healthy employees decreases, reducing the loss of human resources caused by sick leave and improving the effective utilization rate of human resources, thus reducing the enterprise's medical costs and human resources costs.

[0077] Enhancement of Enterprise Competitiveness and Image: Paying attention to employees' health reflects the enterprise's social responsibility, helps to shape a good enterprise image, and attracts and retains outstanding talents. A healthy workforce can create higher value for the enterprise, improve the enterprise's production efficiency and innovation ability, enhance the enterprise's competitiveness in the market, and promote the sustainable development of the enterprise.

[0078] Risks and Countermeasures:

[0079] Data Security Risks: Use advanced encryption algorithms (AES, RSA) to encrypt and store employees' health data during transmission to ensure the confidentiality of data on the cloud platform. Implement strict access control policies, assign different data access permissions according to user roles (employees, managers, doctors), and prevent unauthorized access and data leakage. Establish a regular data backup mechanism, back up the data to multiple geographical locations, and prevent data loss caused by hardware failures and natural disasters. At the same time, formulate a data recovery plan to ensure that data can be quickly restored in case of data loss.

[0080] Technical risks: When selecting technical solutions and products, we fully research mature and stable big data technology frameworks (Hadoop, Spark) and cloud service providers (Alibaba Cloud, Tencent Cloud) in the market, conduct technical feasibility assessments and product selection tests. During the platform development process, we follow software development best practices, conduct code review and optimization, and ensure code quality. We establish a professional technical support team to be responsible for the daily maintenance of the platform and technical problem solving, and timely track and update related technologies to cope with possible technical compatibility issues and challenges brought about by technological upgrades.

[0081] Personnel training risks: Develop detailed and targeted training plans, and design training content based on the needs and usage scenarios of different user groups (corporate managers, employees). The training content includes an introduction to platform functions, a demonstration of operating procedures, and answers to frequently asked questions. Use a variety of training methods, such as online training courses, offline centralized training, operation manuals, and video tutorials to facilitate user learning. Provide an online help platform and a real-time technical support hotline to promptly answer questions users encounter during use, ensure that corporate managers and employees can master the use of the platform, and improve the platform's utilization rate and user satisfaction.

[0082] Project management risks: Establish an experienced project management team to be responsible for the overall planning, coordination and control of the project. Develop a detailed project plan to clarify the various stages, task breakdown, time schedule and milestones of the project. Use project management tools (Jira, Project) to track and monitor the progress of the project, promptly identify the risk of project delays and take measures to adjust (increase resource investment, optimize task allocation). Establish an effective communication mechanism to ensure smooth information flow between project team members, relevant corporate departments and external partners, and promptly resolve problems that arise during project implementation (demand changes, technical difficulties) to ensure that the project can be completed smoothly as planned.

[0083] The above is a schematic scheme of an enterprise employee health management system based on big data in this embodiment. It should be noted that the technical scheme of the enterprise employee health management system based on big data and the technical scheme of the enterprise employee health management system based on big data belong to the same concept. The details of the technical scheme of the enterprise employee health management method based on big data in this embodiment that are not described in detail can all be referred to the description of the technical scheme of the enterprise employee health management system based on big data.

[0084] Example 2, reference Figure 1 - Figure 2 , is an embodiment of the present invention, which provides a method for enterprise employee health management based on big data, including: the big data analysis technology includes data mining, machine learning and statistical analysis;

[0085] The data mining includes association rule mining and clustering analysis;

[0086] Among them, association rule mining includes discovering the association relationships between data items in the dataset, finding the associations between health indicators, lifestyle habits and specific diseases and health conditions, analyzing the health data of employees, determining frequently occurring item sets, and generating association rules; if it is found that employees who often consume a high-salt diet and have little exercise have a high probability of suffering from hypertension, the association rule of high-salt diet and little exercise → increased risk of hypertension is obtained;

[0087] Among them, clustering analysis includes dividing employees with the same characteristics into different clusters, understanding the distribution pattern of the health status of the employee group, and discovering the group characteristics of different health conditions; clustering according to the so-called health data of employees; clustering employees according to health indicators, exercise frequency and intensity, and diet structure, and obtaining a cluster with good health status and positive lifestyle, a cluster with certain health risks but adjustable lifestyle, and a cluster with poor health status and in need of attention;

[0088] The machine learning includes decision trees and neural networks;

[0089] Among them, decision trees include classifying and predicting the health status of employees based on multiple features by constructing a tree-shaped structure model, using the historical health data and real-time monitoring data of employees; using these as input features to construct a decision tree model; predicting the risk level of employees suffering from diabetes, and formulating physical examination plans and health management programs for employees with different risk levels according to the model results;

[0090] Among them, neural networks include modeling complex non-linear relationships by simulating the working mode of human brain neurons, deeply mining the hidden patterns and rules in the health data of employees, and realizing health status assessment and disease risk prediction; inputting the multi-dimensional health data of employees, the neural network automatically extracts features and performs complex calculations by learning the internal relationships in the data, including analyzing the long-term change trend of employees' physiological indicators, the complex relationship between exercise habits and sleep quality, predicting the change trend of employees' health status in the future time and evaluating their overall health level; according to the prediction results of the neural network model, intervening in employees with health problems in advance, including providing personalized health guidance and adjusting work arrangements;

[0091] The statistical analysis includes descriptive statistics and correlation analysis;

[0092] Among them, descriptive statistics include general descriptions of employees' health data to understand the basic characteristics of the data, including central tendency and dispersion; calculating the mean, median, mode, and standard deviation of health indicators; calculating the distribution ranges of the average blood pressure and blood sugar values of all employees in the enterprise to understand the overall health indicator levels of employees; mastering the overall situation of employees' health data through descriptive statistics and discovering outliers in the data.

[0093] Among them, correlation analysis includes determining the degree of linear relationship between different health data variables, including the correlation between exercise data and physiological indicators, and the correlation between diet data and disease risks; calculating the correlation coefficients between variables and analyzing the positive and negative correlation relationships between variables; if the analysis finds that the weekly exercise time of employees is negatively correlated with blood pressure levels, that is, the longer the exercise time, the lower the blood pressure, and the sugar intake in the diet is positively correlated with blood sugar levels.

[0094] This embodiment also provides a computing device, applicable to a situation of an enterprise employees' health management system based on big data, including:

[0095] A memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement an enterprise employees' health management system based on big data as proposed in the above embodiment.

[0096] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements an enterprise employees' health management system based on big data as proposed in the above embodiment.

[0097] The storage medium proposed in this embodiment and an enterprise employees' health management system based on big data proposed in the above embodiment belong to the same inventive concept. Technical details not described in detail in this embodiment can be referred to in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0098] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0099] The logic and / or steps described otherwise herein, for example, can be considered as a defined sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0100] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0101] Embodiment 3, an embodiment of the present invention, provides an enterprise employee health management system based on big data. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0102] Wearable devices (smart bracelets) are promoted and used in enterprises. After employees wear them, the devices automatically collect heart rate, steps, and sleep data, and upload the data to the data collection layer of the cloud platform through Bluetooth or Wi-Fi wireless transmission. At the same time, enterprises regularly organize employees for health check-ups, and the detection data of the check-up devices (professional sphygmomanometers, blood glucose meters) is directly transmitted to the collection layer. Employees can also record their daily dietary intake through a mobile application, such as food types and intake amounts.

[0103] After the data collection layer receives various types of data, according to the preset data cleaning rules, it removes the obviously incorrect data (data with heart rate exceeding the normal physiological range), converts data in different formats into a unified format (unifying the date format to "YYYY-MM-DD"), and standardizes the data according to the standard data dictionary (unifying the exercise steps in the unit of "steps"). The processed data is stored in the distributed file system of the data storage layer.

[0104] The data analysis layer regularly obtains employees' health data from the data storage layer and conducts analysis using machine learning algorithms (support vector machine algorithm for disease risk prediction) and statistical analysis methods (calculating average blood pressure and blood sugar values). For example, by analyzing employees' exercise data and diet data over a period of time and combining with their basic information, the risk of employees suffering from cardiovascular diseases is predicted, and a personalized health assessment report is generated.

[0105] Employees log in to the cloud platform through the mobile application or web page of the application service layer to view their health records, health assessment results, and personalized health suggestions. For example, adjust their exercise plan according to the health suggestions and increase the time of aerobic exercise; enterprise managers log in to the platform to view the statistical reports of employees' health conditions, such as the passing rates of health indicators of employees in each department, and formulate corresponding health management policies according to the report results, such as organizing more health lectures for departments with poor health conditions.

[0106] During the operation of the platform, the operation and maintenance team monitors the server status in real time. If it is found that the CPU usage rate of the server is too high, the cause is promptly investigated (it may be that a certain data analysis task occupies too many resources) and optimized (adjusting the task priority or increasing server resources). At the same time, according to new health research results and the actual needs of the enterprise, the platform functions are regularly updated, such as adding new health assessment indicators or optimizing the content of health intervention services.

[0107] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. An enterprise employee health management system based on big data, characterized by: include: Data collection layer, data storage layer, data analysis layer, application service layer and functional modules.

2. The enterprise employee health management system based on big data as claimed in claim 1, characterized in that: The data collection layer includes channels for collecting employees' health data through wearable devices, health examination equipment, and mobile applications; The health data includes physiological indicators, exercise data, sleep data and diet data; and the collected data is cleaned, converted and standardized.

3. The enterprise employee health management system based on big data as claimed in claim 2, characterized in that: The data storage layer includes using distributed storage technology to store processed data on a cloud platform, and establishing a data warehouse to classify, organize and archive the stored data.

4. The enterprise employee health management system based on big data as claimed in claim 3, characterized in that: The data analysis layer includes using big data analysis technology to conduct in-depth analysis of employee health data, including analyzing employees' health status, disease risks, exercise habits and dietary preferences, and providing health management recommendations for enterprises.

5. The enterprise employee health management system based on big data as claimed in claim 4, characterized in that: The application service layer includes services for providing health consultation, health assessment, and health intervention services, so that employees can obtain health management services anytime and anywhere through the cloud platform; Provide decision-making support for corporate management, including statistical analysis of employee health status, disease risk warning, and health management policy formulation; and integrate with the company's internal human resources management system and attendance system to achieve data sharing and collaborative management.

6. The enterprise employee health management system based on big data as claimed in claim 5, characterized in that: The functional modules include a health record management module, a health monitoring and early warning module, a health assessment and advice module, a health intervention and management module, and a decision support and reporting module.

7. The enterprise employee health management system based on big data as claimed in claim 6, characterized in that: The health record management module includes functions for establishing the health records of employees, dynamically managing the health records of employees, and updating the health data of employees; The health records include basic information, health examination report, medical history and family history; The health monitoring and early warning module includes real-time monitoring of the health status of employees through wearable devices and health examination equipment; Among them, health conditions include blood pressure, blood sugar and heart rate; when an employee's health indicators are abnormal, an early warning message will be issued; The health assessment and advice module includes assessing the health status of employees based on their health records and health monitoring data, including physical condition, disease risk, exercise ability and diet nutrition; and providing personalized health management advice to employees based on the assessment results, including exercise plans, diet adjustments and disease prevention; The health intervention and management module includes providing health intervention services to employees, including health lectures, psychological counseling and rehabilitation guidance; tracking and evaluating the health management of employees, and adjusting health management plans; The decision support and reporting module includes providing decision support for enterprise management, including statistical analysis of employee health status, disease risk warning and health management policy formulation; generating reports, including employee health record reports, health monitoring reports and health assessment reports.

8. A method for enterprise employee health management based on big data according to claim 4, characterized in that: include: The big data analysis technology includes data mining, machine learning and statistical analysis; The data mining includes association rule mining and cluster analysis; Among them, association rule mining includes discovering the association between data items in the data set, finding the association between health indicators, living habits and specific diseases and health conditions, analyzing the health data of employees, determining frequently occurring item sets, and generating association rules; if it is found that employees who often consume a high-salt diet and exercise less have a high probability of suffering from high blood pressure, the association rule of high-salt diet and less exercise → increased risk of high blood pressure is obtained; Among them, cluster analysis includes dividing employees with the same characteristics into different clusters, understanding the distribution pattern of the health status of the employee group, and discovering the group characteristics of different health status; clustering according to the so-called health data of the employees; clustering the employees according to health indicators, exercise frequency and intensity, and diet structure, and obtaining a cluster of employees with good health and positive lifestyle, a cluster with certain health risks but adjustable lifestyle, and a cluster with poor health and needing attention; The machine learning includes decision trees and neural networks; Among them, the decision tree includes building a tree structure model, classifying and predicting the health status of employees based on multiple features, using the employees' historical health data and real-time monitoring data as input features to build a decision tree model; predicting the risk level of employees suffering from diabetes, and formulating physical examination plans and health management plans for employees with different risk levels based on the model results; Among them, the neural network includes modeling complex nonlinear relationships by simulating the working mode of human brain neurons, deeply mining the hidden patterns and laws in the health data of employees, and realizing health status assessment and disease risk prediction; inputting the multi-dimensional health data of employees, the neural network automatically extracts features and performs complex calculations by learning the inherent relationships in the data, including analyzing the long-term trend of changes in physiological indicators of employees, the complex relationship between exercise habits and sleep quality, predicting the trend of changes in the health status of employees in the future and evaluating their overall health level; according to the prediction results of the neural network model, intervening in advance for employees with health problems, including providing personalized health guidance and adjusting work arrangements; The statistical analysis includes descriptive statistics and correlation analysis; Among them, descriptive statistics include general description of employee health data, understanding the basic characteristics of the data, including central tendency and dispersion; calculating the mean, median, mode and standard deviation of health indicators; calculating the distribution range of the average blood pressure and blood sugar values ​​of all employees in the enterprise to understand the overall health indicator level of employees; using descriptive statistics to grasp the overall situation of employee health data and find abnormal values ​​in the data; Among them, correlation analysis includes determining the degree of linear relationship between different health data variables, including the correlation between exercise data and physiological indicators, and the correlation between dietary data and disease risks; calculating the correlation coefficients between variables, and analyzing the positive and negative correlations between variables; if the analysis finds that the employees' weekly exercise time is negatively correlated with blood pressure levels, that is, the longer the exercise time, the lower the blood pressure, and the sugar intake in the diet is positively correlated with blood sugar levels.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a big data-based enterprise employee health management system according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a big data-based enterprise employee health management system according to any one of claims 1 to 7 are implemented.

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