DeepSeek-based power plant staff care service system and method

Abstract

The invention discloses a DeepSeek-based power plant staff care service system and method, and the system and method integrate a data collection technology, break through a traditional fragmentation management mode, achieve the full-dimensional dynamic monitoring of the physiological, psychological, behavior and life characteristics of staff, and effectively solve a data island problem. Based on a prediction model of a DeepSeek platform and a semantic analysis technology, early recognition of mental health risks, occupational tiredness and health hidden dangers is realized, passive response is converted into active intervention, and the service timeliness is greatly improved; through a reinforcement learning algorithm and a service resource intelligent matching mechanism, a service scheme accurately matched with staff requirements is generated, and the coverage range and service pertinence of mental health support, family care and occupational development guidance are remarkably improved; incremental learning and digital twinning technologies are combined, a prediction model and a service strategy are continuously optimized, and a real-time effect tracking and multi-channel feedback mechanism is adopted.

Description

Technical Field

[0001] The present invention relates to the field of employee care, and in particular to a DeepSeek-based power plant employee care service system and method. Background Art

[0002] As a core sector of energy supply, power plants face long-term, high-intensity, high-risk work environments. Their physical and mental health and work status directly impact the company's production safety and operational efficiency. Currently, the employee care services commonly used in the industry rely primarily on manual management, gathering needs through traditional methods such as regular questionnaires and team meetings. This approach presents significant drawbacks: 1. Serious data fragmentation: Employee health data is scattered across multiple independent modules, such as medical records, attendance systems, and psychological assessments. This lacks multi-dimensional integrated analysis, making it difficult to implement full lifecycle status monitoring. 2. Delayed service response: Traditional methods rely on manual identification of needs, resulting in problems such as mental health crises and burnout often being discovered only after they become apparent, creating a "passive response" model. 3. Insufficient service coverage: Services such as psychological support and family care are limited by human resources, making it difficult to cover all employees in real time. Furthermore, the level of personalization is low, and manual coordination costs remain high.

[0003] With the development of artificial intelligence technology, some companies have tried to introduce basic data analysis tools, but there are still problems such as low prediction accuracy, rigid service solutions, and poor system scalability, which cannot meet the precise service needs in complex scenarios of power plants. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention proposes a power plant employee care service system and method based on DeepSeek.

[0005] The DeepSeek-based power plant employee care service system includes a data acquisition module, a data analysis module, a service plan generation module, a service execution and monitoring module, and a system optimization module; The data acquisition module realizes full-dimensional status monitoring of employees through the data acquisition device; The data analysis module is built based on the DeepSeek platform; The data analysis module includes a feature fusion submodule, a risk prediction submodule, and a demand identification submodule; The service solution generation module automatically generates personalized service solutions based on data analysis results, and achieves accurate demand response through intelligent matching of service resources and reinforcement learning to optimize parameter configuration; The service execution and monitoring module executes the service plan through smart wearable devices and tracks physiological, behavioral and emotional data in real time, and dynamically evaluates the service effect based on multi-source feedback; The system optimization module continuously optimizes the prediction model, adjusts the service strategy and updates the knowledge base through incremental learning and digital twin technology to achieve system adaptive iterative upgrades.

[0006] Furthermore, in the DeepSeek-based power plant employee care service system, the data collection module includes a basic information collection submodule, a vital sign monitoring submodule, a psychological assessment submodule, and a life characteristics collection submodule; The basic information collection submodule is connected to the power plant's internal management system to obtain employee position information, attendance records, and performance evaluation data; The vital signs monitoring submodule integrates smart wearable devices to collect heart rate, blood pressure, sleep quality and movement trajectory data in real time; The psychological assessment submodule collects SCL-90 scale data, emotional logs and social interaction characteristics through embedded psychological assessment tools; The life characteristics collection submodule obtains employee dining preferences, commuting methods and family structure information.

[0007] Furthermore, in the DeepSeek-based power plant employee care service system, the data analysis module includes a feature fusion submodule, a risk prediction submodule, and a demand identification submodule; The feature fusion submodule uses multimodal data fusion technology to integrate structured and unstructured data; The risk prediction submodule deploys the DeepSeek prediction model to establish a dynamic assessment system for mental health, occupational burnout, and health risks; The demand identification submodule generates a demand profile including mental health support, family care services and career development guidance through semantic analysis and clustering algorithms.

[0008] Furthermore, in the DeepSeek-based power plant employee care service system, the risk prediction submodule includes a mental health prediction unit, a job burnout assessment unit, and a health risk early warning unit: The mental health prediction unit uses an LSTM network to analyze the temporal characteristics of the psychological assessment data; The occupational burnout assessment unit integrates work intensity data and physiological indicators to establish a composite evaluation model; The health risk early warning unit uses a survival analysis algorithm to predict the probability of occurrence of chronic diseases.

[0009] Furthermore, in the DeepSeek-based power plant employee care service system, the service plan generation module includes a personalized plan construction submodule, a resource scheduling submodule, and a plan optimization submodule; The personalized solution construction submodule matches the preset service template library according to the demand profile; The resource scheduling submodule dynamically coordinates the service provision of psychological counselors, medical resources and educational institutions; The solution optimization submodule applies a reinforcement learning algorithm to iteratively optimize the service parameter combination.

[0010] Among them, the resource scheduling sub-module includes: A psychological counselor competency matrix database that records counselors’ areas of expertise and service evaluations; Dynamic matching algorithm for medical service resources to optimize registration appointments and remote consultation paths; The educational institution cooperation network management system automatically matches children with suitable educational institutions.

[0011] Furthermore, the power plant employee care service system based on DeepSeek includes a service execution and monitoring module including an intelligent distribution submodule, an effect tracking submodule, and a feedback collection submodule; The intelligent distribution submodule implements service delivery through mobile terminal APP and DeepSeek prediction model; The effect tracking submodule integrates smart wearable devices and NLP sentiment analysis tools to perform real-time effect evaluation; The feedback collection submodule establishes a multi-channel feedback mechanism to collect satisfaction evaluations and service improvement suggestions.

[0012] Among them, the effect tracking submodule integrates: Voice emotion recognition engine, real-time analysis of the emotional state of the speaker in telephone consultation; Galvanic skin response sensors to monitor stress responses during group counseling activities; ECG monitoring equipment to monitor employees' psychological changes.

[0013] Furthermore, the DeepSeek-based power plant employee care service system includes a model update submodule, a service parameter adjustment submodule, and a knowledge base maintenance submodule; The model update submodule continuously optimizes the DeepSeek prediction model based on incremental learning technology; The service parameter adjustment submodule automatically adjusts the solution generation strategy based on historical service data; The knowledge base maintenance submodule dynamically updates the typical case library and service resource map.

[0014] The DeepSeek-based caring service method for power plant employees includes the following steps: S1: Build a multi-dimensional data collection network: Use smart wearable devices to collect real-time heart rate, blood pressure, sleep quality, and movement trajectory data. Use smart work badges to record work intensity distribution and job rotation characteristics. Integrate electronic health records of medical institutions to establish employee health baselines. S2: In-depth data analysis and demand mining: Using the DeepSeek platform's text generation engine to analyze employee interview records and psychological counseling texts, and using a time series neural network to analyze the dynamic change patterns of physiological indicators; S3: Personalized service plan generation: Establish a multi-dimensional service feature space, map employee needs to the three-dimensional coordinate system of mental health, family support, and career development, call on the service resource knowledge graph to optimize service matching, and generate a visual plan that includes service content, execution path, and expected results; S4: Intelligent service execution and closed-loop monitoring: Deploy a service effect evaluation model, quantify the effect by combining changes in physiological indicators and service usage frequency, establish a service anomaly early warning mechanism, and trigger an alarm when the service effect is monitored to deviate from the expected threshold.

[0015] The visualization solutions include: A three-color warning sign system that dynamically adjusts service priorities; An interface for displaying service evaluations; Predict trend curves and key indicators of service effectiveness.

[0016] Furthermore, in the power plant employee care service method based on DeepSeek, the establishment of a service abnormality warning mechanism includes establishing a multi-indicator joint deviation evaluation function, setting a dynamic warning threshold based on a sliding window, and generating a warning report including a root cause analysis.

[0017] The beneficial effects of the present invention are: 1. Data integration and precise monitoring: Integrating multi-source heterogeneous data collection technologies, this system breaks through the traditional fragmented management model and enables full-dimensional dynamic monitoring of employees' physiological, psychological, behavioral, and lifestyle characteristics, effectively resolving data silos. 2. Intelligent prediction and proactive service: Leveraging the DeepSeek platform's predictive models and semantic analysis technology, this platform enables early identification of mental health risks, burnout, and other health hazards, shifting from passive response to proactive intervention and significantly improving service timeliness. 3. Personalized service adaptation: Through reinforcement learning algorithms and intelligent matching mechanisms for service resources, we generate service solutions that precisely match employee needs, significantly improving the coverage and relevance of mental health support, family care, and career development guidance. 4. Dynamic Optimization and Closed-Loop Management: Integrating incremental learning and digital twin technology, we continuously optimize predictive models and service strategies. Through real-time performance tracking and multi-channel feedback mechanisms, we form an adaptive closed loop of service execution, monitoring, and optimization, ensuring continuous system iteration and upgrades. 5. Cost reduction and efficiency improvement: Automated service generation and intelligent resource scheduling significantly reduce manual coordination costs. The modular architecture design supports flexible expansion to adapt to the changing needs of power plants of different sizes and employees. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a structural diagram of the DeepSeek-based power plant employee care service system.

[0019] Figure 2 This is a flow chart of the DeepSeek-based caring service method for power plant employees. DETAILED DESCRIPTION

[0020] The present invention is further described below, but the protection scope of the present invention is not limited to the following description.

[0021] As attached Figure 1 As shown in the following example, the module composition of the power plant employee care service system based on DeepSeek is shown in the following example. The DeepSeek-based power plant employee care service system includes the following modules: 1. Data acquisition module: By connecting the smart ID card with the power plant DCS system, real-time data on employee job rotation and work intensity distribution can be collected; Deploy smart wristbands (with integrated heart rate sensors and accelerometers) to continuously monitor employees’ heart rate variability and sleep quality; Embed a psychological assessment app on employees' mobile devices to periodically collect SCL-90 scale data and emotional keyword logs; Through the enterprise OA system, we obtain employees’ commuting clock-in records and dining consumption data to build a family structure inference model.

[0022] 2. Data Analysis Module: The feature fusion submodule uses graph neural networks to perform cross-modal correlation analysis on physiological data (heart rate, blood pressure), work logs (overtime hours, operational error records), and psychological assessment results; In the risk prediction submodule, the mental health prediction unit uses the LSTM network to identify the periodic fluctuation patterns of psychological assessment data, the occupational burnout assessment unit uses the random forest algorithm to integrate work intensity index and cortisol level data, and the health risk warning unit uses the Cox proportional hazards model to predict cardiovascular disease risk. The demand identification submodule uses the BERT model to parse employee consultation records and combines K-means clustering to generate personalized demand labels.

[0023] 3. Service plan generation module: When an employee is identified as having anxiety tendencies, the personalized solution building submodule automatically matches the psychological counseling service template and recommends a psychological counselor with expertise in anxiety intervention based on the service resource knowledge graph; The resource scheduling submodule uses a path optimization algorithm to plan the shortest treatment route for employees who need medical treatment, and at the same time collaborates with cooperative educational institutions to provide online tutoring courses for their children; The solution optimization submodule adopts the DQN algorithm to dynamically adjust the frequency of psychological intervention and the weight of medical resource allocation based on the historical service success rate.

[0024] 4. Service execution and monitoring module: The intelligent distribution submodule pushes customized service plans through WeChat for Business and simultaneously displays intervention reminders on employees’ smart wristbands; The effect tracking submodule uses a voice emotion recognition engine to analyze the voiceprint characteristics of employees' conversations with psychological counselors, and combines the skin electrical response data collected by the smart bracelet to evaluate the intervention effect; The feedback collection sub-module collects employee satisfaction data through multiple channels such as QR code scanning evaluation, voice feedback and questionnaire surveys.

[0025] 5. System optimization module: The model update submodule incrementally trains the DeepSeek prediction model daily, incorporating the latest collected employee physiological indicators and service feedback data; The service parameter adjustment sub-module automatically lowers the psychological assessment trigger threshold for employees in high-load positions based on the service records of the past 30 days; The knowledge base maintenance submodule uses entity linking technology to dynamically update the information of newly contracted medical institutions to the service resource map.

[0026] As attached Figure 2 As shown in the embodiment 2, a method for caring for power plant employees based on DeepSeek is provided, and the specific steps are as follows: 1. Data collection phase: Environmental sensors are deployed in the power plant control room to collect real-time noise decibel levels, temperature, and humidity data, which are then spatially and temporally aligned with stress response data, such as heart rate spikes, monitored by employees' smart wristbands. The built-in positioning module of the smart helmet records the activity trajectory and work break frequency of employees in high-risk positions; Integrate liver function indicators and blood sugar data from employees' annual physical examination reports to build an individual health baseline database.

[0027] 2. Data analysis and solution generation stage: An attention mechanism model was used to analyze employee psychological consultation texts, identifying key semantic features such as "high work pressure" and "family conflicts," and mining their associations with recent operational error records. For employees whose sleep quality index is below the threshold for two consecutive weeks, the mental health prediction model is triggered and an intervention priority score is generated based on their job risk level; When it is predicted that the probability of occupational burnout of an employee in the next three months exceeds 65%, the service plan generation module automatically combines the three services of "job transfer recommendation", "stress management training" and "family care plan" and optimizes the service order.

[0028] 3. Service execution and dynamic optimization stage: Using digital twin technology, a virtual employee model is constructed to simulate the impact of different service plans on blood pressure indicators and work efficiency, and the optimal plan is selected for implementation; When the system detects that the standard deviation of an employee's heart rate variability is still 20% higher than the baseline after participating in group psychological counseling, it automatically triggers the early warning mechanism to call the multi-indicator deviation evaluation function to calculate the comprehensive deviation index of physiological data, work performance, and psychological evaluation; If the deviation index exceeds the dynamic threshold for three consecutive monitoring cycles, an early warning report will be generated and upgraded intervention measures (such as specialist consultation) will be recommended.

[0029] Those skilled in the art will appreciate that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The DeepSeek-based power plant employee care service system is characterized by: Including data collection module, data analysis module, service plan generation module, service execution and monitoring module, and system optimization module; The data acquisition module realizes full-dimensional status monitoring of employees through the data acquisition device; The data analysis module is built based on the DeepSeek platform; The data analysis module includes a feature fusion submodule, a risk prediction submodule, and a demand identification submodule; The service solution generation module automatically generates personalized service solutions based on data analysis results, and achieves accurate demand response through intelligent matching of service resources and reinforcement learning to optimize parameter configuration; The service execution and monitoring module executes the service plan through smart wearable devices and tracks physiological, behavioral and emotional data in real time, and dynamically evaluates the service effect based on multi-source feedback; The system optimization module continuously optimizes the prediction model, adjusts the service strategy and updates the knowledge base through incremental learning and digital twin technology to achieve system adaptive iterative upgrades.

2. The DeepSeek-based power plant employee care service system according to claim 1 is characterized in that: The data acquisition module includes a basic information acquisition submodule, a physical sign monitoring submodule, a psychological assessment submodule, and a life characteristics acquisition submodule; The basic information collection submodule is connected to the power plant's internal management system to obtain employee position information, attendance records, and performance evaluation data; The vital signs monitoring submodule integrates smart wearable devices to collect heart rate, blood pressure, sleep quality and movement trajectory data in real time; The psychological assessment submodule collects SCL-90 scale data, emotional logs and social interaction characteristics through embedded psychological assessment tools; The life characteristics collection submodule obtains employee dining preferences, commuting methods and family structure information.

3. The DeepSeek-based power plant employee care service system according to claim 1 is characterized in that: The data analysis module includes a feature fusion submodule, a risk prediction submodule, and a demand identification submodule; The feature fusion submodule uses multimodal data fusion technology to integrate structured and unstructured data; The risk prediction submodule deploys the DeepSeek prediction model to establish a dynamic assessment system for mental health, occupational burnout, and health risks; The demand identification submodule generates a demand profile including mental health support, family care services and career development guidance through semantic analysis and clustering algorithms.

4. The DeepSeek-based power plant employee care service system according to claim 3 is characterized in that: The risk prediction submodule includes a mental health prediction unit, a job burnout assessment unit, and a health risk early warning unit: The mental health prediction unit uses an LSTM network to analyze the temporal characteristics of the psychological assessment data; The occupational burnout assessment unit integrates work intensity data and physiological indicators to establish a composite evaluation model; The health risk early warning unit uses a survival analysis algorithm to predict the probability of occurrence of chronic diseases.

5. The DeepSeek-based power plant employee care service system according to claim 1 is characterized in that: The service solution generation module includes a personalized solution construction submodule, a resource scheduling submodule, and a solution optimization submodule; The personalized solution construction submodule matches the preset service template library according to the demand profile; The resource scheduling submodule dynamically coordinates the service provision of psychological counselors, medical resources and educational institutions; The solution optimization submodule applies a reinforcement learning algorithm to iteratively optimize the service parameter combination.

6. The DeepSeek-based power plant employee care service system according to claim 1, characterized in that: The service execution and monitoring module includes an intelligent distribution submodule, an effect tracking submodule, and a feedback collection submodule; The intelligent distribution submodule implements service delivery through mobile terminal APP and DeepSeek prediction model; The effect tracking submodule integrates smart wearable devices and NLP sentiment analysis tools to perform real-time effect evaluation; The feedback collection submodule establishes a multi-channel feedback mechanism to collect satisfaction evaluations and service improvement suggestions.

7. The DeepSeek-based power plant employee care service system according to claim 1, characterized in that: The system optimization module includes a model update submodule, a service parameter adjustment submodule, and a knowledge base maintenance submodule; The model update submodule continuously optimizes the DeepSeek prediction model based on incremental learning technology; The service parameter adjustment submodule automatically adjusts the solution generation strategy based on historical service data; The knowledge base maintenance submodule dynamically updates the typical case library and service resource map.

8. A DeepSeek-based power plant employee care service method, implemented based on the DeepSeek-based power plant employee care service system according to any one of claims 1 to 7, characterized in that: The following steps are involved: S1: Build a multi-dimensional data collection network: Use smart wearable devices to collect real-time heart rate, blood pressure, sleep quality, and movement trajectory data. Use smart work badges to record work intensity distribution and job rotation characteristics. Integrate electronic health records of medical institutions to establish employee health baselines. S2: In-depth data analysis and demand mining: Using the DeepSeek platform's text generation engine to analyze employee interview records and psychological counseling texts, and using a time series neural network to analyze the dynamic change patterns of physiological indicators; S3: Personalized service plan generation: Establish a multi-dimensional service feature space, map employee needs to the three-dimensional coordinate system of mental health, family support, and career development, call on the service resource knowledge graph to optimize service matching, and generate a visual plan that includes service content, execution path, and expected results; S4: Intelligent service execution and closed-loop monitoring: Deploy a service effect evaluation model, quantify the effect by combining changes in physiological indicators and service usage frequency, establish a service anomaly early warning mechanism, and trigger an alarm when the service effect is monitored to deviate from the expected threshold.

9. According to the DeepSeek-based power plant employee care service method of claim 8, establishing a service abnormality early warning mechanism includes establishing a multi-indicator joint deviation evaluation function, setting a dynamic early warning threshold based on a sliding window, and generating an early warning report including a root cause analysis.

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