Personal risk identification method and device, computer equipment, storage medium and program product

Abstract

The invention relates to a personal risk identification method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: collecting a plurality of personal safety data information from a multi-source information system, wherein each piece of personal safety data information is matched with corresponding risk information; the risk information comprises a risk level and a risk type; preprocessing the multiple pieces of personal safety data information to obtain multiple pieces of preprocessed data information; the preprocessed data information accords with a data quality strategy; performing data analysis on each piece of preprocessed data information based on a data mining technology, and extracting personal safety features from each piece of preprocessed data information; and based on each personal safety feature, performing risk prediction through the constructed initial personal risk identification model to obtain predicted risk information corresponding to each piece of personal safety data information. By adopting the method, the reliability and flexibility of personal safety management of power supply enterprise employees by power supply enterprises can be improved.

Description

Technical Field

[0001] The present application relates to the fields of big data analysis, artificial intelligence and risk management technology, and in particular to a method, apparatus, computer equipment, storage medium and program product for personal risk identification. Background Art

[0002] In today's complex and ever-changing industrial and social environment, personal safety risks are becoming increasingly prominent, posing a severe challenge to the safe production of enterprises and institutions and the personal health of employees. Traditional risk management methods often rely on manual inspections, empirical judgments, or single data source analysis. These methods are not only inefficient, but also difficult to fully and accurately capture potential risk factors, resulting in the lag and incompleteness of risk prevention and control. With the rapid development of technologies such as big data and artificial intelligence, intelligent risk identification methods based on multi-source data information have gradually become a research hotspot and a new trend in practical applications.

[0003] Multi-source data information, including but not limited to production data, equipment status, environmental parameters, employee behavior records, mental health assessments, social media activities, etc., covers all aspects of employees' work and life, and provides a rich data foundation for comprehensive and in-depth analysis of personal safety risks. However, these data come from a wide range of sources, in various formats, and of varying quality, and there are many challenges in directly applying them to risk identification. First, the data formats, units, dimensions, etc. between different data sources may be inconsistent, requiring data cleaning and preprocessing; second, the timeliness and completeness of the data are also important factors that affect the accuracy of risk identification; finally, how to extract useful information related to risk identification from massive data and build an effective model for prediction and judgment is the core issue of intelligent risk identification methods.

[0004] At present, although some risk identification methods based on a single data source or simple data fusion have been proposed and applied, these methods often have the following shortcomings: first, the data source is single and cannot fully reflect the diversity of risk factors; second, the data processing method is simple and it is difficult to fully explore the hidden information and value in the data; third, the model construction lacks specificity and cannot be customized according to specific risk scenarios and needs. Summary of the invention

[0005] Based on this, it is necessary to provide a method, device, computer equipment, storage medium and program product for personal risk identification that can integrate multi-source data information and perform complex data processing in response to the above-mentioned technical problems.

[0006] In a first aspect, the present application provides a method for identifying personal risks, comprising:

[0007] Collect multiple personal safety data information from multi-source information systems, each personal safety data information is matched with corresponding risk information; the risk information includes risk level and risk type;

[0008] Preprocessing multiple personal safety data information to obtain multiple preprocessed data information; the preprocessed data information complies with the data quality strategy;

[0009] Perform data analysis on each pre-processed data information based on data mining technology, and extract personal safety features from each pre-processed data information;

[0010] Based on the personal safety characteristics of each person, risk prediction is performed through the constructed initial personal risk identification model to obtain the predicted risk information corresponding to each personal safety data information.

[0011] In one embodiment, the method for personal risk identification further includes:

[0012] Through each predicted risk information and each risk information, the model parameters of the initial personal risk identification model are adjusted to obtain a personal risk identification model for personal risk identification;

[0013] The personal risk identification model is deployed to the actual business system, and the real-time risk information of the power supply enterprise employees is predicted based on the real-time personal safety data information of the power supply enterprise employees through the personal risk identification model; the real-time risk information includes the real-time risk level and the real-time risk type; the real-time personal safety data information at least includes the working environment and behavior pattern;

[0014] Identify risk warnings that match real-time risk information, and identify intervention measures that match real-time risk information.

[0015] In one embodiment, determining a risk warning that matches the real-time risk information includes:

[0016] Determine the potential risks of power supply enterprise employees based on real-time risk level and real-time risk type;

[0017] A risk warning is triggered when the potential risk of power supply company employees reaches the preset threshold.

[0018] In one embodiment, determining interventions that match the real-time risk information includes:

[0019] Determine the intervention strategy for each real-time risk type;

[0020] Establish a feedback collection mechanism based on the real-time risk types in the real-time risk information, and collect feedback for different real-time risk types based on the feedback collection mechanism;

[0021] The intervention strategy for each real-time risk type is optimized and adjusted based on the feedback from each real-time risk type.

[0022] In one embodiment, the initial personal risk identification model is constructed as follows:

[0023] Based on each personal safety feature, a matched personal risk identification model to be verified is constructed respectively through a plurality of model construction algorithms; the plurality of model construction algorithms include at least one of a logistic regression algorithm, a decision tree algorithm, a random forest algorithm, a gradient boosting tree algorithm, and a neural network algorithm;

[0024] Based on the personal safety characteristics of each person, each personal risk identification model to be verified is cross-validated to obtain the model performance corresponding to each personal risk identification model to be verified;

[0025] The personal risk identification model to be verified with the best model performance is determined as the initial personal risk identification model.

[0026] In one embodiment, data analysis is performed on each pre-processed data information based on data mining technology, and personal safety features are extracted from each pre-processed data information, including:

[0027] Using any one of correlation analysis, principal component analysis and mutual information method to analyze the pre-processed data information to obtain data analysis results;

[0028] Based on the data analysis results, a feature subset with the highest relevance to personal safety and the largest amount of information is selected from each pre-processed data information, and the feature subset is determined as a personal safety feature;

[0029] Based on the personal safety characteristics of each person, risk prediction is performed through the constructed initial personal risk identification model to obtain the predicted risk information corresponding to each personal safety data information, including:

[0030] Using the constructed initial personal risk identification model, at least one of cluster analysis and association rule mining techniques is used to identify potential patterns and associations in each personal safety feature;

[0031] The initial personal risk identification model adopts at least one of the time series analysis method and the machine learning regression model method, and predicts the predicted risk information corresponding to each personal safety data information based on the collected potential patterns and correlation relationships.

[0032] In a second aspect, the present application also provides a personal risk identification device, including:

[0033] The data information collection module is used to collect multiple personal safety data information from multi-source information systems. Each personal safety data information is matched with corresponding risk information; the risk information includes risk level and risk type;

[0034] The data information processing module is used to pre-process a plurality of personal safety data information to obtain a plurality of pre-processed data information; the pre-processed data information complies with the data quality strategy;

[0035] A data analysis module is used to analyze the pre-processed data information based on data mining technology and extract personal safety features from the pre-processed data information;

[0036] The model prediction module is used to perform risk prediction based on the personal safety characteristics of each person through the constructed initial personal risk identification model to obtain the predicted risk information corresponding to each personal safety data information.

[0037] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0038] Collect multiple personal safety data information from multi-source information systems, and each personal safety data information is matched with corresponding risk information; the risk information includes risk level and risk type;

[0039] Preprocessing multiple personal safety data information to obtain multiple preprocessed data information; the preprocessed data information complies with the data quality strategy;

[0040] Perform data analysis on each pre-processed data information based on data mining technology, and extract personal safety features from each pre-processed data information;

[0041] Based on the personal safety characteristics of each person, risk prediction is performed through the constructed initial personal risk identification model to obtain the predicted risk information corresponding to each personal safety data information.

[0042] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0043] Collect multiple personal safety data information from multi-source information systems, and each personal safety data information is matched with corresponding risk information; the risk information includes risk level and risk type;

[0044] Preprocessing multiple personal safety data information to obtain multiple preprocessed data information; the preprocessed data information complies with the data quality strategy;

[0045] Perform data analysis on each pre-processed data information based on data mining technology, and extract personal safety features from each pre-processed data information;

[0046] Based on the personal safety characteristics of each person, risk prediction is performed through the constructed initial personal risk identification model to obtain the predicted risk information corresponding to each personal safety data information.

[0047] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0048] Collect multiple personal safety data information from multi-source information systems, each personal safety data information is matched with corresponding risk information; the risk information includes risk level and risk type;

[0049] Preprocessing multiple personal safety data information to obtain multiple preprocessed data information; the preprocessed data information complies with the data quality strategy;

[0050] Perform data analysis on each pre-processed data information based on data mining technology, and extract personal safety features from each pre-processed data information;

[0051] Based on the personal safety characteristics of each person, risk prediction is performed through the constructed initial personal risk identification model to obtain the predicted risk information corresponding to each personal safety data information.

[0052] The above-mentioned personal risk identification method, device, computer equipment, computer-readable storage medium and computer program product collect multiple personal safety data information from a multi-source information system, and each personal safety data information is matched with corresponding risk information; the risk information includes risk level and risk type; multiple personal safety data information are preprocessed to obtain multiple preprocessed data information; the preprocessed data information complies with the data quality strategy; data analysis is performed on each preprocessed data information based on data mining technology, and personal safety features are extracted from each preprocessed data information; based on each personal safety feature, risk prediction is performed through the constructed initial personal risk identification model to obtain the predicted risk information corresponding to each personal safety data information. First, by introducing artificial intelligence and machine learning technology, intelligent identification, early warning and management of personal safety risks are realized, which can not only significantly improve the efficiency and accuracy of safety management of power supply enterprises, but also greatly reduce the subjectivity and uncertainty of human judgment, making safety management more scientific and accurate. Secondly, it fully considers multi-dimensional information such as personal characteristics, working environment, historical records, etc. of employees, and can generate personalized risk assessment and early warning for different employees and different scenarios, further improving the overall safety management level. It can be seen from this that the reliability and flexibility of power supply companies' personal safety management of power supply company employees can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0054] FIG1 is an application environment diagram of a method for identifying personal risk in one embodiment;

[0055] Figure 2 A flowchart of a method for identifying personal risk in one embodiment;

[0056] Figure 3 A flowchart of a method for identifying personal risk in another embodiment;

[0057] Figure 4 is an overall flow chart of a method for identifying personal risk based on multi-source data information in one embodiment;

[0058] Figure 5 is a flowchart of an implementation process of a personal risk identification model of a personal risk identification method based on multi-source data information in an embodiment;

[0059] Figure 6is a structural block diagram of a personal risk identification device in one embodiment;

[0060] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0062] The method for personal risk identification provided in the embodiment of the present application can be applied in the application environment shown in FIG1 . The terminal 102 communicates with the server 104 via a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers.

[0063] Specifically, taking the application to server 104 as an example, multiple personal safety data information is collected from a multi-source information system, each personal safety data information matches the corresponding risk information, and the risk information includes risk level and risk type. Then, multiple personal safety data information is preprocessed to obtain multiple preprocessed data information, and the preprocessed data information meets the data quality strategy. Thus, data analysis is performed on each preprocessed data information based on data mining technology, personal safety features are extracted from each preprocessed data information, and based on each personal safety feature, risk prediction is performed through the constructed initial personal risk identification model to obtain the predicted risk information corresponding to each personal safety data information. By reducing the subjectivity and uncertainty of human judgment, safety management is made more scientific and accurate. And multi-dimensional information such as employee personal characteristics, work environment, historical records, etc. is fully considered to further improve the overall safety management level. It can be seen from this that the reliability and flexibility of power supply enterprises in personal safety management of power supply enterprise employees can be improved.

[0064] The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. Head-mounted devices may be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server 104 may be an independent physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides cloud computing services.

[0065] In an exemplary embodiment, Figure 2 As shown, a method for personal risk identification is provided. This embodiment uses the method applied to the server 104 shown in FIG. 1 as an example for illustration. It can be understood that the method can also be applied to the terminal 102 shown in FIG. 1, and can also be applied to a system including the terminal 102 and the server 104, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0066] Step 202, collect multiple personal safety data information from multi-source information systems, each personal safety data information is matched with corresponding risk information; the risk information includes risk level and risk type.

[0067] Among them, the multi-source information system includes external systems and various business systems under the power supply enterprise. The external system is the business system that does not belong to the power supply enterprise. Therefore, the external system includes but is not limited to the meteorological information system, traffic management system, and public safety information platform. Secondly, the business systems under the power supply enterprise include but are not limited to the production management system, human resources management system, and safety management system.

[0068] Secondly, the aforementioned personal safety data information includes but is not limited to: basic employee information, historical accident records, equipment status monitoring data, work environment monitoring data, weather condition data, traffic condition data, public safety incident data, as well as workload and stress assessment data, safety training and qualification certification information data, special work permits and approval data, personal health monitoring data, emergency response and rescue capability data, social network and sentiment analysis data, external threat and risk intelligence data.

[0069] The aforementioned workload and stress assessment data collects data on employees’ work stress, workload, and mental health through questionnaires and heart rate monitoring to assess their indirect impact on personal safety. The aforementioned safety training and qualification certification information data analyzes the impact on employees’ safety awareness and mastery of operating specifications by investigating the safety training courses they have participated in, the qualification certificates they have obtained, and the test scores, thereby assessing potential risks. The aforementioned special work permit and approval data targets high-risk operations, including aerial operations and live-line operations, and collects monitoring data during the application, approval process, and execution of work permits to ensure compliance with the regulations and reduce risks.

[0070] The aforementioned personal health monitoring data collects employees’ physical health indicators, including blood pressure, blood sugar, and heart rate variability, through wearable devices and regular physical examinations to assess the impact of their physical condition on work safety. The aforementioned emergency response and rescue capability data assesses the completeness of the company’s emergency response system, including the determination of emergency plans, drill records, training and equipment of rescue teams, and the efficiency and effectiveness of historical rescue operations, providing data support for rapid response to emergencies. The aforementioned social network and sentiment analysis data uses natural language processing technology to analyze employees’ comments on social media, identify negative emotions and potential psychological pressure, and intervene in time to avoid work errors and safety accidents caused by personal emotional problems. The aforementioned external threat and risk intelligence data collects and analyzes external threat information from the Internet, media, and other channels about influenza transmission, natural disasters, and special events that may affect corporate operations and employee safety, and make preventive preparations in advance.

[0071] Specifically, the server collects personal safety data information from multiple source information systems, that is, collects data from meteorological information systems, traffic management systems, public safety information platforms, production management systems, human resource management systems, and safety management systems to obtain personal safety data information. And the collected personal safety data information all matches the corresponding risk information, and the aforementioned risk information includes risk levels and risk types. The risk levels include at least high risk, medium risk, and low risk, and the risk types include at least electric shock risk, high-altitude fall risk, mechanical injury risk, fire and explosion risk, chemical leakage risk, radiation injury risk, infectious disease risk, human error risk, natural disaster risk, violent incident risk, and psychological problem risk. The risk information matched by the personal safety data information can be obtained by performing a manual risk assessment on each personal safety data information after information collection.

[0072] Step 204, pre-processing the multiple personal safety data information to obtain multiple pre-processed data information; the pre-processed data information complies with the data quality strategy.

[0073] The preprocessed data information complies with the data quality strategy, and the aforementioned data quality strategy at least includes: unified format, no data duplication, no missing data values, no data abnormal values, etc. Specifically, the server preprocesses multiple personal safety data information to ensure that each preprocessed data information obtained complies with the data quality strategy.

[0074] Since the data quality strategy at least includes: unified format, no duplication of data, no missing data, no abnormal data, etc., then in the process of preprocessing personal safety data information, it is necessary to perform corresponding processing based on the aforementioned data quality strategy. Based on this, in a specific embodiment, the preprocessing method includes at least one of the following: format unification, data cleaning, data deduplication, missing value processing, abnormal value correction, standardization processing, and data integration.

[0075] The following is a detailed description of the above preprocessing method:

[0076] The aforementioned format unification is to convert various types of data into a unified format that can be recognized by the system, including date format, timestamp, and currency unit standardization, that is, to convert various personal safety data information into a unified format that can be recognized by the system. The aforementioned data cleaning is to remove and correct erroneous, incomplete, and redundant records, that is, to remove and correct erroneous, incomplete, and redundant records in personal safety data information to reduce the interference of noise on the analysis results.

[0077] The aforementioned data deduplication is to delete duplicate records by comparing the unique identifiers in the data, that is, to match the unique identifiers in each personal safety data information to delete the personal safety data information with duplicate identifiers to avoid bias in subsequent analysis. The aforementioned missing value processing adopts interpolation, mean substitution and filling methods based on business rules, that is, interpolation, mean substitution and filling methods based on business rules are used to process the missing values ​​of personal safety data information to ensure data integrity.

[0078] The above-mentioned abnormal value correction is to identify and adjust those values ​​that deviate extremely from the normal range according to the data distribution rules and business logic. That is, to identify the abnormalities of personal safety data information according to the data distribution rules and business logic in the power supply field, and then adjust the values ​​in the personal safety data information that deviate from the normal range through the abnormal identification results to make them more in line with the actual situation.

[0079] The aforementioned standardization process is to scale the data in proportion, that is, to scale the personal safety data information in proportion so that it falls into a small specific range for unified comparison and analysis. Finally, the aforementioned data integration is to integrate the pre-processed data into a unified data warehouse, that is, to integrate the personal safety data information after the aforementioned processing into a unified data warehouse to provide comprehensive data support for subsequent data mining and model training.

[0080] Step 206: Perform data analysis on each preprocessed data information based on data mining technology, and extract personal safety features from each preprocessed data information.

[0081] Specifically, the server performs data analysis on each pre-processed data information based on data mining technology, that is, by using a variety of algorithms and models, feature selection is performed on the pre-processed data information to extract personal safety features from each pre-processed data information. The feature selection method in data analysis includes at least correlation analysis, principal component analysis, and mutual information method. This is introduced in detail below:

[0082] In a specific embodiment, data analysis is performed on each preprocessed data information based on data mining technology, and personal safety features are extracted from each preprocessed data information, including: using any one of correlation analysis, principal component analysis, and mutual information methods to perform data analysis on each preprocessed data information to obtain data analysis results; based on the data analysis results, a feature subset with the highest relevance to personal safety and the largest amount of information is screened out from each preprocessed data information, and the feature subset is determined as a personal safety feature. Specifically, the process of feature selection by the server needs to use any one of correlation analysis, principal component analysis (PCA), and mutual information (MI) methods to perform data analysis on each preprocessed data information, and based on the data analysis results, a feature subset with the highest relevance to personal safety and the largest amount of information is screened out from each preprocessed data information, and then the feature subset is determined as a personal safety feature to complete feature screening.

[0083] Step 208, based on each personal safety feature, risk prediction is performed through the constructed initial personal risk identification model to obtain predicted risk information corresponding to each personal safety data information.

[0084] Among them, the predicted risk information includes the predicted risk level and the predicted risk type. Specifically, based on each personal safety feature, the risk prediction is performed through the constructed initial personal risk identification model to obtain the predicted risk information corresponding to each personal safety data information, that is, each personal safety feature is input into the pre-constructed initial personal risk identification model, and the initial personal risk identification model performs risk prediction and outputs the predicted risk level and predicted risk type corresponding to each personal safety data information, and the aforementioned predicted risk level and predicted risk type are the predicted risk information.

[0085] The following will detail how to construct the initial personal risk identification model: In a specific embodiment, the initial personal risk identification model is constructed as follows: based on each personal safety feature, the matched personal risk identification models to be verified are constructed respectively through a plurality of model construction algorithms; the plurality of model construction algorithms include at least one of a logistic regression algorithm, a decision tree algorithm, a random forest algorithm, a gradient boosting tree algorithm, and a neural network algorithm; each personal risk identification model to be verified is cross-validated to obtain the model performance of each personal risk identification model to be verified; the personal risk identification model to be verified with the best model performance is determined as the initial personal risk identification model.

[0086] That is to say, building an initial personal risk identification model requires at least data feature engineering, algorithm selection, and model construction, which are introduced below:

[0087] 1. Data feature engineering: According to the needs of personal safety risk assessment of power supply enterprise employees, further feature engineering is performed on the processed data, including feature construction, feature scaling, and encoding conversion, to enhance the interpretability of the model and improve the learning efficiency of the model. Specifically, categorical variables are converted into numerical features through One-Hot Encoding and Label Encoding, continuous features are normalized, and composite features are constructed according to business logic.

[0088] 2. Algorithm selection and model construction: that is, the server constructs the matching personal risk identification models to be verified through logistic regression, decision tree, random forest, gradient boosting tree, and neural network algorithms, and cross-validates each personal risk identification model to be verified to obtain the model performance of each personal risk identification model to be verified. At this time, the server needs to select logistic regression, decision tree, random forest, gradient boosting decision tree (GBDT), and neural network algorithm to build a personal risk identification model based on the complexity and diversity of multi-source data information, as well as the real-time and accuracy requirements of personal risk identification; in the model construction stage, first cross-validation is performed to evaluate the model performance under different algorithms and parameter combinations, select the optimal model configuration, and further optimize the model's prediction accuracy and generalization ability by adjusting the model's hyperparameters (tree depth, learning rate, regularization coefficient).

[0089] The method of how to proceed is described in detail below: In a specific embodiment, based on each personal safety feature, risk prediction is performed through the constructed initial personal risk identification model to obtain predicted risk information corresponding to each personal safety data information, including: using at least one of clustering analysis and association rule mining technology through the constructed initial personal risk identification model to identify potential patterns and correlation relationships in each personal safety feature; using at least one of a time series analysis method and a machine learning regression model method through the initial personal risk identification model to predict the predicted risk information corresponding to each personal safety data information based on the collected potential patterns and correlation relationships.

[0090] That is, the initial personal risk identification model constructed by the server performs model identification and trend prediction. Pattern recognition is the initial personal risk identification model model that uses at least one of cluster analysis and association rule mining technology to identify the potential patterns and correlations in each personal safety feature. Then, trend prediction is performed, that is, at least one of the time series analysis method and the machine learning regression model method is used to predict the predicted risk information corresponding to each personal safety data information based on the potential patterns and correlations obtained by model identification. The aforementioned predicted risk information can be risk information within a preset time period in the future.

[0091] Secondly, risk information within a preset time period in the future may include but is not limited to electric shock, falling from height, mechanical injury, fire and explosion, chemical leakage, radiation injury, traffic accidents, occupational diseases, infectious diseases, human errors, natural disasters, violent incidents, biosafety threats, health problems caused by excessive psychological stress, food poisoning, etc.

[0092] It should be understood that the corresponding examples in the embodiments of the present application are used to understand the present solution, but should not be understood as specific limitations of the present solution.

[0093] In the above-mentioned personal risk identification method, by introducing artificial intelligence and machine learning technology, intelligent identification, early warning and management of personal safety risks can be realized, which can not only significantly improve the efficiency and accuracy of power supply enterprise safety management, but also greatly reduce the subjectivity and uncertainty of human judgment, making safety management more scientific and accurate. Secondly, it fully considers multi-dimensional information such as employee personal characteristics, work environment, historical records, etc., and can generate personalized risk assessments and early warnings for different employees and different scenarios, further improving the overall safety management level. It can be seen that the reliability and flexibility of power supply enterprises in personal safety management of power supply enterprise employees can be improved.

[0094] In an exemplary embodiment, Figure 3 As shown, the method of personal risk identification also includes:

[0095] Step 302: adjusting the model parameters of the initial personal risk identification model through each predicted risk information and each risk information, so as to obtain a personal risk identification model for personal risk identification.

[0096] Specifically, the server adjusts the model parameters of the initial personal risk identification model through each predicted risk information and each risk information to obtain a personal risk identification model for personal risk identification. That is, the server needs to at least train and optimize the model for the initial personal risk identification model. The following is an introduction to model training and optimization: After determining the initial personal risk identification model, the server uses the preprocessed and feature-engineered data set to train the model. During the training process, a suitable loss function (cross entropy loss, mean square error) is used to measure the difference between the model prediction value and the actual value, and the model parameters are updated through the gradient descent optimization algorithm to minimize the loss function.

[0097] It can be seen that the server uses a suitable loss function (such as cross entropy loss, mean square error) to calculate the loss function of each predicted risk information and each risk information to obtain a loss value. At this time, the loss value can be a cross entropy loss value or a mean square error loss value. The aforementioned loss value is the difference between the predicted value and the actual value. Then, the model parameters of the initial personal risk identification model are adjusted based on the aforementioned loss value through the gradient descent optimization algorithm to minimize the loss function, thereby obtaining a personal risk identification model for personal risk identification.

[0098] It is understandable that in practical applications, after adjusting the model parameters of the initial personal risk identification model, it is also possible to conduct model evaluation and interpretation, as well as model deployment and monitoring. This is described in detail below:

[0099] 1. Model evaluation and interpretation: After adjusting the model parameters of the initial personal risk identification model, use an independent test set to evaluate the model to ensure that the model has good generalization ability, that is, it can maintain stable prediction performance on unknown data. When evaluating the model, not only pay attention to common indicators such as accuracy, recall rate, and F1 score, but also combine the model's interpretability, stability, and real-time performance. Specifically, a model with strong interpretability helps companies understand the inherent mechanism of risk factors and determine targeted prevention and control measures; a model with good stability can maintain consistent prediction performance at different time points and data distributions; real-time requires that the model be able to complete the prediction task in a short time to meet the company's needs to respond quickly to risk changes.

[0100] 2. Model deployment and monitoring: Deploy the trained and evaluated personal risk identification model into the actual business system, integrate it with the production management system, human resource management system, and safety management system of the power supply company to achieve real-time monitoring and analysis. During the operation of the model, set up a monitoring mechanism to regularly check the prediction accuracy and stability of the model, and promptly discover and deal with model degradation problems. At the same time, regularly update and optimize the model according to business needs and data changes to maintain its optimal performance.

[0101] Step 304, deploy the personal risk identification model to the actual business system, and predict the real-time risk information of the power supply enterprise employees based on the real-time personal safety data information of the power supply enterprise employees through the personal risk identification model; the real-time risk information includes the real-time risk level and the real-time risk type; the real-time personal safety data information at least includes the working environment and behavior pattern.

[0102] Among them, the real-time risk information includes real-time risk level and real-time risk type. Similar to the above-mentioned embodiment, the real-time risk level includes at least high risk, medium risk, and low risk, and the real-time risk type includes at least electric shock risk, high-altitude fall risk, mechanical injury risk, fire and explosion risk, chemical leakage risk, radiation injury risk, infectious disease risk, human error risk, natural disaster risk, violent incident risk, and psychological problem risk.

[0103] Specifically, the personal risk identification model is deployed to the actual business system, so as to obtain the working environment and behavior pattern of the power supply enterprise employees in the actual business system, and the working environment and behavior pattern of the power supply enterprise employees are input into the personal risk identification model, so as to output the real-time risk information of the power supply enterprise employees through the personal risk identification model. The working environment and behavior pattern of the power supply enterprise employees in the actual business system belong to the personal safety data information introduced in the aforementioned embodiment, that is, the working environment can be the working environment monitoring data, and the behavior pattern can be the workload and stress assessment data, and the personal health monitoring data.

[0104] Step 306, determining risk warnings that match the real-time risk information, and determining intervention measures that match the real-time risk information.

[0105] Specifically, determine the risk warning that matches the real-time risk information, and determine the intervention measures that match the real-time risk information. It can be seen from the above embodiments that the real-time risk information includes the real-time risk level and the real-time risk type. Therefore, at this time, the matching risk warning can be determined based on the real-time risk level and the real-time risk type, and the matching intervention measures can be determined based on the real-time risk type in the real-time risk information to reduce the risk of personal safety accidents of power supply enterprise employees.

[0106] The following introduces the methods of determining risk warnings and intervention policies respectively. First, the method of determining risk warnings that match real-time risk information is introduced: In an optional embodiment, determining risk warnings that match real-time risk information includes: determining the potential risks of power supply enterprise employees based on real-time risk levels and real-time risk types; triggering risk warnings when the potential risks of power supply enterprise employees reach a preset threshold.

[0107] Among them, risk warnings are pushed through at least one of SMS, email, and mobile applications. That is, risk warnings are pushed through SMS, email, and mobile applications to ensure that relevant managers and employees can obtain risk information in a timely manner. Specifically, based on the real-time risk level and real-time risk type, the potential risk of power supply enterprise employees in real time is triggered when the potential risk of power supply enterprise employees reaches the preset threshold, so as to ensure that power supply enterprise managers and power supply enterprise employees can obtain risk information in a timely manner.

[0108] In an optional embodiment, determining intervention measures that match real-time risk information includes: determining intervention strategies corresponding to each real-time risk type; establishing a feedback collection mechanism according to the real-time risk type in the real-time risk information, and collecting feedback for different real-time risk types based on the feedback collection mechanism; and optimizing and adjusting the intervention strategies for the real-time risk types through the feedback from each real-time risk type.

[0109] From the above introduction, we can know that the real-time risk types include at least electric shock risk, height fall risk, mechanical injury risk, fire and explosion risk, chemical leakage risk, radiation injury risk, infectious disease risk, human error risk, natural disaster risk, violent incident risk and psychological problem risk, etc. Therefore, we should determine and implement corresponding intervention measures for the real-time risk types, and establish a feedback collection mechanism to collect employees' feedback on the intervention measures. That is, based on the feedback collection mechanism, we should collect feedback on different real-time risk types, and optimize and adjust the intervention strategies for the real-time risk types, so as to continuously optimize and adjust the intervention strategies to ensure the effectiveness and adaptability of the intervention measures.

[0110] The following is an introduction to the corresponding intervention measures for different real-time risk types:

[0111] When the real-time risk type is electric shock risk, the matched intervention measures can be to strengthen on-site safety inspections to ensure the integrity of electrical equipment and safe operation of employees.

[0112] When the real-time risk type is the risk of falling from height, the matched intervention measures may be strengthening the use of safety belts and the setting of protective nets.

[0113] When the real-time risk type is mechanical injury risk, the matched intervention measures can be regular maintenance and inspection of mechanical equipment to ensure its safety performance, and safety training for operators to improve their safety awareness and operating skills.

[0114] When the real-time risk type is fire and explosion risk, the matched intervention measures can be to strengthen the construction and maintenance of fire-fighting facilities, ensure that fire passages are unobstructed, conduct fire drills regularly, and improve employees' fire emergency response capabilities.

[0115] When the real-time risk type is chemical leakage risk, the matched intervention measures can be strict management of chemical storage and use areas to ensure compliance with safety regulations and equip employees with necessary protective equipment, such as chemical protective clothing and gas masks.

[0116] When the real-time risk type is radiation injury risk, the matched intervention measures can be strict control of radioactive sources, ensuring that they are used under safe shielding conditions, and conducting regular health checks on employees who may be exposed to radiation.

[0117] When the real-time risk type is infectious disease risk, the matching intervention measures can be to strengthen workplace hygiene management, conduct regular disinfection, and encourage employees to develop good personal hygiene habits, such as washing hands frequently and wearing masks.

[0118] When the real-time risk type is human error risk, the matching intervention measures can be to reduce it by optimizing the work process and improving the quality of employee training. At the same time, an intelligent monitoring system is introduced to monitor abnormal behaviors in the work process in real time and intervene in time.

[0119] When the real-time risk type is natural disaster risk, the matching intervention measures can be to establish a comprehensive emergency plan, provide natural disaster response training for employees, and conduct regular drills to ensure that a quick and effective response can be carried out when a natural disaster occurs.

[0120] When the real-time risk type is the risk of violent incidents, the matching intervention measures can be to strengthen safety monitoring in the workplace, improve employees' safety awareness, and establish an effective reporting and response mechanism to ensure timely handling and intervention when violent incidents occur.

[0121] When the real-time risk type is the risk of psychological problems, the matched intervention measures can be the provision of psychological counseling and stress management workshops.

[0122] In this embodiment, the safety awareness and self-protection ability of employees are enhanced: through the implementation of risk warning and intervention measures, the present invention can not only effectively prevent risks before they occur, but also respond quickly when risks occur to reduce losses. Secondly, through continuous feedback and adjustment, employees can gradually enhance their safety awareness, improve their self-protection ability, and further adjust strategies to ensure the effectiveness of prevention and form a good safety culture atmosphere.

[0123] The following is an introduction to the complete implementation of the method for personal risk identification based on multi-source data information. In an exemplary embodiment, Figure 4 As shown, a method for identifying personal risk based on multi-source data information is provided. This embodiment uses the method applied to the server 104 shown in FIG. 1 as an example for illustration. It can be understood that the method can also be applied to the terminal 102 shown in FIG. 1 , and can also be applied to a system including the terminal 102 and the server 104, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0124] Step 401: Collect multiple personal safety data information from multiple source information systems, thereby realizing internal and external multi-source data fusion.

[0125] Step 402: Preprocess the multiple personal safety data information to obtain multiple preprocessed data information, thereby ensuring data quality and providing an accurate basis for subsequent analysis.

[0126] Step 403: Perform data analysis on each preprocessed data information based on data mining technology, and extract personal safety features from each preprocessed data information.

[0127] Step 404, construct an initial personal risk identification model, use a machine learning algorithm to train and learn personal safety features, and form a personal risk identification model for personal risk identification.

[0128] Step 405: deploy the personal risk identification model to the actual business system, and predict the real-time risk information of the power supply enterprise employees based on the real-time personal safety data information of the power supply enterprise employees, including at least the working environment and behavior patterns, so as to timely discover potential personal safety risks.

[0129] Step 406: determine risk warnings that match the real-time risk information, and determine intervention measures that match the real-time risk information, so as to reduce the risk of personal safety accidents.

[0130] The aforementioned multi-source information system includes external systems and various business systems under the power supply enterprise. The external system is the business system that does not belong to the power supply enterprise. The various business systems under the power supply enterprise include but are not limited to the production management system, human resources management system, and safety management system, while the external system includes but is not limited to the meteorological information system, traffic management system, and public safety information platform. Personal safety data information includes but is not limited to basic employee information, historical accident records, equipment status monitoring data, work environment monitoring data, weather condition data, traffic condition data, public safety incident data, as well as workload and stress assessment data, safety training and qualification certification information data, special operation permits and approval data, personal health monitoring data, emergency response and rescue capability data, social network and sentiment analysis data, external threat and risk intelligence data.

[0131] In the embodiment of the present application, a comprehensive and dynamic personal risk identification system is constructed by integrating a wide range of data sources inside and outside the power supply enterprise. This system not only covers internal information such as employee personal characteristics and historical safety records, but also incorporates external environmental factors such as weather, traffic, public safety incidents and other external data, thereby achieving all-round and multi-angle monitoring and analysis of personal safety risks. This multi-source data fusion method greatly enriches the dimensions of risk assessment and makes the risk assessment results more comprehensive and accurate. By comprehensively considering internal and external factors, the embodiment of the present application can more accurately identify potential risk points and avoid the one-sidedness and limitations that may be brought about by a single data source.

[0132] Further, the specific implementation method of risk prediction based on the personal risk identification model is described in detail below. In an exemplary embodiment, Figure 5 As shown, a process for implementing a personal risk identification model of a personal risk identification method based on multi-source data information is provided. In this embodiment, the implementation process includes the following steps:

[0133] Step 501, data feature engineering: according to the needs of personal safety risk assessment of power supply enterprise employees, further feature engineering is performed on the processed data, including feature construction, feature scaling, and encoding conversion, to enhance the interpretability of the model and improve the learning efficiency of the model. Specifically, the categorical variables are converted into numerical features through One-Hot Encoding and Label Encoding, the continuous features are normalized, and composite features are constructed according to business logic.

[0134] Step 502, algorithm selection and model construction: The server constructs the matched personal risk identification models to be verified through logistic regression, decision tree, random forest, gradient boosting tree, and neural network algorithms, and cross-validates each personal risk identification model to be verified to obtain the model performance of each personal risk identification model to be verified. At this time, the server needs to select logistic regression, decision tree, random forest, gradient boosting decision tree (GBDT), and neural network algorithms to construct personal risk identification models based on the complexity and diversity of multi-source data information and the real-time and accuracy requirements of personal risk identification; in the model construction stage, first cross-validation is performed to evaluate the model performance under different algorithms and parameter combinations, select the optimal model configuration, and further optimize the prediction accuracy and generalization ability of the model by adjusting the model's hyperparameters (tree depth, learning rate, regularization coefficient).

[0135] Step 503, model training and optimization: After determining the initial personal risk identification model, the server uses the preprocessed and feature-engineered data set to train the model. During the training process, a suitable loss function (either cross entropy loss or mean square error) is used to measure the difference between the model prediction value and the actual value, and the model parameters are updated through the gradient descent optimization algorithm to minimize the loss function.

[0136] Step 504, model evaluation and interpretation: After adjusting the model parameters of the initial personal risk identification model, use an independent test set to evaluate the model to ensure that the model has good generalization ability, that is, it can maintain stable prediction performance on unknown data. When evaluating the model, not only common indicators such as accuracy, recall rate, and F1 score are considered, but also the interpretability, stability, and real-time performance of the model are considered. Specifically, a model with strong interpretability helps enterprises understand the internal mechanism of risk factors and determine targeted prevention and control measures; a model with good stability can maintain consistent prediction performance at different time points and data distributions; and real-time performance requires that the model can complete the prediction task in a short time to meet the needs of enterprises to respond quickly to risk changes.

[0137] Step 505, model deployment and monitoring: deploy the trained and evaluated personal risk identification model to the actual business system, integrate it with the production management system, human resource management system, and safety management system of the power supply enterprise, and realize real-time monitoring and analysis. During the operation of the model, set up a monitoring mechanism to regularly check the prediction accuracy and stability of the model, and promptly discover and deal with model degradation problems. At the same time, according to business needs and data changes, regularly update and optimize the model to maintain its optimal performance.

[0138] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0139] Based on the same inventive concept, the embodiment of the present application also provides a personal risk identification device for implementing the personal risk identification method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations of one or more personal risk identification device embodiments provided below can refer to the limitations of the personal risk identification method above, and will not be repeated here.

[0140] In an exemplary embodiment, Figure 6 As shown, a personal risk identification device is provided, including: a data information collection module 602, a data information processing module 604, a data analysis module 606 and a model prediction module 608, wherein:

[0141] The data information collection module 602 is used to collect multiple personal safety data information from multiple source information systems, each personal safety data information is matched with corresponding risk information; the risk information includes risk level and risk type;

[0142] The data information processing module 604 is used to pre-process the multiple personal safety data information to obtain multiple pre-processed data information; the pre-processed data information complies with the data quality strategy;

[0143] A data analysis module 606 is used to perform data analysis on each pre-processed data information based on data mining technology, and extract personal safety features from each pre-processed data information;

[0144] The model prediction module 608 is used to perform risk prediction based on each personal safety feature through the constructed initial personal risk identification model to obtain predicted risk information corresponding to each personal safety data information.

[0145] In an exemplary embodiment, the personal risk identification device further includes a model parameter adjustment module, a risk prediction module, and an early warning and intervention module;

[0146] A model parameter adjustment module is used to adjust the model parameters of the initial personal risk identification model through each predicted risk information and each risk information to obtain a personal risk identification model for personal risk identification;

[0147] The risk prediction module is used to deploy the personal risk identification model into the actual business system, and predict the real-time risk information of the power supply enterprise employees based on the real-time personal safety data information of the power supply enterprise employees through the personal risk identification model; the real-time risk information includes the real-time risk level and the real-time risk type; the real-time personal safety data information at least includes the working environment and behavior pattern;

[0148] The early warning and intervention module is used to determine risk warnings that match real-time risk information, and to determine intervention measures that match real-time risk information.

[0149] In an exemplary embodiment, the early warning and intervention module is specifically used to determine the potential risks of power supply enterprise employees based on real-time risk levels and real-time risk types; and trigger a risk early warning when the potential risks of power supply enterprise employees reach a preset threshold.

[0150] In an exemplary embodiment, the early warning and intervention module is specifically used to determine the intervention strategy corresponding to each real-time risk type; establish a feedback collection mechanism according to the real-time risk type in the real-time risk information, and collect feedback for different real-time risk types based on the feedback collection mechanism; and optimize and adjust the intervention strategy for each real-time risk type through the feedback from each real-time risk type.

[0151] In an exemplary embodiment, the personal risk identification device further includes a model building module;

[0152] A model building module is used to build the matched personal risk identification models to be verified respectively through multiple model building algorithms based on various personal safety features; the multiple model building algorithms include at least one of a logistic regression algorithm, a decision tree algorithm, a random forest algorithm, a gradient boosting tree algorithm, and a neural network algorithm; based on various personal safety features, each personal risk identification model to be verified is cross-validated to obtain the model performance corresponding to each personal risk identification model to be verified; the personal risk identification model to be verified with the best model performance is determined as the initial personal risk identification model.

[0153] In an exemplary embodiment, the data analysis module is used to perform data analysis on each pre-processed data information by using any one of correlation analysis, principal component analysis and mutual information method to obtain a data analysis result; based on the data analysis result, a feature subset with the highest relevance to personal safety and the largest amount of information is screened out from each pre-processed data information, and the feature subset is determined as a personal safety feature;

[0154] The model prediction module is used to identify the potential patterns and correlation relationships in each personal safety feature by using at least one of cluster analysis and association rule mining techniques through the constructed initial personal risk identification model; and to predict the predicted risk information corresponding to each personal safety data information based on the collected potential patterns and correlation relationships by using at least one of time series analysis methods and machine learning regression model methods through the initial personal risk identification model.

[0155] Each module in the above-mentioned personal risk identification device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0156] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store personal safety data information, risk information and other data related to the embodiments of the present application. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for identifying personal risks is implemented.

[0157] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0158] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0159] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0160] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0161] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0162] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0163] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0164] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for identifying personal risk, characterized in that: The method comprises: Collecting multiple personal safety data information from a multi-source information system, each of the personal safety data information is matched with corresponding risk information; the risk information includes risk level and risk type; Preprocessing the plurality of personal safety data information to obtain a plurality of preprocessed data information; the preprocessed data information complies with the data quality strategy; Performing data analysis on each of the pre-processed data information based on data mining technology, and extracting personal safety features from each of the pre-processed data information; Based on each of the personal safety features, risk prediction is performed through the constructed initial personal risk identification model to obtain predicted risk information corresponding to each of the personal safety data information.

2. The method according to claim 1, characterized in that The method further comprises: Adjusting the model parameters of the initial personal risk identification model through each of the predicted risk information and each of the risk information to obtain a personal risk identification model for personal risk identification; The personal risk identification model is deployed into the actual business system, and the real-time risk information of the power supply enterprise employees is predicted based on the real-time personal safety data information of the power supply enterprise employees through the personal risk identification model; the real-time risk information includes the real-time risk level and the real-time risk type; the real-time personal safety data information includes at least the working environment and behavior pattern; Determine a risk warning that matches the real-time risk information, and determine an intervention measure that matches the real-time risk information.

3. The method according to claim 2, characterized in that The determining of a risk warning matching the real-time risk information includes: Determining the potential risk of the power supply enterprise employees based on the real-time risk level and the real-time risk type; When the potential risk of the employees of the power supply enterprise reaches a preset threshold, a risk warning is triggered.

4. The method according to claim 2, characterized in that: The determining of intervention measures matching the real-time risk information comprises: Determining intervention strategies corresponding to each of the real-time risk types; Establishing a feedback collection mechanism according to the real-time risk type in the real-time risk information, and collecting feedback for different real-time risk types based on the feedback collection mechanism; The intervention strategy for each real-time risk type is optimized and adjusted based on the feedback from each real-time risk type.

5. The method according to any one of claims 1 to 4, characterized in that The initial personal risk identification model is constructed as follows: Based on each of the personal safety features, a matched personal risk identification model to be verified is constructed respectively by using a plurality of model construction algorithms; the plurality of model construction algorithms include at least one of a logistic regression algorithm, a decision tree algorithm, a random forest algorithm, a gradient boosting tree algorithm, and a neural network algorithm; Based on the personal safety features, cross-validate the personal risk identification models to be verified to obtain the model performances corresponding to the personal risk identification models to be verified; The personal risk identification model to be verified with the best model performance is determined as the initial personal risk identification model.

6. The method according to any one of claims 1 to 4, characterized in that The data mining technology is used to analyze the pre-processed data information, and personal safety features are extracted from the pre-processed data information, including: Using any one of correlation analysis, principal component analysis and mutual information method to analyze the pre-processed data information to obtain data analysis results; Based on the data analysis result, a feature subset with the highest relevance to personal safety and the largest amount of information is screened out from each of the pre-processed data information, and the feature subset is determined as a personal safety feature; Based on each of the personal safety features, risk prediction is performed through the constructed initial personal risk identification model to obtain predicted risk information corresponding to each of the personal safety data information, including: Using the constructed initial personal risk identification model, at least one of cluster analysis and association rule mining techniques is used to identify potential patterns and association relationships in each of the personal safety features; The initial personal risk identification model adopts at least one of a time series analysis method and a machine learning regression model method to predict the predicted risk information corresponding to each of the personal safety data information based on the collected potential patterns and the association relationships.

7. A personal risk identification device, characterized in that: The device comprises: A data information collection module is used to collect a plurality of personal safety data information from a multi-source information system, each of which is matched with corresponding risk information; the risk information includes risk level and risk type; A data information processing module, used to pre-process the plurality of personal safety data information to obtain a plurality of pre-processed data information; the pre-processed data information complies with the data quality strategy; A data analysis module, used to perform data analysis on each of the pre-processed data information based on data mining technology, and extract personal safety features from each of the pre-processed data information; The model prediction module is used to perform risk prediction based on each of the personal safety features through the constructed initial personal risk identification model to obtain the predicted risk information corresponding to each of the personal safety data information.

8. 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 the method according to any one of claims 1 to 6 are implemented.

9. 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 the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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