Security risk assessment method and device, computer equipment and readable storage medium

Abstract

The invention relates to a security risk assessment method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a data set for security risk assessment; performing security risk assessment index extraction according to the data set to obtain respective security risk assessment indexes of the plurality of security feature layers; performing statistical analysis on the respective security risk assessment indexes of the plurality of security feature layers to obtain a statistical analysis result, and screening out a plurality of key indexes for security risk assessment from the respective security risk assessment indexes of the plurality of security feature layers according to the statistical analysis result; and performing security risk assessment analysis on the plurality of key indexes for security risk assessment, and constructing a security risk assessment model which is used for outputting a security risk assessment portrait according to the input security risk assessment data of the to-be-assessed user. By adopting the method, the security risk assessment accuracy can be improved.

Description

Technical Field

[0001] The present application relates to the field of security protection technology, and in particular to a security risk assessment method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] With the rapid development of industrial technology and the continuous expansion of production scale, the various safety risks faced by enterprises in the production process are increasing. Among them, personal safety, as the core of enterprise safety management, is of self-evident importance.

[0003] In traditional methods, safety risk assessment for personal safety focuses on safety issues at the level of working environment or safety training, and conducts safety risk assessment based on the working environment or safety training. However, the safety risk assessment method used in traditional methods is relatively simple and cannot accurately reflect the safety risks in complex production environments, resulting in low accuracy of safety risk assessment. Summary of the invention

[0004] Based on this, it is necessary to provide a security risk assessment method, apparatus, computer equipment, computer-readable storage medium and computer program product that can improve the accuracy of security risk assessment in response to the above technical problems.

[0005] In a first aspect, the present application provides a security risk assessment method, comprising:

[0006] Acquire data sets for security risk assessment;

[0007] Extracting security risk assessment indicators based on the data set to obtain security risk assessment indicators for each of the multiple security feature layers;

[0008] Performing statistical analysis on the security risk assessment indicators of each of the multiple security feature layers to obtain statistical analysis results, and screening out multiple key indicators of security risk assessment from the security risk assessment indicators of each of the multiple security feature layers according to the statistical analysis results;

[0009] A security risk assessment analysis is performed on multiple key indicators of the security risk assessment to construct a security risk assessment model, and the security risk assessment model is used to output a security risk assessment portrait based on the input security risk assessment data of the user to be assessed.

[0010] In a second aspect, the present application also provides a security risk assessment device, comprising:

[0011] A data acquisition module, used to acquire a data set for security risk assessment;

[0012] An indicator extraction module, used to extract security risk assessment indicators according to the data set to obtain security risk assessment indicators for each of the multiple security feature layers;

[0013] An indicator screening module is used to perform statistical analysis on the security risk assessment indicators of each of the multiple security feature layers to obtain statistical analysis results, and screen out multiple key indicators of security risk assessment from the security risk assessment indicators of each of the multiple security feature layers according to the statistical analysis results;

[0014] The model building module is used to perform security risk assessment analysis on multiple key indicators of the security risk assessment and build a security risk assessment model. The security risk assessment model is used to output a security risk assessment portrait based on the input security risk assessment data of the user to be assessed.

[0015] 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 the processor implements the above-mentioned security risk assessment method when executing the computer program.

[0016] In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, and the computer program implements the above-mentioned security risk assessment method when executed by a processor.

[0017] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the above-mentioned security risk assessment method when executed by a processor.

[0018] The above-mentioned security risk assessment method, device, computer equipment, computer-readable storage medium and computer program product, when a data set for security risk assessment is obtained, can accurately obtain security risk assessment indicators from the perspective of multiple security feature layers by extracting security risk assessment indicators according to the data set, and statistically analyze the security risk assessment indicators of each of the multiple security feature layers to obtain statistical analysis results. According to the statistical analysis results, multiple key indicators of security risk assessment are screened out from the security risk assessment indicators of each of the multiple security feature layers, and multiple key indicators that can be used for accurate security risk assessment can be obtained by further screening. Then, security risk assessment analysis can be performed on multiple key indicators of security risk assessment to achieve the construction of a security risk assessment model, so that the security risk assessment model can be used to output a security risk assessment portrait according to the input security risk assessment data of the user to be assessed, so as to achieve accurate security risk assessment. In the whole process, by using the security risk assessment indicators of multiple security feature layers extracted from the data set to construct a security risk assessment model, various factors affecting personal safety can be comprehensively analyzed, thereby achieving accurate assessment of security risks and improving the accuracy of security risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] 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.

[0020] FIG1 is a diagram of an application environment of a security risk assessment method in one embodiment;

[0021] FIG2 is a schematic diagram of a flow chart of a security risk assessment method in one embodiment;

[0022] FIG3 is a schematic diagram of a risk matrix in one embodiment;

[0023] FIG4 is a schematic diagram of a flow chart of a security risk assessment method in another embodiment;

[0024] FIG5 is a schematic diagram of a flow chart of a security risk assessment method in yet another embodiment;

[0025] FIG6 is a block diagram of a security risk assessment device in one embodiment;

[0026] FIG. 7 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0027] 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.

[0028] The security risk assessment method provided in the embodiment of the present application can be applied in the application environment shown in FIG. 1 . Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, can be integrated on the server 104, or can be placed on the cloud or other network servers. The server 104 obtains a data set for security risk assessment, extracts security risk assessment indicators based on the data set, obtains security risk assessment indicators for each of the multiple security feature layers, performs statistical analysis on the security risk assessment indicators for each of the multiple security feature layers, obtains statistical analysis results, and according to the statistical analysis results, screens out multiple key indicators of security risk assessment from the security risk assessment indicators for each of the multiple security feature layers, performs security risk assessment analysis on the multiple key indicators of security risk assessment, and constructs a security risk assessment model. When a security risk assessment request for a user to be assessed is received through the terminal 102, the security risk assessment data of the user to be assessed is obtained, and the security risk assessment model is used to output a security risk assessment portrait of the user to be assessed.

[0029] 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.

[0030] In an exemplary embodiment, as shown in FIG2 , a security risk assessment method is provided, which can be executed by a terminal or a server alone, or by a terminal and a server in collaboration. The method is applied to the server 104 in FIG1 as an example for description, and includes the following steps 202 to 208. Among them:

[0031] Step 202: Acquire a data set for security risk assessment.

[0032] Among them, the data set used for security risk assessment refers to the data set collected when a security risk assessment is needed. For example, the data set used for security risk assessment can specifically be the security policies, procedures and operating guidelines related to various departments and levels within the enterprise, covering multiple business areas and job responsibilities. For another example, the data set used for security risk assessment can specifically be the security management requirements outside the enterprise, including laws and regulations, industry standards and local safety regulations. For another example, the data set used for security risk assessment can specifically be historical safety accident information, including accident type, accident cause, scope of impact and handling results. It can be understood that when the data set includes security management requirements outside the enterprise, it can ensure that the enterprise safety management system is consistent with the security management requirements outside the enterprise. When the data set includes historical safety accident information, it can provide actual case support for security risk assessment.

[0033] Exemplarily, when a security risk assessment is required, the server acquires a data set for the security risk assessment to construct a security risk assessment model using the data set.

[0034] Step 204 , extracting security risk assessment indicators based on the data set to obtain security risk assessment indicators for each of the multiple security feature layers.

[0035] Among them, the security risk assessment indicators refer to the indicators for assessing security risks, that is, the indicators that have a significant impact on assessing and managing personal safety risks. It is understandable that the security risk assessment indicators of different security feature layers are different. The security feature layer refers to the multiple dimensions that need to be comprehensively considered when conducting a security risk assessment to ensure comprehensive identification and assessment of possible security risks.

[0036] Exemplarily, after acquiring the data set, the server acquires multiple security feature layers, extracts security risk assessment indicators based on the data set, and obtains security risk assessment indicators for each of the multiple security feature layers.

[0037] In a specific application, after obtaining the data set, the server will first preprocess the data set, and then extract security risk assessment indicators from the preprocessed data set, and extract the security risk assessment indicators of multiple security feature layers from the preprocessed data set.

[0038] In a specific application, data preprocessing includes data cleaning, data standardization, and data normalization steps. Data cleaning is used to remove noise and outliers in the data and improve data quality. Data standardization ensures that the data is compared and analyzed under a unified dimension. Data normalization scales the data to a specific range to facilitate the subsequent extraction of security risk assessment indicators and the construction of security risk assessment models.

[0039] In a specific application, the multiple security feature layers obtained can be configured according to the actual application scenario, or can be obtained by classification using a historical data set. In a specific application, the method for obtaining multiple security feature layers can be: the server obtains a historical data set, and inputs the historical data in the historical data set into a pre-trained natural language model in sequence, obtains the security feature layers involved in the historical data output by the pre-trained natural language model, and counts the security feature layers involved in all historical data as multiple security feature layers. Among them, the pre-trained natural language model can be trained according to the actual application scenario. Specifically, it can be a question-and-answer model, which can answer the security feature layers involved based on the input historical data.

[0040] Step 206, statistical analysis is performed on the security risk assessment indicators of each of the multiple security feature layers to obtain statistical analysis results, and based on the statistical analysis results, multiple key indicators for security risk assessment are screened out from the security risk assessment indicators of each of the multiple security feature layers.

[0041] Among them, statistical analysis refers to the use of statistical methods to collect, process, analyze and interpret data in order to extract useful information, discover patterns, verify hypotheses or predict future trends. Through statistical analysis, we can draw conclusions from a large amount of data to support decision making. In this embodiment, statistical methods are used to analyze the security risk assessment indicators of each of the multiple security feature layers to obtain statistical analysis results, so that the security risk assessment indicators of each of the multiple security feature layers can be screened using the statistical analysis results to obtain multiple key indicators for security risk assessment.

[0042] Among them, the multiple key indicators of security risk assessment refer to a series of important factors used to identify, analyze and evaluate risks when conducting security risk assessment. It can be understood that in this embodiment, the multiple key indicators of security risk assessment are actually part of the security risk assessment indicators of each of the multiple security feature layers.

[0043] Exemplarily, the server performs statistical analysis on the security risk assessment indicators of each of the multiple security feature layers to obtain statistical analysis results, and based on the statistical analysis results, screens out multiple key indicators for security risk assessment from the security risk assessment indicators of each of the multiple security feature layers.

[0044] In specific applications, the statistical analysis may be specifically a correlation analysis. The server may perform a correlation analysis on the security risk assessment indicators of multiple security feature layers to determine the indicator correlation between the security risk assessment indicators of multiple security feature layers, that is, the statistical analysis results. Then, based on the indicator correlation, multiple key indicators for security risk assessment are screened out from the security risk assessment indicators of multiple security feature layers. Among them, indicator correlation refers to the relationship and influence between different security risk assessment indicators. These indicators usually do not exist in isolation, but are interconnected and influence each other. Understanding the correlation between these indicators is crucial to accurately assessing and managing security risks.

[0045] In a specific application, the indicator correlation can be specifically represented by the indicator correlation coefficient. The server can screen out multiple key indicators of security risk assessment from the security risk assessment indicators of multiple security feature layers through the indicator correlation coefficients between the security risk assessment indicators of multiple security feature layers.

[0046] In a specific application, the statistical analysis may be a regression analysis. The server may perform regression analysis on the security risk assessment indicators of each of the multiple security feature layers to determine the indicator influence relationship between the security risk assessment indicators of each of the multiple security feature layers, i.e., the statistical analysis result. Then, based on the indicator influence relationship, multiple key indicators of security risk assessment are screened out from the security risk assessment indicators of each of the multiple security feature layers. Among them, the indicator influence relationship refers to the interaction and influence between different security risk assessment indicators.

[0047] In a specific application, the indicator impact relationship can be specifically represented by the indicator causal relationship. Then, the server can screen out multiple key indicators of security risk assessment from the security risk assessment indicators of multiple security feature layers through the indicator causal relationship between the security risk assessment indicators of multiple security feature layers. Among them, the indicator causal relationship refers to the relationship between the change of one security risk assessment indicator causing the change of another security risk assessment indicator.

[0048] Step 208, perform security risk assessment analysis on multiple key indicators of security risk assessment, and construct a security risk assessment model. The security risk assessment model is used to output a security risk assessment portrait based on the input security risk assessment data of the user to be assessed.

[0049] Exemplarily, the server will perform security risk assessment analysis on multiple key indicators of security risk assessment, determine the security risk level of each key indicator, and then construct a security risk assessment model based on the security risk level of each key indicator. The constructed security risk assessment model can output a security risk assessment portrait based on the input security risk assessment data of the user to be assessed. Among them, the user to be assessed refers to the user who needs to undergo a security risk assessment. For example, the user to be assessed may specifically refer to an employee of a company who needs to undergo a security risk assessment. Security risk assessment data refers to the data required to perform a security risk assessment on the user to be assessed, which may specifically include information about the user to be assessed, job-related data, security management-related data, etc.

[0050] In specific applications, the server can use priority classification method, risk matrix method, critical value method and other methods to determine the security risk level of each key indicator. Among them, the priority classification method refers to the priority classification of multiple key indicators to determine the security risk level of each key indicator. It can be understood that the higher the priority of the key indicator, the higher the security risk level. The risk matrix method is a commonly used tool in risk management and assessment. It helps organizations identify, evaluate and prioritize risks by quantifying the possibility (probability) and impact (severity) of risks. This method is usually represented by a two-dimensional matrix, in which the horizontal axis represents the possibility of risk occurrence and the vertical axis represents the degree of impact after the risk occurs. By dividing the different levels of possibility and impact into several discrete levels, risks can be divided into different categories, which is convenient for identification and priority. In this embodiment, the risk matrix method can be used to construct a risk matrix of multiple key indicators, and then determine the security risk level of each key indicator. The critical value method refers to determining the security risk level of each key indicator by setting a critical value.

[0051] In a specific application, the priority of multiple key indicators can be divided into the following ways: first, the multiple key indicators are sorted based on their respective indicator correlations, and then the security risk level of each key indicator is determined according to the order of the key indicators. Among them, the key indicator with a higher correlation with other key indicators has a higher security risk level. In addition, the priorities of multiple key indicators can also be configured according to the actual application scenario.

[0052] The above-mentioned security risk assessment method, when a data set for security risk assessment is obtained, can accurately obtain security risk assessment indicators from the perspective of multiple security feature layers by extracting security risk assessment indicators based on the data set, and obtain statistical analysis results by statistically analyzing the security risk assessment indicators of each of the multiple security feature layers. According to the statistical analysis results, multiple key indicators of security risk assessment are screened out from the security risk assessment indicators of each of the multiple security feature layers, and multiple key indicators that can be used for accurate security risk assessment can be obtained by further screening. Then, security risk assessment analysis can be performed on multiple key indicators of security risk assessment to achieve the construction of a security risk assessment model, so that the security risk assessment model can be used to output a security risk assessment portrait according to the input security risk assessment data of the user to be assessed, so as to achieve accurate security risk assessment. In the whole process, by using the security risk assessment indicators of multiple security feature layers extracted from the data set to construct a security risk assessment model, various factors affecting personal safety can be comprehensively analyzed, thereby achieving accurate assessment of security risks and improving the accuracy of security risk assessment.

[0053] In an exemplary embodiment, the security risk assessment method further includes:

[0054] When receiving a security risk assessment request for a user to be assessed, obtaining security risk assessment data of the user to be assessed, and obtaining indicator information corresponding to each of a plurality of key indicators of the security risk assessment from the security risk assessment data;

[0055] The indicator information corresponding to multiple key indicators of security risk assessment is input into the security risk assessment model to obtain a security risk assessment portrait of the user to be assessed.

[0056] Among them, the user to be evaluated refers to the user who needs to be evaluated for safety risk assessment. For example, the user to be evaluated may specifically refer to the enterprise employee who needs to be evaluated for safety risk assessment. Safety risk assessment data refers to the data required for the safety risk assessment of the user to be evaluated, which may specifically include the user information to be evaluated, operation-related data, safety management-related data, etc. For example, the user information to be evaluated may specifically include the user's age, length of service, health status and other basic information, occupational health status such as occupational disease incidence, health examination coverage, personal behavior characteristics such as the number of illegal operations, safety compliance, use of personal protective equipment, and historical accident records such as the number of accidents, accident types, and accident severity. Operation-related data may specifically include operation environment data such as operation area, operation conditions, and operation time. Safety management-related data may specifically include safety training records, safety equipment use, safety culture-related data, safety supervision information, emergency response capabilities, safety performance indicators, safety technology application, safety investment data, safety regulations compliance, safety information management, safety psychological status, safety environment data, etc.

[0057] Among them, the indicator information of the key indicator refers to the specific information used to measure the key indicator. For example, the indicator information of the key indicator can specifically refer to the specific value used to measure the key indicator, or specific descriptive information. It can be understood that the indicator information of the key indicator is usually used to evaluate the performance of the user to be evaluated on the key indicator. The security risk assessment portrait refers to the result obtained after the security risk assessment of the user to be evaluated, including the corresponding indicator information of multiple key indicators, risk assessment results and improvement suggestions. For example, the security risk assessment portrait can specifically be a personal safety portrait of the user to be evaluated.

[0058] Exemplarily, when a security risk assessment request for a user to be assessed is received, the server obtains the security risk assessment data of the user to be assessed, and uses multiple key indicators of the security risk assessment to extract indicator information corresponding to each of the multiple key indicators of the security risk assessment from the security risk assessment data, and then inputs the indicator information corresponding to each of the multiple key indicators of the security risk assessment into the security risk assessment model. The security risk assessment model is used to analyze the indicator information corresponding to each of the multiple key indicators to obtain a security risk assessment portrait of the user to be assessed.

[0059] In specific applications, after the indicator information corresponding to multiple key indicators of security risk assessment is input into the security risk assessment model, the security risk assessment model will first evaluate the security risk level of each key indicator, and compare the indicator critical value and indicator information of each key indicator to determine whether there is a potential security risk, and then combine the security risk level and comparison results of each key indicator to output a security risk assessment portrait.

[0060] In a specific application, the security risk levels of multiple key indicators are pre-stored in the security risk assessment model. After the indicator information corresponding to the multiple key indicators of the security risk assessment is input into the security risk assessment model, the security risk assessment model can directly determine the security risk level of each input key indicator through the stored security risk levels of the multiple key indicators.

[0061] In a specific application, for example, the safety risk assessment data of the user to be evaluated may include: personal basic information, work environment data, safety training records, safety equipment usage, historical accident records, safety culture-related data, safety supervision information, emergency response capabilities, safety performance indicators, safety technology application, safety investment data, safety law and regulation compliance, occupational health status, safety information management, safety psychological status, safety environment data, personal behavior characteristics, safety responsibility allocation and fulfillment, safety innovation, environmental monitoring data, etc.

[0062] In a specific application, by obtaining the indicator information corresponding to multiple key indicators from the above-mentioned security risk assessment data, and inputting the indicator information corresponding to multiple key indicators into the security risk assessment model, the security risk assessment portrait of the user to be assessed can specifically include the indicator information corresponding to multiple key indicators, risk assessment results and improvement suggestions.

[0063] Among them, the corresponding indicator information of multiple key indicators can be specifically as follows: 1. Personal basic information characteristic layer: age 30 years old, 5 years of work experience, and good health; 2. Working environment characteristic layer: the working area is production line area A, the working conditions are high temperature and high humidity, and the working hours are from 8 am to 8 pm; 3. Safety training characteristic layer: received 3 safety trainings in the past year, and the training effect was good; 4. Safety equipment characteristic layer: personal protective equipment is equipped with a 100% rate, a 95% utilization rate, and a 98% intact rate; 5. Historical accident characteristic layer: no accident record in the past five years; 6. Safety culture characteristic layer: strong safety awareness and active participation in safety activities; 7. Safety supervision characteristic layer: receive a safety inspection once a month, and the supervision effect is good; 8. Emergency response characteristic layer: familiar with emergency plans and have participated in two emergency drills; 9. Safety performance characteristic layer: safety target completion rate is 100%, the assessment results are excellent, and there is no penalty record; 10. Safety technology characteristic layer: the production line is updated The frequency is high and the application effect is good; 11. Safety investment characteristic layer: sufficient investment in personal protective equipment and good training investment; 12. Safety regulations compliance characteristic layer: familiar with relevant regulations and no violation records; 13. Occupational health characteristic layer: the risk of occupational diseases in the position is low, and regular health checks are carried out; 14. Safety information management characteristic layer: complete information collection, accurate processing, and timely transmission; 15. Safety psychology characteristic layer: moderate stress level, high satisfaction, and good loyalty; 16. Safety environment characteristic layer: high frequency of environmental monitoring, good risk assessment, and improvement measures in place; 17. Personal behavior characteristic layer: no record of illegal operations, high safety compliance, and good use of personal protective equipment; 18. Safety responsibility characteristic layer: clear responsibility allocation, good performance, and strict accountability; 19. Safety innovation characteristic layer: the team has innovative projects and a high rate of results conversion; 20. Environmental monitoring characteristic layer: high frequency of environmental monitoring, accurate monitoring data, and good risk warning capabilities.

[0064] In this embodiment, when a security risk assessment request is received, by obtaining the security risk assessment data of the user to be assessed, indicator information corresponding to multiple key indicators of the security risk assessment can be extracted from the security risk assessment data, and then the security risk assessment model can be used to implement the security risk assessment of the user to be assessed based on the extracted indicator information, and a security risk assessment portrait can be obtained, which can improve the accuracy of the security risk assessment.

[0065] In an exemplary embodiment, security risk assessment indicators are extracted based on the data set to obtain security risk assessment indicators for each of the multiple security feature layers, including:

[0066] Based on multiple security feature layers, the data set is classified to obtain data subsets of each of the multiple security feature layers;

[0067] For each security feature layer, feature extraction is performed on a data subset of the security feature layer to obtain a security risk assessment indicator of the security feature layer.

[0068] For example, the server will classify the data set according to its content and attributes based on multiple security feature layers to ensure that the data points in the data set are classified into the corresponding security feature layer, and obtain the data subsets of multiple security features. For each security feature layer, the server will extract features from the data subset of the security feature layer to obtain the security risk assessment index of the security feature layer.

[0069] In specific applications, the server can classify the data set through a pre-trained classification model to obtain data subsets for multiple security feature layers. The pre-trained classification model can be trained according to the actual application scenario, and the classification categories involved are multiple security feature layers. When classifying the data set, the server will split the data in the data set into input data that can be recognized by the pre-trained classification model, and then input the split data into the pre-trained classification model to obtain the classification results of the split data. Based on the classification results of each split data, the data subsets for multiple security feature layers can be obtained. The splitting method can be configured according to the actual application scenario. For example, the splitting method can be specifically to split the data in the data set according to delimiters (such as periods, commas, semicolons, etc.) to obtain the split data.

[0070] In specific applications, the purpose of feature extraction is to extract the most valuable information for security risk assessment from a large amount of data. In this embodiment, principal component analysis, linear discriminant analysis, automatic encoder, etc. can be used to identify and extract key information in the data subset of the security feature layer as the security risk assessment indicator of the security feature layer.

[0071] Among them, principal component analysis is a statistical method that transforms a set of possibly correlated variables into a set of linearly uncorrelated variables through orthogonal transformation. These new variables are called principal components. In safety risk assessment, principal component analysis is used for dimensionality reduction, that is, to extract the most important principal components from high-dimensional data, which can capture most of the variability in the data. In this way, redundant information in the original data set can be removed, and those features that have the greatest impact on safety risk assessment can be retained. For example, in safety inspection records, there may be multiple indicators (such as temperature, humidity, chemical concentration, etc.) that are closely related to personal safety. Principal component analysis can help identify which of these indicators are the most important and use them as key features for subsequent risk assessment.

[0072] Among them, linear discriminant analysis is a supervised learning method used to find a linear combination of features that can maximize the differences between different categories while minimizing the differences within the same category. In safety risk assessment, linear discriminant analysis can be used for classification problems, such as distinguishing between safe and unsafe working environments. Through linear discriminant analysis, those features that show the greatest difference in performance under different safety conditions can be identified, which are crucial for distinguishing different safety levels. For example, in safety operating procedures, certain operating steps may show significant differences in the frequency of accidents. Linear discriminant analysis can help identify these steps as key features, so that they can be given special attention in training and monitoring.

[0073] Among them, the autoencoder is a deep learning model, which is usually used for unsupervised learning tasks. Its purpose is to learn the effective representation (encoding) of data and then reconstruct the input data through the decoding process. In security risk assessment, the autoencoder can be used to extract key features from large amounts of complex data. The autoencoder automatically learns the important features in the data through the training process, and these features can represent the inherent structure and pattern of the original data. For example, in safety training records, the autoencoder can identify those features that perform well in the evaluation of training effectiveness, such as the degree of employees' mastery of specific safety knowledge. These features can be used to optimize training content and methods and improve training effectiveness.

[0074] In a specific application, the multiple security feature layers and the respective security risk assessment indicators of the multiple security feature layers in this embodiment can be as follows: personal basic information feature layer, including key indicators of age, length of service, and health status; working environment feature layer, including key indicators of working area, working conditions, and working time; safety training feature layer, including key indicators of number of trainings, training effects, and training content; safety equipment feature layer, including key indicators of equipment allocation rate, equipment utilization rate, and equipment integrity rate; historical accident feature layer, including key indicators of number of accidents, accident types, and accident severity; safety culture feature layer, including key indicators of safety awareness, safety behavior, and safety atmosphere; safety supervision feature layer, including key indicators of supervision frequency, supervision effects, and supervision measures; emergency response feature layer, including key indicators of emergency plans, emergency drills, and emergency disposal; safety performance feature layer, including key indicators of safety target completion rate, safety assessment results, and safety rewards and penalties; safety technology feature layer, including key indicators of technology update frequency, technology application effects, and technology maintenance status; safety investment feature layer , including key indicators of safety facility investment, safety training investment, and safety R&D investment; the safety law compliance characteristic layer, including key indicators of timeliness of law update, strictness of law enforcement, and number of law violations; the occupational health characteristic layer, including key indicators of occupational disease incidence, health examination coverage, and occupational health improvement measures; the safety information management characteristic layer, including key indicators of information collection integrity, information processing accuracy, and information transmission timeliness; the safety psychological characteristic layer, including key indicators of employee stress level, employee satisfaction, and employee loyalty; the safety environment characteristic layer, including key indicators of environmental monitoring frequency, environmental risk assessment, and environmental improvement measures; the personal behavior characteristic layer, including key indicators of the number of illegal operations, safety compliance, and use of personal protective equipment; the safety responsibility characteristic layer, including key indicators of clarity of responsibility allocation, responsibility fulfillment, and strictness of responsibility accountability; the safety innovation characteristic layer, including key indicators of the number of innovation projects, innovation results conversion rate, and innovation incentive mechanism; the environmental monitoring characteristic layer, including key indicators of environmental monitoring frequency, monitoring data accuracy, and environmental risk early warning capabilities.

[0075] In this embodiment, by classifying the data set, it is possible to accurately divide the data in the data set and obtain data subsets for multiple security feature layers. Furthermore, at each security feature layer, feature extraction can be performed on the data subset of the security feature layer to obtain security risk assessment indicators for the security feature layer.

[0076] In an exemplary embodiment, statistical analysis is performed on the security risk assessment indicators of each of the multiple security feature layers to obtain statistical analysis results. According to the statistical analysis results, multiple key indicators for security risk assessment are screened out from the security risk assessment indicators of each of the multiple security feature layers, including:

[0077] Performing correlation analysis on the security risk assessment indicators of the multiple security feature layers to determine the indicator correlation between the security risk assessment indicators of the multiple security feature layers;

[0078] Performing regression analysis on the security risk assessment indicators of each of the multiple security feature layers to determine the indicator influence relationship between the security risk assessment indicators of each of the multiple security feature layers;

[0079] According to the indicator correlation and indicator impact relationship, multiple key indicators for security risk assessment are screened out from the security risk assessment indicators of multiple security feature layers.

[0080] Among them, indicator correlation refers to the mutual relationship and influence between different security risk assessment indicators. These indicators usually do not exist in isolation, but are interconnected and influence each other. Understanding the correlation between these indicators is crucial for accurately assessing and managing security risks. Indicator influence relationship refers to the interaction and influence between different security risk assessment indicators. Indicator causality refers to the relationship in which the change of one security risk assessment indicator causes the change of another security risk assessment indicator.

[0081] Exemplarily, the statistical analysis includes correlation analysis and regression analysis, and the statistical analysis results include indicator correlation and indicator influence relationship. The server can determine the indicator correlation between the security risk assessment indicators of multiple security feature layers by performing correlation analysis on the security risk assessment indicators of multiple security feature layers. The server can determine the indicator influence relationship between the security risk assessment indicators of multiple security feature layers by performing regression analysis on the security risk assessment indicators of multiple security feature layers. Furthermore, the server can combine the indicator correlation and indicator influence relationship to screen out multiple key indicators for security risk assessment from the security risk assessment indicators of multiple security feature layers.

[0082] In specific applications, the correlation of indicators can be specifically represented by the indicator correlation coefficient. The server can evaluate the linear relationship strength and direction between the security risk assessment indicators of multiple security feature layers by calculating the indicator correlation coefficient between them. The indicator influence relationship can be specifically represented by the indicator causal relationship. The server can obtain the indicator causal relationship between the security risk assessment indicators of multiple security feature layers by constructing a multivariate regression model, using a stepwise regression method, and evaluating the goodness of fit of the regression model.

[0083] In a specific application, the indicator correlation coefficient can specifically be the Pearson correlation coefficient, which is a statistic used to measure the degree of linear correlation between two variables. In this embodiment, it is a statistic used to measure the degree of linear correlation between two security risk assessment indicators. The value range of the Pearson correlation coefficient is between -1 and 1. When the correlation coefficient is 1, it indicates that there is a complete positive correlation between the two variables, that is, when one variable increases, the other variable also increases. When the correlation coefficient is -1, it indicates that there is a complete negative correlation between the two variables, that is, when one variable increases, the other variable decreases. When the correlation coefficient is 0, it indicates that there is no linear correlation between the two variables. It should be noted that the Pearson correlation coefficient can only measure the strength of the linear relationship, and cannot explain the causal relationship. Therefore, it is also necessary to consider the indicator causal relationship between the security risk assessment indicators of multiple security feature layers to screen out multiple key indicators for security risk assessment.

[0084] In a specific application, constructing a multivariate regression model means taking one safety risk assessment indicator as the dependent variable and other safety risk assessment indicators as independent variables to evaluate the causal relationship between them. The stepwise regression method refers to screening out the independent variables that have a significant impact on the dependent variable through stepwise regression to eliminate the problem of multicollinearity. Evaluating the goodness of fit of the regression model means evaluating the fit of the regression model through the model fitting index value to ensure that the model can better explain the changes in the dependent variable. Among them, the model fitting index value can be specifically the R² value, which is also called the coefficient of determination, which is an indicator used in statistics to measure the quality of the regression model. It indicates the degree of fit of the model to the data, that is, the percentage of the dependent variable variation that the model can explain.

[0085] In a specific application, after obtaining the indicator correlation coefficients and indicator causal relationships between the security risk assessment indicators of each of the multiple security feature layers, the server can screen out highly correlated security risk assessment indicator pairs and security risk assessment indicators that have a causal relationship with other security risk assessment indicators from the security risk assessment indicators of each of the multiple security feature layers as multiple key indicators for security risk assessment. Among them, a highly correlated security risk assessment indicator pair refers to a security risk assessment indicator pair whose indicator correlation coefficient is greater than a correlation coefficient threshold. A security risk assessment indicator that has a causal relationship with other security risk assessment indicators refers to a security risk assessment indicator that serves as an independent variable of at least N security risk assessment indicators. Among them, N can be configured according to the actual application scenario and is any positive integer.

[0086] In a specific application, the server can construct a correlation coefficient matrix based on the indicator correlation coefficients between the security risk assessment indicators of each of the multiple security feature layers to visualize the correlation between the security risk assessment indicators of each of the multiple security feature layers and identify highly correlated security risk assessment indicator pairs. Furthermore, the server can also determine whether the indicator correlation of the security risk assessment indicator pair is statistically significant through a significance test (such as a t-test or an F-test) to exclude the indicator correlation caused by accidental factors. That is, after determining the highly correlated security risk assessment indicator pair, the server can further test the highly correlated security risk assessment indicator pair through a significance test to exclude the indicator correlation caused by accidental factors.

[0087] In this embodiment, by determining the indicator correlation and indicator influence relationship between the security risk assessment indicators of multiple security feature layers, the indicator correlation and indicator influence relationship can be utilized to consider both linear relationship and causal relationship, and the security risk assessment indicators of multiple security feature layers can be screened to obtain multiple key indicators for accurate security risk assessment.

[0088] In an exemplary embodiment, performing security risk assessment analysis on multiple key indicators of security risk assessment and constructing a security risk assessment model includes:

[0089] For each key indicator of the safety risk assessment, determine the personal safety impact level and probability of occurrence of the key indicator;

[0090] Construct a risk matrix based on the personal safety impact level and probability of occurrence of each key indicator;

[0091] Based on the risk matrix, determine the safety risk level of each key indicator;

[0092] Build a security risk assessment model based on the security risk level of each key indicator.

[0093] Exemplarily, for each key indicator of security risk assessment, the server determines the personal safety impact level and occurrence probability of the key indicator, constructs a risk matrix based on the personal safety impact level and occurrence probability of each key indicator, divides the risk matrix into regions, determines multiple security risk level regions in the risk matrix, determines the security risk level of each key indicator based on the multiple security risk level regions, and constructs a security risk assessment model based on the security risk level of each key indicator.

[0094] In specific applications, the personal safety impact level refers to the degree of impact of key indicators on personal safety, which can be determined through expert evaluation, accident consequence analysis, hierarchical analysis method and other methods. The probability of occurrence refers to the frequency of occurrence of key indicators in the past period of time. In this embodiment, the past period of time can be configured according to the actual application scenario. For example, the past period of time can be specifically the past three months, the past six months, etc. The probability of occurrence can be obtained using historical accident data statistics, and can be specifically quantified through data such as the number of accidents in the historical accident data, the number of violations, or the number of problems found in the inspection.

[0095] In specific applications, the personal safety impact level can be specifically quantified into multiple levels. After obtaining the probability of occurrence of each key indicator, the server will quantify the probability of occurrence of each key indicator and quantify it into multiple levels that are the same as the personal safety impact level. After the quantification is completed, the server will cross-analyze the personal safety impact level and the quantified probability of occurrence to form a two-dimensional matrix, namely the risk matrix. Each key indicator can be placed in a corresponding position in the risk matrix based on its personal safety impact level and probability of occurrence.

[0096] In a specific application, the personal safety impact level can be specifically three levels, including low level, medium level and high level. The low level means that the impact is small and generally does not cause serious injury or loss. The medium level means that the impact is medium and may cause a certain degree of injury or loss. The high level means that the impact is serious and may cause major injury or loss. The probability of occurrence can also be specifically three levels, including low level, medium level and high level. The low level means that the probability of occurrence is low, usually below 10%. The medium level means that the probability of occurrence is medium, usually between 10% and 50%. The high level means that the probability of occurrence is high, usually above 50%.

[0097] In a specific application, the security risk level can also be specifically divided into three levels, including a low risk level, a medium risk level, and a high risk level. When dividing the risk matrix into regions and determining multiple security risk level regions in the risk matrix, the server can designate regions where both the personal safety impact level and the probability of occurrence are low as low risk level regions, and regions where one of the personal safety impact level and the probability of occurrence is low and the other is medium or high, and regions where both the personal safety impact level and the probability of occurrence are medium as medium risk level regions, and the remaining regions as high risk level regions.

[0098] In a specific application, for example, the constructed risk matrix can be shown in Figure 3, in which different areas represent different risk levels. Among them, the low-risk level area refers to an area with a low probability of occurrence and a low level of personal safety impact, the medium-risk level area refers to an area with a medium probability of occurrence and a medium level of personal safety impact, and the high-risk level area refers to an area with a high probability of occurrence and a high level of personal safety impact. It can be understood that in this embodiment, the risk matrix method provides an intuitive visual tool to help decision makers quickly identify and prioritize high-risk key indicators. The grading based on the level of personal safety impact and the probability of occurrence provides an initial priority assessment for each key indicator. The grading of the level of personal safety impact and the probability of occurrence, as well as the risk matrix method, are used simultaneously. Combining these two methods can more comprehensively evaluate and manage safety risks and ensure that key indicators receive proper attention and control.

[0099] In this embodiment, by determining the personal safety impact level and occurrence probability of each key indicator, a risk matrix is ​​constructed using the personal safety impact level and occurrence probability of each key indicator. The risk matrix can be used to accurately determine the safety risk level of each key indicator, and then the safety risk assessment model can be constructed based on the safety risk level of each key indicator.

[0100] In an exemplary embodiment, constructing a security risk assessment model according to the security risk level of each key indicator includes:

[0101] Obtain the critical value of each key indicator, and build a security risk assessment model based on the critical value of each key indicator and the security risk level.

[0102] The critical value of an indicator refers to a value determined according to the nature of the key indicator and the security requirements and used to determine whether there is a security risk. For example, the critical value of an indicator can be a specific interval, which represents a safety range. For another example, the critical value of an indicator can also be a single value, such as the highest value, which represents the upper limit of the safety range. For another example, the critical value of an indicator can also be a specific minimum value, which represents the lower limit of the safety range.

[0103] For example, the server will obtain the critical value of each key indicator, and build a security risk assessment model based on the critical value of each key indicator and the security risk level. That is, the critical value of each key indicator and the security risk level are stored in the security risk assessment model. When the security risk assessment data of the user to be assessed is input into the security risk assessment model, the security risk assessment model can output a security risk assessment portrait. Among them, the critical value of the indicator can be configured according to the actual application scenario.

[0104] In specific applications, if the key indicator is temperature or pressure, the critical value of the indicator is an area, which indicates a safe range. Any value outside this range may indicate a potential risk. If the key indicator is an environmental monitoring indicator, such as the concentration of harmful substances in the air, the critical value of the indicator is an area, which indicates a safe range. Concentrations below or above this range may indicate potential risks. If the key indicator is the number of safety training sessions or the utilization rate of safety equipment, the critical value of the indicator is a specific value, indicating whether a certain target level has been reached or not. For example, if the minimum requirement for safety training is at least once a year, then the critical value is 1 time / year. If the utilization rate target of safety equipment is 95%, then the critical value is 95%.

[0105] In this embodiment, by obtaining the indicator critical value of each key indicator, the indicator critical value of each key indicator and the security risk level can be used to build a security risk assessment model.

[0106] In an exemplary embodiment, as shown in FIG4 , a safety risk assessment method of the present application is described by a flowchart, and the safety risk assessment method specifically includes the following steps:

[0107] Step 402, obtaining a data set for security risk assessment;

[0108] Step 404, classifying the data set based on the multiple security feature layers to obtain data subsets of the multiple security feature layers;

[0109] Step 406, for each security feature layer, extracting features from a data subset of the security feature layer to obtain a security risk assessment index of the security feature layer;

[0110] Step 408, performing correlation analysis on the security risk assessment indicators of the multiple security feature layers to determine the indicator correlation between the security risk assessment indicators of the multiple security feature layers;

[0111] Step 410, performing regression analysis on the security risk assessment indicators of each of the multiple security feature layers to determine the indicator influence relationship between the security risk assessment indicators of each of the multiple security feature layers;

[0112] Step 412, based on the indicator correlation and the indicator influence relationship, multiple key indicators for security risk assessment are screened out from the security risk assessment indicators of each of the multiple security feature layers;

[0113] Step 414, for each key indicator of the security risk assessment, determine the personal safety impact level and occurrence probability of the key indicator;

[0114] Step 416, constructing a risk matrix according to the personal safety impact level and occurrence probability of each key indicator;

[0115] Step 418, determining the security risk level of each key indicator based on the risk matrix;

[0116] Step 420, obtaining the critical value of each key indicator, and constructing a security risk assessment model based on the critical value of each key indicator and the security risk level;

[0117] Step 422, when a security risk assessment request for the user to be assessed is received, security risk assessment data of the user to be assessed is obtained, and indicator information corresponding to each of a plurality of key indicators of the security risk assessment is obtained from the security risk assessment data;

[0118] In step 424, the indicator information corresponding to each of the multiple key indicators of the security risk assessment is input into the security risk assessment model to obtain a security risk assessment portrait of the user to be assessed.

[0119] In an exemplary embodiment, the security risk assessment method of the present application is taken as an example and applied to personal safety risk assessment in enterprise operations to illustrate the security risk assessment method of the present application. As shown in FIG5 , the security risk assessment method specifically includes the steps of collecting information sets, data fusion, feature construction, key indicator analysis, model construction, and decision support. Each step is described below.

[0120] First, the server collects data sets for security risk assessment.

[0121] In specific applications, data sets include safety regulations and systems, safety operating procedures, safety training records, safety inspection records, accident hazard investigation records, accident handling records, etc.

[0122] Secondly, the server will deeply integrate the information set through data preprocessing, feature extraction and data fusion technology.

[0123] In specific applications, data preprocessing includes data cleaning, data standardization, and data normalization steps. Data cleaning is used to remove noise and outliers in the data and improve data quality; data standardization ensures that the data is compared and analyzed under a unified dimension; and data normalization scales the data to a specific range. After data preprocessing is completed, the server will use feature extraction technology, including principal component analysis, linear discriminant analysis, and autoencoders to identify and extract key information in the data. Finally, through data fusion technology, including decision layer fusion, feature layer fusion, and data layer fusion, the preprocessed data is deeply fused with the extracted features to form a comprehensive data set.

[0124] It is understandable that data preprocessing can effectively remove redundancy and inconsistency in data, and improve data availability and accuracy. Data cleaning can identify and remove erroneous, missing or abnormal data points to avoid these data from having a negative impact on subsequent analysis. Data standardization and normalization ensure that data from different sources and dimensions can be compared and analyzed under the same standard, which facilitates subsequent processing.

[0125] In specific applications, data fusion can further integrate data from different sources and types to obtain more comprehensive information. Decision-level fusion integrates the views of different decision makers through decision support systems, feature-level fusion integrates the results obtained by different feature extraction methods to obtain a more comprehensive feature description, and data-level fusion directly fuses the original data to obtain a richer data representation.

[0126] In specific applications, multiple safety feature layers may include a personal basic information feature layer, a working environment feature layer, a safety training feature layer, a safety equipment feature layer, a historical accident feature layer, a safety culture feature layer, a safety supervision feature layer, an emergency response feature layer, a safety performance feature layer, a safety technology feature layer, a safety investment feature layer, a safety law compliance feature layer, an occupational health feature layer, a safety information management feature layer, a safety psychology feature layer, a safety environment feature layer, a personal behavior feature layer, a safety responsibility feature layer, a safety innovation feature layer, an environmental monitoring feature layer, and the like.

[0127] Among them, the personal basic information feature layer can help enterprises understand the background information of employees, so as to formulate training plans and safety measures in a targeted manner. The working environment feature layer helps to identify and improve potential dangerous areas and ensure that employees work in a safe environment. The analysis results of the safety training feature layer and the safety equipment feature layer can guide enterprises to optimize training content and improve equipment utilization, thereby improving employees' safety awareness and operating skills. The historical accident feature layer provides an important basis for analyzing the causes of accidents and formulating preventive measures. The comprehensive analysis of the safety culture feature layer, the safety supervision feature layer and the emergency response feature layer helps enterprises establish a positive safety culture atmosphere, strengthen supervision, and improve emergency response capabilities. The safety performance feature layer, the safety technology feature layer and the safety investment feature layer can help enterprises evaluate the benefits of safety investment and ensure the effectiveness of technology updates and applications. The analysis results of the safety regulations compliance feature layer, the occupational health feature layer and the safety information management feature layer can help enterprises ensure compliance with regulations, pay attention to employees' occupational health, and improve the efficiency of safety information management. The safety psychology feature layer, the safety environment feature layer and the personal behavior feature layer help enterprises understand employees' psychological state, working environment and behavioral habits, so as to take corresponding measures. The analysis results of the safety responsibility characteristic layer and the safety innovation characteristic layer can help enterprises clarify the allocation of safety responsibilities, strengthen accountability, and encourage safety innovation, thereby continuously improving the company's safety management level.

[0128] It is understandable that multiple security feature layers can fully cover all aspects of enterprise security management, thereby ensuring the accuracy and reliability of the assessment results. By comprehensively analyzing these feature layers and key indicators, enterprises can obtain a comprehensive personal safety portrait, providing strong support for the formulation of effective security strategies and improvement measures. Furthermore, by comprehensively using these security feature layers and security risk assessment indicators, enterprises can not only comprehensively evaluate and improve the current security situation, but also prepare for possible security risks in the future, ensure the personal safety of employees, and promote the sustainable development of the enterprise.

[0129] Secondly, the server will build a personal safety portrait with multiple security feature layers based on a diversified indicator quantification method. Each security feature layer contains at least one quantifiable security risk assessment indicator.

[0130] Secondly, the server will conduct a comprehensive analysis including statistical analysis on multiple security risk assessment indicators of the security feature layer, extract key indicators of personal safety portraits and form a set of targeted calculation methods.

[0131] In specific applications, the comprehensive analysis can specifically include the following steps: S1: independently analyze the security risk assessment indicators of each security feature layer to evaluate the degree and trend of its impact on personal safety; S2: explore the interaction and impact of security risk assessment indicators between different security feature layers through correlation analysis and regression analysis statistical methods; S3: use data mining technology, including cluster analysis and association rule mining, to identify potential risk groups and high-risk behavior patterns; S4: verify and correct the analysis results in combination with expert experience and actual cases to ensure the accuracy and practicality of the analysis, S5: integrate the analysis results to form a personal safety portrait of each employee and employee group, including quantitative key indicator values, as well as qualitative risk assessments and improvement suggestions.

[0132] It is understandable that a comprehensive and multi-dimensional perspective can be provided through all-round analysis to examine all aspects of enterprise safety management. Through this analysis method, enterprises can not only identify current problems and potential risk points, but also predict possible safety hazards in the future, so as to take preventive measures and avoid accidents.

[0133] Based on the results of the comprehensive analysis, enterprises can formulate personalized safety improvement plans and implement differentiated safety management strategies for different employee groups and working environments. For example, for high-risk groups, enterprises can increase the frequency of safety training, provide more targeted personal protective equipment, and strengthen daily supervision and emergency drills. Enterprises can also continuously optimize the safety management system. By regularly reviewing and updating key indicators, enterprises can ensure that the safety management system keeps pace with the development of the enterprise and changes in the external environment, thereby improving the adaptability and flexibility of safety management.

[0134] Through comprehensive analysis, enterprises can also establish a data-driven safety decision-making mechanism: by collecting and analyzing a large amount of safety data, enterprises can more scientifically evaluate the return on safety investment, reasonably allocate resources, and ensure the effectiveness of safety investment. It can also promote the construction of safety culture within the enterprise: by sharing the analysis results with employees, enterprises can improve employees' safety awareness, encourage employees to actively participate in safety management, and form a safety atmosphere of full participation. It can also help enterprises establish a continuously improved safety performance evaluation system: by regularly evaluating safety performance, enterprises can promptly discover and solve existing problems, continuously optimize the safety management system, and thus achieve continuous improvement of the enterprise's safety management level.

[0135] In specific applications, the calculation methods can be weighted average method based on weights, scoring method based on rules, prediction method based on models, etc. The weighted average method based on weights can ensure that the evaluation results are more in line with the actual situation of the enterprise by assigning appropriate weights to different key indicators. The scoring method based on rules can quantitatively score each indicator according to the safety standards and specifications set by the enterprise, thereby providing an intuitive evaluation result. The prediction method based on models uses historical data and statistical analysis to predict possible safety risks in the future, providing a scientific basis for enterprises to formulate preventive measures.

[0136] Secondly, based on key indicators, the server will use priority classification method, risk matrix method, critical value method, and continuous comparison method to build a security risk assessment model to comprehensively assess the company's personal safety status.

[0137] In specific applications, the way to construct a safety risk assessment model is as follows: first, use the risk matrix method to conduct a safety risk assessment on the key indicators of each safety feature layer. By cross-analyzing the possibility of the occurrence of key indicators (i.e., the probability of occurrence) and the severity of the impact of the indicators on personal safety (i.e., the level of personal safety impact), a two-dimensional matrix is ​​formed. According to different areas in the matrix, the risks are divided into three levels: high, medium, and low, so as to determine the safety risk level of each key indicator. Secondly, according to historical data and industry standards, one or more critical values ​​are set for each key indicator. When the indicator value exceeds the critical value, it indicates that there is a potential safety risk. Among them, the setting of the critical value needs to be combined with the actual situation of the enterprise and the opinions of experts to ensure its scientificity and practicality. Finally, the continuous comparison method can also be used to dynamically monitor the key indicators. By regularly collecting and analyzing data, comparing the difference between the current indicator value and the historical data, abnormal fluctuations in the indicator value can be discovered in time, thereby warning of potential safety risks. In addition, the server can also determine the priority of key indicators of each security feature layer based on the enterprise security policy and historical accident data. The priorities are divided according to the severity and frequency of the impact of the indicators on personal safety, and the indicators are divided into three levels: high, medium and low. Among them, high-priority indicators need to be monitored and managed, medium-priority indicators need to be kept under attention, and low-priority indicators can be appropriately relaxed in management.

[0138] The above security risk assessment model can provide a structured and systematic approach to assessing and managing enterprise security risks. This model not only takes into account the interaction between different security feature layers, but also responds to changes in security risks in a timely manner through dynamic monitoring and early warning mechanisms, thereby providing real-time security decision support for enterprises.

[0139] At the same time, the model is customizable: enterprises can adjust the key indicators and weights in the model according to their own industry characteristics, scale and safety needs to ensure the pertinence and applicability of the evaluation results. The model is easy to understand and operate: through the risk matrix method and continuous comparison method, enterprises can intuitively identify and understand the safety risk level of each key indicator, which is convenient for management and employees to quickly grasp and take corresponding safety measures. The model is preventive: by predicting possible safety risks in the future, enterprises can take preventive measures in advance to avoid or reduce the occurrence of accidents, thereby reducing potential economic losses and reputation risks.

[0140] Finally, the server will provide decision-making basis for the enterprise based on the security risk assessment results output by the security risk assessment model.

[0141] In specific applications, the decision-making basis includes the following: First, according to the risk assessment results of the security risk assessment model, formulate targeted risk control measures and improvement plans; second, continuously optimize the risk management system through the evaluation of the implementation effect of risk control measures; then, establish a risk information feedback mechanism to ensure that the risk assessment results can be delivered to relevant departments and personnel in a timely manner; finally, regularly review and update the security risk assessment model to adapt to changes in the internal and external environment of the enterprise and ensure the continued effectiveness and applicability of the security risk assessment method.

[0142] The decision-making basis provided above can provide a scientific and systematic decision-making support framework for enterprises. Through the evaluation results based on the safety risk assessment model, enterprises can clearly identify the safety risk points that need to be dealt with first, so as to formulate risk control measures and improvement plans in a targeted manner. This not only helps to improve the efficiency of enterprise safety management, but also ensures that resources are reasonably allocated to avoid blind investment and waste.

[0143] Among them, implementation effect evaluation is a key link to ensure the effectiveness of risk control measures. By regularly evaluating the implementation effect of measures, enterprises can promptly discover and correct deficiencies and continuously optimize the risk management system. This continuous improvement process helps enterprises gradually improve their safety management level and form a virtuous circle.

[0144] Among them, establishing a risk information feedback mechanism is an important means to ensure that risk assessment results can be delivered to relevant departments and personnel in a timely manner. Through effective information feedback, the company can ensure that every employee understands the current safety status and potential risks, thereby improving employees' safety awareness and participation. This safety management method of full participation helps to build a more solid safety line of defense.

[0145] Among them, regular review and update of risk assessment models is a necessary measure to ensure the continued effectiveness and applicability of security risk assessment methods. As the internal and external environment of the enterprise changes, new risk points may appear, while existing risk points may weaken or disappear. Through regular review and update, enterprises can ensure that the security risk assessment model always reflects the latest security situation, thereby providing a continuous scientific basis for the enterprise's security management.

[0146] In summary, the safety risk assessment method of this application, through comprehensive analysis and multi-level feature layer comprehensive consideration, constructs a scientific and practical safety risk assessment model, provides strong technical support for the safety management of enterprises, effectively improves the accuracy of personal safety assessment, risk warning capabilities, management pertinence and effectiveness, promotes the construction of corporate safety culture, and optimizes resource allocation. Specifically, the safety risk assessment method of this application has the following advantages:

[0147] (1) Improved accuracy of personal safety assessment: Through comprehensive analysis and multi-level feature layer consideration, potential safety risks can be more comprehensively identified and assessed, thus providing a scientific basis for formulating effective risk control measures.

[0148] (2) Enhanced risk warning capabilities: Using data mining technology and dynamic monitoring methods, abnormal fluctuations in indicator values ​​can be discovered in a timely manner, warning of potential safety risks, thereby taking preventive measures in advance and reducing the probability of accidents.

[0149] (3) Improved the pertinence and effectiveness of safety management: Through the construction of a safety risk assessment model and the provision of decision-making basis, targeted risk control measures and improvement plans can be formulated to ensure the continuous optimization of the risk management system and improve the pertinence and effectiveness of safety management.

[0150] (4) Promoted the construction of corporate safety culture: By establishing a risk information feedback mechanism and regularly reviewing and updating the safety risk assessment model, the continued effectiveness and applicability of the safety risk assessment method can be ensured, thereby promoting the construction of corporate safety culture and improving employees' safety awareness and behavioral compliance.

[0151] (5) Optimized resource allocation: By determining the priority of key indicators at each security feature layer, it is possible to rationally allocate enterprise resources, focus on monitoring and managing high-priority indicators, improve resource utilization efficiency, and reduce security management costs.

[0152] 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.

[0153] Based on the same inventive concept, the embodiment of the present application also provides a security risk assessment device for implementing the security risk assessment 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 in the one or more security risk assessment device embodiments provided below can refer to the limitations on the security risk assessment method above, and will not be repeated here.

[0154] In an exemplary embodiment, as shown in FIG6 , a security risk assessment device is provided, including: a data acquisition module 602, an indicator extraction module 604, an indicator screening module 606 and a model building module 608, wherein:

[0155] A data acquisition module 602 is used to acquire a data set for security risk assessment;

[0156] An indicator extraction module 604 is used to extract security risk assessment indicators according to the data set to obtain security risk assessment indicators for each of the multiple security feature layers;

[0157] The indicator screening module 606 is used to perform statistical analysis on the security risk assessment indicators of each of the multiple security feature layers to obtain statistical analysis results, and screen out multiple key indicators of security risk assessment from the security risk assessment indicators of each of the multiple security feature layers according to the statistical analysis results;

[0158] The model building module 608 is used to perform security risk assessment analysis on multiple key indicators of security risk assessment and build a security risk assessment model. The security risk assessment model is used to output a security risk assessment portrait based on the input security risk assessment data of the user to be assessed.

[0159] The above-mentioned security risk assessment device, when obtaining a data set for security risk assessment, can accurately obtain security risk assessment indicators from the perspective of multiple security feature layers by extracting security risk assessment indicators based on the data set, and obtain statistical analysis results by statistically analyzing the security risk assessment indicators of each of the multiple security feature layers. According to the statistical analysis results, multiple key indicators of security risk assessment are screened out from the security risk assessment indicators of each of the multiple security feature layers, and multiple key indicators that can be used for accurate security risk assessment can be obtained by further screening. Then, security risk assessment analysis can be performed on multiple key indicators of security risk assessment to achieve the construction of a security risk assessment model, so that the security risk assessment model can be used to output a security risk assessment portrait according to the input security risk assessment data of the user to be assessed, so as to achieve accurate security risk assessment. In the whole process, by using the security risk assessment indicators of multiple security feature layers extracted from the data set to construct a security risk assessment model, various factors affecting personal safety can be comprehensively analyzed, thereby achieving accurate assessment of security risks and improving the accuracy of security risk assessment.

[0160] In an exemplary embodiment, the security risk assessment device also includes an assessment module, which is used to obtain security risk assessment data of the user to be assessed when a security risk assessment request for the user to be assessed is received, obtain indicator information corresponding to each of multiple key indicators of the security risk assessment from the security risk assessment data, input the indicator information corresponding to each of the multiple key indicators of the security risk assessment into the security risk assessment model, and obtain a security risk assessment portrait of the user to be assessed.

[0161] In an exemplary embodiment, the indicator extraction module is also used to classify the data set based on multiple security feature layers to obtain data subsets for each of the multiple security feature layers, and for each security feature layer, perform feature extraction on the data subset of the security feature layer to obtain security risk assessment indicators for the security feature layer.

[0162] In an exemplary embodiment, the indicator screening module is also used to perform correlation analysis on the security risk assessment indicators of each of the multiple security feature layers, determine the indicator correlation between the security risk assessment indicators of each of the multiple security feature layers, perform regression analysis on the security risk assessment indicators of each of the multiple security feature layers, determine the indicator influence relationship between the security risk assessment indicators of each of the multiple security feature layers, and screen out multiple key indicators for security risk assessment from the security risk assessment indicators of each of the multiple security feature layers based on the indicator correlation and indicator influence relationship.

[0163] In an exemplary embodiment, the model building module is also used to determine the personal safety impact level and occurrence probability of each key indicator of the security risk assessment, construct a risk matrix based on the personal safety impact level and occurrence probability of each key indicator, determine the security risk level of each key indicator based on the risk matrix, and construct a security risk assessment model based on the security risk level of each key indicator.

[0164] In an exemplary embodiment, the model building module is also used to obtain the indicator critical value of each key indicator, and build a security risk assessment model based on the indicator critical value of each key indicator and the security risk level.

[0165] Each module in the above security risk assessment device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module 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 operations corresponding to each module.

[0166] In an exemplary embodiment, a computer device is provided, which can be a server or a terminal. Taking the computer device as a server as an example, its internal structure diagram can be shown in Figure 7. 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 data such as data sets. 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 security risk assessment method is implemented.

[0167] Those skilled in the art will understand that the structure shown in FIG. 7 is merely a block diagram of a partial 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 shown in the figure, or combine certain components, or have a different arrangement of components.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] It should be noted that the user information (including but not limited to security risk assessment data, 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.

[0172] 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.

[0173] 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.

[0174] 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 security risk assessment method, characterized in that: The method comprises: Acquire data sets for security risk assessment; Extracting security risk assessment indicators based on the data set to obtain security risk assessment indicators for each of the multiple security feature layers; Performing statistical analysis on the security risk assessment indicators of each of the multiple security feature layers to obtain statistical analysis results, and screening out multiple key indicators of security risk assessment from the security risk assessment indicators of each of the multiple security feature layers according to the statistical analysis results; A security risk assessment analysis is performed on multiple key indicators of the security risk assessment to construct a security risk assessment model, and the security risk assessment model is used to output a security risk assessment portrait based on the input security risk assessment data of the user to be assessed.

2. The method according to claim 1, characterized in that: The method further comprises: When receiving a security risk assessment request for the user to be assessed, obtaining security risk assessment data of the user to be assessed, and obtaining indicator information corresponding to each of a plurality of key indicators of the security risk assessment from the security risk assessment data; The indicator information corresponding to each of the multiple key indicators of the security risk assessment is input into the security risk assessment model to obtain a security risk assessment portrait of the user to be assessed.

3. The method according to claim 1, characterized in that: The extracting of security risk assessment indicators according to the data set to obtain security risk assessment indicators of each of the multiple security feature layers includes: Based on multiple security feature layers, classifying the data set to obtain data subsets of each of the multiple security feature layers; For each of the security feature layers, feature extraction is performed on a data subset of the security feature layer to obtain a security risk assessment indicator of the security feature layer.

4. The method according to claim 1, characterized in that: The statistical analysis is performed on the security risk assessment indicators of each of the multiple security feature layers to obtain statistical analysis results. According to the statistical analysis results, multiple key indicators for security risk assessment are screened out from the security risk assessment indicators of each of the multiple security feature layers, including: Performing correlation analysis on the security risk assessment indicators of the multiple security feature layers to determine the indicator correlation between the security risk assessment indicators of the multiple security feature layers; Performing regression analysis on the security risk assessment indicators of each of the multiple security feature layers to determine the indicator influence relationship between the security risk assessment indicators of each of the multiple security feature layers; According to the indicator correlation and the indicator influence relationship, a plurality of key indicators for security risk assessment are screened out from the security risk assessment indicators of each of the plurality of security feature layers.

5. The method according to any one of claims 1 to 4, characterized in that: The performing of security risk assessment analysis on the multiple key indicators of the security risk assessment and constructing a security risk assessment model comprises: For each key indicator of the safety risk assessment, determine the personal safety impact level and probability of occurrence of the key indicator; Construct a risk matrix based on the personal safety impact level and probability of occurrence of each key indicator; Based on the risk matrix, determine the safety risk level of each key indicator; A security risk assessment model is constructed based on the security risk level of each of the key indicators.

6. The method according to claim 5, characterized in that The construction of a security risk assessment model according to the security risk level of each key indicator includes: The critical value of each key indicator is obtained, and a security risk assessment model is constructed based on the critical value of each key indicator and the security risk level.

7. A security risk assessment device, characterized in that: The device comprises: A data acquisition module, used to acquire a data set for security risk assessment; An indicator extraction module is used to extract security risk assessment indicators according to the data set to obtain security risk assessment indicators for each of the multiple security feature layers; An indicator screening module is used to perform statistical analysis on the security risk assessment indicators of each of the multiple security feature layers to obtain statistical analysis results, and screen out multiple key indicators of security risk assessment from the security risk assessment indicators of each of the multiple security feature layers according to the statistical analysis results; The model building module is used to perform security risk assessment analysis on multiple key indicators of the security risk assessment and build a security risk assessment model. The security risk assessment model is used to output a security risk assessment portrait based on the input security risk assessment data of the user to be assessed.

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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