346 results about "Risk characteristics" patented technology

The invention provides a multi-modal enterprise credit risk assessment method based on a knowledge graph, which integrates data such as enterprise relationships, industry policies and supply chain information by constructing an enterprise financial knowledge graph, processes entity static attributes and associated information by using a multi-modal embedding technology, captures the associated information in combination with a heterogeneous graph neural network, and evaluates the credit risk of an enterprise. And the dynamic space-time attention mechanism mines time and space features of the time series data, identifies a core risk conduction path based on an attention weight, and finally fuses graph-level features, dynamic space-time features and business rules to output a structured evaluation result. According to the method, multi-modal data is effectively integrated, the problem of incidence relation modeling deficiency is solved, deep fusion of enterprise multi-source data and accurate extraction of risk features are realized, and the accuracy and interpretability of enterprise credit risk assessment can be effectively improved.

The invention relates to the technical field of data security management, in particular to a self-adaptive data security management and risk early warning system based on intelligent analysis under a cloud platform. Comprising a multi-dimensional data acquisition module; an intelligent analysis module; a self-adaptive strategy generation module; a risk early warning module; and a user behavior portrait construction module. In the design, the security policy can be dynamically adjusted along with the risk situation of the cloud platform, the problem that a static policy cannot adapt to real-time change is solved, and dynamic mapping of risk characteristics-policy parameters is realized; according to the design, the one-sidedness of single-dimension analysis is broken through, multi-modal feature association modeling of user behaviors is achieved, an abnormal behavior triggering threshold value is accurately recognized, and the integrity and accuracy of risk feature analysis are improved; the security policy can be continuously optimized through historical event data, so that protection efficiency attenuation caused by long-term static operation is avoided, and an autonomous lifting link of data driving, algorithm optimization and policy evolution is realized.

The invention discloses a risk control credit monitoring method based on cloud computing, and belongs to the technical field of cloud computing, and the risk control credit monitoring method based on cloud computing comprises the following steps: S1, collecting user transaction data and behavior track data in real time; s2, cleaning and standardizing the data; s3, constructing a multi-dimensional risk assessment model based on machine learning; s4, dynamically generating a credit score according to the risk characteristics; s5, triggering an early warning mechanism for abnormal transactions in real time; and S6, generating a visual risk control report and updating a monitoring strategy. According to the method, multi-source heterogeneous data are integrated through federated learning, hierarchical privacy protection is realized in combination with homomorphic encryption and differential privacy, risk assessment real-time performance is improved by using a hybrid cloud resource scheduling and dynamic model updating technology, and a compliance audit closed loop is constructed based on a block chain and interpretability analysis.

The invention discloses an oil and gas pipe network operation state risk perception early warning method and system, and the method comprises the steps: carrying out the fusion of the multi-dimensional features of the operation state of a pipe network according to the real-time operation data of the oil and gas pipe network, and generating a spatial-temporal feature matrix; inputting the spatial-temporal feature matrix into a deep Gaussian process model, performing probabilistic modeling on abnormal features in the pipe network operation state, and outputting a risk feature vector with confidence; constructing a three-dimensional risk concentration distribution field according to the risk feature vector, and predicting a propagation path and a diffusion trend of the risk concentration distribution field; and for the prediction result, constructing a self-adaptive early warning threshold generation model based on the generative adversarial network, generating a multi-stage early warning threshold changing along with the operation state of the pipe network, and realizing risk perception early warning of the operation state of the oil and gas pipe network according to the comparison result of the real-time risk characteristics and the early warning threshold. According to the embodiment of the invention, dynamic risk assessment and self-adaptive early warning can be realized, and the safety and emergency response capability of the pipe network are enhanced.

The invention discloses a neurology nursing potential safety hazard monitoring system and method, and relates to the technical field of medical information safety monitoring, and the system comprises a first collection processing unit which is used for collecting monitoring data of a target patient, inputting the monitoring data into a time sequence deep network for processing, and obtaining risk feature data; the second collecting and processing unit is used for collecting physiological state data of the target patient, constructing a risk assessment rule base based on a medical knowledge graph to analyze the physiological state data, and generating risk probability data; and the risk early warning unit is used for inputting the risk feature data and the risk probability data into a Bayesian fusion network, generating risk early warning information and sending the risk early warning information to the medical care terminal. According to the invention, collaborative analysis of multi-source information is realized through the Bayesian fusion network, the problem of low early warning accuracy of a traditional single monitoring method is solved, and the reliability of potential safety hazard monitoring is improved.

The invention discloses a rock slope risk assessment method and system based on artificial intelligence, and belongs to the technical field of geological disaster risk assessment.The rock slope risk assessment method comprises the steps that a slope three-dimensional digital model is constructed, and geological-environment-monitoring data are integrated; extracting spatio-temporal characteristics, and predicting a landslide risk probability; quantifying a risk space diffusion path, and identifying a high-risk area; the early warning threshold value is dynamically adjusted, and accurate early warning is achieved; the problems that in the prior art, how to integrate multi-modal data (geological data, environment data and monitoring data) to achieve real-time sensing of the slope state and how to construct a deep learning model with high generalization ability to deal with risk prediction under the complex geological condition are solved. The problem of how to establish a risk transfer model considering spatial heterogeneity to improve high-risk area identification precision and how to realize dynamic adaptive adjustment of an early warning threshold to match personalized risk features of different slopes is solved.

The invention relates to an infection risk assessment method and system for nursing, and the method comprises the steps: collecting multi-source heterogeneous data of a patient, and fusing the multi-source heterogeneous data into a unified original data set; obtaining a standardized risk feature vector based on the original data set; inputting the risk feature vectors into a multi-modal risk assessment model, and outputting risk probabilities of individual infection and group infection; forming a personalized risk index based on the risk probability; according to the personalized risk indexes, the decision support system outputs hierarchical intervention measures matched with the risk levels, and records and feeds back execution conditions and intervention effect data of the hierarchical intervention measures for dynamically updating parameters of the risk assessment model; and integrating the intervention effect data and the early warning signal into feedback information to form a dynamic infection risk assessment system adapted to specific hospital characteristics. The accuracy of infection risk assessment of nursing can be improved.

The invention discloses a construction site safety early warning method and system based on multi-dimensional data mining, and relates to the technical field of construction site early warning, and the method comprises the steps: collecting and preprocessing multi-dimensional data, and forming a multi-dimensional data set; performing feature extraction on the multi-dimensional data set by using a convolutional neural network, and fusing time sequence information through a space-time dynamic weighting mechanism to generate a multi-dimensional risk feature set; based on the multi-dimensional risk feature set, the risk features are converted into nodes, the incidence relation between the risk features is converted into edges, a causal correlation structure of the nodes and the edges is formed, and a dynamic risk map is constructed; analyzing the association strength among the risk factors by using the dynamic risk map, evaluating the overall risk level, and generating a risk event list; performing trend prediction on the risk events in the risk event list based on the time sequence to obtain a risk trend index; and performing dynamic early warning according to the risk trend index, and triggering a safety response. According to the invention, through multi-dimensional data mining and analysis, intelligentization of construction site safety early warning is realized.

The invention discloses an edge calculation method and system applied to construction site safety management. The method comprises the following steps: S1, collecting and preprocessing multi-modal data; s2, extracting and sharing risk features, and generating a preliminary risk feature map; s3, generating a local abnormal projection result by using a distributed high-dimensional abnormal mode projection module, and splicing to form a high-dimensional risk feature curved surface; s4, monitoring the data flow by using a dynamic event sensing module, identifying a high-risk event and outputting a high-risk event mark; s5, calling a dynamic risk weight tree search algorithm in a path optimization module to generate a dynamic security path; s6, generating a local risk prediction result by using the space-time hybrid prediction model; s7, generating a global dynamic prediction result through a layered space-time cooperation mechanism; and S8, providing real-time risk early warning and path optimization suggestions for construction management personnel through the mobile terminal. According to the invention, edge calculation and artificial intelligence technologies are combined, so that construction site dynamic risk identification and safety management are realized.

The invention discloses a project performance evaluation and risk early warning method for multi-source heterogeneous scientific research data, and relates to the technical field of scientific research project management, and the method comprises the steps: collecting the multi-source heterogeneous scientific research data, cleaning, converting, filling missing values, and storing in a unified format; extracting performance and risk features, and fusing to obtain a fused feature set; combining an analytic hierarchy process, an entropy weight method and the like to construct a dynamic weight performance evaluation model; constructing a multi-modal risk early warning model by adopting multiple algorithms and optimizing a threshold value; collecting data in real time to update a feature set, and dynamically adjusting the model; and visually outputting a result, generating an improvement suggestion, and forming closed-loop feedback. According to the method, the multi-source data processing efficiency and evaluation accuracy are improved, the risk early warning timeliness and adaptability are enhanced, a management closed loop is formed, and the problems of difficult data integration, evaluation lagging, insufficient early warning and the like of a traditional method are solved.

The invention provides a risk data analysis method based on an LLM large language model, risk data analysis is carried out based on the LLM large language model, data identification, classification and risk feature extraction can be carried out by utilizing the LLM large language model in the whole process, risk feature vector training learning is carried out on the LLM large language model, and the risk data analysis efficiency is improved. The defect that a large number of training samples are needed can be reduced; meanwhile, the LLM big language model cue words on all dimensions are constructed and input on one LLM big language model, the LLM big language model can continue to further carry out risk analysis on the classification and recognition result on all dimensions, and the risk analysis result of the risk data on all dimensions is rapidly and deeply mined and output. Therefore, the risk data can be intelligently processed by the LLM in the whole process, the risk types and all dimensions can be analyzed through one model, and the risk data analysis efficiency is greatly improved.

The invention discloses an AI-based database operating system risk prevention and control system and method, belongs to the technical field of deep learning, and aims to solve the technical problem of how to comprehensively identify, prevent and control database operation risks and improve data management efficiency. Comprising a data processing module for performing data preprocessing on historical operation logs; the risk identification module is used for carrying out grammar and semantic analysis on the SQL statements in the processed operation logs, constructing risk characteristics of the SQL statements, carrying out risk prediction through a trained risk identification model and outputting risk levels; the risk response module is used for carrying out risk warning level by level and carrying out classification and priority ranking on the risk warning based on the Bayesian network; and the user analysis module is used for monitoring user behaviors in real time based on the user portrait and an anomaly detection algorithm, forming alarm information based on a detection result and pushing the alarm information.

The invention relates to the technical field of computers, particularly discloses an engineering investment project multi-dimensional risk dynamic assessment and early warning system, and aims to solve the problems that existing risk assessment is single in dimension, insufficient in timeliness and lack of dynamic early warning and intelligent decision support. The system comprises a data acquisition and preprocessing module, a multi-dimensional risk feature construction module, a dynamic risk assessment and prediction module, a risk early warning and visualization module and an intelligent decision support and optimization module. Through integration of multivariate data, machine learning and deep learning algorithms, risk dynamic modeling, real-time evaluation and trend prediction are realized, and in combination with intelligent early warning and decision support, risk management is changed from post-remedy to beforehand prevention.

The invention discloses a specific scene safety risk identification method based on visual dynamic analysis, and belongs to the technical field of safety monitoring, and the method specifically comprises the steps: collecting environment parameter time sequence data and cargo state time sequence data of a monitoring region; respectively extracting an environment characteristic component and a cargo response component; calculating a quantized value of correlation strength of the two through an information entropy algorithm, and marking the quantized value as a cargo response entropy value; a cargo response entropy evolution curve is monitored, and when the change rate of the evolution curve exceeds a historical threshold value and the change acceleration is not zero, it is judged that a state transition event occurs; extracting environmental incentive parameters and cargo response parameters of a state transition event, encoding the environmental incentive parameters and the cargo response parameters into risk feature tensors, inputting the risk feature tensors into the accident incentive chain knowledge base, outputting a matched critical intervention parameter set, and formulating an environmental reconstruction instruction set and a cargo intervention instruction set according to the critical intervention parameter set; according to the invention, the intelligent level and risk response capability of dangerous cargo safety early warning are effectively improved.

The invention discloses an engineering safety early warning method and system based on artificial intelligence real-time risk identification, and relates to the technical field of engineering safety, and the method comprises the steps: collecting scattered engineering safety data from each engineering platform in batches, and carrying out the preprocessing of the data, and constructing a dynamic database; according to the engineering safety data collected in batches, an engineering safety knowledge graph is constructed, and different risk levels are preset according to engineering safety standards. According to the method, multi-source engineering safety data are integrated, dynamically changing risk characteristics are analyzed in real time by using an AI risk identification model, the hysteresis of traditional manual inspection and static analysis is overcome, a nonlinear relationship among the risk characteristics is captured by using a random forest model through integrated learning of multiple decision trees, and the risk characteristics are analyzed in real time. And the probability values of high, medium and low risk levels are output in combination with Softmax probability normalization, so that the evaluation precision is remarkably improved, the risk features are positioned, the scientificity of risk traceability is ensured, and data-driven decision support is provided for engineering safety management.

The invention relates to the technical field of electric power, in particular to an electric power circuit alarm method and system with an AI function, and the method comprises the steps: collecting multi-source data, and carrying out the time-space calibration of the information of the multi-source data through a Kalman filtering algorithm; constructing a multi-physics field coupling prediction model, and calculating standardized scores of different risk types; different types of risk feature weights are calculated by using a quantum-graph attention network fusion model, and fault severity scores corresponding to different types of risks are calculated through a migration reinforcement learning fault diagnosis model; constructing a comprehensive risk value calculation formula by combining the standardized scores of the different risk types, the feature weights of the different types of risks and the fault severity scores corresponding to the different types of risks, and calculating a comprehensive risk value; and carrying out grade division on the risk according to a threshold interval in which the comprehensive risk value is located, and pushing different early warning information and processing strategies to different responsible personnel according to different risk grades. According to the scheme, accurate early warning is realized through multi-source data space-time calibration and AI fusion.

The invention relates to the technical field of information security, in particular to a network security risk assessment method and system based on fuzzy mathematics, and the method comprises the following steps: based on dynamic monitoring data of node data in a network environment, extracting multiple risk factor parameters, dividing a parameter value range, comparing risk level intervals, and carrying out the statistics of a differentiation interval distribution proportion. And generating a network risk factor membership distribution table. According to the method, network node data are dynamically monitored, multiple risk factors are accurately analyzed, attribution range division is refined, the distribution relation between parameters and risk levels is defined, the distribution proportion and weight influence are analyzed, an interactive risk distribution model is formed, factor relevance is revealed, a significant characteristic group is screened, and the time dimension change rule is combined, so that the risk level of the network is determined. And key risk feature data is extracted, the dynamic monitoring capability is enhanced, the accuracy, comprehensiveness and dynamic adaptability of risk assessment are improved, the conversion from static analysis to real-time response is realized, and complex network threats are effectively dealt with.

The invention provides a risk early warning method and system for medical insurance settlement data based on a knowledge graph, and relates to the technical field of medical insurance settlement, and the method comprises the steps: formulating a risk rule, obtaining the medical insurance settlement data, extracting a first entity and a relation thereof from the data, constructing the knowledge graph, and storing the knowledge graph in a graph database; and searching the second entity according with the risk rule and the relationship thereof in the graph database by utilizing the reasoning ability of the knowledge graph, extracting the risk characteristics of the second entity, determining the risk level of the second entity according to the risk characteristics, and sending a corresponding early warning signal, thereby improving the recognition rate of the complex associated risk in the process of medical insurance settlement.

The invention relates to the technical field of computers, and discloses a network security assessment method and system, and the system comprises an acquisition module, an analysis module, a processing module and an assessment module. The acquisition module acquires a plurality of network risk data through a network tool, the analysis module acquires a historical risk feature data set, obtains a risk prediction type of the network risk data based on a convolutional neural network model, verifies the risk prediction type, and if the risk prediction type is judged to be incorrect, determines that the risk prediction type is correct. Re-determining the risk prediction type of the network risk data by using a clustering algorithm; the processing module obtains a network risk value of each piece of network risk data based on the risk characteristics, establishes a time-network function based on the risk characteristics and the network risk values, mines association results of the network risk data of different risk prediction types according to an association rule algorithm, and sends the association results to the server; and the evaluation module determines the network security level according to the network anomaly level and the change trend. According to the invention, the accuracy of network security assessment is improved.

The invention provides a customer risk report generation method and device, electronic equipment, a medium and a product, and the method comprises the steps: obtaining a plurality of pieces of feature information of a customer, and determining a prediction risk level of the customer according to the plurality of pieces of feature information; according to the multiple pieces of feature information and the predicted risk level, determining the contribution degree of each piece of feature information to the predicted risk level; for any risk feature information, determining risk description corresponding to the risk feature information based on a natural language model according to the contribution degree corresponding to the risk feature information; wherein the risk feature information is screened from multiple pieces of feature information; and generating the first customer risk report according to the risk description corresponding to each piece of risk feature information, so that a clear risk profile and detailed risk description can be provided for the customer, and the efficiency of writing the customer risk report and the accuracy of the confirmed customer risk point can be improved.

The invention discloses a risk state sensing method and device for power grid safety production operation and equipment, and relates to the technical field of power grid operation management. The method comprises the following steps: acquiring power grid production operation data in real time; processing the power grid production operation data based on the feature extraction model to obtain a comprehensive feature vector; processing the comprehensive feature vector based on a BiLSTM model to obtain a dynamic risk feature sequence; calculating an attention score corresponding to the risk feature of each time point in the dynamic risk feature sequence, weighting the corresponding risk feature based on the attention score of each time point, and generating a comprehensive risk perception feature vector; and determining a real-time risk state of the power grid production operation data at the current moment based on the comprehensive risk perception feature vector. Potential risks in the power grid production scene can be recognized in time, the risk state of power grid production operation and equipment is comprehensively evaluated, and accurate perception of the global risk state of the power grid production scene is achieved.

The invention relates to the technical field of energy storage risk prediction, and particularly discloses an electrochemical energy storage risk prediction method, which comprises the following steps of: determining a risk characteristic value of an electrochemical energy storage system, obtaining an abnormal deviation value of an operation environment of the electrochemical energy storage system, and obtaining a risk judgment threshold value of the electrochemical energy storage system; according to the method, the real-time operation state of the electrochemical energy storage system is monitored, whether the electrochemical energy storage system has risks or not is judged by combining a risk judgment threshold value of the electrochemical energy storage system, and the risk level of the electrochemical energy storage system with the risks is determined. According to the method, the problems that the existing risk management system is not easy to carry out and is lack of comprehensive analysis are solved, rapid response and implementation of effective rectification measures are facilitated, the accident occurrence probability is remarkably reduced, the efficiency and pertinence of maintenance work are improved, a perfect risk judgment threshold system is established, potential risks can be systematically identified and evaluated, and the overall risk management capability is improved.

The invention relates to the technical field of customer relationship management, and discloses an information data analysis method and system based on CRM. An information data analysis method based on CRM comprises the following steps: S1, collecting user original information data based on CRM, and screening out target users with loss risks according to the user original information data; s2, extracting customer loss risk features from the original information data of the target users, importing the customer loss risk features into the trained risk assessment calculation model, and outputting results to indicate loss risk levels of the target users; and S3, generating a reservation scheme according to the loss risk level of the target user and the corresponding original information data, and outputting the reservation scheme to the corresponding target user. According to the invention, grading intervention is carried out on the target users based on different risk grades, a uniform retention strategy is prevented from being adopted for all users, and the retention rate of clients is improved.

The invention relates to the technical field of laboratory safety management, and discloses an AI model fused laboratory safety management risk intelligent pre-judgment method and system. The method comprises the steps of collecting original monitoring data of a laboratory monitoring area, and extracting risk signals and abnormal signals corresponding to abnormal events from the original monitoring data; obtaining relevance characteristics between the risk signals and adjustment characteristics of the risk signals by abnormal events; risk features representing risk differences are generated according to the features, and target risk probability distribution is obtained based on the risk features; and performing interference suppression processing on the original monitoring data according to the target risk probability distribution to separate a prediction risk waveform of the target risk source. According to the method, through deep mining of the relevance of risk signals and the adjustment effect of abnormal events, the accuracy of risk pre-judgment is improved, potential risks can be effectively captured, and a more intelligent and more reliable risk pre-judgment means is provided for laboratory safety management.

The invention relates to the technical field of engineering safety management, and particularly discloses an engineer safety management method and system based on big data, and the method comprises the steps: collecting all data of a construction site in real time; performing space-time alignment and fusion processing on the collected data to generate multi-modal fusion data; training a multi-modal risk assessment model based on the obtained construction big data; performing construction risk feature extraction on the collected data based on the multi-modal risk assessment model, and outputting construction risk features; and performing safety response analysis on the multi-modal fusion data based on the construction risk features, and generating a safety response result. All data of a construction site are fused, and a multi-modal data set is constructed, so that full-dimensional monitoring of the construction site is realized; a big data training model is matched based on construction types, and space-time correlation analysis of risk features is achieved; a differentiated early warning mechanism for personnel, equipment and environment is generated, multi-agent co-processing is supported, and accident loss is reduced.

The invention discloses a commercial subject risk assessment method and system based on machine learning, and belongs to the technical field of knowledge maps. Basic features of an enterprise are obtained, a knowledge graph is constructed, and risk features are identified through graph mining; carrying out qualitative analysis and quantitative analysis on the enterprise to obtain a feature tag description; constructing a sample set, setting a misjudgment cost and a class weight based on the sample set, performing parameter tuning and verification set feedback, adjusting a decision threshold, performing binning on each feature, and obtaining a final risk score; and threshold selection and probability calibration are carried out to obtain risk grade division and decision suggestions of the enterprise, and risk label interpretation and large model auxiliary interpretation are carried out. The problems that manual collection and analysis of supervision policy documents consumes time and labor, careless omissions easily occur, and final risk judgment is prone to misalignment, misjudgment and frequent missed judgment are solved. According to the method, key rules can be accurately extracted, and hidden risk conduction links and potential risk points can be mined.

The invention discloses a reservoir desilting body layout scheme evaluation method based on siltation risk characteristics, belongs to the technical field of reservoir desilting, and can solve the problem that an existing method is low in evaluation precision and accuracy. The method comprises the following steps: S1, determining a plurality of subareas of a reservoir according to historical operation data of the reservoir, and determining sedimentation risk characteristics and sediment distribution characteristics of each subarea in each of a plurality of continuous time periods; s2, according to the siltation risk characteristics and the sediment distribution characteristics, constructing a sediment distribution prediction model of the reservoir; and S3, utilizing the sediment distribution prediction model to predict the sediment distribution situation after the reservoir implements the desilting body layout scheme, and evaluating the desilting body layout scheme according to the sediment distribution situation. The method is used for evaluating the reservoir desilting body layout scheme.

The invention provides an engineering dynamic risk assessment early warning system and method, and belongs to the technical field of engineering risk assessment. According to the method, engineering stages are divided, a stage label incidence matrix is constructed, historical cases are collected, an engineering stage-risk feature incidence matrix is constructed, a weight dynamic adjustment mechanism is set, construction data are collected in real time, the current stage is identified, risk features are extracted, risk weights are automatically adjusted, and dynamic risk comprehensive scores are calculated. And then the preset risk threshold is automatically called to divide the risk early warning level, so that scientific and dynamic assessment and early warning of the engineering risk are realized, core risk characteristics of different engineering stages can be accurately identified, the risk weight is dynamically adjusted according to real-time data, the risk assessment is enabled to better fit the actual construction condition, the risk early warning level is timely and accurately divided, and the risk early warning efficiency is improved. Powerful support is provided for engineering safety management, and the probability of occurrence of engineering accidents is effectively reduced.

The invention discloses a low-altitude aircraft risk management method and system based on machine learning. The method comprises the following steps: S1, generating a preprocessed flight path data set; s2, extracting a complexity description vector of a flight path from the fractal flight path data set by applying a fractal dimension analysis method; s3, constructing a multi-dimensional risk feature data set; s4, automatically selecting an optimal risk evaluation model with the minimum description length and the optimal data fitting precision; s5, performing real-time risk assessment on the newly collected flight trajectory data set and the corresponding multi-dimensional risk feature data set by using the selected optimal risk assessment model, and generating flight trajectory risk assessment output; and S6, dynamic risk monitoring and flight path adjustment are realized, and a decision basis is provided for low-altitude aircraft risk management. According to the method, the high-risk feature segment with the high turning rate or the speed dramatic change in the track is accurately captured, and the recognition accuracy of the local risk event is greatly improved.

The invention belongs to the technical field of mine safety mixed reality interactive training, and particularly provides a mine safety training detection method and system based on mixed reality, and the method mainly comprises the steps: obtaining a mine basic data set, and generating enhanced safety constraint boundary data according to the mine basic data set; generating environmental risk features according to the enhanced security constraint boundary data; generating security behavior features according to the enhanced security constraint boundary data and the environmental risk features; generating risk evolution characteristics according to the environmental risk characteristics and the safety behavior characteristics; generating emergency response characteristics according to the safety behavior characteristics and the risk evolution characteristics; and generating a dynamic risk visualization picture according to the risk evolution characteristics and the emergency response characteristics. According to the method, training scene dynamic, operation behavior compliance detection automation and risk evolution process visualization are realized, and safety training effectiveness and emergency processing capability are improved.