93 results about "Risk mapping" patented technology

The invention provides a multi-source software supply chain intelligent analysis method and system. The method comprises the following steps: analyzing software component identification information submitted by a user, extracting attribute characteristics, positioning node paths among components, and generating a weighted dependency chain list containing direct and indirect dependency chains; a Bayesian network model is combined with a predefined risk index system, the risk score of each dependency relationship chain is calculated, potential supply chain threat points are identified through an anomaly detection algorithm, and a risk report is generated; the report covers the overall risk score and the threat point of the key link or component; based on the report, drawing a comprehensive risk map by using a multi-dimensional visualization algorithm, and displaying component groups with similar risk modes; and a genetic algorithm and a simulated annealing algorithm are fused to explore a high-risk area, an adjustment suggestion is proposed, and a risk mitigation strategy is generated to reduce the overall risk score. According to the technical scheme provided by the embodiment of the invention, the security and integrity of the multi-source software supply chain are improved.

The invention provides a network attack risk mapping assessment method and system, and relates to the technical field of network attack risks, and the method comprises the steps: collecting a network flow data packet to extract a multi-dimensional attack feature vector, employing a deep neural network to carry out feature learning to obtain an attack type recognition result and an attack intensity assessment value, and constructing an attack feature standard model; based on the attack type identification result and the attack strength evaluation value, combining an asset weight coefficient, a vulnerability hazard level and a vulnerability utilization difficulty coefficient to construct an asset vulnerability evaluation model; and performing association analysis on the real-time attack feature data and the asset vulnerability assessment score, constructing a risk mapping matrix, calculating a comprehensive risk score, and generating risk early warning information. According to the invention, accurate identification, dynamic assessment and timely early warning of the network attack risk are realized, and the accuracy and real-time performance of risk assessment are improved.

The invention discloses an intelligent data management system, and relates to the field of intelligent data management. The system comprises a data acquisition module for acquiring and preprocessing multi-source data and extracting field information; the decoupling analysis module is used for calculating a mutual information index and an information cementation degree according to the field information; the map generation module is used for constructing an attribute fusion map according to the field information and generating risk mapping skewness and a risk control map; the behavior analysis module is used for collecting action execution logs, extracting field behavior sequences and calculating field behavior interference factors; and the quality evaluation module is used for extracting a key field missing proportion and information entropy based on the field information to calculate an information completeness index, and fusing multiple indexes to generate a comprehensive quality score. Through graph structure construction, behavior sequence analysis and quality scoring fusion, the multi-source data field fusion accuracy and risk identification precision are improved, and intelligent assessment and compression abstract generation of high-quality data are realized.

The invention discloses a laboratory whole-process intelligent management and control system based on AI and Internet of Things fusion, which relates to the technical field of laboratory management and comprises an equipment access unit, a central decision unit and a business application unit. According to the system, a risk model library and a real-time risk map can be constructed through the risk assessment module, accident risks caused by human errors are reduced through the real-time risk map, automatic emergency disposal and personnel behavior compliance monitoring, and when the equipment operation and maintenance module detects that the equipment is abnormal, predictive maintenance can be automatically carried out, so that the downtime is shortened, and the maintenance efficiency is improved. The resource scheduling module can be used for optimizing the inventory and scheduling the equipment, and carrying out global allocation based on an inventory sharing model, so that resource idleness is reduced through cross-courtyard inventory sharing, resource sharing is facilitated, cross-courtyard collaboration is completed, and the situation that data cannot be synchronized due to independent inventory management of each courtyard is avoided; and further, global optimization can be realized.

The invention relates to the technical field of construction safety, in particular to a trestle construction hazard source assessment method and system, and the method comprises the steps: constructing a hazard source data set corresponding to a trestle construction process, carrying out the entry recording of various potential hazard sources, and setting impact factors for representing risks; collecting field data of the construction process, and associating the field data with the influence factors to obtain risk characterization forming a hazard source; fusing the risk characterization with the environmental parameters, and constructing a space-time risk mapping layer; based on the space-time risk mapping layer, performing quantitative analysis on the hazard source through a multi-factor comprehensive model to obtain risk distribution characteristics in different level intervals; and according to the risk distribution characteristics, generating a space-marked trestle risk map. According to the invention, the problems of strong subjectivity and lack of systematic analysis of existing trestle construction hazard source assessment are effectively solved, the objectivity of risk assessment is enhanced, and the capability of accurately judging the risk state in a complex construction environment is improved.

The invention relates to the field of clinical diagnostics, and discloses an elderly NSC emergency risk layering method and device and a medium, and the method comprises the steps: S1, obtaining multi-dimensional evaluation data of an elderly NSC patient; s2, adopting a preset risk mapping rule to convert the risk values into single risk values of a unified scale; s3, dividing the single risk value into a plurality of risk sub-models, and calculating a dimension risk score of each risk sub-model; s4, based on a preset fusion strategy, integrating the risk sub-model and the single risk value interaction effect, and calculating to obtain a total risk score of the patient; and S5, determining the risk level of the patient according to the total risk score, and outputting a clinical diagnosis and treatment suggestion. According to the method, multiple biomarkers and key clinical parameters are integrated, a multi-dimensional combined risk assessment model is constructed, and the potential pathological state of a patient can be reflected more comprehensively.

The invention relates to the technical field of coal mine fire prevention and control, and discloses a coal mine fire disaster prevention and control system and method based on artificial intelligence. A data preprocessing module of the system cleans and converts the data to obtain high-quality data; the fire prevention knowledge base construction module constructs a coal mine fire prevention ontology knowledge base in combination with the preprocessed data and a preset fire prevention knowledge text; the risk map construction module constructs a risk knowledge map containing risk nodes, attribute edges and relation edges based on the knowledge base and historical disaster data; a risk factor extraction module extracts fire risk factors from real-time monitoring data, and a risk mapping module maps the fire risk factors to a risk knowledge graph to obtain risk association nodes; and the risk reasoning module performs reasoning analysis on the risk association nodes according to the risk knowledge graph and a preset fireproof rule to form a preliminary risk assessment result, so that the comprehensiveness and effectiveness of coal mine fireproof disaster prevention and control can be improved.

The invention discloses a plateau mountain area avalanche susceptibility intelligent assessment method, and relates to the technical field of high mountain ice and snow disaster space early warning and risk mapping. According to the method, the influence of the microtopography is captured by adopting high resolution, the evaluation accuracy is improved, the control effect of the slope microtopography (the scale of 30-50 m) on the accumulated snow stress field can be accurately captured by adopting a factor map layer (based on data such as 12.5 mALOSPALSARDEM and Landsat-8OLI images) with the spatial resolution of 12.5 m, and compared with traditional 30mDEM and hectometer-level meteorological reanalysis data, the method has the advantages that the evaluation accuracy is greatly improved, and the evaluation efficiency is improved. Spatial differences of key factors such as terrains, earth surfaces and meteorology can be depicted more carefully, more accurate basic data support is provided for avalanche susceptibility assessment, and missing of key microtopography influence factors due to insufficient resolution is avoided.

The invention discloses an agent-based security risk assessment and operation management integrated method and system, and relates to the technical field of industrial security production and artificial intelligence, and the method comprises the steps: obtaining enterprise security production operation data, carrying out the preprocessing of the data, obtaining a standardized data set, and carrying out the deep transfer learning through an agent, and extracting scene risk features; calculating a propagation entropy value of the risk mapping data and constructing a propagation path, and performing dynamic time sequence fusion to generate a risk assessment result; generating a scene control instruction based on group game operation and performing optimization to obtain a baseline check instruction; operating data are collected, the conduction probability is calculated, a safety response rule is generated, linkage optimization is carried out, and a cooperative defense strategy is formed. According to the invention, risk feature extraction, propagation path analysis and cooperative prevention and control are realized through the intelligent agent, and the intelligent management level of enterprise safety production is improved.

The invention provides a risk assessment early warning method and device based on mechanism and data fusion, equipment and a medium, and the method comprises the steps: obtaining historical data, including variables of a production field sensing part, a logic part and an execution part; the method comprises the following steps of: preprocessing data, performing alignment and fusion processing on the preprocessed data, determining a causal relationship among factors in a production process through a causal analysis method, describing a quantitative relationship among nodes by estimating conditional probability density among the factors, and constructing a causal probability graph of a system; constructing a risk assessment model; and inputting the risk data with the risk label and the risk node label and normal data into the risk assessment model for training until a convergence condition is met. According to the method, a high-precision and high-robustness model is established, and the risk mapping relationship is clarified by mining the joint features of the data under different dimensions and scales including time, so that comprehensive security risk perception and decision support are realized.

The invention provides an unmanned aerial vehicle ground risk map construction method based on a gridding risk matrix, and the method comprises the steps: dividing a city region into a plurality of grid units, and obtaining unmanned aerial vehicle feature parameters, ground personnel density and city ground surface features; according to the unmanned aerial vehicle characteristic parameters and the ground personnel density, an inherent ground risk matrix of the unmanned aerial vehicle to the ground crowd is determined; obtaining a first mitigation matrix according to the urban surface features; obtaining a second mitigation matrix according to the unmanned aerial vehicle failure protection measures; adopting the first mitigation matrix and the second mitigation matrix to construct an overall ground risk mitigation matrix; obtaining a to-ground risk matrix according to the inherent to-ground risk matrix and the overall to-ground risk mitigation matrix; and according to the to-the-ground risk matrix, constructing a static to-the-ground risk map. According to the method, the unmanned aerial vehicle ground risk is comprehensively considered from multiple angles, the accuracy and reliability of risk assessment are improved, support is provided for the development of the urban unmanned aerial vehicle industry, and the safety and efficiency of unmanned aerial vehicle flight are improved.

The invention relates to the technical field of food safety risk management, in particular to an aquatic product risk analysis system based on multi-factor analysis, which comprises a pollution factor response module, a path aggregation module, a channel decision module, a pollution risk simulation module and a risk mapping collection module. According to the method, by extracting the concentration change direction of the pollution factor in the specific water area in the continuous time period and establishing the sequence combination of the concentration rising section, the time sequence behavior characteristics of the pollution factor can be identified, and the overlapping relation between the risk factor and the biological class group exposure can be effectively judged; in a standard exposure period, contact frequencies are integrated, sensitive time periods are screened, and generality factor grouping is completed by analyzing frequency coincidence conditions of a plurality of aquatic products under the same factors, so that a risk influence map can reflect a factor interaction correlation and category common inductance response relationship; and the comprehensiveness, granularity and prediction capability of risk assessment are improved in a dynamic scene.

The invention relates to the field of geological disaster risk assessment, and discloses a geological disaster risk assessment method and device based on remote sensing big data, and the method comprises the following steps: S1, collecting disaster trigger factors related to multi-source data and geological disasters; s2, establishing a disaster risk distribution model in the source region; s3, constructing a disaster risk distribution model of the target area in the target area based on the limited observation data; s4, calculating a transmission mapping relation between the source area distribution and the target area distribution by using a mapping method between disaster distributions; s5, predicting disaster risk dynamic distribution in the target area through the transmission mapping in the step S4; and S6, dynamically updating the disaster risk distribution model according to the actual observation data of the target area, and generating a disaster risk map. Through the source-target area distribution mapping based on the optimal transmission theory, the technical effect of accurately predicting the disaster risk distribution of the target area under the condition of data scarcity is achieved.

The invention belongs to the technical field of boiler maintenance, and particularly relates to a boiler four-pipe foreign matter blockage early warning method and system. The method comprises the steps of obtaining boiler four-tube foreign matter blockage historical data; collecting boiler four-tube real-time state data of a reheater, an induced draft fan and a flame detection cooling fan; a fault mode-influence-risk mapping relation is established by adopting FMEA based on historical data, and a risk assessment framework is formed; generating an initial risk level; training a machine learning algorithm model by adopting historical data and generating a blockage probability; and dynamically adjusting the initial risk level according to the blockage probability, generating a final risk level and outputting a corresponding maintenance strategy. The system comprises a data acquisition module, an equipment monitoring module, a risk division module, a risk assessment module, a jam prediction module and a decision module. According to the scheme, the FMEA and the machine learning algorithm are fused, early warning, accurate risk assessment and dynamic maintenance decision making of boiler foreign matter blockage are achieved, and boiler operation safety and maintenance efficiency are improved.

The invention discloses an authority management method and system based on user behaviors, and belongs to the field of artificial intelligence, and the method comprises the following steps: S1, obtaining behavior data of a power user, and carrying out the preliminary data cleaning of the data; s2, performing multi-dimensional feature extraction on the cleaned behavior data, and generating a user behavior portrait in combination with a user identity tag; s3, inputting the user behavior portrait into a behavior scoring model, and outputting a behavior score and a risk coefficient; and S4, dynamically configuring an authority level by an authority control engine according to a scoring risk mapping rule. By introducing a dynamic permission adjustment mechanism based on the user behavior score and risk assessment, the behavior mode of the user can be monitored and analyzed in real time, the permission level of the user can be dynamically adjusted according to the real-time demand and risk level of the user, the permission demand of the user can be judged according to the actual operation behavior of the user, and the user experience is improved. Excessive or insufficient authority authorization is avoided, and intelligence and dynamics of authority management are improved.

The invention relates to the technical field of hospital robot inspection, and discloses a hospital logistics robot security inspection path planning system. The system comprises a data acquisition module, a region partitioning module, a risk mapping module, a path combination module, a dynamic updating module and an anomaly detection module. The data acquisition module collects robot sensor data and environment data; the area partitioning module identifies a hospital area security risk intention based on the data, and divides a plurality of risk partitions; a risk mapping module obtains risk events corresponding to the risk partitions and constructs a mapping relationship between the risk events; a path combination module extracts key risk partitions and generates a path deviation combination; the dynamic updating module is combined with real-time monitoring data to update the mapping relation; and the anomaly detection module analyzes the behavior data of the robot based on the updated mapping relation to identify the abnormal activity. The system can adapt to the complex environment of a hospital, improves the polling pertinence and timeliness, and guarantees the security and protection safety of the hospital.

The invention discloses an intelligent ship navigation system based on a space-time prediction risk field and deep reinforcement learning, relates to the technical field of ship navigation, and aims to solve the problem that a conventional navigation system is insufficient in dynamic risk assessment and prospective planning in the prior art. The technical key points are that the system comprises a multi-source data acquisition module, a dynamic risk mapping module, an intelligent path planning module, a visual interaction module, a risk early warning module and a self-checking module. According to the system, multi-source data such as weather, ocean current and ship dynamics are collected in real time, a predictive risk field is generated by using a long-short-term memory network, a route is optimized in combination with an adaptive deep reinforcement learning algorithm, and risk distribution and a recommended route are visually presented in a multi-scale three-dimensional form; the system can early warn a high-risk area in real time, and the reliability is ensured through a self-checking function. According to the invention, the sailing safety and the intelligent level of the ship in a complex environment are obviously improved.

The invention relates to the technical field of construction safety management, in particular to a cloud platform-based construction industry operation condition risk evaluation system, which integrates cloud computing, Internet of Things and AI (artificial intelligence) technologies, and constructs a whole-process safety management system. Risk indexes are intelligently calculated in combination with an improved LEC-M model, four levels of risk levels are divided, and parameter customization is supported; multi-mode early warning is automatically triggered based on a risk threshold value, and rectification closed-loop management is realized through dynamic recheck; cloud architecture deployment is adopted, multi-terminal access and data interaction are supported, and data security is guaranteed through HTTPS encryption and an AES-256 technology; and the supervision docking module realizes real-time sharing of risk data, provides risk map visualization, historical data mining and AI auxiliary analysis, and supports risk trend prediction and intelligent recommendation of rectification schemes. According to the system, the accuracy and efficiency of building safety management are improved, and the intelligence from risk identification to supervision decision making is realized.

The invention relates to the technical field of infusion nursing, and discloses an intelligent monitoring method and system for nursing infusion in an operating room, and the method comprises the steps: collecting the pressure intensity, flow velocity and liquid level height of an infusion pipeline, and obtaining physiological parameters; in a non-transfusion state, collecting N frames of drip chamber images to establish a coordinate system and a background template; m frames of images are obtained in the infusion process, a binary foreground difference image is constructed, a connected domain is extracted, barycentric coordinates are calculated, and whether the images are the same liquid drop track or not is judged through the barycentric distance; counting the number of liquid drops and calculating the flow velocity by combining the drop ratio; constructing infusion indexes including a time sequence consistency score and a pressure diagnosis factor; risk vectors are constructed through patient parameters and infusion indexes, and normalization processing is carried out to generate dynamic risk mapping; and designing a risk scoring algorithm, setting grades according to risk values, and taking different treatment measures.

The invention provides a method and a system for generating a dynamic risk map of unmanned aerial vehicle operation on the ground, which provide detailed risk assessment for unmanned aerial vehicle flight, identify potential risks of a flight area, avoid a high-risk area, improve flight safety and provide a scientific basis for low-altitude flight path planning. And the damage to ground personnel and facilities caused by falling of the unmanned aerial vehicle or other accidents is reduced, so that the threat to the ground personnel and facilities caused by air crash accidents is reduced. According to the method and the system for generating the dynamic risk map of the unmanned aerial vehicle operation on the ground, the problems of insufficient multi-factor fusion, insufficient static data precision, poor dynamic adaptability and the like in an existing unmanned aerial vehicle risk assessment method can be efficiently solved.

A system for determining a subjective risk score may include a vehicle and / or a computing device associated with a user travelling within the vehicle. The computing device may receive input from the user when the user feels a sense of unease regarding a particular road segment upon which the vehicle is traveling. The system may further include a subjective risk analysis computing system that may be communicatively coupled to the computing device. The subjective risk analysis computing system may receive subjective risk information corresponding to the user's sense of unease regarding particular road segments and may process the subjective risk information to determine a subjective risk score for each of a plurality of road segments along a route. An insurance company may use this information to determine whether to adjust a quote or premium of an insurance policy.

The invention provides an allergy risk prediction method and device based on indoor physical environment simulation and electronic equipment, and relates to the technical field of medical health data analysis, and the method comprises the steps: constructing a parameterized three-dimensional building model based on spatial information; based on the three-dimensional building model and the external meteorological parameters, coupled physical field simulation is carried out on sunlight, natural lighting and natural ventilation conditions in the target building space, and quantitative environment field distribution data is obtained; matching and fusing the environment field distribution data with a preset risk mapping rule based on allergen breeding physical conditions, identifying and dividing different levels of environment risk areas in the target building space, and generating a visual risk distribution map with space coordinates; and generating a personalized report containing a targeted environment intervention strategy bound with the spatial position based on the specific risk area position and the grade thereof in the risk distribution map. According to the method, quantitative environment data support is provided, and health suggestions are upgraded from experience judgment to data decision.

This invention provides a method and system for constructing a negative obstacle risk map, relating to the field of robot environmental perception technology. The method includes: acquiring multimodal sensing data of a legged robot in the current operating environment to obtain the types of negative obstacles, multiple candidate regions, and the corresponding confidence and uncertainty of each candidate region; using a risk diffusion algorithm, performing ink blurring processing based on the type, confidence, and uncertainty to generate a continuous risk field corresponding to each candidate region; dividing the continuous risk field to generate a continuous risk gradient distribution corresponding to each candidate region; performing terrain modeling based on laser point clouds, updating the continuous risk gradient distribution using Bayesian fusion to obtain a multi-level grid map, and then performing tactile closed-loop correction on the multi-level grid map to generate a risk cost map. This invention improves the accuracy of constructing a negative obstacle risk map.

The application discloses a kind of based on chip drive automatic adaptation and performance optimization method, specifically related to chip performance adjustment technical field;First, construct operation characteristic tensor, extract and piecewise polynomial fitting by extreme value envelope to construct stability risk boundary function, establish risk mapping relationship and calculate risk gradient vector, in real-time operation process, input stability risk prediction model with real-time operation characteristic set and real-time phase characteristic vector, obtain risk coefficient and risk gradient vector, and under the condition that risk coefficient is less than preset risk threshold, construct multi-objective constraint optimization process, solve optimal feature adjustment vector, and core voltage, core frequency and load scheduling phase are optimized in coordination;The application can realize the prospective control of stability risk under complex transient load scene, improve unit power consumption performance under the premise of guaranteeing reliability, with higher adjustment precision and energy efficiency optimization effect.

The invention relates to a transformer defect early warning method based on time sequence fragment association evidence fusion and a dynamic risk map. The method comprises the following steps: acquiring a standardized time sequence data set with a state label; obtaining a time sequence evidence body; calculating a dynamic fault probability and generating a dynamic risk curve; constructing and continuously iterating a dynamic risk association graph; and outputting quantitative risk early warning and generating a self-adaptive optimization operation and maintenance strategy based on the cloud edge cooperative computing framework. By introducing preprocessing based on data fluctuation characteristics and time sequence evidence body construction, deep mining and uncertainty quantification of a multi-modal long-term evolution law are realized, and the early recognition capability and evaluation reliability of latent defects are remarkably improved; through cloud edge cooperation and multi-objective optimization strategy generation, the unification of efficient utilization of computing resources and decision scientificity is realized, the output quantitative early warning and priority strategy directly supports field lean operation and maintenance, and a complete intelligent operation and maintenance closed loop is formed.

The present application relates to the technical field of fire accident monitoring and early warning and fire rescue, in particular to a simulation deduction and auxiliary decision system for multi-point concurrent leakage accidents of hydrogen stations, which comprises an accident scene dynamic risk mapping module, which is used for receiving and gridding a rescue area, collecting real-time monitored hydrogen concentration, heat radiation flux and explosion overpressure values for each grid. In the present application, by receiving and gridding the rescue area, collecting real-time monitored hydrogen concentration, heat radiation flux and explosion overpressure values, and combining the path interruption probability caused by hydrogen storage and hydrogenation structure collapse, a unified grid risk value is obtained, and a time-varying three-dimensional risk field data covering the entire accident scene is established, which converts discrete and multidimensional on-site monitoring data into an intuitive global danger situation picture, enabling fire rescue commanders to master the evolution law of risk in space and time.

The invention discloses a public security digital certificate dynamic supervision and management method and system fused with big data analysis, and relates to the technical field of digital certificate management. All credible operation feature points and corrected feature points are integrated, a feature set of the operation is output, operation data of the feature set are input into a behavior-risk mapping model, and a behavior-risk mapping model is established; and deducing a corresponding risk quantized value, establishing a risk probability assessment function according to the risk quantized value, fusing prior knowledge provided by the macroscopic baseline scanning layer, performing inversion by utilizing a probabilistic reasoning technology to obtain posterior probability distribution of the operation on each risk dimension, and summarizing the comprehensive risk level of the digital certificate operation according to the posterior probability distribution. And the corresponding hierarchical management and control actions are automatically triggered. According to the management system, through fusion of big data analysis and a multi-level risk perception technology, full-process dynamic accurate supervision of public security digital certificate operation behaviors is realized.

The invention relates to a risk aggregation method based on improved conditional value-at-risk in an off-road scene, belongs to the technical field of vehicle control, and solves the problem that off-road environment traffic risks are difficult to accurately and reliably assess due to the fact that risk indexes are isolated analysis or simple fusion and uncertainty of data is not fully considered in the prior art. Comprising the following steps: establishing a conversion relation among a planning coordinate system, a vehicle body coordinate system and a sensor coordinate system by taking a world coordinate system as a reference; acquiring current pose information of the vehicle and current point cloud data of the sensor in real time; performing horizontal processing on the current point cloud data to obtain horizontal point cloud data, and constructing a 2.5 D grid elevation map by adopting a sparse Gaussian process regression method; obtaining the cost of each traffic risk index of each grid based on a 2.5 D grid elevation map; and converting the cost of each traffic risk index to a planning coordinate system, and generating a current comprehensive traffic risk map by adopting an improved conditional value-at-risk method.

The embodiment of the application belongs to the field of artificial intelligence and financial technology, is applied to the field of insurance risk map construction, and relates to a business risk map construction method and device, computer equipment and a storage medium, which comprises risk factor normalization processing; an area map to be subjected to risk map construction is acquired and subjected to rasterization processing; property type business data is subjected to normalization processing; the correlation between different risk factors and the property type business data is analyzed by using a typical correlation analysis method to obtain business weight coefficients of different risk factors; multiplication calculation is performed according to the business weight coefficients and the property type business data in each grid map block to obtain corresponding business data of different risk factors in each grid map block, and the business data is marked in the area map corresponding to a specific map area to complete business risk map construction. The application combines actual business data to perform risk map construction, thereby guaranteeing the scientificity of business risk map construction.

The invention relates to the technical field of fire accident monitoring and early warning and fire rescue, in particular to a hydrogen refueling station multi-point concurrent leakage accident simulation deduction and auxiliary decision making system which comprises an accident site dynamic risk composition module, an accident site dynamic risk composition module, an accident site dynamic risk composition module and an accident site dynamic risk composition module, and the hydrogen concentration, the heat radiation flux and the explosion overpressure value monitored in real time are integrated. According to the method, a rescue area is received and gridded, hydrogen concentration, thermal radiation flux and explosion overpressure numerical values monitored in real time are integrated, and path interruption probabilities caused by collapse of hydrogen storage and hydrogenation structures are combined, so that a unified grid risk numerical value is obtained through calculation, and then time-varying three-dimensional risk field data covering a whole accident scene is established; discrete and multi-dimensional field monitoring data are converted into a visual global danger situation picture, so that fire rescue commanders can master evolution rules of risks in space and time.