45 results about "Hazard index" patented technology

The invention relates to the field of industrial safety monitoring, provides a real-time risk identification and active safety control method and system for industrial operating personnel in order to improve the accuracy and efficiency of a safety monitoring technology, and realizes real-time acquisition and edge preprocessing of human-machine-environment data by integrating a physiological / environmental / equipment multi-mode sensor. An FPN network is utilized to fuse spatial-temporal characteristics, Temporal-PC causal inference and a GBDT integration algorithm are combined, an interpretable risk propagation model is constructed, risk indexes are calculated, three-level early warning is divided, and hierarchical intervention is executed for different risk levels, including local early warning, environment regulation and control, emergency evacuation and multi-department emergency linkage. Through LSTM trend prediction and reinforcement learning dynamic parameter adjustment, closed-loop optimization of an intervention strategy is realized, the problems of information splitting, response lag and the like of a traditional monitoring system are solved, the risk identification precision and emergency disposal efficiency of a chemical high-risk scene are remarkably improved, and the method is suitable for operation safety control in high-temperature, high-pressure, toxic and harmful environments.

The invention discloses a lightning multi-parameter space-time analysis method based on clustering tracking and a thunderstorm risk early warning method and system, and relates to the technical field of severe convective weather early warning. The method comprises the following steps: collecting and analyzing lightning data; data preprocessing and space-time collaborative clustering: clustering lightning belonging to the same thunderstorm monomer in the current time window by using optimized clustering parameters; cross-period target tracking and association: constructing a lightning cluster tracking chain of a continuous time window; performing multi-factor dynamic feature extraction, namely capturing real-time dynamic feature parameters on the associated cluster sequence; and constructing a hazard index model and carrying out risk early warning. According to the method, dynamic tracking, strength evaluation and danger grading of thunderstorm activities can be realized by utilizing lightweight and easily acquired lightning positioning data, the method has the characteristics of light calculation load and high real-time performance, and objective and quantitative technical support can be provided for refined short-term and temporary early warning decision making.

The invention relates to the technical field of environment monitoring, in particular to a building decoration environment harmful gas dynamic monitoring method based on deep learning, which comprises the following steps: acquiring temperature, humidity, volatile organic compound concentration, carbon dioxide concentration, energy consumption and equipment attitude in a building model coordinate system, and eliminating redundant channels according to spatial topology to form a simplified data stream; dense features are extracted from the simplified data flow through a space-time auto-encoder, query key values are generated through quantification, and adjacent historical features are retrieved and spliced into a fusion feature vector; inputting the fused feature vector and a sensing node coordinate into a physical neural network of an embedded gas diffusion and heat conduction equation, and predicting a temperature gradient and regional carbon equivalent emission; calculating a hazard index field according to prediction, heat flow simulation, construction progress and a material release rate, iteratively optimizing a sensing node set and writing the sensing node set into an environment mirror image body; and reconstructing a coupling field in the environment mirror image body, comparing a measured value to obtain a residual field, and updating a hazard index field and a sensing node set when the environment mirror image body is abnormal.

The invention discloses a radar sounding-based hail grading early warning method and system for a convective monomer hazard index and jump increase, and relates to the technical field of severe convective weather early warning. According to the method, monomer segmentation is carried out and a severe convection monomer hazard model is constructed based on regional multi-weather radar data and air sounding data, a discrimination threshold is determined based on local air sounding data, and segmented convection monomers are screened for three times to obtain classification types of the convection monomers; and on the basis, tracking a hazard index track of a convective monomer core area in real time, and when a monomer hazard index shows a remarkable jump characteristic in a set time window, dynamically judging the occurrence probability and grade of hail, and pushing a digital alarm. The classification, digitization and progressive algorithm of the invention fuses multi-source data collaborative analysis, dynamic threshold determination and real-time evolution tracking, and significantly improves the timeliness and accuracy of the turning point of the heavy rainfall hail severe convection weather process and hail classification early warning identification.

The invention relates to the technical field of data processing, in particular to a threat recognition system and method.The method comprises the steps that a self-adaptive collection module obtains network flow data and operation log output feature vectors; the multi-dimensional rule module generates rule confidence; the statistical modeling module outputs an abnormal probability score; the machine learning module outputs a classification probability in response to the abnormal probability score dynamic selection model combination; the simulation decision module is fused with the multi-source data to execute threat diffusion simulation and output hazard indexes; the grading execution module triggers grading response according to the hazard index; and the feedback optimization module adjusts model parameters in a cross-layer manner. According to the method, noise interference is eliminated through feature optimization, a multi-dimensional model is fused to calibrate an output result, a threat grading mechanism preferentially responds to key threats, and closed-loop feedback is continuously injected into a false alarm data training model, so that the problem of high false alarm rate caused by insufficient training data coverage of a machine learning model in a complex dynamic network environment is solved; security resource allocation is optimized and key threat response delay is shortened.

The invention belongs to the technical field of image data processing, and particularly relates to an electron beam welding quality detection method and system, and the method comprises the steps: obtaining a welding seam image under the irradiation of a light source in a fixed direction; identifying and extracting defect plaques and expanded neighborhoods thereof and reconstructing the defect plaques and the expanded neighborhoods thereof into a local three-dimensional surface; obtaining a shadow depth index representing the depth of the defect plaque by analyzing the shadow feature of the defect plaque under directional illumination; based on the maximum principal curvature and the direction of each point of the reconstructed three-dimensional surface, obtaining a stress concentration ratio for evaluating the sharpness degree and the direction consistency of the micromorphology of the defect plaque; and obtaining a comprehensive hazard index through the shadow depth index and the stress concentration ratio, and carrying out detection and quality evaluation on the defect plaque according to the index. According to the method, the high-risk defect patches and the surface flaws are distinguished by evaluating the depth and the stress concentration ratio of the defect patches, and a reliable basis is provided for welding quality evaluation.

This invention discloses a method for earthquake landslide hazard zoning based on subtractive clustering ANFIS, belonging to the field of earthquake landslide hazard zoning technology. The method includes the following steps: collecting earthquake landslide monitoring data including seismic data, topographic data, meteorological and hydrological data, and image data; extracting features from the image data to obtain feature-extracted data; based on the earthquake landslide monitoring data, using a subtractive clustering algorithm to determine the initial cluster centers of the earthquake landslide area, thereby obtaining initial clustering information for the earthquake landslide; and combining the earthquake landslide hazard levels and their monitoring data within each geographic grid unit to draw an earthquake landslide hazard zoning map. The data acquisition technology, subtractive clustering algorithm application technology, ANFIS model construction technology, and data calibration technology in this method are closely integrated with modern information technology. The hazard calibration coefficient is used to calibrate the hazard index, improving the accuracy of earthquake landslide hazard assessment.

This invention relates to a method for risk assessment of multiple ecological factors in lakes based on DOM (Dissipative Oxygen Species), nutrients, and the coupling of functional bacteria and pathogen resistance. The invention aims to address the technical problems of current lake risk assessment methods, which primarily focus on evaluating single resistance risks, neglect the ecological buffering role of functional bacteria, and fail to reflect the coupled risks of multiple ecological factors in lakes. This invention obtains DOM parameters, nutrient parameters, functional bacteria indicators, pathogenic bacteria indicators, and drug resistance transmission factor indicators; calculates a comprehensive risk value R based on the environmental stress index E, ecological buffering index B, transmission hazard index H, and DOM-mediated transmission triggering coefficient K, and performs risk classification and spatial zoning assessment. This invention incorporates DOM activity, nutrient imbalance, the ecological buffering capacity of functional bacteria, and the drug resistance transmission potential of pathogenic bacteria into a unified evaluation framework, applicable to comprehensive ecological security assessment, risk early warning, and zoning management of lakes such as Poyang Lake.

The invention relates to the field of gas detection and early warning, in particular to an intelligent gas detection and early warning method and device for a multi-module integrated device. The method comprises the following steps: collecting multi-dimensional gas parameters of a scene, a real-time spectrum monitoring image and real-time positioning information of a user; performing environment temperature and humidity compensation on the multi-dimensional gas parameters to generate environment compensation gas parameters; performing abnormal gas concentration identification and toxic gas detection on the environmental compensation gas parameters to obtain an abnormal gas identification result; performing targeted gas spectrum analysis on the real-time spectrum monitoring image according to an abnormal gas identification result, and performing concentration situation change prediction so as to generate concentration situation prediction data; and performing comprehensive gas risk index evaluation according to the concentration situation prediction data to generate an environmental risk index. The mining area abnormal gas early warning is accurately and efficiently completed, and the safety and efficiency of mining area management are improved.

The invention relates to a method for determining a navigation route (1) of a vehicle, in which further road users (2) are detected by means of a sensor system (3), and sensor data generated by the sensor system (3) are evaluated by a computing device (4) in order to determine the behavior of the respective road user (2) in the road traffic. The method according to the invention is characterized in that the computing device (4) stores a database (5) comprising behavior limit values; -the computing device (4) compares the behavior observed by the road user (2) with the behavior limit values stored in the database (5) and counts the events (E) on the road section on which the road user (2) stays if the respective observed behavior of the road user (2) exceeds the associated behavior limit value; -the computing device (4) computes a hazard index (GI) for at least one road section as a function of the number and value of events counted over the respective road section over the specified time period; and-the navigation unit takes into account the risk index (GI) assigned to the respective route section for route planning, the route section of which the risk index (GI) exceeds a specified risk index threshold value being avoided in order to determine the navigation route (1).

The invention provides a regional soil heavy metal water input type pollution risk assessment method and system, and relates to the technical field of soil pollution risk assessment. Aiming at the problem of insufficient accuracy of existing pollution risk assessment, the method comprises the following steps: acquiring a sub-basin boundary, a cadmium-containing wastewater discharge point, cultivated land distribution data, a grain yield, cultivated land irrigation water consumption and a flood-submerged cultivated land area; calculating a cadmium emission hazard index, an irrigation exposure index, a flood exposure index and a vulnerability index according to the acquired data; after standardization processing is conducted on the key indexes, cadmium pollution risk values of the irrigation approaches and the flood approaches of all the sub-basins are calculated; dividing an irrigation exposure type and a flood exposure type according to a set threshold value, and determining a comprehensive risk type by combining the dual-path risk value; a sensitive analysis model is adopted, the key index is used as an input quantity, the double-path risk value is used as an output quantity, and a sampling and statistical method is set to identify a sensitive factor with the maximum influence of the risk value. According to the method, comprehensive and accurate evaluation of the watershed scale pollution risk is realized.

The application discloses an electrical fire hazard detection and early warning method based on an intelligent sensor, relates to the technical field of intelligent sensors, and comprises the following steps: collecting data, generating a sensing data set and a loop structure table; mapping channels and arranging multi-source hazard time sequence samples; extracting an electrical fire hazard time-frequency feature set based on an improved WFTNet model; separating a zero sequence sine trajectory by adopting an SGMD algorithm, and recalibrating an electrical heating sine characteristic value; constructing a component-loop matrix and generating a hazard index; calculating component responses and generating a hazard discrimination matrix; and mapping early warning levels and disposal objects to output results. By introducing the improved WFTNet model and the SGMD algorithm, the electrical fire hazard time-frequency identification, mode decomposition, loop positioning and hierarchical early warning of multi-source sensing data of an electrical loop under complex electrical working conditions are realized.

The invention provides a method and device for estimating vegetation growth vigor change influenced by high temperature-drought composite disasters, and relates to the technical field of disaster assessment, and the method comprises the steps: obtaining historical satellite remote sensing data and historical meteorological station data corresponding to a research area; according to the historical satellite remote sensing data and the historical meteorological station data, identifying a high temperature-drought composite event historically occurring in the research area and corresponding historical risk index data, and determining historical growth period distribution corresponding to the research area; determining a historical growth change based on the high temperature-drought composite event and the historical growth period distribution; and training the machine learning model by taking the historical risk index data as input and the historical growth vigor change as a label to obtain a growth vigor change pre-estimation model which is used for pre-estimating the growth vigor change of the vegetation in the research area. According to the method, the vegetation growth vigor change can be estimated according to disasters.

This invention relates to a method and system for unmanned aerial vehicle (UAV) power line inspection based on multimodal fusion, belonging to the field of partial discharge detection. The system includes a meteorological station along the power line, a quadcopter UAV, a remote cloud server, and a mobile terminal. The meteorological station collects environmental meteorological information; the quadcopter UAV collects UHF and infrared temperature signals, receives flight control commands, and completes power line inspection tasks; the remote cloud server uses a multimodal fusion method to determine the type and location of partial discharge, calculates the discharge risk hazard index according to the discharge risk hazard index formula, incorporates it into the power system maintenance work plan, generates disposal suggestions, and sends them to the mobile terminals of maintenance personnel to complete the maintenance tasks. This invention integrates multimodal data from the power line environment, UHF data, and infrared thermal imaging data to detect partial discharge type and location, calculate the discharge hazard index, and generate disposal suggestions, achieving integrated management of power line inspection and maintenance.

The application discloses a hail prevention operation method and system based on a convection cell hazard index and hail identification, and relates to the technical field of weather monitoring and artificial weather modification command operation. The method is based on multi-source observation such as weather radar and sounding, establishes a strong convection cell hazard model and a hail identification model, identifies the type of each strong convection cell in the hail identification model by combining two segment score thresholds, and determines the development stage corresponding to the strong convection cell. According to the identification type and stage of the strong convection cell, a segmented operation strategy is constructed, a task allocation mechanism for multiple convection cells and multiple operation points is established, and an element method for calculating cannonball launching parameters is established. The application provides a precise operation process for hail prevention command, reduces human intervention in hail prevention operation, and improves the efficiency and accuracy of decision execution.

This invention discloses a method for monitoring the safety of tailings dam waterlogged areas based on deep learning, comprising: Step 1, acquiring panoramic images of the waterlogged area and close-up images of the drainage outlet, preprocessing the images, and extracting basic features; Step 2, calculating the initial image texture entropy, and calculating the actual water level change based on the basic features; Step 3, calculating the real-time turbidity based on the basic features and the initial image texture entropy; Step 4, calculating the water flow state quantification value, and calculating the siltation coefficient based on the water flow state quantification value to determine the degree of siltation; Step 5, calculating the comprehensive hazard index based on the basic features, the actual water level change, the real-time turbidity, and the siltation coefficient, determining the risk level, and identifying the hazard type. This invention solves the problems of traditional machine vision monitoring algorithms having poor adaptability to dynamic interference in waterlogged areas, water quality assessment not being correlated with particle settling characteristics, water flow analysis not being coupled with hydrostatic resistance, and hazard judgment relying on a single indicator, which is prone to misjudgment.

This invention relates to the field of hazard assessment of hydrogen sulfide gas in tunnels, specifically to a method and equipment for classifying the hazard level of hydrogen sulfide gas in tunnels. The classification method includes the following steps: establishing assessment standard one based on the presence of sulfur-bearing strata and the temperature and pressure environment of the tunnel area; establishing assessment standard two based on the presence of structural fractures in the tunnel; establishing assessment standard three based on the development level and migration path of groundwater; establishing assessment standard four based on the lithology of the tunnel strata; calculating the assessment scores obtained according to assessment standards one, two, three, and four to obtain a hazard index score; and determining the hazard level of hydrogen sulfide gas in the tunnel based on the hazard index score. The classification method of this invention can simply and quickly evaluate the hazard level of hydrogen sulfide gas in tunnels and has strong engineering practicality.

The application provides a mass concrete crack evaluation method and system for a fan foundation, relates to the technical field of building engineering and structure monitoring, and calculates crack hazard indexes, influence indexes and expansion indexes according to the parameters, forms characteristic indexes representing crack conditions, establishes a concrete crack risk prediction model based on a convolutional neural network, receives the characteristic indexes through an input layer, processes the indexes through a hidden layer, outputs risk grade indexes, considers construction design parameters to generate a crack risk correction factor, calculates a crack risk correction coefficient, compares the coefficient with a preset correction threshold, uses a correction ascending and descending mechanism, and determines the final risk grade indexes of the concrete cracks. The application realizes accurate evaluation of the concrete crack risk of the fan foundation by monitoring the characteristic parameters of the concrete cracks in real time and combining the convolutional neural network model.

The invention provides a mass concrete crack evaluation method and system for a fan foundation, and relates to the technical field of constructional engineering and structural monitoring, and the method comprises the steps: collecting the characteristic parameters of a crack in real time, calculating a crack hazard index, an influence index and an expansion index according to the parameters, and forming a characteristic index representing the crack condition; establishing a concrete crack risk prediction model based on a convolutional neural network, receiving the characteristic index through an input layer, outputting a risk grade index after hidden layer processing, considering construction design parameters to generate a crack risk correction factor, calculating a crack risk correction coefficient, and comparing the coefficient with a preset correction threshold value, and determining a final risk grade index of the concrete crack by using a correction lifting mechanism. By monitoring the characteristic parameters of the concrete crack in real time and combining the convolutional neural network model, accurate assessment of the concrete crack risk of the fan foundation is realized.

This invention provides a method, apparatus, and equipment for risk assessment of marine aquaculture caused by sea ice disasters, relating to the field of geographic environment technology. The method includes: acquiring multiple indicator factors corresponding to the hazard criterion, vulnerability criterion, and exposure criterion of disaster-causing factors, which determine the impact of sea ice disasters on marine aquaculture; determining the target weights for each of the multiple indicator factors corresponding to these criteria; determining the hazard index, vulnerability index, and exposure index; and determining the risk assessment index of sea ice disasters on marine aquaculture in a target area based on these indices. The solution of this invention can provide targeted solutions for marine aquaculture by combining the sea ice disaster risk assessment index and provide early warning of sea ice disasters.

The invention discloses an underground water level rising risk influence assessment method. The method comprises the following steps: acquiring underground water monitoring data; determining an underground water level rising early warning area according to the underground water monitoring data; the underground water level rising early warning area is subjected to type division, division results are obtained, and the division results comprise a salinization risk area, an urban building risk area and an urban water supply risk area; performing preliminary evaluation on the division result to obtain a risk level; the risk level is input into a risk influence quantitative evaluation model, an evaluation result is obtained, and the risk influence quantitative evaluation model comprises a salinity balance sub-model, a bearing capacity sub-model and a comprehensive hazard index sub-model; the salinity balance sub-model is used for evaluating the salinization risk area; the bearing capacity sub-model is used for evaluating the urban building risk area; and the comprehensive hazard index sub-model is used for evaluating the urban water supply risk area.

The invention relates to the technical field of traditional Chinese medicine quality supervision, and discloses a traditional Chinese medicine heavy metal probability risk assessment system and a visual platform, and the system comprises a data recognition module, an analysis calculation module and an assessment management and control module. According to the traditional Chinese medicinal material heavy metal probability risk assessment system, an assessment data set is collected through a data identification module, a target medicinal material consumption characteristic data set and a target medicinal material concentration characteristic data set are screened and analyzed according to a user instruction, a Monte Carlo (MC) algorithm is adopted to approach real data distribution, and the daily exposure EDI of target medicinal material heavy metal is calculated; distribution hypothesis is high in applicability and wide in range, a probability risk calculation unit calculates a hazard entropy HQ, a hazard index HI, a single heavy metal element carcinogenic risk index CRi and all heavy metal combined carcinogenic risks CRt, analysis automation and result visualization are achieved, a priority management and control list is determined, a corresponding evaluation report is generated, and evaluation is real, accurate and traceable.

This invention discloses a comprehensive assessment method for the risk index of crack flow hazards on rhythmic beaches, comprising: simulating the evolution of beach topography using a topographic linear instability model based on set initial equilibrium beach geomorphological characteristic parameters and incident wave parameters to generate rhythmic beach topography; performing crack flow simulation calculations using a simplified model based on nonlinear gentle slope equations based on the rhythmic beach topography and incident wave parameters to obtain the horizontal velocity field at the still water level; calculating the time-averaged Lagrange crack flow velocity distribution based on the horizontal velocity field to determine the crack flow risk area and crack flow velocity magnitude; obtaining the float drift trajectory based on numerical buoy simulation deployed in the nearshore area and calculating the float retention rate to characterize the crack flow cycle type; and quantitatively calculating the crack flow hazard risk index using a comprehensive assessment formula based on the crack flow velocity magnitude, the range of the crack flow risk area, and the float retention rate.

This invention relates to the field of monitoring and identification technology, and discloses a comprehensive identification and evaluation method for risk zones and hidden danger points of rainfall-induced cluster landslides. The method includes: determining the landslide hazard index and the vulnerability index of the affected body for each calculation unit in the target area based on an updated comprehensive database; determining the risk assessment result for each sub-region based on the landslide risk index and the vulnerability index of the affected body, including the landslide risk level corresponding to each sub-region; determining the deformation area in the target area based on synthetic aperture radar data; determining the hidden danger point identification result corresponding to the deformation area, including the disaster-prone points in the deformation area and the corresponding risk level of the disaster-prone points; adjusting the hidden danger point identification result based on the risk assessment result; and adjusting the risk assessment result based on the hidden danger point identification result and the mitigation results of the disaster-prone points. This invention can achieve dynamic feedback and optimization of risk and hazard assessment, reducing misjudgments.

This invention discloses an intelligent scanning method for software security vulnerabilities based on static analysis, specifically relating to the field of software security testing technology. The method acquires a snapshot of the baseline version source code and a snapshot of the modified version source code corresponding to the software project under test, establishes alignment relationships between program elements of the previous and current versions, and forms a set of modified elements. Combining the baseline version program graph cache, function summary cache, and historical hazard index, a comprehensive quantity of the impact of changes is formed, and a subgraph of the impact of changes is constructed. Static incremental vulnerability analysis is performed on the subgraph of the impact of changes to identify newly added risk paths and activated historical hazard paths, and a trusted comprehensive quantity of risk activation is formed. Based on the trusted comprehensive quantity of risk activation, confirmation screening and baseline updates are performed, and valid incremental alerts or paths awaiting review are output. This invention can narrow the analysis scope, reduce the resource consumption of full scans, and improve the ability to identify incremental vulnerabilities and the activation of historical hazard paths.

The invention discloses a nondestructive testing and risk assessment method and system for clogging of a tunnel drainage pipe. The method comprises the following steps: detecting the drainage pipe according to a surface-line-point gridding testing process by adopting a nondestructive testing technology to obtain the position and the deposition height of a clogging object; determining a clogging degree factor based on the deposition height and the diameter of the drainage pipe; determining a position weight factor based on the topological distance from the position of the clogging object to the outlet of the drainage pipe and the total length of the drainage pipe; determining a clogging hazard index according to the clogging degree factor and the position weight factor; and carrying out risk assessment on drainage pipe clogging according to the clogging hazard index. The problem that a traditional robot cannot enter a small-diameter pipeline and is blocked by hard clogging can be effectively solved, and single clogging judgment can be changed into systematic hazard quantitative evaluation.

This invention discloses a method and system for underground safety detection in coal mines based on facial image recognition, belonging to the field of coal mine safety detection technology. The specific implementation steps include: identifying the miners' identities and safety equipment wearing status in each work area using video target recognition technology and adaptive facial recognition methods, obtaining miner numbers and a first hazard index; secondly, analyzing the hazard level of various miners' behaviors based on motion trajectory matching and behavior analysis technology, obtaining a second hazard index; then, assessing the hazard level of the miners' working environment based on environmental parameters, calculating a third hazard index; finally, combining the miners' hazard index and the hazard level of the work area, comprehensively assessing the miners' work safety situation, obtaining a comprehensive hazard index, and classifying the hazard level to issue timely warning information, effectively reducing the safety risks of underground coal mine operations, ensuring the safety of workers, and reducing the occurrence of safety accidents.

The invention discloses an occupational health hazard monitoring method, device and equipment and a storage medium, and the method comprises the steps: obtaining the time-varying concentration data of hazard factors in different stations of a production line, and generating the hazard factor monitoring data of each station; obtaining the stroke data of each worker on the production line, and obtaining the hazard contact duration of each worker on the corresponding station according to the stroke data; and calculating the average contact concentration of each worker and the dust hazard index of each station according to the hazard factor monitoring data and the hazard contact duration of each worker at the corresponding station. By associating the monitoring data with the actual contact conditions of the individual workers, the problem that the detection data is disjointed with the station features and the contact conditions of the individual workers in the prior art is solved, and the accuracy and effectiveness of occupational health monitoring are improved.

The invention discloses an intelligent security and protection monitoring system and method for a community, and belongs to the technical field of security and protection monitoring. The system comprises a data acquisition module, a monitoring analysis module, a security management module and a visualization module. The data acquisition module is used for acquiring a facility equipment three-dimensional diagram and a video image of a community; the monitoring analysis module builds a three-dimensional scene through a digital twinning technology and performs real-time mapping, takes a human body target in the three-dimensional scene as an object, divides an attention area according to the position of the object, analyzes and calculates an active index of each object and a transaction index of each attention area, and sets an abnormal area according to the transaction index; the security and protection management module is used for analyzing position distribution, action information and active index change of each object in the abnormal area so as to calculate a danger index of each abnormal area, and setting a risk area and performing early warning; and the visualization module displays a three-dimensional scene through a visualization interface, displays a real-time video image of the risk area, and automatically gives an early warning to a manager for processing.

This invention provides a method, apparatus, electronic device, and storage medium for hail disaster risk assessment, belonging to the technical field of risk assessment. When determining the target disaster hazard index for each grid, it utilizes not only hail observation data from various meteorological observation stations but also historical disaster data and geographic grid data. This results in high accuracy of the target disaster hazard index for each grid, and the distribution of disaster hazard levels within the target area matches the actual disaster hazard distribution within the target area. Furthermore, when determining the risk assessment index for different disaster-bearing bodies within each grid, it uses accurate target disaster hazard indices for each grid and also employs vulnerability and disaster-prone environment sensitivity indices for different disaster-bearing bodies. This results in high accuracy of the risk assessment index for different disaster-bearing bodies within each grid, and the risk level distribution of the target disaster-bearing bodies within the target area matches the actual situation.