51 results about "Risk mapping" patented technology

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 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 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 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.

The invention discloses a mine robot emergency risk avoiding intelligent decision-making system, and the system comprises a disaster risk prediction module which is used for carrying out the time sequence prediction based on the data of a multi-source sensor, and outputting a disaster occurrence probability and a risk comprehensive index; the dynamic risk avoiding path planning module is used for constructing a dynamic risk map and performing path planning by adopting an improved A * algorithm; according to the system, advanced early warning, dynamic path planning, hierarchical response and multi-agent coordination in a mine catastrophe environment are realized by constructing an intelligent closed-loop architecture of perception, prediction, decision making, execution and coordination. Multi-source sensor data are fused, an LSTM time sequence prediction model is adopted to carry out disaster probability prediction, an A * algorithm is improved to realize risk sensitive path planning, a hierarchical response strategy is realized based on a finite-state machine, and robot group collaborative risk avoiding is realized through a publishing and subscribing mechanism. According to the method, the survivability, the decision-making efficiency and the collaborative operation level of the mine robot in the catastrophe environment are improved.

The invention provides an intelligent early warning and protection method and system for falling of an inpatient based on multi-modal data fusion. According to the method, individual risk data, behavior posture data and equipment state data of an inpatient are collected, a tumble risk index of the patient is calculated from the three aspects of potential risk, behavior risk and environmental risk by using a tumble risk prediction model, the tumble risk level of the patient is comprehensively evaluated, and then corresponding protection measures are taken. Specifically, each risk factor is quantified through a weight coefficient and a risk mapping function, and finally, an S-type normalization function is utilized to obtain a comprehensive risk index, so that accurate and personalized early warning is realized. The system comprises a sensing layer, a data processing layer, an intelligent analysis layer and an intervention execution layer, and can realize full-process automation and intellectualization. According to the invention, the problems of inaccurate evaluation, incapability of personalized early warning, lack of active intervention and the like in the prior art are effectively solved, and the occurrence rate of tumble of the inpatient is remarkably reduced.

The invention relates to the technical field of staged consumption of home decoration, in particular to a dynamic risk monitoring method for staged consumption of home decoration, comprising the following steps: S1, acquiring multi-stage consumption data and construction stage information formed in the process of staged consumption of home decoration; s2, based on a time sequence neural network, extracting a time sequence risk feature vector used for representing time evolution of a consuming behavior; s3, inputting the construction site image data into a convolutional neural network (CNN), and generating an image risk feature vector for representing the consistency of the construction progress and the declaration plan; s4, performing calculation through a risk mapping function to obtain a staging risk score of the staging node; s5, the comprehensive feature vector and the staging risk score are updated, and continuous dynamic risk monitoring of the whole home decoration staging consumption process is achieved; according to the invention, automatic and dynamic analysis and monitoring of the consistency of the fund flow and the real flow are realized.

The invention provides a three-level environmental risk assessment linkage method and system for an industrial park, relates to the technical field of environmental risk management, and aims to realize a unified semantic framework of enterprise-level, special-level and park-level data by constructing an environmental risk assessment ontology. According to the method, an event-driven dynamic updating mechanism is adopted, data recalculation and risk map adjustment are automatically triggered, and the timeliness and accuracy of environmental risk response are improved. The whole system effectively solves the problems of data islands, updating lagging, verification missing and the like, and provides real-time and accurate decision support for environment management of industrial parks.

The invention discloses a risk control method and system for real-time transfer transaction, and relates to the technical field of finance. The method comprises the steps of collecting transaction related information, and establishing an initial transaction intention fingerprint; obtaining transfer execution behavior information, updating the initial transaction intention fingerprint, and generating a transaction intention evolution track; performing consistency analysis on the transfer execution behavior information and the transaction intention evolution trajectory to obtain a transaction intention deviation result; generating a corresponding risk energy variation according to a preset risk mapping rule, and constructing a risk energy change track; judging the risk state of the transfer transaction request, and determining a risk control level; setting a risk control permission condition, judging whether the risk control permission condition corresponding to the risk control level is met or not, and executing a risk control action matched with the risk control level according to a judgment result. According to the method and the device, the accuracy of transfer transaction risk identification and the performability of risk control are remarkably improved.

The application discloses a kind of underground pipe network comprehensive risk assessment monitoring method and system, it is related to risk assessment monitoring field, including feature extraction and feature labeling processing to historical operation time series dataset;Based on the correlation analysis between pipe network operation time series curve and historical risk event record, construct dynamic mapping model;According to the pipe network operation time series curve of real-time acquisition determines candidate reference curve set;Similarity is calculated using dynamic time warping algorithm and is sorted;Risk factor details are analyzed by dynamic mapping model, and risk assessment probability interval is output;According to the deviation of actual risk occurrence and risk assessment result, curve-risk mapping rule set and similarity matching threshold are adaptively corrected, and typical risk curve template library is updated, realize underground pipe network comprehensive risk assessment and adaptive optimization based on time series curve dynamic matching, can automatically identify risk type, quantitative assessment probability, and continuously self-modification in actual feedback.

The invention discloses a coal mine operator behavior monitoring and early warning method, device, equipment and medium, and relates to the technical field of coal mine operation safety early warning, and the method comprises the steps: S1, multi-source time sequence data collection and time synchronization; s2, extracting edge privacy features; s3, performing individual behavior baseline establishment and situation labeling; s4, performing anomaly quantification and scene risk mapping; according to the method, a short-term and long-term double-layer statistical base line is established for each operator, and the real-time behavior deviation degree is weighted according to the field situation, so that the same behavior can be treated in different risk contexts, a deviation signal is amplified for the real danger situation, and accidental deviation is inhibited for the safety context. According to the judgment framework of individual reference and situation adjustment, the sensitivity to anomalies is improved, and false alarms caused by individual habit differences or operation differences are remarkably reduced, so that a better judgment boundary and stronger scene adaptability are realized on statistical judgment.

This invention relates to the field of smart grid inspection and edge computing technology, specifically to a cloud-edge collaborative computing resource balancing scheduling system for unmanned vehicles used in converter station inspections. It includes modules for collaborative management and control, risk mapping, demand prediction, resource scheduling, and feedback. The system identifies computing power sensitive areas and passage zones through physical entropy value correlation analysis. Its core is to combine scenario risk coefficients with visual semantic pre-simulation to determine instantaneous computing power peaks and demand types. When strong real-time, heavy computation, or low-risk signals are generated, edge exclusive locking, cloud high-priority slice offloading, or energy-saving degradation strategies are executed respectively. This invention changes the current scheduling situation that relies solely on CPU load, ensuring that resources are preferentially tilted towards high-risk areas, and realizing dynamic reconfiguration of computing power resources.

The invention discloses a food production supervision inspection result evaluation model construction system and method, and belongs to the field of food safety supervision. According to the system, real-time acquisition, standardization and fusion processing of structural and semi-structural supervision and inspection data of multiple links in the whole process of food production are realized through multiple types of terminals such as an Internet of Things sensing node and a mobile law enforcement terminal. The system adopts an adaptive semantic quantification algorithm to carry out automatic quantification and normalization on qualitative items, and realizes index same-scale fusion in combination with enterprise scale, category characteristics and the like. The evaluation model is based on a mechanism combining multilevel causal reasoning and a dynamic trusted network, integrates enterprise historical compliance behaviors and personalized scene factors, and realizes intelligent and accurate evaluation of enterprise risks by applying evolution collaborative weight distribution and adaptive optimization. The result is subjected to spatial positioning and graded display through a GIS risk map and multi-dimensional trend visualization, risk early warning is supported, and the scientificity and the intelligent level of food production supervision and inspection are improved.

The application discloses a rare earth mine area ecological risk assessment method and system based on rare earth element migration prediction, and belongs to the field of environmental risk assessment. The method comprises the following steps: acquiring environmental characteristic data sets of soil profiles at different depths in a target area; performing interface coupling effect calculation on the environmental characteristic data sets to generate a multi-medium migration flux data set, wherein the interface coupling effect calculation is used to determine interface characteristic parameters evolving over time according to third characteristic data, and to determine dynamic source and sink items based on the interface characteristic parameters, and the multi-medium migration flux data set indicates the migration flux of different phase states of rare earth at each depth evolving over time; and outputting a three-dimensional risk map according to the multi-medium migration flux data set, wherein the spatial dimension of the three-dimensional risk map indicates the migration and diffusion range of the rare earth, and the chemical dimension of the three-dimensional risk map indicates the bioavailability of the rare earth. The application improves the pertinence and accuracy of risk assessment.

The invention discloses a low-altitude risk map generation method and device, a medium and a product, and relates to the field of risk map generation, and the method comprises the steps: obtaining low-altitude risk related data; preprocessing the low-altitude risk related data, and calculating a corresponding risk value; determining a risk factor grid map according to the risk value; constructing a space-time diagram data set according to the risk factor grid map; according to the space-time diagram structure data set, extracting spatial features and time features of each node based on a space-time diagram convolutional network, and determining a dynamic risk occurrence feature tensor of each node under multiple time steps; according to the dynamic risk occurrence feature tensor, determining an overall coupling weight tensor of the risk factor by adopting a dynamic coupling reasoning model; performing nonlinear fusion on the dynamic risk occurrence feature tensor and the overall coupling weight tensor to determine comprehensive risk strength; determining a low-altitude risk two-dimensional thermodynamic diagram according to the comprehensive risk intensity; according to the invention, the accuracy and credibility of risk prediction in a complex environment can be improved.

The application discloses a cyanobacteria accumulation prediction method and system based on discontinuous data, and belongs to the technical field of cyanobacteria monitoring. The method comprises the following steps: collecting historical discontinuous data of multiple monitoring stations in a target water area and processing the data to obtain pretreated data; establishing a cyanobacteria morphology-risk mapping rule, extracting cyanobacteria condition information from the pretreated data, and converting the information into corresponding risk probability values as cyanobacteria accumulation risk labels; generating a feature variable set to construct an accumulation prediction model, using the accumulation prediction model to output an initial value of cyanobacteria accumulation probability of a target prediction point; constructing a correction function based on spatial position and meteorological factors to correct the initial value of cyanobacteria accumulation probability to obtain a final cyanobacteria accumulation probability; and matching a risk level according to the final cyanobacteria accumulation probability to generate corresponding response measures. The application establishes a cyanobacteria morphology-risk mapping rule, realizes quantitative evaluation of cyanobacteria accumulation risk, and provides clear risk reference basis for subsequent targeted decision-making.

This invention relates to a multi-parameter dynamic assessment system and method for early warning of urosepsis, belonging to the field of data processing technology. The system includes: a data acquisition and preprocessing module for obtaining unified time-series multi-source pathological data; a feature extraction and interactive evolution modeling module for obtaining a temporal evolution feature representation of the patient's pathological state; a risk fusion assessment module for obtaining the patient's dynamic urosepsis risk score; a risk mapping and graded early warning module for generating corresponding level of visualized early warning information; a risk attribution and clinical intervention prompt module for generating risk attribution results and outputting corresponding clinical intervention prompt information; and an adaptive learning and cross-domain transfer module for online updating of the early warning model based on the doctor's records of handling the visualized early warning information and the patient's final outcome information. This system can improve the accuracy of urosepsis early warning.

This invention discloses an intelligent system for HIV prevention and detection, belonging to the field of medical and health data processing technology. The system includes an intelligent testing kit, a user terminal, and a disease control management terminal. The intelligent testing kit has a built-in image acquisition unit for acquiring the colorimetric image of the HIV test strip; the user terminal receives the colorimetric image and extracts colorimetric feature parameters, generates a continuously variable risk index through a risk mapping model, and simultaneously identifies high-risk scenarios through a behavior perception module, dynamically pushing intervention information based on the risk index and / or high-risk scenarios; the disease control management terminal receives the desensitized risk index and corresponding anonymous identifiers and spatiotemporal data, constructs a risk propagation model based on a graph neural network, traces the spatiotemporal companions of risk nodes, and pushes anonymous early warning information. This invention achieves a closed loop from individual detection to group early warning, which can improve the early detection rate of HIV, achieve precise intervention, and optimize resource allocation.

The invention relates to the field of ecological environment risk assessment, in particular to a regional ecological restoration engineering risk early warning system and method based on a DPSIR model. The method comprises the following steps: firstly, dividing grids, and carrying out standardization processing on obtained driving force index data and pressure index data of the grids to obtain a driving force standardization value and a pressure standardization value; based on the driving force standardized value, a dynamic driving force index is obtained through a spatial one-way infectious driving force dynamic index algorithm; then, on the basis of the dynamic driving force index, the pressure standardization value and the obtained historical state value, a state value is obtained through a pressure-state transmission algorithm; calculating a risk index of the grid based on the state value, and determining a risk level of the grid; and finally, performing spatial visualization rendering on the risk level, and outputting a risk map layer. The technical problems that ecological risk assessment is static, historical state memory and tolerance threshold change of an ecological system are neglected, and complex cross influence is not modeled are solved.

The invention relates to the technical field of artificial intelligence security, and discloses a security situation awareness and risk assessment method and system for a large model. The method comprises the steps of obtaining an input data stream, an output data stream, intermediate state information and context information of a target large model; constructing a multi-level hierarchical attack path map; executing a cross-dimensional risk mapping task by adopting a dynamic multi-factor scoring mechanism; generating a defense strategy suggestion set by adopting a fuzzy reinforcement learning mechanism; and constructing a security situation map. Compared with the prior art, a large model faces multi-dimensional input disturbance and is deeply hidden in vulnerability, and especially under the condition of composite attacks such as input trigger semantic camouflage attacks, intermediate state pollution and context interference, attack path positioning and defense strategy generation cannot be achieved. Due to the fact that hierarchical attack graph reasoning and fuzzy reinforcement learning type defense strategy generation are combined, the self-adaptive defense capacity of a large model in a complex confrontation environment is improved.

The invention discloses a protection configuration comparison and consistency checking method, system and device based on an SCD file and a medium, and belongs to the technical field of power system automation, and the method comprises the steps that a reference SCD file and a to-be-checked configuration file are analyzed respectively, and a structured data model is obtained; processing and outputting an initial difference set through a structured comparison algorithm; performing hierarchical discrimination to obtain a discrimination difference set; verifying the discriminated difference set to obtain a final difference set; and presetting a risk mapping rule, performing risk assessment on the final difference set, and generating a risk assessment result. According to the method, automatic identification of the configuration difference is realized through a structured comparison algorithm, and the influence range of the configuration difference is positioned by combining a hierarchical check model of an interval level, an equipment level and a virtual loop level; consistency verification of on-site line protection device configuration is achieved through a check code mechanism, risk assessment is conducted by presetting a risk mapping rule, and the accuracy, efficiency and safety of intelligent substation configuration management are improved.

This invention relates to the field of food safety risk management technology, specifically to a risk analysis system for aquatic products based on multi-factor analysis. The system includes a contaminant response module, a path aggregation module, a channel decision module, a contamination risk simulation module, and a risk mapping and aggregation module. In this invention, by extracting the concentration change direction of contaminants in specific water areas over continuous time periods and establishing sequence combinations of concentration increase segments, the temporal behavioral characteristics of contaminants can be identified. This effectively determines the overlap between risk factors and biological group exposure, integrates exposure frequencies within a standard exposure cycle, and filters sensitive periods. By analyzing the frequency overlap of multiple aquatic products under the same factor, common factors are grouped, enabling the risk impact map to reflect factor interaction and category-based empathetic response relationships. This improves the comprehensiveness, granularity, and predictive ability of risk assessment in dynamic scenarios.

This application proposes a method and equipment for identifying risk sections of high-speed railway bridges and overhead contact lines. Through regional system parameterization, structural network modeling, construction of seismic influence fields along the line, integrated algorithm calculation of bridge-net coupling probability prediction and progressive screening of the most unfavorable sections based on risk upper bound, risk mapping and progressive screening, and bridge-net risk dominance classification, the most unfavorable sections can be quickly identified and sorted.