35 results about "Natural hazard" patented technology

The embodiment of the invention provides a power grid natural disaster early warning method and system based on artificial intelligence, and belongs to the technical field of artificial intelligence. The method comprises the following steps of: firstly, constructing a multi-source power grid and disaster information evolution association set which comprises power grid equipment operation dynamic information and natural disaster occurrence dynamic information of a corresponding region and is associated in real time through a region identifier and annotated with an information evolution trend, and then carrying out sequential risk conduction analysis on the multi-source power grid and disaster information evolution association set; then calling a pre-trained power grid disaster intelligent hierarchical prediction model to carry out multi-stage feedback prediction processing on the result, generating a power grid natural disaster hierarchical prediction result, executing early warning strategy dynamic iteration optimization according to the result, generating early warning strategy dynamic iteration adjustment parameters, and carrying out early warning strategy dynamic iteration adjustment. And finally, generating a power grid natural disaster accurate early-warning instruction based on the early-warning strategy dynamic iteration adjustment parameters, and sending the power grid natural disaster accurate early-warning instruction to a power grid monitoring center terminal. The power grid natural disaster early-warning method can comprehensively, accurately and dynamically perform early warning on the power grid natural disaster.

The invention discloses an elasticity evaluation and improvement method for a power transmission system under the action of debris flow, is used for solving the technical problem of single fault rate consideration factor in the prior art, and belongs to the technical field of evaluation of anti-disaster capability and recovery capability of the power transmission system under extreme natural disasters (such as debris flow). The method comprises the following specific steps: step 1, constructing a debris flow model considering an impact force attenuation effect; s11, describing the influence area of the debris flow by using the three parameters; s12, carrying out stress analysis on the tower impacted by the debris flow; 2, constructing a fault model considering the accumulated impact process of debris flow on the power transmission element; s21, calculating a tower failure probability; s22, calculating the failure probability of the power transmission line; s23, calculating the overall failure probability of the power transmission line; 3, sampling at a constant time interval delta t based on the solved failure probability of the power transmission line within the debris flow duration delta T; 4, drawing an expected power supply power curve of the power transmission system; and 5, calculating an elastic index.

The invention discloses a mountain disaster susceptibility prediction method and device considering a composite terrain distance, and relates to the field of natural disaster risk assessment, and the method comprises the steps: obtaining a mountain disaster sample and a multi-source environment factor in a research area range; terrain factors are screened from the multi-source environment factors, and the weight of each terrain factor is calculated based on an entropy weight method; on the basis of the Euclidean space distance, a weighted terrain difference distance is introduced, and a composite terrain distance is constructed through fusion; sorting and screening the mountain disaster sample data according to the composite terrain distance, and determining a local reference sample set and a geographic weighted weight corresponding to each local reference sample; adopting a Bayesian optimization method to perform joint optimization on the bandwidth parameter of the geographical weighted weight, the weight parameter of the composite terrain distance and the structure parameter of the random forest model to obtain an optimal parameter combination; and under the constraint of the optimal parameter combination, carrying out point-by-point prediction on the research area. According to the invention, the precision of mountain disaster susceptibility prediction can be improved.

The application discloses a kind of based on ST-GAN's multi-source data dynamic risk early warning method and system, the method includes the following steps: S1. constructing the spatiotemporal generation confrontation network suitable for spatiotemporal data characteristics, utilize the network to generate extreme precipitation data, realize spatiotemporal unbalanced data reduction, obtain the equalization data of final output;S2. the equalization data of final output with high-dimensional space variable is carried out multi-source data fusion, and nonlinear spatiotemporal information conversion equation, and by local linearization obtains linear approximation model, to predict future time series;S3. under the present situation that extreme rainfall data amount is relatively insufficient, neural network based on dual learning theory accurately learns the parameter of nonlinear spatiotemporal conversion, estimates extreme weather event.The application is through spatiotemporal generation confrontation network (ST-GAN) and multi-source data fusion engine, significantly improves the precision and efficiency of natural disaster warning.

This invention relates to a method for constructing a disaster-bearing entity based on a structure-function-behavior model, comprising the following steps: S1 entity structural semantic modeling; S2 entity functional semantic modeling; S3 behavioral semantic modeling; S4 constructing a semantic fusion module and building urban natural disaster risk factor data and knowledge data, and using the risk factor data and knowledge data to fuse the semantics formed in the models built in steps S1-S3 to realize the construction of the disaster-bearing entity. The method also includes a data and mechanism-driven twin model update method, specifically including: updating the twin model using a change detection model, and update management, wherein the change detection model includes the detection of structural and semantic changes. The method also includes a disaster risk prediction method, which uses a hybrid approach consisting of decision trees and node data formed by urban natural disaster risk factor data and knowledge data. This achieves data analysis and prediction of urban entity disaster situations with visualization of the sum of structure-function-behavior factors.

1. The name of the design product: lighting column assembly. 2. The use of the design product: used as a lighting column assembly for safety and hazard identification in various work areas (such as infrastructure, road construction and maintenance, aviation, port, railway, mining, traffic congestion area, and public access area); or used to guide pedestrian traffic during work area, public activity, during natural disaster, and help identify the location of various landmarks. 3. The design points of the design product: in shape. 4. The picture or photo that best shows the design points: perspective view 1. 5. The rear view and right view are omitted as the design points have been shown in other views. 6. Other changes in the state of the case need to be explained: the product with color is shown in the figure.

This invention discloses a method and system for comprehensive risk assessment of natural disasters in coal mines under complex geological conditions, belonging to the field of coal mine disaster risk assessment technology. The method involves collecting relevant coal mine data; determining comprehensive risk assessment indicators for natural disasters in coal mines under complex geological conditions, and calculating the comprehensive indicator weights using a combined weighting method; constructing an improved transfer learning-BP neural network model, optimizing scoring parameters by combining relevant coal mine data under complex geological conditions, and determining the scoring values ​​for the comprehensive risk assessment of natural disasters in coal mines under complex geological conditions; and conducting a comprehensive risk assessment of natural disasters in coal mines under complex geological conditions based on the comprehensive indicator weights and the scoring values ​​to obtain the assessment results. This invention achieves comprehensive, accurate, and real-time assessment of natural disasters in coal mines under complex geological conditions, effectively preventing and reducing the risks of underground natural disasters under complex geological conditions, and ensuring safe production and the safety of life and property of coal mine workers.

A method and system for monitoring low-temperature rain, snow, and freezing in complex terrain areas comprises the following steps: Step 1: Collecting satellite observation data of the monitoring area for large-scale monitoring of cold air movement, snow cover, and surface temperature, and identifying lower atmospheric inversion layer data; Step 2: Obtaining atmospheric vertical structure data through a ground-based remote sensing vertical observation system; Step 3: Collecting image data of the monitoring area through UAV-based remote sensing; Step 4: Deploying ground data acquisition units to collect data including rainfall and snowfall, snow thickness, temperature, road icing, and power line icing; Step 5: Collecting historical disaster data for the monitoring area; Step 6: Cleaning, fusing, and extracting features from various data using machine learning algorithms; Step 7: Constructing an assessment model reflecting low-temperature rain, snow, and freezing disasters based on the extracted feature data, and generating prediction results for low-temperature rain, snow, and freezing natural disasters in the monitoring area.

The utility model discloses a curtain wall truss with an anti-seismic structure, which relates to the technical field of curtain walls and comprises an inner vertical frame and an inner transverse frame which are used for mounting curtain wall glass. Wherein the inner side vertical frame is also fixedly connected with the connecting piece; the outer end of the connecting piece is connected with a C-shaped connecting piece through a corner connector; an outer side vertical frame is fixed in the C-shaped connecting piece, and outer side transverse frames are fixed on the two sides of the outer side vertical frame; according to the utility model, the rubber strip has better sealing and protecting effects on the curtain wall glass, and when vibration occurs, the rubber strip has a buffering effect on the curtain wall glass, so that the condition of stress cracking can be avoided; due to the structural design of the C-shaped connecting piece, the whole curtain wall truss system has a certain displacement space when subjected to external force, and therefore vibration energy is effectively absorbed and dispersed; when natural disasters such as earthquakes occur, the anti-seismic structure can guarantee the overall stability of the curtain wall, and the risks of glass breakage and structural damage caused by vibration are reduced.

The application provides a kind of glacier mud-rock flow dynamic risk early warning method and system based on seismic signal, belongs to natural disaster warning technical field, first constructs the response chain of seismic signal glacier, obtains the information such as the propagation path of seismic signal, the response order of each circle of glacier and the response characteristic transmission relationship, then analyzes the risk evolution node in response chain information, accurately determines the key nodes such as response trigger, energy accumulation and diffusion start, forms the glacier mud-rock flow risk evolution node set, then links the glacier mud-rock flow risk evolution node set with the topographic features of glacier area, generates the dynamic risk evolution path that can reflect the dynamic development process of glacier mud-rock flow, generates the dynamic risk warning content containing detailed warning information based on this dynamic risk evolution path, finally reversely associates the on-site response data to the response chain construction process, realizes the optimization update of the response chain information of seismic signal glacier, effectively improves the early warning ability to glacier mud-rock flow disaster.

The invention discloses a power distribution network multi-disaster toughness improvement strategy, an evaluation method, a system, equipment and a medium, and belongs to the technical field of power distribution network toughness evaluation and toughness improvement, and the method comprises the steps: obtaining meteorological and geological disaster data and power grid structure data, and constructing a multi-disaster coupling fault probability model; based on the multi-disaster coupling fault probability model, a comprehensive toughness evaluation index is obtained by combining a power distribution network information entropy index, a graph structure toughness index and a load recovery rate; carrying out joint representation learning on the topological structure and node operation data of the power distribution network through a graph convolutional network, extracting high-order topological features through multilayer message transmission, and carrying out state integration to obtain a comprehensive power distribution network state vector; and constructing a soft actor-commentator reinforcement learning model, and dynamically outputting an optimal resource scheduling strategy by taking maximization of a comprehensive toughness index as a target. According to the invention, the restoring force and the operation efficiency of the power distribution network in the face of natural disasters are effectively improved.

The invention discloses a pole and tower inclination monitoring device based on an AI learning module and a working method thereof. A multi-sensor fusion monitoring mode is adopted, a single set of device can realize omnibearing state monitoring of a base pole and tower, important power transmission channel poles and towers need to be installed on each base, and a set of device can be configured for each 3-5 base pole and towers according to geological conditions in a general line. When a tower inclines due to factors such as foundation settlement, external force damage or natural disasters, the spatial attitude of the tower changes continuously, and systematic deviation of a measured value of a tilt angle sensor and Beidou positioning coordinates is caused. The change forms digital representation of the health state of the tower through time-space correlation analysis of multi-source data. The device performs fusion processing on acquired multi-dimensional data such as inclination angles, coordinates and environments, identifies an inclination mode and predicts a development trend by using an intelligent algorithm, and realizes early warning and accurate diagnosis of tower inclination.

The invention relates to the technical field of building structures, in particular to a multidirectional composite shock insulation support for a building structure, which comprises an embedded part, and a shock insulation support is arranged in the middle of the embedded part. By arranging the embedded part and the shock insulation support, an efficient and stable shock insulation support mounting mode is realized; the stability and reliability of the shock insulation support in a building structure are greatly improved through ingenious cooperation of the embedded part and the shock insulation support and cooperative action of all the assemblies, various shocks and displacements possibly encountered in the using process of a building can be effectively resisted through the unique installation mode, the durability of the shock insulation effect is ensured, and the service life of the building is prolonged. In practical application, the multidirectional composite seismic isolation support can remarkably reduce the influence of natural disasters such as earthquakes on a building structure, the multidirectional composite characteristic of the multidirectional composite seismic isolation support enables the multidirectional composite seismic isolation support to effectively isolate and buffer vibration in different directions, vibration energy transmitted to a building body is reduced, and therefore the integrity and safety of the building structure are protected.

The invention relates to the technical field of natural disaster monitoring and early warning, in particular to a plateau avalanche risk dynamic early warning and disaster deduction method based on digital twinning, and the method comprises the steps: constructing a high-precision three-dimensional digital twinning model; adopting a collaborative Kriging interpolation algorithm to generate a full-field accumulated snow thickness distribution map and mapping the full-field accumulated snow thickness distribution map to the digital twin model; simulating an avalanche motion and accumulation process by adopting an improved Voellmy model and an improved finite difference method; an improved K-epsilon-LES hybrid model is adopted to simulate an air wave power process and obtain shock wave pressure field distribution; generating a dynamic disaster-causing envelope diagram based on the accumulation depth and the shock wave pressure field distribution; executing space collision detection, identifying a risk situation and triggering a differentiated early warning level; real-time mapping and data synchronization of the physical world and the virtual model are achieved, and the method can be widely applied to disaster prevention and reduction work of avalanche high-risk areas such as plateau snow mountain construction areas and mountainous area residential areas.

An object of a system according to an embodiment is to predict the risk of a natural disaster and provide early warning.SOLUTION: A system according to an embodiment includes a disaster risk prediction unit and an early warning provision unit. The disaster risk prediction unit predicts a disaster risk of an earthquake, a flood, or a forest fire. The early warning providing unit provides an early warning based on the disaster risk predicted by the disaster risk predicting unit.SELECTED DRAWING: Figure 1

The application provides a power distribution network resilience evaluation method considering typhoon disasters and secondary faults, which comprises the following steps: using an extreme weather model to perform load calculation on a to-be-processed line to obtain a line load of the to-be-processed line; analyzing the line load and a preset maximum load capacity value of the to-be-processed line to obtain a line fault rate of the to-be-processed line; using a fault probability resilience index algorithm to obtain a line fault resilience index; using a preset PageRank algorithm to update preset importance values of each power supply node corresponding to the to-be-processed line to obtain current importance values of the power supply nodes and determine load resilience indexes of the corresponding power supply nodes; and determining a comprehensive resilience index of the to-be-processed line according to the line fault resilience index and the corresponding load resilience indexes. The method can adapt to the demand of extreme coupling natural disasters, so as to more accurately evaluate and improve the resistance and recovery level of the power distribution network to extreme disasters.

The invention discloses a site selection method and device for an electrochemical energy storage station, electronic equipment and a storage medium, and belongs to the technical field of power distribution network energy storage. The method comprises the following steps: calculating a safety score of a natural disaster risk assessment index of a site selection scheme, a safety score of a crowd and social environment risk assessment index, a safety score of an emergency response capability assessment index and a safety score of a fire protection resource and protection capability assessment index; calculating safety scores of the site selection schemes, and selecting a preset number of site selection schemes as target site selection schemes; according to the construction cost and investment recovery data of the electrochemical energy storage station, the economic score corresponding to each target site selection scheme is calculated, and the target site selection scheme with the highest economic score is used as the site selection scheme of the electrochemical energy storage station. The system can solve the problem of lack of system assessment of multi-disaster linkage risks such as natural disasters, crowd safety, emergency response and fire protection resources in the prior art.

The invention provides a reinforcement learning-driven dynamic line selection method for frontier defense emergency treatment. The method comprises the following steps of data acquisition and processing, road network topology modeling, recommended route optimization and dynamic line selection. According to the line selection method, the action route can be recommended according to the emergency event and the superior intention, the problem of line adaptive selection under the condition that different emergency events such as border public health, natural disasters and enemy sudden attack occur is solved, and a reliable guarantee is provided for border management and control support. The self-adaptive dynamic programming algorithm in reinforcement learning is adopted, environment changes are sensed in real time, better adaptability is achieved, and the method is suitable for different scenes and emergencies. According to the method, the route recommendation results of the reinforcement learning algorithm under different emergency events can be utilized, and resource allocation is optimized.

To provide a detection means for detecting sudden natural disasters such as rockfalls, rockfalls, landslides, mudslides, and debris flows in real time, and a disaster detection device equipped with this detection means. [Solution] The detection means supports a detection body 3 installed on the ground surface and has transforming parts 4, 47, 60 whose shape changes when an external force of a predetermined energy E or more is applied and the detection body 3 is displaced and / or deformed. The disaster detection device 1 includes a detection unit 20 that operates when the shape of the transforming parts 4, 47, 60 changes, and a communication unit 30 that transmits information to the outside when the detection unit 20 is activated.

The invention discloses a multi-carbon balance defense method, system and device fusing power grid information physics social factors and a storage medium, and the method comprises the steps: constructing an information physics social system multi-dimensional coupling model, and simulating a dynamic evolution trajectory of a system under an extreme natural disaster condition in combination with a finite-state machine and historical disaster data; a risk assessment model is constructed based on an event-driven mechanism, and the risk assessment model carries out quantitative assessment on multi-dimensional coupling factors based on natural disaster events and artificial carbon emission emergencies; and a stage collaborative defense strategy is preset, a carbon balance defense effect evaluation model is constructed, and quantitative verification is performed on the emission reduction and recovery capability of each stage strategy in combination with a quantitative evaluation result. The carbon emission and carbon sink layout can be synchronously optimized in an extreme disaster scene, and the toughness and low-carbon transformation capability of a power system are enhanced; dynamic regulation and control of carbon unbalance induced by disasters are realized, and scientificity and performability of the scheme under complex conditions are ensured.

This invention belongs to the field of highway safety operation management and disaster prevention and mitigation technology. It discloses a method for classifying and grading the risks of highway structural points by comprehensively considering environmental and technical conditions. The method includes the following steps: S1. Quantifying the threat of natural disasters to structural points using a natural disaster risk index to obtain the natural disaster risk level of the structural point; S2. Quantifying and assessing the technical condition of different types of structural points, such as roadbeds, pavements, bridges, tunnels, and slopes, using standards and regulations to obtain a technical condition level; S3. Combining the obtained natural disaster risk level and technical condition level, and using an evaluation matrix, determining the final comprehensive risk level of the structural point. This invention reflects both the health status of the structural point itself and its vulnerability to natural environmental factors, organically combining internal and external factors to comprehensively and accurately classify and grade the comprehensive risk of structural points.

The application discloses a mountain disaster susceptibility prediction method and device considering composite terrain distance, and relates to the field of natural disaster risk assessment, wherein the method comprises the following steps: obtaining mountain disaster samples and multi-source environmental factors in a study area; selecting terrain factors from the multi-source environmental factors, and calculating the weight of each terrain factor based on an entropy weight method; introducing a weighted terrain difference distance on the basis of a Euclidean space distance, and fusing and constructing a composite terrain distance; sorting and screening the mountain disaster sample data according to the composite terrain distance, determining a local reference sample set and a geographical weighted weight corresponding to each local reference sample; adopting a Bayesian optimization method to jointly optimize the bandwidth parameter of the geographical weighted weight, the weight parameter of the composite terrain distance and the structure parameter of a random forest model, and obtaining an optimal parameter combination; and under the constraint of the optimal parameter combination, predicting the study area point by point. The application can improve the precision of mountain disaster susceptibility prediction.

The application belongs to the technical field of automobiles and provides a vehicle-mounted natural disaster detection system, a working method thereof and a vehicle, which comprise a vehicle-mounted host, a vehicle-mounted communication module and a plurality of types of sensors configured on the vehicle; the vehicle-mounted host identifies a natural disaster type and intensity based on data collected by the sensors; the vehicle-mounted communication module sends the natural disaster type and intensity to a satellite and / or a base station; wherein the sensors comprise a rainfall detection device, the rainfall detection device comprises a pressure sensor arranged on a fender inner lining, the pressure sensor detects a pressure signal caused by impact potential energy of rainwater when the rainwater rotates with a wheel, converts the pressure signal into an electric signal and sends the electric signal to the vehicle-mounted host, and the vehicle-mounted host identifies rainfall size based on the pressure signal. Not only does it help improve the detection and early warning capability of natural disasters, but it also makes up for the shortcomings of a traditional rainfall sensor on the windshield, such as a small sensing area and low sensing accuracy.

The invention discloses a geological landslide natural disaster early warning system and method based on causal path recognition, and relates to the technical field of disaster early warning. Geological comprehensive dynamic and static feature processing is performed on geological internal and external related data to obtain comprehensive geological feature coefficients, and causal confirmation geological data is obtained through screening based on the comprehensive geological feature coefficients; performing causal relationship conversion extraction on the causal confirmation geological data to obtain a geological landslide causal relationship path, performing node risk quantification based on the geological landslide causal relationship path to obtain a node risk value, and performing path causal risk calculation based on the node risk value to obtain a geological landslide comprehensive risk index. And carrying out early warning on geological landslide natural disasters based on the geological landslide comprehensive risk index, thereby realizing a function of carrying out accurate early warning on the geological landslide through a complete causal mechanism generated based on external causes and disasters.

The utility model relates to the technical field of information communication, and discloses an emergency communication device comprising a processor module; the wireless communication module is connected with the processor module through an SPI (Serial Peripheral Interface) bus; the transmitting module is electrically connected with the processor module through an I2C bus; the mediation module is electrically connected with the processor module through an I2C bus; and the display module is electrically connected with the processor module through an I2C bus. The emergency communication device solves the problems that in the prior art, signals of the emergency communication device are seriously influenced in mountain forests or severe weather, when a resident residence place is far away from an FM broadcast transmitting station, the signals are blocked easily, and when natural disasters occur, alarm signals cannot be received and cannot be avoided in time, so that the occurrence of the natural disasters is influenced. And serious personnel and property loss is caused.

The present application relates to the vulnerability evaluation method of immovable cultural relics, comprising the following steps: 1, determining the vulnerability evaluation index; 2, determining the evaluation index vulnerability value grading standard; 3, obtaining evaluation index data and vulnerability value assignment; 4, determining the vulnerability evaluation index weight; 5, constructing the vulnerability evaluation model; 6, dividing the vulnerability level; 7, calculating the vulnerability index; 8, determining the vulnerability level of immovable cultural relics. The method of the present application can well evaluate the vulnerability of immovable cultural relics under the influence of natural disasters such as flood, earthquake and other disasters at different scales, and has universal applicability.

The invention relates to an island ecosystem risk evaluation method and system. The method comprises the following steps: acquiring spatial geographic data of an island whole domain, and constructing a multi-scale evaluation grid containing three subsystem areas of an island land, an intertidal zone and an offshore sea area; according to environment quality, biological ecology and landscape pattern data in each evaluation grid, constructing an ecological feature-process-service coupling index system; an ecological system health index is calculated through a combined weighting algorithm; natural disasters and human interference sources are identified, and comprehensive risk intensity is calculated; calculating an ecological vulnerability coefficient by using a nonlinear vulnerability function; and combining the comprehensive risk intensity and the ecological vulnerability coefficient to obtain a comprehensive ecological risk index, and generating an island ecological risk spatial distribution map. By adopting the method, the island ecosystem risk can be comprehensively and accurately evaluated, and a scientific basis is provided for island ecological protection and management decision.

The invention relates to the technical field of natural disaster risk assessment, and discloses a drainage basin risk assessment method and system based on geographic space and a Bayesian network, and the method comprises the steps: obtaining a digital elevation model diagram, a multi-disaster survey record and a simulation research result of a target drainage basin, and extracting a key point location; according to the key point location, establishing a disaster chain path, and completing Bayesian network modeling; performing node probability modeling, and initializing parameters; establishing a disaster chain probability propagation mechanism, and describing a sequential conduction process of disasters among nodes; establishing a high-dimensional joint distribution model, and performing forward reasoning propagation of the Bayesian network for multiple times; carrying out uncertainty compression on the disaster reasoning chain; probabilistic propagation and reasoning are carried out, and the disaster probability of population loss and economic loss is calculated. By utilizing the scheme of the invention, the overall collaboration of disaster chain reasoning and the meticulousness of risk expression under a complex system can be improved, and the risk assessment capability of the model under the complex chain disaster system is improved.

This invention discloses a method and system for identifying post-disaster cracks in arch dams based on curvature anomaly measurement. The method includes selecting multiple virtual arches and cantilever arms within the arch dam body and performing virtual structural reparameterization of the arch dam based on deformation monitoring facilities; extracting horizontal displacement monitoring values ​​at various measuring points of the arch dam before and after the natural disaster; calculating the bidirectional curvature of the horizontal displacement monitoring values ​​for each measuring point controlled by the virtual arches and cantilever arms; calculating the curvature anomaly measurement for each measuring point based on the bidirectional curvature calculated before and after the disaster; calculating the anomaly index for each region based on the dam body's zoning, and calculating the probability of crack occurrence in each region, determining its statistical significance. This invention proposes a method for identifying arch dam cracks using the static displacement curvature of the dam body before and after a natural disaster, effectively overcoming the limitations of current arch dam crack identification methods.

This invention belongs to the field of highway safety management and risk assessment technology, and discloses a method for assessing the traffic risk of key infrastructure structural points on mountainous highways. The method includes the following steps: S1. Acquiring multi-source data for the target structural point, including natural disaster data, technical condition data, road alignment data, meteorological data, traffic flow data, and special control measures data; S2. Determining the basic risk value for classification and grading of the structural point based on the natural disaster data and technical condition data through a risk assessment matrix; S3. Calculating the basic risk value of the structural point's alignment characteristics based on the road alignment data; S4. Determining dynamic risk adjustment coefficients based on meteorological data and traffic flow data; S5. Determining additional risk correction coefficients based on special control measures data; S6. Calculating the traffic risk value of the target structural point. This invention achieves quantitative assessment and classification of the traffic risk of structural points, providing a scientific basis for monitoring, early warning, and special rectification of high-risk locations.