Disaster condition-based highway traffic operation state evaluation zoning method and system

Abstract

The application discloses a highway traffic operation state evaluation zoning method and system based on disaster conditions, and fuses highway geological disaster data and meteorological disaster data to perform disaster risk evaluation and zoning. The method adopts a disaster-pregnant geological and meteorological index system analysis and an information amount model solution method to perform traffic operation state risk evaluation and zoning under highway disaster conditions. The result is basically reliable and is suitable for highway traffic operation state risk zoning work under disaster conditions.

Description

Technical Field

[0001] This invention relates to the field of highway transportation technology, and specifically to a method and system for assessing and classifying highway traffic operation status based on disaster conditions. Background Technology

[0002] With the rapid development of my country's economy, transportation demand is constantly increasing, and highway construction is continuously advancing into mountainous and western regions. Traffic flow on mountain trunk roads is gradually increasing, and the main highway network is becoming increasingly dense. Although highway service facilities are constantly improving, and transportation practitioners have summarized a series of experiences and research results in practice, mountain roads, especially those in the western plateau and mountainous areas, are often subject to harsh natural environments and poor alignment conditions. Their traffic operation is highly susceptible to the significant impacts of geological and meteorological disasters. These natural disasters have a major impact on the smoothness and safety of traffic operations. In particular, the frequent occurrence of extreme weather events under the influence of the global climate change trend in recent years has exacerbated the impact of geological and climatic disasters on the safe operation of highway traffic in extreme environments.

[0003] Based on the first national comprehensive survey of natural disaster risks, supplementary data on highway facilities and information on natural disaster risk points were collected through the natural disaster risk highway disaster-bearing body survey and data collection system. Risk assessments of natural disaster risk points were conducted, and data verification of high-level risk points was carried out. The basic number of natural disaster risk hazards on highway disaster-bearing bodies was basically ascertained, and the work of highway natural disaster risk zoning was initially explored.

[0004] However, highway natural disaster risk zoning is merely a single zoning of natural disasters, without considering the factors affecting highway traffic safety operation under disaster conditions. Road traffic operation risk analysis often focuses on the analysis of factors such as road traffic accidents, vehicle accidents, highway alignment, and weather conditions alone, without researching risk assessment methods for highway traffic operation under the coupled effects of disasters. Currently, the assessment and zoning methods for highway traffic operation under disaster conditions use only single indicators, resulting in poor applicability of the results. Summary of the Invention

[0005] This application provides a zoning method and system for assessing highway traffic operation status based on disaster conditions, in order to solve the problems of traditional calculation methods having simple considerations, large deviations in calculation results, and inability to meet the requirements of actual railway station design.

[0006] According to the first aspect, one embodiment provides a method for assessing and zoning highway traffic operation status based on disaster conditions, the method comprising:

[0007] Acquire data on natural disaster risk points along highways, collect data on disaster-prone geological factors, topographical factors and meteorological factors, and collect data on highway traffic operation status under disaster conditions;

[0008] The highway area is divided into evaluation units based on the main disaster-prone factors in the area, and different combinations of evaluation factors are selected for each evaluation unit according to its characteristics.

[0009] Based on the combination of evaluation factors for each evaluation unit, the information content model method is used to solve the total information content of geological disasters under the corresponding combination of evaluation factors in order to evaluate the probability of geological disasters and obtain disaster probability information for different evaluation factors.

[0010] Based on the acquired data on natural disaster risk points along the highway, the density information of highway disaster points is obtained. The disaster probability information of different evaluation factors and the density information of highway disaster points are weighted and superimposed to obtain the geological disaster susceptibility evaluation results of each evaluation unit.

[0011] The geological hazard risk assessment results are obtained by weighting and overlaying the geological hazard susceptibility assessment results with the probability information of major disaster-inducing factors and geological hazard points in the region;

[0012] Based on the collected data on highway traffic operation status under disaster conditions, a road traffic impact index is obtained. A disaster risk level matrix is ​​established by combining the geological disaster risk assessment results with the road traffic impact index to obtain disaster risk assessment results and form a risk level zoning map of highway traffic operation status under disaster conditions.

[0013] Furthermore, data on natural disaster risk points along the highway will be obtained, including data on disaster-prone geological factors, topographical factors, and meteorological factors, as well as data on highway traffic operation status under disaster conditions. Specifically, this includes:

[0014] Based on the results of the comprehensive natural disaster risk survey and the data collection system for the natural disaster risk highway disaster-bearing bodies survey, information on natural disaster risk points is collected, risk assessments of natural disaster risk points are conducted, data on high-level risk points are verified, and the baseline data on natural disaster risk hazards of highway disaster-bearing bodies are obtained.

[0015] Collect data on disaster-prone geological factors, topographic and geomorphological factors, and meteorological factors for highways, including topography, geomorphology, engineering rock groups, geological structures, surface water systems, seismic intensity, and precipitation data. Collect traffic operation status data under disaster conditions, including road blockage location, frequency, and duration data.

[0016] Furthermore, the highway areas are divided into evaluation units based on the main disaster-prone factors within each unit. Different combinations of evaluation factors are then selected for each of these units based on their characteristics. Specifically, these combinations include:

[0017] The main disaster-prone factors in each area of ​​the highway were analyzed and identified, and the main disaster-prone factors were selected as the theme for dividing the evaluation units in each area.

[0018] Susceptibility assessment factors include geological factors: engineering rock group, hydrogeology, and structural features; topographic and geomorphological factors: elevation, slope, aspect, curvature, gradient, landform, and surface water system; as well as vegetation cover factors, rainfall factors, and seismic intensity factors.

[0019] During the evaluation, different evaluation factors are selected and combined based on the characteristics of the evaluation unit and the judgment of experts.

[0020] Furthermore, the highway areas are divided into evaluation units based on the main disaster-prone factors within each unit. Different combinations of evaluation factors are then selected for each of these units based on their characteristics. Specifically, these combinations include:

[0021] Based on multi-source, multi-period data from optical remote sensing and DEM covering the highway disaster risk assessment area, we conduct comprehensive interpretation of various typical geological disaster evaluation factors and use different algorithms to extract information on various typical geological disaster evaluation factors.

[0022] A sample library is established based on the interpreted typical geological hazard evaluation factor information. The deep learning model is then trained iteratively multiple times using a classification-supervised approach based on the sample library. The trained deep learning model is then used to obtain various atypical geological hazard evaluation factor information.

[0023] Furthermore, the information content model method is used to calculate the total information content of geological disasters under the corresponding combination of evaluation factors in order to evaluate the probability of geological disasters occurring. Specifically, this includes:

[0024] The formula for disaster information content is expressed as:

[0025]

[0026] Where A represents a certain disaster-prone condition factor; J represents the state classification or interval under disaster-prone condition A; I Aj→B This refers to the amount of information related to the occurrence of geological disaster B under the corresponding factor A, state j, or interval; N j S represents the number of geological hazard distribution points under the corresponding factor A, state j, or interval; N represents the total number of known geological hazard hazard points in the evaluation area; S represents the number of geological hazard hazard distribution points. j The number of grid cells for the state or interval distribution of factor A and j; S is the total number of grid cells in the evaluation area;

[0027] When I Aj→B When I > 0, it reflects a large amount of information about the tendency of geological disasters to occur under the corresponding factor A, j state or interval, indicating a higher probability of geological disasters occurring, or in other words, a favorable environment for geological disasters to occur; when I Aj→B When I < 0, it indicates that under the conditions of factor A, j, state, or interval, the occurrence of geological disasters is unfavorable; when I Aj→BWhen the value is 0, it indicates that the state or interval of factor A or j does not provide any information about whether a geological disaster has occurred. In other words, the state or interval of factor A or j can be eliminated and excluded as a geological disaster prediction factor.

[0028] Each evaluation unit is influenced by a combination of numerous factors, and these factors exist in several states. The probability of geological disasters occurring is expressed by the total amount of information about the occurrence of geological disasters under the combined conditions of these states, serving as a geological disaster susceptibility index. The specific formula is as follows:

[0029]

[0030] Where I represents the total information content corresponding to the occurrence of geological disasters in a specific unit, indicating the likelihood of geological disasters occurring, and serving as a geological disaster susceptibility index; N i This refers to the area or number of geological hazards corresponding to specific factors, the i-th state, or the interval condition; S i N represents the number of raster cells corresponding to a specific factor, state i, or interval; N represents the total area of ​​geological hazards or the total number of geological hazard points in the evaluation area; and S represents the total number of raster cells in the evaluation area.

[0031] Furthermore, based on the acquired data on natural disaster risk points along the highway, the density information of highway disaster points is obtained. The disaster probability information of different evaluation factors and the density information of highway disaster points are then weighted and superimposed to obtain the geological disaster susceptibility evaluation results for each evaluation unit. Specifically, this includes:

[0032] Based on the acquired data of natural disaster risk points along the highway, the density analysis module in the SpatialAnalyst tool under the ArcGIS platform is used. The density analysis module provides three calculation methods: kernel density analysis, point density analysis, and line density analysis. The kernel density analysis method is selected to obtain the kernel density map of highway disaster points.

[0033] Using the spatial analysis overlay function of ArcGIS, specifically by accessing the overlay analysis module in the Spatial Analyst tool under the ArcGIS platform, selecting the weighted overlay calculation method, inputting the disaster probability layer and kernel density layer, and obtaining the geological disaster susceptibility evaluation index for each evaluation unit through calculation to form the susceptibility evaluation result.

[0034] Furthermore, the geological hazard susceptibility assessment results are weighted and superimposed with the probability information of major disaster-inducing factors and geological hazard point information in the region to obtain the geological hazard risk assessment results, specifically including:

[0035] The weighted overlay calculation method provided by the ArcGIS software platform is selected. The geological hazard susceptibility layer, the probability layer of major disaster-inducing factors, and geological hazard point information are input. The geological hazard point information includes the coordinates of the hazard point, the degree of hazard, the hazard level, the hazard type, the length of the impact, and the frequency of occurrence. The geological hazard risk assessment results are obtained, thereby evaluating the spatial location, scale or intensity, and frequency of occurrence of a certain scale of disaster in a certain region within a certain time period.

[0036] Furthermore, based on the collected data on highway traffic operation status under disaster conditions, a road traffic impact index is obtained. A disaster risk level matrix is ​​then established by combining the geological disaster risk assessment results with the road traffic impact index to obtain disaster risk assessment results, specifically including:

[0037] The collected data on highway traffic operation status under disaster conditions includes the location, frequency, duration, and length of highway traffic blockages. The data is normalized by assigning values ​​to each highway traffic operation status indicator to obtain the road traffic impact index. A disaster risk and road traffic impact level matrix is ​​then established, with four levels—extremely high, high, medium, and low—defined and color-coded.

[0038] Furthermore, obtaining disaster risk assessment results specifically includes:

[0039] Based on the principles of similarity and proximity, the disaster risk assessment results are generalized from raster to vector. Specifically, the disaster risk assessment results are input into the ENVI software platform, the Classification tool is invoked, the PostClassification module is selected, Sieve classes are calculated to remove scattered patches, and then parameters are input to calculate Clumpclasses, completing the patch generalization and cluster analysis, thereby realizing the generalized raster processing of disaster risk assessment information. Subsequently, the raster to surface submodule under the raster to surface module of the conversion tool in the ArcGIS platform is used to realize the vectorization of raster data.

[0040] According to a second aspect, one embodiment provides a highway traffic operation status assessment zoning system based on disaster conditions, the system comprising:

[0041] The data acquisition module is used to acquire data on natural disaster risk points along the highway, collect data on disaster-prone geological factors, topographic factors and meteorological factors, and collect data on highway traffic operation status under disaster conditions.

[0042] The evaluation factor determination module is used to divide the highway area into evaluation units based on the main disaster-prone factors in the area, and select different combinations of evaluation factors for each evaluation unit according to the characteristics of the evaluation unit.

[0043] The information content calculation module is used to calculate the total information content of geological disasters under the corresponding evaluation factor combination based on the evaluation factor combination of each evaluation unit, so as to evaluate the probability of geological disasters and obtain disaster probability information of different evaluation factors.

[0044] The susceptibility assessment module is used to obtain highway disaster point density information based on the acquired natural disaster risk point data along the highway, and to obtain the geological disaster susceptibility assessment results for each assessment unit by weighted superposition of the disaster probability information of different assessment factors and the highway disaster point density information.

[0045] The risk assessment module is used to weight and superimpose the geological hazard susceptibility assessment results with the probability information of major disaster-inducing factors and geological hazard point information in the region to obtain the geological hazard risk assessment results;

[0046] The risk assessment module is used to obtain the road traffic impact index based on the collected data on highway traffic operation status under disaster conditions, establish a disaster risk level matrix by combining the geological disaster risk assessment results with the road traffic impact index, obtain disaster risk assessment results, and form a risk level zoning map of highway traffic operation status under disaster conditions.

[0047] This application provides a method and system for assessing and zoning highway traffic operation status based on disaster conditions. It integrates highway geological disaster data with meteorological disaster data to conduct disaster risk assessment and zoning. It adopts the analysis of disaster-prone geological and meteorological index system and the information volume model solution method to conduct risk assessment and zoning of highway traffic operation status under disaster conditions. The results are basically reliable and suitable for highway traffic operation status risk zoning work under disaster conditions. Attached Figure Description

[0048] Figure 1 A flowchart illustrating a zoning method for assessing highway traffic operation status based on disaster conditions, provided as an embodiment of the present invention;

[0049] Figure 2 A flowchart illustrating the specific implementation of a highway traffic operation status assessment and zoning method based on disaster conditions, as provided in one embodiment of the present invention;

[0050] Figure 3 A kernel density map of disaster points along a highway is provided in a highway traffic operation status assessment zoning method based on disaster conditions, as an embodiment of the present invention.

[0051] Figure 4 This invention provides a disaster probability layer in a disaster-based highway traffic operation status assessment zoning method according to disaster conditions;

[0052] Figure 5 A risk assessment level matrix diagram is provided in a disaster-condition-based highway traffic operation status assessment zoning method according to an embodiment of the present invention;

[0053] Figure 6 This invention provides a comprehensive risk level zoning map of highway traffic operation status under disaster conditions in a highway traffic operation status assessment zoning method based on disaster conditions, as an embodiment of the present invention.

[0054] Figure 7 This is a schematic diagram of the logical structure of a highway traffic operation status assessment and zoning system based on disaster conditions, provided as an embodiment of the present invention. Detailed Implementation

[0055] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings. Similar elements in different embodiments are referred to by associated similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of this application. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, certain operations related to this application are not shown or described in the specification. This is to avoid obscuring the core parts of this application with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0056] Furthermore, the features, operations, or characteristics described in the specification can be combined in any suitable manner to form various embodiments. At the same time, the steps or actions in the method description can be rearranged or adjusted in a manner obvious to those skilled in the art. Therefore, the various orders in the specification and drawings are only for the clear description of a particular embodiment and do not imply a necessary order, unless otherwise stated that a particular order must be followed.

[0057] The first embodiment of this invention provides a zoning method for assessing highway traffic operation status based on disaster conditions. The following is a combination of... Figure 1 and Figure 2 Please provide a detailed explanation.

[0058] like Figure 1 As shown, in step S100, data on natural disaster risk points along the highway are acquired, data on disaster-prone geological factors, topographic factors and meteorological factors are collected, and data on highway traffic operation status under disaster conditions are collected.

[0059] Specifically, by integrating the results of the first national comprehensive natural disaster risk survey and the natural disaster risk highway disaster-bearing body survey and data collection system, information on natural disaster risk points was collected; risk assessments of natural disaster risk points were conducted, data on high-level risk points were verified, and the baseline data on natural disaster risk hazards of highway disaster-bearing bodies were obtained, which were used to form the geological disaster development kernel density factor information in the highway disaster susceptibility assessment.

[0060] Collect data on geological and meteorological factors that contribute to highway disasters, including topography, geomorphology, engineering rock formations, geological structures, surface water systems (for highway disaster susceptibility assessment), seismic intensity, and precipitation (for highway disaster susceptibility assessment). Collect data on traffic operation status under disaster conditions, including road blockage locations, blockage frequency, and duration (for highway disaster risk assessment and zoning).

[0061] like Figure 1 As shown, in step S200, the highway area is divided into evaluation units according to the main disaster-prone factors in the area, and different combinations of evaluation factors are selected for each evaluation unit according to the characteristics of the evaluation unit.

[0062] Specifically, due to the significant differences in climate, geography, and geology across the regions traversed by the highway, a unified zoning evaluation is not feasible. Therefore, the evaluation must consider the entire region involved in the highway, dividing it into different evaluation units based on the main disaster-inducing or triggering factors for each region. These main disaster-inducing or triggering factors can be determined manually. Based on the classification of the main disaster types in each block, key disaster-inducing factors, such as geomorphic units, seismic zoning, and slope, are selected as the themes for dividing the evaluation units. For example, for the Sichuan-Tibet Highway section, the main controlling factor influencing disaster formation is geomorphic units. Therefore, spatial division can be carried out according to primary geomorphic units such as high mountains and canyons, lake basins and valleys, and plains to form independent evaluation units. After completing the evaluation unit zoning, evaluation factors are selected for each evaluation unit, substituted into the evaluation model, and evaluation parameters are set.

[0063] Susceptibility evaluation factors mainly include geological factors such as engineering rock group, hydrogeology, and structural features; topographic and geomorphological factors such as elevation, slope, aspect, curvature, gradient, landform, and surface water system; as well as vegetation cover, rainfall, and seismic intensity factors. In specific evaluations, different evaluation factors are selected and combined according to the characteristics of the evaluation unit. For example, in high mountain and canyon areas, landform, slope, engineering rock group, and vegetation cover are selected to form a combination of evaluation factors, while in lake basin and valley areas, landform, water system, and vegetation cover are selected to form a combination of evaluation factors.

[0064] The above steps also include: based on multi-source, multi-period data from optical remote sensing and DEM covering the highway disaster risk assessment area, conducting comprehensive interpretation of various typical geological disaster evaluation factors, and extracting information on various typical geological disaster evaluation factors using different algorithms; establishing a sample library based on the interpreted typical geological disaster evaluation factor information, and iteratively training the constructed deep learning model using a classification-supervised approach based on the sample library, and obtaining information on various atypical geological disaster evaluation factors through the trained deep learning model.

[0065] Specifically, the process begins by acquiring multi-source, multi-period data, including optical remote sensing and DEM data, covering the highway disaster risk assessment area. Various algorithms are then used to extract assessment factors for typical geological hazards. Specifically, optical remote sensing data is used to comprehensively interpret assessment factors such as geomorphology, geological engineering rock groups, fault structures, and surface water systems. The National Disaster Risk Index (NDVI) model is also used to calculate vegetation grade factors. DEM data is used to calculate slope using the SLOPE model to extract topographic slope factors, and the Aspect model is used to extract topographic aspect factors. Multi-period sub-meter level optical remote sensing data is then used for comprehensive interpretation of geological hazards, obtaining information such as hazard type, spatial location, development scale, and hazard deformation development trends (comparison of long-term optical remote sensing images). Combined with the highway natural disaster risk point data obtained in step S100, the severity of hazard and road disaster characteristics are analyzed, ultimately obtaining baseline data for highway disaster risk assessment.

[0066] The calculation formulas for various evaluation factor information are as follows:

[0067] ① Vegetation Index (NDVI) model:

[0068] NDVI = (NIR - R) / (NIR + R)

[0069] In the formula: NIR represents the reflectance value of the near-infrared band, and R represents the red band.

[0070] ② Slope model

[0071]

[0072] In the formula, Slope is the slope; Slopewe is the slope in the X direction; Slopesn is the slope in the Y direction; and the ground slope (slope) is the angle between the tangent plane passing through the point and the horizontal ground.

[0073] ③ Aspect model

[0074] Equation of the tangent plane of inclination angle:

[0075] Z=Ax+By+C=fx(x0,y0)x+fy(x0,y0)y+C

[0076] Slope aspect:

[0077] β = arctan(A / B)

[0078] In the formula, x and y are planar coordinates; z is the vertical coordinate; and β is the slope azimuth angle.

[0079] Furthermore, in this embodiment, based on the interpreted typical geological hazard evaluation factor information, atypical geological hazard factor information is obtained using deep learning methods to supplement and improve the geological hazard factor information needed for subsequent geological hazard susceptibility assessment. Specifically, this embodiment collects various typical geological hazard evaluation factor information corresponding to geological hazard development areas, establishes a sample library, forms a deep learning model, and performs multiple iterative trainings on the ENVI software platform using a classification-supervised approach to obtain a multi-factor evaluation factor automatic identification optimization model (a training sample library identification model formed after multiple classification-supervised iterative trainings of geological hazard samples; this model is used to obtain atypical geological hazard factor information for disaster susceptibility assessment).

[0080] In this embodiment, evaluation factors are selected based on the characteristics of the evaluation unit area and expert judgment. Furthermore, weighted calculations are used to form a candidate model structure with multiple combinations of evaluation factors. Based on the obtained information values ​​of typical and atypical geological hazard factors, weight calculations and normalization are performed on the candidate models with multiple evaluation factor combinations to obtain new information values ​​as the final data for each evaluation factor, which are then used in the calculation of the information value formula below.

[0081] like Figure 1 As shown, in step S300, based on the combination of evaluation factors for each evaluation unit, the information content model method is used to solve the total information content value of geological disaster occurrence under the corresponding combination of evaluation factors in order to evaluate the probability of geological disaster occurrence and obtain disaster probability information for different evaluation factors.

[0082] Specifically, in this embodiment of the invention, based on the investigation and analysis of disaster-prone geological conditions, the main controlling factors that lead to disasters are identified, an index system is constructed, and the corresponding information value is solved using the information quantity model method; based on the hierarchical evaluation, the results of comprehensive superposition analysis are used to classify the disaster susceptibility level; based on susceptibility, the information quantity of the main inducing factors in the region, such as rainfall and earthquakes, is superimposed to calculate the disaster risk assessment and zoning results.

[0083] The information content of geological disasters under a specific factor is calculated using a formula corresponding to the information content of geological disasters under a specific state of a certain factor. The specific formula is as follows:

[0084]

[0085] Where A represents a certain disaster-prone condition factor (information on factors such as engineering rock group, hydrogeology, structural features, elevation, slope, aspect, curvature, gradient, geomorphology, surface water system, and vegetation cover obtained in step S200); J represents the state classification or interval under disaster-prone condition A; I Aj→B This refers to the amount of information related to the occurrence of geological disaster B under the corresponding factor A, state j, or interval; N j S represents the number of geological hazard distribution points under the corresponding factor A, state j, or interval; N represents the total number of known geological hazard hazard points in the evaluation area; S represents the number of geological hazard hazard distribution points. j The number of raster cells for the state or interval distribution of factors A and j (where various evaluation factor information is unified into a raster file, different scales are set according to different evaluation accuracy requirements, and then the raster processing / resampling tool under the data management module of ArcGIS software is used to unify the pixel size of the raster cells. For example, for a 1:50000 accuracy requirement, the resampled pixel size of the raster cell is 25*25m. Then, the system symbol is retrieved in the layer properties, and the raster cell count is queried in the classification statistics table to finally obtain the raster cells and their quantity); S is the total number of raster cells in the evaluation area.

[0086] When I Aj→B When I > 0, it reflects a large amount of information about the tendency of geological disasters to occur under the corresponding factor A, j state or interval, indicating a higher probability of geological disasters occurring, or in other words, a favorable environment for geological disasters to occur; when I Aj→B When I < 0, it indicates that under the conditions of factor A, j, state, or interval, the occurrence of geological disasters is unfavorable; when I Aj→B When the value is 0, it indicates that the state or interval of factor A or j does not provide any information about whether a geological disaster has occurred. In other words, the state or interval of factor A or j can be eliminated and excluded as a geological disaster prediction factor.

[0087] Each evaluation unit is influenced by a combination of numerous factors, and these factors exist in several states. The probability of geological disasters occurring is expressed by the total amount of information about the occurrence of geological disasters under the combined conditions of these states, serving as a geological disaster susceptibility index. The specific formula is as follows:

[0088]

[0089] Where I represents the total information content corresponding to the occurrence of geological disasters in a specific unit, indicating the likelihood of geological disasters occurring, and serving as a geological disaster susceptibility index; N i This refers to the area or number of geological hazards corresponding to specific factors, the i-th state, or the interval condition; S i N represents the number of raster cells corresponding to a specific factor, state i, or interval; N represents the total area of ​​geological hazards or the total number of geological hazard points in the evaluation area; and S represents the total number of raster cells in the evaluation area.

[0090] like Figure 1 As shown, in step S400, based on the acquired natural disaster risk point data along the highway, the highway disaster point density information is obtained, and the disaster probability information of different evaluation factors and the highway disaster point density information are weighted and superimposed to obtain the geological disaster susceptibility evaluation results of each evaluation unit.

[0091] Specifically, a buffer zone of 500m is set up on both sides of the highway, centered on the highway route. Based on the baseline data of disaster points along the highway obtained in step S100, the density analysis module of the Spatial Analyst tool in the ArcGIS platform is used. This module provides three calculation methods: kernel density analysis, point density analysis, and line density analysis. This technique selects the kernel density analysis method to obtain the kernel density map of highway disaster points (e.g., Figure 3 (As shown).

[0092] By combining the kernel density information of highway disaster points with the evaluation factors in different evaluation units and weighting them, the highway disaster susceptibility evaluation information can be obtained. Specifically, using the formula for calculating the information value of geological disasters under specific factors, the probability of geological disasters occurring in a certain area, controlled by disaster-inducing geological conditions such as topographic features, engineering geological rock groups, slope structure types, vegetation coverage, and surface water and groundwater characteristics, is evaluated. Using the aforementioned information value calculation model and the mathematical analysis module of the SpatialAnalyst tool under the ArcGIS platform, the above evaluation factors are calculated to obtain disaster probability layers for different disaster-inducing conditions (such as...). Figure 4 As shown in the figure, the probability of disaster under different disaster-prone conditions refers to the information value calculated according to the information value calculation formula under the corresponding disaster-prone conditions. Using the spatial analysis overlay function of ArcGIS, specifically by accessing the overlay analysis module in the SpatialAnalyst tool under the ArcGIS platform, selecting the weighted overlay calculation method, inputting the disaster probability layer and kernel density layer, and obtaining the geological disaster susceptibility evaluation index for each evaluation unit through computer calculation, a susceptibility evaluation result is formed.

[0093] like Figure 1 As shown, in step S500, the geological hazard susceptibility assessment results are weighted and superimposed with the probability information of major disaster-inducing factors and geological hazard point information in the region to obtain the geological hazard risk assessment results.

[0094] Specifically, based on the geological hazard susceptibility assessment, a probability layer of major hazard-inducing factors in the region is overlaid (the information value is calculated according to the information value calculation formula under the conditions of major hazard-inducing factors such as rainfall and earthquakes). The specific method is to use the weighted overlay calculation method provided by the ArcGIS software platform, input the susceptibility assessment layer, the probability layer of major hazard-inducing factors such as rainfall and earthquakes, and geological hazard point information (including hazard point coordinates, hazard degree, hazard level, hazard type, impact length, occurrence frequency, etc.) to obtain the geological hazard risk assessment results, thereby evaluating the spatial location, scale (intensity), and frequency of occurrence of a certain scale of disaster in a certain area within a certain time period.

[0095] like Figure 1 As shown, in step S600, based on the collected data on highway traffic operation status under disaster conditions, a road traffic impact index is obtained. A disaster risk level matrix is ​​established by combining the geological disaster risk assessment results with the road traffic impact index to obtain the disaster risk assessment results and form a risk level zoning map of highway traffic operation status under disaster conditions.

[0096] Specifically, the geological hazard risk assessment results will be used to establish a level matrix with the road traffic impact index to obtain the hazard risk assessment results. Using data reflecting traffic operation status, such as the location, frequency, and duration of road traffic blockages under disaster conditions, collected in step S100, the data is normalized by assigning tiered values ​​to indicators such as blockage frequency, blockage duration, and blockage length to obtain the road traffic impact index. This establishes a hazard risk and road traffic impact level matrix, defining hazard risk levels as extremely high, high, medium, and low, and assigning colors accordingly (e.g., ...). Figure 5 (As shown).

[0097] Finally, based on the principles of similarity and proximity, the raster data was generalized to vector data. Specifically, the disaster risk assessment results were input into the ENVI software platform, the Classification tool was invoked, the Post Classification module was selected, and Sieve classes were calculated to remove scattered patches. Then, parameters were input to calculate Clump classes, completing patch generalization and cluster analysis, thus achieving generalized raster processing of the disaster risk assessment information. Subsequently, the raster-to-surface sub-module under the raster-to-raster conversion module in the ArcGIS platform was used to vectorize the raster data. The final result is a risk zoning map of highway traffic operation status under disaster conditions, as shown below. Figure 6 As shown.

[0098] Corresponding to the aforementioned method for assessing and zoning highway traffic operation status based on disaster conditions, this invention also discloses a system for assessing and zoning highway traffic operation status based on disaster conditions, such as... Figure 7 As shown, it specifically includes:

[0099] The data acquisition module is used to acquire data on natural disaster risk points along the highway, collect data on disaster-prone geological factors, topographic factors and meteorological factors, and collect data on highway traffic operation status under disaster conditions.

[0100] The evaluation factor determination module is used to divide the highway area into evaluation units based on the main disaster-prone factors in the area, and select different combinations of evaluation factors for each evaluation unit according to the characteristics of the evaluation unit.

[0101] The information content calculation module is used to calculate the total information content of geological disasters under the corresponding evaluation factor combination based on the evaluation factor combination of each evaluation unit, so as to evaluate the probability of geological disasters and obtain disaster probability information of different evaluation factors.

[0102] The susceptibility assessment module is used to obtain highway disaster point density information based on the acquired natural disaster risk point data along the highway, and to obtain the geological disaster susceptibility assessment results for each assessment unit by weighted superposition of the disaster probability information of different assessment factors and the highway disaster point density information.

[0103] The risk assessment module is used to weight and superimpose the geological hazard susceptibility assessment results with the probability information of major disaster-inducing factors and geological hazard point information in the region to obtain the geological hazard risk assessment results;

[0104] The risk assessment module is used to obtain the road traffic impact index based on the collected data on highway traffic operation status under disaster conditions, establish a disaster risk level matrix by combining the geological disaster risk assessment results with the road traffic impact index, obtain disaster risk assessment results, and form a risk level zoning map of highway traffic operation status under disaster conditions.

[0105] It should be noted that for a detailed description of the highway traffic operation status assessment and zoning system based on disaster conditions provided in the embodiments of the present invention, please refer to the relevant description of the highway traffic operation status assessment and zoning method based on disaster conditions provided in the embodiments of this application, which will not be repeated here.

[0106] In addition, embodiments of the present invention also provide an electronic device, the device comprising: a processor and a memory; the memory being used to store one or more program instructions; the processor being used to execute one or more program instructions to perform the steps of a disaster-condition-based highway traffic operation status assessment zoning method as described in any of the preceding embodiments.

[0107] It should be noted that for a detailed description of an electronic device provided in the embodiments of the present invention, please refer to the relevant description of a method for assessing and classifying highway traffic operation status based on disaster conditions provided in the embodiments of this application, which will not be repeated here.

[0108] In addition, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the disaster-condition-based highway traffic operation status assessment zoning method as described in any of the preceding claims.

[0109] It should be noted that for a detailed description of the computer-readable storage medium provided in the embodiments of the present invention, please refer to the relevant description of the zoning method for assessing the road traffic operation status based on disaster conditions provided in the embodiments of this application, which will not be repeated here.

[0110] Those skilled in the art will understand that all or part of the functions of the various methods in the above embodiments can be implemented by hardware or by computer programs. When all or part of the functions in the above embodiments are implemented by computer programs, the program can be stored in a computer-readable storage medium, which may include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to achieve the above functions. For example, the program can be stored in the memory of a device, and when the program in the memory is executed by the processor, all or part of the above functions can be achieved. In addition, when all or part of the functions in the above embodiments are implemented by computer programs, the program can also be stored in a server, another computer, disk, optical disk, flash drive, or external hard drive, etc., and can be downloaded or copied to the memory of a local device, or the system of the local device can be updated. When the program in the memory is executed by the processor, all or part of the functions in the above embodiments can be achieved.

[0111] The above examples illustrate the present invention only to aid in understanding it and are not intended to limit the scope of the invention. Those skilled in the art can make various simple deductions, modifications, or substitutions based on the principles of this invention.

Claims

1. A method for assessing and zoning highway traffic operation status based on disaster conditions, characterized in that, The method includes: Acquire data on natural disaster risk points along highways, collect data on disaster-prone geological factors, topographical factors and meteorological factors, and collect data on highway traffic operation status under disaster conditions; The highway area is divided into evaluation units based on the main disaster-prone factors within the area. Different combinations of evaluation factors are then selected for each of the divided evaluation units according to their characteristics. Specifically, this includes: Based on multi-source, multi-period data from optical remote sensing and DEM covering the highway disaster risk assessment area, we conduct comprehensive interpretation of various typical geological disaster evaluation factors and use different algorithms to extract information on various typical geological disaster evaluation factors. A sample library is established based on the interpreted typical geological hazard evaluation factor information. The deep learning model is trained iteratively multiple times using a classification-supervised method based on the sample library. The trained deep learning model is used to obtain various atypical geological hazard evaluation factor information. Based on the combination of evaluation factors for each evaluation unit, the information content model method is used to solve the total information content of geological disasters under the corresponding combination of evaluation factors in order to evaluate the probability of geological disasters and obtain disaster probability information for different evaluation factors. Based on the acquired data on natural disaster risk points along the highway, the density information of highway disaster points is obtained. The disaster probability information of different evaluation factors and the density information of highway disaster points are weighted and superimposed to obtain the geological disaster susceptibility evaluation results of each evaluation unit. The geological hazard risk assessment results are obtained by weighting and overlaying the geological hazard susceptibility assessment results with the probability information of major disaster-inducing factors and geological hazard points in the region; Based on collected data on highway traffic operation status under disaster conditions, a road traffic impact index is obtained. A disaster risk level matrix is ​​established by combining the geological disaster hazard assessment results with the road traffic impact index to obtain disaster risk assessment results and generate a risk level zoning map of highway traffic operation status under disaster conditions. Specifically, this includes: The collected data on highway traffic operation status under disaster conditions includes the location, frequency, duration, and length of highway traffic blockages. The data is normalized by assigning values ​​to each highway traffic operation status indicator to obtain the road traffic impact index. A disaster risk and road traffic impact level matrix is ​​then established, with four levels—extremely high, high, medium, and low—defined and color-coded.

2. The method for assessing and zoning highway traffic operation status based on disaster conditions as described in claim 1, characterized in that, Acquire data on natural disaster risk points along highways, collect data on disaster-prone geological factors, topographical factors, and meteorological factors, and collect data on highway traffic operation status under disaster conditions, specifically including: Based on the results of the comprehensive natural disaster risk survey and the data collection system for the natural disaster risk highway disaster-bearing bodies survey, information on natural disaster risk points is collected, risk assessments of natural disaster risk points are conducted, data on high-level risk points are verified, and the baseline data on natural disaster risk hazards of highway disaster-bearing bodies are obtained. Collect data on disaster-prone geological factors, topographic and geomorphological factors, and meteorological factors for highways, including topography, geomorphology, engineering rock groups, geological structures, surface water systems, seismic intensity, and precipitation data. Collect traffic operation status data under disaster conditions, including road blockage location, frequency, and duration data.

3. The method for assessing and zoning highway traffic operation status based on disaster conditions as described in claim 1, characterized in that, The highway area is divided into evaluation units based on the main disaster-prone factors within the area. Different combinations of evaluation factors are then selected for each of the divided evaluation units according to their characteristics. Specifically, these include: The main disaster-prone factors in each area of ​​the highway were analyzed and identified, and the main disaster-prone factors were selected as the theme for dividing the evaluation units in each area. Susceptibility assessment factors include geological factors: engineering rock group, hydrogeology, and structural features; topographic and geomorphological factors: elevation, slope, aspect, curvature, gradient, landform, and surface water system; as well as vegetation cover factors, rainfall factors, and seismic intensity factors. During the evaluation, different evaluation factors are selected and combined based on the characteristics of the evaluation unit and the judgment of experts.

4. The method for assessing and zoning highway traffic operation status based on disaster conditions as described in claim 1, characterized in that, Based on the acquired data on natural disaster risk points along the highway, the density information of highway disaster points is obtained. The disaster probability information of different evaluation factors and the density information of highway disaster points are then weighted and superimposed to obtain the geological disaster susceptibility evaluation results for each evaluation unit. Specifically, this includes: Based on the acquired data of natural disaster risk points along the highway, the density analysis module in the Spatial Analyst tool under the ArcGIS platform is used. The density analysis module provides three calculation methods: kernel density analysis, point density analysis, and line density analysis. The kernel density analysis method is selected to obtain the kernel density map of highway disaster points. Using the spatial analysis overlay function of ArcGIS, specifically by accessing the overlay analysis module in the Spatial Analyst tool under the ArcGIS platform, selecting the weighted overlay calculation method, inputting the disaster probability layer and kernel density layer, and obtaining the geological disaster susceptibility evaluation index for each evaluation unit through calculation to form the susceptibility evaluation result.

5. The method for assessing and zoning highway traffic operation status based on disaster conditions as described in claim 1, characterized in that, The geological hazard risk assessment results are obtained by weighting and overlaying the geological hazard susceptibility assessment results with the probability information of major disaster-inducing factors and geological hazard point information in the region. Specifically, these include: The weighted overlay calculation method provided by the ArcGIS software platform is selected. The geological hazard susceptibility layer, the probability layer of major disaster-inducing factors, and geological hazard point information are input. The geological hazard point information includes the coordinates of the hazard point, the degree of hazard, the hazard level, the hazard type, the length of the impact, and the frequency of occurrence. The geological hazard risk assessment results are obtained, thereby evaluating the spatial location, scale or intensity, and frequency of occurrence of a certain scale of disaster in a certain region within a certain time period.

6. The method for assessing and zoning highway traffic operation status based on disaster conditions as described in claim 1, characterized in that, Obtaining disaster risk assessment results specifically includes: Based on the principles of similarity and proximity, the disaster risk assessment results are generalized from raster to vector. Specifically, the disaster risk assessment results are input into the ENVI software platform, the Classification tool is invoked, the PostClassification module is selected, Sieve classes are calculated to remove scattered patches, and then parameters are input to calculate Clumpclasses, completing the patch generalization and cluster analysis, thereby realizing the generalized raster processing of disaster risk assessment information. Subsequently, the raster to surface submodule under the raster to surface module of the conversion tool in the ArcGIS platform is used to realize the vectorization of raster data.

7. A highway traffic operation status assessment zoning system based on disaster conditions, characterized in that, The system includes: The data acquisition module is used to acquire data on natural disaster risk points along the highway, collect data on disaster-prone geological factors, topographic factors and meteorological factors, and collect data on highway traffic operation status under disaster conditions. The evaluation factor determination module is used to divide highway areas into evaluation units based on the main disaster-prone factors within the area, and to select different combinations of evaluation factors for each evaluation unit according to its characteristics; specifically, it includes: Based on multi-source, multi-period data from optical remote sensing and DEM covering the highway disaster risk assessment area, we conduct comprehensive interpretation of various typical geological disaster evaluation factors and use different algorithms to extract information on various typical geological disaster evaluation factors. A sample library is established based on the interpreted typical geological hazard evaluation factor information. The deep learning model is trained iteratively multiple times using a classification-supervised method based on the sample library. The trained deep learning model is used to obtain various atypical geological hazard evaluation factor information. The information content calculation module is used to calculate the total information content of geological disasters under the corresponding evaluation factor combination based on the evaluation factor combination of each evaluation unit, so as to evaluate the probability of geological disasters and obtain disaster probability information of different evaluation factors. The susceptibility assessment module is used to obtain highway disaster point density information based on the acquired natural disaster risk point data along the highway, and to obtain the geological disaster susceptibility assessment results for each assessment unit by weighted superposition of the disaster probability information of different assessment factors and the highway disaster point density information. The risk assessment module is used to weight and superimpose the geological hazard susceptibility assessment results with the probability information of major disaster-inducing factors and geological hazard point information in the region to obtain the geological hazard risk assessment results; Based on collected data on highway traffic operation status under disaster conditions, a road traffic impact index is obtained. A disaster risk level matrix is ​​established by combining the geological disaster hazard assessment results with the road traffic impact index to obtain disaster risk assessment results and generate a risk level zoning map of highway traffic operation status under disaster conditions. Specifically, this includes: The collected data on highway traffic operation status under disaster conditions includes the location, frequency, duration, and length of highway traffic blockages. The data is normalized by assigning values ​​to each highway traffic operation status indicator to obtain the road traffic impact index. A disaster risk and road traffic impact level matrix is ​​then established, with four levels—extremely high, high, medium, and low—defined and color-coded.

Images