Geological disaster risk and risk area analysis method based on surveying and mapping technology
By using data association and impact factor quantification based on surveying and mapping technology, the problem of multi-source geological disaster data has been solved, improving the accuracy and coverage of geological disaster prediction and supporting scientific prediction, early warning and management.
Patent Information
- Application Number
- CN202511232457.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-29
- Publication Date
- 2026-02-13
AI Technical Summary
Existing technologies are insufficient to effectively quantify multi-source geological disaster data, leading to difficulties in geological disaster information management, low prediction accuracy, and the impact of historical disaster point data offset on prediction accuracy.
Using a surveying and mapping-based approach, a geological hazard risk assessment model was constructed by establishing a geological hazard database module, a remote hazard monitoring module, and a hazard analysis and prediction module, combined with an UAV oblique photogrammetry system and GIS technology, to perform data association, real-scene 3D modeling, and quantification of influencing factors.
It has achieved the quantification and unification of multi-source data, improved the accuracy and coverage of geological disaster prediction, provided refined data support, supported scientific prediction, early warning and management, and reduced the loss of life and property caused by geological disasters.
Smart Images
Figure CN121524232A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the technical field of geological disaster analysis system, and particularly relates to a geological disaster danger and risk area analysis method based on surveying and mapping technology. BACKGROUND
[0002] Geological disasters refer to disasters caused by natural or human factors, and are characterized by complex inducing factors, poor predictability, suddenness, etc., and are easy to cause significant loss of human life and property. Humans cannot accurately predict and prevent geological disasters, especially the time of disaster occurrence, so the "monitoring-prediction and early warning" and disaster prevention and mitigation work for geological disasters have always been valued and researched by domestic and foreign universities and professional research institutions, and the development and application of technology are having a major impact on this field. Due to the different research degrees of countries around the world on related disciplines involving geological disasters, the methods used in the construction of early warning systems are different, resulting in some differences in the reliability and accuracy of early warning.
[0003] The most prominent problem in geological disaster information management is the multi-source, non-deterministic and difficult-to-quantify nature of data, which makes it difficult to centrally manage geological disaster information on a large scale, and there is a lack of effective means to implement scientific organization and management. In the sampling process of historical disaster points, most of them are distributed in residential areas affected by geological disasters, and more attention is paid to the impact on residents, resulting in a shift of the collected geological disaster points relative to the actual occurrence, so the factor statistical information corresponding to the existing disaster points will also deviate from the true situation, which will adversely affect subsequent prediction.
[0004] Therefore, it is a technical problem to be solved to design a geological disaster danger and risk area analysis method based on surveying and mapping technology, which can effectively quantify multi-source data and improve the accuracy of disaster point prediction. SUMMARY
[0005] The application provides a geological disaster danger and risk area analysis method based on surveying and mapping technology.
[0006] The technical scheme of the application is a geological disaster danger and risk area analysis method based on surveying and mapping technology, which comprises the following modules,
[0007] The geological disaster database module is used for collecting historical geological disaster data, screening the data, classifying the effective data according to a geological disaster type classification standard, obtaining spatial data and attribute data, associating the spatial data and the attribute data, using spatial GIS layer data management, constructing a geological disaster hidden danger database, and generating a geological disaster risk assessment.
[0008] The disaster analysis and prediction module is used for constructing a suspected geological disaster risk area analysis model based on GIS technology, obtaining historical disaster point information from the historical geological disaster data, analyzing the regularity and characteristics of the disaster point information, finding out influence factors of the geological disaster from the surrounding environment information of the disaster point, quantifying the influence factors, and obtaining the weight of each influence factor by using the analytic hierarchy process.
[0009] The analysis method is used for obtaining the geological disaster risk assessment by analyzing the geological disaster data of the geological disaster hidden danger database, obtaining the geological disaster hazard evaluation by combining the risk level and the risk assessment of the disaster remote monitoring module, correcting the disaster point information by using the influence factor weight, drawing a geological disaster grid map, obtaining the regional risk assessment of the geological disaster according to the grid map, and comprehensively comparing the geological disaster hazard evaluation and the regional risk assessment to obtain a comprehensive comparison evaluation of the geological disaster and to establish a time-sequenced comparison database.
[0010] As a further improvement of the present application, the data type of the spatial data includes vector data and raster data, the vector data includes collected basic geographic information, basic geological information and thematic maps, the basic geographic information data includes administrative division, road and water system information, the basic geological information includes stratum lithology and fault information, the thematic maps include existing geological disaster distribution maps and risk zoning maps, and the raster data includes collected remote sensing images.
[0011] As a further improvement of the present application, the oblique photogrammetry system platform carries a multi-lens aerial photogrammetry camera by using a UAV, regularly carries out phased measurement on geological disaster hidden danger points, collects POS data and shoots original images of the UAV oblique photogrammetry, uses aerial triangulation and encryption technology of the UAV to carry out texture matching mapping on the collected data, carries out real scene three-dimensional modeling on geological disasters and surrounding areas, and generates real scene three-dimensional model results for precision statistics and quality analysis and evaluation, the real scene three-dimensional model includes a digital elevation model DEM, a digital orthographic image DOM and a digital surface model DSM, and the SuperMap software is used to carry out spatial analysis on the three digital models to calculate the risk level of geological disasters.
[0012] As a further improvement of the present application, the influence factors of the geological disaster include a slope factor, an elevation factor, a terrain relief factor, a land use factor, a slope direction factor, a distance from water system factor and a distance from disaster point factor; the quantification of the influence factors is based on setting a fixed window with a fixed size centered on the geological disaster point, for the land use factor and the slope direction factor, the mode value with the highest frequency of all grid attribute values in the entire window is calculated, and the calculation formula is Wherein, c j represents the number of the jth grid attribute value corresponding to each type of factor; for the elevation factor and the slope factor, the average value of the factors in the window is used to analyze the float type attribute values of the two, and the calculation formula is Wherein, N i represents the ith grid attribute value, and n represents the number of grid attributes, and the terrain relief factor, the distance from water system factor and the distance from disaster point factor are not additionally quantified.
[0013] As a further improvement of the present application, the weight of each influence factor calculated by the analytic hierarchy process includes that according to whether the terrain relief of the terrain relief factor is greater than 30 meters as a benchmark, the geological disaster point is divided into mountainous areas and plain areas, and two importance matrices of the mountainous areas and the plain areas are constructed respectively according to the quantified influence factors, and then a judgment matrix is constructed according to the distribution law and influence of the influence factors, the matrix calculates the weight of each influence factor in the mountainous areas and the plain areas, and a comparison table of the influence factor weights under different terrain conditions is established; the judgment mode of the judgment matrix includes that 1) the influence of the slope factor and the terrain relief factor on the geological disaster is maximized in the mountainous areas and the plain areas, 2) for the mountainous area with single land use type, the influence of the land use factor is reduced, and for the plain area with greater human activity influence, the influence of the land use factor is increased, 3) for the plain area with slow water flow and wide water surface, the influence of the distance from water system factor is reduced, and 4) the influence of the elevation factor in the plain area is reduced.
[0014] After the above method, the three modules of the analysis system cooperate, the historical geological disaster data is collected for big data analysis, the multi-source and non-deterministic data are effectively quantified and unified, the prediction accuracy of geological disasters is improved, the topography, landform special conditions of the occurrence of various geological disasters and the meteorological conditions such as precipitation, temperature and humidity at the time of occurrence are collected, a multi-condition dimension geological disaster space-time information database is formed, and on this basis, big data analysis is carried out, a geological disaster prediction and early warning model is trained and optimized, and the coverage, accuracy and timeliness of prediction and early warning are improved.
[0015] The three-dimensional real scene shooting of the geological disaster hidden danger point is carried out by the unmanned aerial vehicle, the topography and landform conditions of the hidden danger point and the roads, water systems, buildings and other buildings or facilities affected are comprehensively mastered, fine data support is provided for disaster risk assessment, the geological disaster environment can be truly restored, and the fine management of geological disasters is promoted. By quantifying the influence factors, constructing the importance matrix and the judgment matrix, the weight of the influence factors is calculated, and finally the regional risk assessment of geological disasters is obtained, so as to achieve the purpose of assisting the investigation, treatment and plan making of geological disaster hidden dangers.
[0016] Using the danger assessment based on the real scene three-dimensional model, the harm evaluation based on the historical geological disaster data, and the regional risk assessment based on the geological disaster grid map, a comprehensive comparison and evaluation of geological disasters is formed, which can more effectively support the scientific planning of geological disaster prediction and early warning, strengthen the prevention and management of geological disasters, avoid and reduce the loss of people's life and property caused by geological disasters, and play an important role in protecting the ecological environment and promoting sustainable development. BRIEF DESCRIPTION OF DRAWINGS
[0017] Figure 1 The technical roadmap of the prediction system is shown. DETAILED DESCRIPTION
[0018] As Figure 1 shown, a geological disaster danger and risk area analysis method based on surveying and mapping technology includes establishing the following modules: a geological disaster database module, historical geological disaster data is collected, effective data is classified according to geological disaster type classification standards, spatial data and attribute data are obtained, spatial data and attribute data are associated, spatial GIS layer data management is used, a geological disaster hidden danger database is constructed, and a danger assessment of geological disasters is generated; a disaster remote monitoring module, an unmanned aerial vehicle and a multi-lens aerial photography camera are used to build a tilt photogrammetry system platform, original images and survey area data are obtained, real scene three-dimensional modeling is carried out on the original images, spatial analysis is carried out in combination with the survey area data, the danger level of geological disasters is calculated, the danger level and the danger assessment are combined, and a harm evaluation of geological disasters is obtained.
[0019] The disaster analysis and prediction module is based on GIS technology to construct a suspected geological disaster risk area analysis model, obtains historical disaster point information from historical geological disaster data, analyzes the regular characteristics of the disaster point information, finds out the influence factors of geological disasters from the surrounding environment information of the disaster point, quantifies the influence factors, and obtains the weight of each influence factor by using the analytic hierarchy process;
[0020] The analysis method obtains the risk assessment of geological disasters by analyzing the geological disaster data of the geological disaster hidden danger database, combines the risk level of the disaster remote monitoring module and the risk assessment, obtains the geological disaster hazard evaluation, corrects the disaster point information using the influence factor weight, draws a geological disaster grid map, obtains the regional risk assessment of geological disasters according to the grid map, and comprehensively compares the geological disaster hazard evaluation and the regional risk assessment to obtain the comprehensive comparison evaluation of geological disasters and establish a time-series comparison database.
[0021] The data types of the spatial data include vector data and raster data, the vector data includes collected basic geographic information, basic geological information and thematic maps, the basic geographic information data includes administrative division, road and water system information, the basic geological information includes stratum lithology and fault information, and the thematic maps include existing geological disaster distribution maps and risk zoning maps, and the raster data includes collected remote sensing images.
[0022] The oblique photogrammetry system platform uses a multi-lens aerial photogrammetry camera carried by a UAV to regularly perform phase control measurement on geological disaster hidden danger points, collects POS data and shoots original oblique photogrammetry images of the UAV, uses aerial triangulation and encryption technology of the UAV to perform texture matching mapping on the collected data, performs real scene three-dimensional modeling on geological disasters and surrounding areas, generates real scene three-dimensional model results for precision statistics and quality analysis and evaluation, the real scene three-dimensional model includes a digital elevation model DEM, a digital orthographic image map DOM and a digital surface model DSM, and the three digital models are subjected to spatial analysis by using SuperMap software to calculate the risk level of geological disasters.
[0023] The influence factors of the geological disasters include a slope factor, an elevation factor, a terrain relief factor, a land use factor, a slope direction factor, a distance from a water system factor and a distance from a disaster point factor, the quantification of the influence factors is based on setting a fixed window with a fixed size centered on a geological disaster point, for the land use factor and the slope direction factor, the mode is calculated by calculating the value with the highest frequency of all grid attribute values in the entire window, and the calculation formula is Wherein, c j represents the number of the jth grid attribute value corresponding to each factor, and for the elevation factor and the slope factor, the average value of the factors in the window is used to analyze the float type attribute values of the two, and the calculation formula is wherein, N i represents the i-th grid attribute value, n represents the number of grid attributes, and no additional quantitative processing is performed on the terrain relief factor, the distance from water system factor, and the distance from disaster point factor.
[0024] The weight of each influence factor calculated by the analytic hierarchy process includes, according to whether the terrain relief factor is greater than 30 meters as a benchmark, dividing the geological disaster point into mountainous and plain areas, constructing two importance matrices of the mountainous and plain areas respectively with the quantified influence factors, and then constructing a judgment matrix according to the distribution law and influence of the influence factors, and calculating the weight of each influence factor in the mountainous and plain areas by the matrix, and establishing a comparison table of the influence factor weights under different terrain conditions; the judgment mode of the judgment matrix includes, 1) the influence of the slope factor and the terrain relief factor on the geological disaster is maximized in both the mountainous and plain areas, 2) for the mountainous area with single land use type, the influence of the land use factor is reduced, and for the plain area with greater human activity influence, the influence of the land use factor is increased, 3) for the plain area with slow water flow and wide water surface, the influence of the distance from water system factor is reduced, and 4) the influence of the elevation factor in the plain area is reduced.
[0025] The three modules of the analysis system cooperate, the historical geological disaster data is collected for big data analysis, the multi-source and non-deterministic data is effectively quantified and unified, the prediction accuracy of geological disasters is improved, the terrain, topography special conditions of the occurrence place of various geological disasters and the meteorological conditions such as precipitation, temperature and humidity at the time of occurrence are collected, a multi-condition dimension geological disaster space-time information database is formed, and on this basis, big data analysis is carried out, the geological disaster prediction and early warning model is trained and optimized, and the coverage, accuracy and timeliness of prediction and early warning are improved.
[0026] The three-dimensional real scene shooting of the geological disaster hidden danger point is carried out by the unmanned aerial vehicle, the terrain and topography conditions of the hidden danger point and the affected roads, water systems, buildings and other buildings or facilities are comprehensively mastered, fine data support is provided for disaster risk assessment, the geological disaster environment can be truly restored, and the fine management of geological disasters is promoted. By quantifying the influence factors and constructing the importance matrix and the judgment matrix, the weight of the influence factors is calculated, and finally the regional risk assessment of geological disasters is obtained, so as to achieve the purpose of assisting the investigation, governance and plan making of geological disaster hidden dangers.
[0027] The danger assessment based on the real three-dimensional model, the harm evaluation based on the historical geological disaster data, and the regional risk assessment based on the geological disaster grid map are used to form a comprehensive comparison and evaluation of geological disasters, which can more effectively support the scientific planning of geological disaster prediction and early warning, strengthen the prevention and management of geological disasters, avoid and reduce the loss of people's life and property caused by geological disasters, and play an important role in protecting the ecological environment and promoting sustainable development.
[0028] Through the statistical probability of historical disaster points in Wenzhou, the following examples of calculating the weights of geological disaster influence factors are obtained.
[0029] Table 1: Classification and quantification table of geological disaster influence factors.
[0030]
[0031]
[0032] Table 2: Mountain area influence factor matrix and weight value table.
[0033]
[0034] Table 3: Plain area influence factor matrix and weight value table.
[0035]
[0036]
[0037] Table 4: Comparison table of influence factor weights under different terrain conditions.
[0038]
Claims
1. A method for analyzing geological hazard risk and risk zones based on surveying and mapping technology, characterized in that: This includes establishing the following modules: The geological disaster database module collects historical geological disaster data, filters the data for validity, refines the valid data according to the classification standards of geological disaster types, obtains spatial data and attribute data, associates the spatial data and attribute data, uses spatial GIS layer data management, constructs a geological disaster hazard database, and generates a geological disaster risk assessment. The disaster remote monitoring module uses drones and multi-lens aerial photography cameras to build an oblique photogrammetry system platform to acquire raw images and survey area data. The raw images are used to create a real-world 3D model, and then combined with the survey area data for spatial analysis to calculate the risk level of geological disasters. The risk level and risk assessment are combined to obtain a geological disaster hazard assessment. The disaster analysis and prediction module uses GIS technology to construct an analysis model for suspected geological disaster risk zones. It obtains information on historical disaster points from historical geological disaster data, analyzes the regularity and characteristics of disaster point information, identifies the influencing factors of geological disasters from the surrounding environmental information of disaster points, quantifies the influencing factors, and uses the analytic hierarchy process (AHP) to obtain the weights of each influencing factor. The analytical method analyzes geological disaster data from a geological disaster hazard database to obtain a geological disaster risk assessment. It combines the risk level and risk assessment from the remote disaster monitoring module to obtain a geological disaster hazard evaluation. It uses influence factor weights to correct disaster point information, draws a geological disaster raster map, obtains a regional risk assessment of geological disasters based on the raster map, and comprehensively compares the geological disaster hazard evaluation and the regional risk assessment to obtain a comprehensive comparative evaluation of geological disasters and establish a time-series comparative database.
2. The method for analyzing geological hazard risk and risk zones based on surveying technology according to claim 1, characterized in that: The spatial data types include vector data and raster data. The vector data includes collected basic geographic information, basic geological information, and thematic maps. The basic geographic information includes administrative divisions, roads, and water system information. The basic geological information includes stratigraphy, lithology, and fault information. The thematic maps include existing geological hazard distribution maps and hazard zoning maps. The raster data includes collected remote sensing images.
3. The method for analyzing geological hazard risk and risk zones based on surveying technology according to claim 1, characterized in that: The oblique photogrammetry system platform uses a drone carrying a multi-lens aerial camera to periodically conduct phased-array measurements of potential geological hazard points, collect POS data, and capture raw oblique photogrammetric images. Using aerial triangulation and encryption technology from the drone, texture matching and mapping are performed on the collected data to create a realistic 3D model of the geological hazard and surrounding area. The resulting realistic 3D model is then subjected to accuracy statistics and quality analysis and evaluation. The realistic 3D model includes a digital elevation model (DEM), a digital orthophoto map (DOM), and a digital surface model (DSM). SuperMap software is used to perform spatial analysis on the three digital models and calculate the hazard level of the geological hazard.
4. The method for analyzing geological hazard risk and risk zones based on surveying technology according to claim 2, characterized in that: The influencing factors of geological hazards include slope factor, elevation factor, topographic relief factor, land use factor, aspect factor, distance from water system factor, and distance from hazard point factor. The quantification of these influencing factors is based on setting a fixed-size window centered on the geological hazard point. For land use factor and aspect factor, the mode is calculated by determining the most frequent value among all raster attribute values within the entire window. The calculation formula is as follows: Among them, c j This represents the number of raster attribute values for the j-th item corresponding to each factor category; for elevation and slope factors, the floating-point attribute values are analyzed using the average factor value within the window, calculated as follows: Where, N i This represents the value of the i-th raster attribute, and n represents the number of raster attributes. No additional quantization processing is performed on the terrain relief factor, the distance factor from the water system, or the distance factor from the disaster point.
5. The method for analyzing geological hazard risk and risk zones based on surveying technology according to claim 4, characterized in that: The weights of each influencing factor calculated by the analytic hierarchy process include: dividing geological hazard points into mountainous and plain areas based on whether the topographic relief factor is greater than 30 meters; constructing two importance matrices for mountainous and plain areas respectively based on the quantified influencing factors; constructing a judgment matrix based on the distribution pattern and influence of the influencing factors; calculating the weights of each influencing factor for mountainous and plain areas based on the matrix; and establishing a comparison table of influencing factor weights under different topographic conditions. The judgment methods of the judgment matrix include: 1) the influence of slope factor and topographic relief factor on geological hazards is maximized in both mountainous and plain areas; 2) the influence of land use factor decreases in mountainous areas with a single land use type, while the influence of land use factor increases in plain areas with greater human activity; 3) the influence of distance factor from water system decreases in plain areas with slow water flow and wide water surface; and 4) the influence of elevation factor decreases in plain areas.