Comprehensive Identification and Evaluation Method for Risk Zones and Hazard Points of Rainfall-Induced Landslides

Abstract

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

Description

Technical Field

[0001] This invention relates to the field of monitoring and identification technology, specifically to a method for comprehensive identification and evaluation of risk areas and potential hazards of rain-induced cluster landslides. Background Technology

[0002] Landslides are a common and serious form of geological disaster prevention and control, and landslide hazards and disaster risks have attracted much attention.

[0003] Currently, landslide hazard identification primarily targets slopes with visible deformation at the individual scale. This involves periodically comparing high-resolution optical remote sensing images from different periods to determine slope morphological changes, monitoring displacement and settlement using total stations and GPS monitoring stations, and conducting on-site inspections to observe abnormal phenomena. Landslide risk assessment is mostly based on a regional scale, while hazard identification is mostly based on a slope scale, resulting in inconsistent assessment scales. The methods for risk assessment and hazard identification are relatively independent and lack systematic integration, leading to a low overlap between actual geological hazard sites and registered hazard sites. Summary of the Invention

[0004] Therefore, the technical problem this invention aims to solve is to overcome the inconsistency in scale between landslide risk assessment, which is often based on a regional scale, and hazard identification, which is often based on a slope scale. Furthermore, the risk assessment and hazard identification methods are relatively independent and lack systematic integration, resulting in a low overlap between actual geological disaster sites and registered hazard sites.

[0005] To address the aforementioned technical problems, this invention provides a comprehensive identification and evaluation method for risk areas and potential hazards of rain-induced cluster landslides. The method includes:

[0006] When an extreme rainfall event occurs, the comprehensive database is updated; when the comprehensive database is updated, the landslide hazard index and the vulnerability index of the disaster-bearing body for each calculation unit in the target area are determined based on the updated comprehensive database; the target area includes multiple sub-regions, and each sub-region includes multiple calculation units;

[0007] For each sub-region, the landslide risk index of the sub-region is determined based on the landslide hazard index and the vulnerability index of the disaster-bearing body of each calculation unit under the sub-region;

[0008] The risk assessment results are determined based on the landslide risk index of each sub-region; the risk assessment results include: the landslide risk level corresponding to each sub-region;

[0009] Deformation regions within the target area are determined based on synthetic aperture radar data;

[0010] The identification results of potential hazard points in the deformed area are determined based on the mathematical elevation model imagery; the identification results of potential hazard points include the potential hazard points in the deformed area and the risk level of the potential hazard points.

[0011] Slope division is performed based on the risk assessment results to adjust the hazard point identification results;

[0012] The risk assessment results are adjusted based on the results of the hazard identification or the results of the disaster hazard mitigation.

[0013] In one optional implementation, when the landslide risk level includes high-risk, medium-risk, and low-risk areas, and a preset number of hazard risk levels are provided, the step of dividing the slope according to the risk assessment results to adjust the hazard point identification results includes:

[0014] The first region is divided into slope units, and these slope units are identified as potential disaster hazard points. The first region is a region in which there are no disaster hazard points among all high-risk regions. The hazard risk level corresponding to the potential disaster hazard point is the lowest hazard risk level among a preset number of hazard risk levels.

[0015] In one optional implementation, adjusting the risk assessment result based on the hazard identification result or the disaster hazard mitigation result includes:

[0016] The landslide risk level of the second area is adjusted according to the risk level of the target disaster hazard point; the target disaster hazard point includes disaster hazard points located in low-risk areas; the second area is the sub-area to which the target disaster hazard point belongs;

[0017] The risk level of the standard disaster hazard points will be re-determined based on the treatment results of the standard disaster hazard points.

[0018] The landslide risk level of the third region is adjusted according to the risk level of the newly determined standard disaster hazard points; the standard disaster hazard points include disaster hazard points located in high-risk and medium-risk areas; the third region is the sub-region to which the standard disaster hazard points belong.

[0019] In one optional implementation, determining the hazard point identification result corresponding to the deformed area based on the mathematical elevation model image includes:

[0020] High-resolution optical remote sensing data are obtained from the mathematical elevation model image.

[0021] The high-resolution optical remote sensing data is subjected to preset processing to extract the features of disaster hazard points; the preset processing includes: filtering, edge detection, texture analysis, contrast enhancement, pattern recognition and image segmentation;

[0022] Based on the characteristics of the disaster hazard points, the disaster hazard points in the deformation area are determined;

[0023] Determine the geological elements of the disaster hazard points and the corresponding weights of the geological elements; the geological elements include: deformation rate, slope, topographic relief, surface roughness, topographic curvature, and volume;

[0024] The risk level of the disaster hazard points in the deformation area is determined based on the geological elements of the disaster hazard points and the weights corresponding to the geological elements.

[0025] In one optional implementation, the comprehensive database includes: a baseline database and a monitoring database; the baseline database includes: geological structure data, topographic data, hydrological and meteorological data, socio-economic data, disaster management data, and historical case data; the monitoring database includes: ground monitoring data, aerospace monitoring data, and manual inspection data, and the monitoring database is used to determine the management results of the disaster hazard points.

[0026] In one optional implementation, determining the landslide hazard index and the vulnerability index of the affected body for each calculation unit in the target area based on the updated comprehensive database includes:

[0027] The landslide hazard index of each calculation unit in the target area is determined based on the geological structure data, the topographic data, and the hydrological and meteorological data.

[0028] The vulnerability index of each computing unit in the target area is determined based on the socioeconomic data and the historical case data.

[0029] In one optional implementation, determining the landslide hazard index of each calculation unit in the target area based on the geological structure data, the topographic data, and the hydrological and meteorological data includes:

[0030] Obtain a landslide hazard assessment model pre-constructed using logistic regression;

[0031] The geological structure data, topographic data, and hydrological and meteorological data are input into the landslide hazard assessment model to obtain the landslide hazard index for each calculation unit.

[0032] The process of determining the vulnerability index of each computing unit in the target area based on the socioeconomic data and the historical case data includes:

[0033] Obtain a vulnerability assessment model for disaster-bearing bodies that has been pre-constructed using logistic regression.

[0034] The socioeconomic data and historical case data are input into the disaster-bearing body vulnerability assessment model to obtain the disaster-bearing body vulnerability index for each calculation unit.

[0035] Secondly, the present invention provides a comprehensive identification and evaluation device for risk areas and hidden danger points of rain-induced cluster landslides, the device comprising:

[0036] The first processing module is used to update the comprehensive database when an extreme rainfall event occurs; when the comprehensive database is updated, the landslide hazard index and the vulnerability index of the disaster-bearing body for each calculation unit in the target area are determined according to the updated comprehensive database; the target area includes multiple sub-regions, and each sub-region includes multiple calculation units;

[0037] The second processing module is used to determine the landslide risk index of each sub-region based on the landslide hazard index and the vulnerability index of the disaster-bearing body of each calculation unit under the sub-region.

[0038] The third processing module is used to determine the risk assessment result based on the landslide risk index of each sub-region; the risk assessment result includes: the landslide risk level corresponding to each sub-region;

[0039] The fourth processing module is used to determine the deformation area in the target region based on synthetic aperture radar data; and to determine the hazard point identification results corresponding to the deformation area based on mathematical elevation model imagery; the hazard point identification results include the hazard points in the deformation area and the hazard risk level corresponding to the hazard points;

[0040] The fifth processing module is used to divide the slope according to the risk assessment results in order to adjust the hazard point identification results;

[0041] The sixth processing module is used to adjust the risk assessment results based on the hazard identification results or the disaster hazard mitigation results.

[0042] Thirdly, the present invention provides a computer device, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the comprehensive identification and evaluation method for risk areas and hidden danger points of rain-induced mass landslides described in the first aspect or any corresponding embodiment.

[0043] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions, which are used to cause a computer to execute the method for comprehensive identification and evaluation of risk areas and hidden danger points of rain-induced mass landslides described in the first aspect or any corresponding embodiment above.

[0044] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the comprehensive identification and evaluation method for risk areas and hidden danger points of rain-induced cluster landslides described in the first aspect or any corresponding embodiment above.

[0045] The technical solution provided by this invention has the following technical effects:

[0046] The technical solution of this invention updates the comprehensive database when extreme rainfall events occur, and re-identifies and evaluates the risk areas and hidden danger points of rainfall-induced mass landslides based on the updated comprehensive database. This enables dynamic updating of disaster hidden danger points and timely detection of disaster hidden danger points.

[0047] After determining the risk assessment results (the landslide risk level corresponding to each sub-region), the results are correlated with the deformation areas identified based on synthetic aperture radar data and the hazard point identification results (slope scale) determined by mathematical elevation model imagery. By adjusting the hazard point identification results according to the slope division based on the risk assessment results, the scale of risk assessment at the regional scale and hazard point identification at the slope scale are unified. For example, for high-risk sub-regions, the slope division can be further refined to more accurately identify hazard points within that region, avoiding the limitations of assessments based solely on the regional scale.

[0048] This involves a two-way feedback mechanism: adjusting the hazard identification results based on risk assessment results, and adjusting the risk assessment results based on the hazard identification results or the mitigation results of disaster hazard points. Once the landslide risk level of a sub-region is determined through risk assessment, the hazard identification results in the deformation area are adjusted accordingly. For example, potentially overlooked slope units in high-risk sub-regions are identified as potential disaster hazard points and included in the hazard identification results, making the identification of disaster hazard points more comprehensive and no longer limited to determination based solely on the deformation and other characteristics of the region itself.

[0049] At the same time, when the disaster hazard points are treated or their identification results change, the information is promptly fed back into the risk assessment to adjust the landslide risk level of the corresponding sub-area.

[0050] This two-way adjustment mechanism transforms risk assessment and hazard identification from independent processes into an organic whole, where they influence and promote each other, greatly enhancing their relevance and consistency. This solves the problem of independent and low-overlapping working methods in related technologies. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the specific embodiments or related technologies of the present invention, the drawings used in the description of the specific embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0052] Figure 1 This is a flowchart illustrating the method for comprehensive identification and evaluation of risk areas and hidden danger points of rain-induced mass landslides according to an embodiment of the present invention.

[0053] Figure 2 This is a schematic diagram of the overall process of the method for comprehensive identification and evaluation of risk areas and hidden danger points of rain-induced mass landslides according to an embodiment of the present invention;

[0054] Figure 3 This is a schematic diagram of the structure of the integrated identification and evaluation device for risk areas and hidden danger points of rain-induced mass landslides according to an embodiment of the present invention;

[0055] Figure 4 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Landslide hazards refer to slopes that already show obvious signs of deformation and may become unstable and cause damage in the near future. Landslide risk refers to the possibility that a natural slope will become unstable and collapse under the influence of rainfall, freeze-thaw cycles, earthquakes, or engineering activities, resulting in damage. Landslide hazard identification often focuses on individual slopes with historical deformation, impending deformation, or current deformation, using methods such as remote sensing image comparison, ground-based instrument monitoring, and manual inspections. Landslide risk identification, on the other hand, focuses on the causative factors, analyzing the possibility of natural slopes in a target area becoming unstable and causing damage under rainfall triggers, and is often conducted through model calculations. A comparison of the two reveals that landslide hazard identification primarily targets slopes with visible deformation at the individual scale, using instrument monitoring, while landslide risk assessment primarily targets slopes with invisible deformation at the regional scale, using model calculations. It can be observed that although there are differences between rainfall-induced landslide hazard identification and risk assessment in terms of evaluation objects, research scale, and identification techniques. Based on previous research and the characteristics of rainfall-induced landslide clusters in recent years, the following shortcomings can be identified in the current work on risk assessment and hazard identification for rainfall-induced landslide clusters:

[0058] (1) Landslide hazards include not only areas undergoing deformation, but also potentially unstable slopes. At present, monitoring and identification methods dominated by surface deformation are difficult to achieve accurate and complete identification of rainfall-induced landslide hazards.

[0059] (2) At present, the risk assessment of rain-induced mass landslides is mostly carried out at the regional scale, while the identification of disaster hazard points is mostly carried out at the slope scale. The assessment results of the two have not achieved scale unification.

[0060] (3) The methods and results of risk assessment and hazard identification are relatively independent and have not been systematically integrated, resulting in actual geological disaster points being located in risk areas, but with low overlap with the disaster hazard points recorded in the register.

[0061] This invention provides a comprehensive identification and evaluation method for risk zones and hidden danger points of rainfall-induced cluster landslides. It aims to construct a comprehensive judgment and analysis system for cluster landslide risks and hazards, featuring dual levels of risk and hazard assessment, dual characteristics of visible and invisible landslides, dual control at regional and slope scales, dual driving forces of instrumental monitoring and mathematical calculation, and dual mechanisms of mutual feedback correction and dynamic updating. This system improves the accuracy of identifying and predicting rainfall-induced cluster landslides, innovates the working mode of risk assessment and hazard identification for cluster geological disasters, and addresses problems in related technologies.

[0062] According to an embodiment of the present invention, a method for comprehensive identification and evaluation of risk areas and hidden danger points of rain-induced mass landslides is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer device such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0063] Figure 1 This is a flowchart illustrating the method for comprehensive identification and evaluation of risk areas and hidden danger points of rain-induced mass landslides according to an embodiment of the present invention.

[0064] like Figure 1 As shown in the figure, this embodiment of the invention provides a method for comprehensive identification and evaluation of risk areas and hidden danger points of rain-induced cluster landslides. This method includes:

[0065] S101: In the event of an extreme rainfall event, update the integrated database. With the integrated database updated, determine the landslide hazard index and vulnerability index of each calculation unit in the target area based on the updated database.

[0066] In this embodiment, the target region includes multiple sub-regions, and each sub-region includes multiple computing units.

[0067] In this invention, before implementing the technical solution for the comprehensive identification and evaluation of risk areas and potential hazards of rainfall-induced landslides, a comprehensive database needs to be pre-constructed. This comprehensive database includes a baseline database and a monitoring database. Initial baseline and monitoring databases need to be pre-constructed. When extreme rainfall events occur, the data in the comprehensive database is systematically updated, and the technical solution for the comprehensive identification and evaluation of risk areas and potential hazards of rainfall-induced landslides is re-implemented based on the updated comprehensive database. This achieves iterative updating of potential hazard points and timely identification of newly emerging potential hazard points.

[0068] The baseline database includes:

[0069] Geological structure data: including but not limited to faults, lithology, and stratigraphic age.

[0070] Topographic data, including but not limited to aspect, elevation, slope, and soil use type.

[0071] Hydrological and meteorological data: including but not limited to average rainfall, cumulative rainfall, river system distribution, and soil moisture data.

[0072] Socioeconomic data: including but not limited to data on housing distribution, population distribution, road distribution, and bridge distribution.

[0073] Disaster management data includes, but is not limited to, the location of registered disaster hazard points, the number of registered disaster hazard points, basic geological data of disaster hazard points, and disaster hazard point management data.

[0074] Historical case data: including but not limited to case rainfall, case landslides, case damage, and disaster recovery data.

[0075] The monitoring database includes:

[0076] Ground monitoring data includes, but is not limited to, soil moisture content, slope deformation, slope acceleration, and rainfall.

[0077] Space-air monitoring data includes, but is not limited to, optical images, interferometric synthetic aperture radar (InSar) data, airborne radar data, and meteorological satellite data.

[0078] Manual inspection data includes, but is not limited to, landslide deformation information, landslide precursor information, disaster hazard avoidance information, and disaster hazard mitigation information.

[0079] This invention does not limit the method of constructing a comprehensive database or obtaining data from the comprehensive database. Conventional methods in the field can be used to obtain the above data, such as geological exploration, remote sensing technology, meteorological station and hydrological station observation, archive retrieval, field surveys, etc.

[0080] In this embodiment, the step of S101 above, which involves determining the landslide hazard index and the vulnerability index of the disaster-bearing body for each calculation unit in the target area based on the updated comprehensive database, specifically includes:

[0081] S1011: Determine the landslide hazard index for each calculation unit in the target area based on geological structure data, topographic data, and hydrological and meteorological data.

[0082] S1012: Determine the vulnerability index of each computing unit in the target area based on socioeconomic data and historical case data.

[0083] The aforementioned S1011 determines the landslide hazard index for each calculation unit in the target area based on geological structure data, topographic data, and hydrological and meteorological data, specifically including:

[0084] Obtain a landslide hazard assessment model pre-constructed using logistic regression.

[0085] Geological structure data, topographic data, and hydrological and meteorological data are input into the landslide hazard assessment model to obtain the landslide hazard index for each calculation unit.

[0086] In this embodiment, geological structure data, topographic data, and hydrological and meteorological data (X1X2…X) are selected from the comprehensive database. n A landslide hazard assessment model based on logistic regression was constructed. Geological structure data, topographic data, and hydrological and meteorological data of the target area were input into the landslide hazard assessment model to calculate the landslide hazard index (H) for each calculation unit. k With a comprehensive database update, the landslide hazard assessment model is reconstructed;

[0087] In this embodiment, the landslide hazard index is calculated as follows:

[0088] (A0,A1,A2,…,A n )=f LOR (X1,X2,…,X n );

[0089] Z H =A0 + A1X1 + A2X2 + ... + A n X n ;

[0090]

[0091] H = ∑H k ;

[0092] Where: f LOR This represents the logistic regression algorithm; Z H Represents all control variables X1X2…X based on weights. n The sum; n represents the number of control variables, k represents the calculation unit number, H k Let A0, A1, A2, ..., A represent the landslide hazard index of calculation unit k, and H represent the landslide hazard index of sub-region. n Determined using logistic regression, A0 represents the constant term, and A1, A2, ..., A... n These represent the weights corresponding to the control variables.

[0093] The aforementioned S1012 determines the vulnerability index of each calculation unit in the target area based on socioeconomic data and historical case data, specifically including:

[0094] Obtain a vulnerability assessment model for disaster-bearing bodies that has been pre-constructed using logistic regression.

[0095] Socioeconomic data and historical case data are input into the disaster vulnerability assessment model to obtain the disaster vulnerability index for each calculation unit.

[0096] In this embodiment, socioeconomic data and historical case data (Y1Y2…Y) are selected from a comprehensive database.n A vulnerability assessment model for disaster-bearing bodies based on logistic regression was constructed. Socioeconomic data and historical case data of the target area were input into the model to calculate the vulnerability index (V) for each calculation unit. k With the comprehensive database updated, the vulnerability assessment model for disaster-bearing bodies is reconstructed.

[0097] In this embodiment, the vulnerability index of the disaster-bearing body is calculated as follows:

[0098] (B0,B1,B2,…,B n )=f LOR (Y1,Y2,…,Y n );

[0099] Z V =B0 + B1Y1 + B2Y2 + ... + B n Y n ;

[0100]

[0101] V=∑V k ;

[0102] Among them: Z V V represents the sum of all control variables based on their weights. k Let B0, B1, B2, ..., B represent the vulnerability index of the disaster-bearing body in calculation unit k, and V represent the vulnerability index of the disaster-bearing body in sub-regions. n Determined through logistic regression, B0 represents the constant term.

[0103] B1,B2,…,B n These represent the weights corresponding to the control variables.

[0104] S102: For each sub-region, determine the landslide risk index of the sub-region based on the landslide hazard index and the vulnerability index of the disaster-bearing body for each calculation unit under the sub-region.

[0105] In this embodiment, the landslide hazard index and the vulnerability index of the disaster-bearing body of the calculation unit are coupled and superimposed by multiplication to generate the landslide risk index (R) of the sub-region.

[0106] R k =H k ×V k ;

[0107] R = ∑R k ;

[0108] Among them, R k This represents the landslide risk index for calculation unit k.

[0109] S103: Determine the risk assessment results based on the landslide risk index of each sub-region.

[0110] In this embodiment, the risk assessment results include: the landslide risk level corresponding to each sub-region, and may also include the sub-region's number, location information, and other relevant information.

[0111] Landslide risk levels are categorized into high-risk, medium-risk, and low-risk areas.

[0112] As an example, landslide risk levels can be divided into five levels, where high-risk areas include extremely high-risk areas and high-risk areas, medium-risk areas include risk areas, and low-risk areas include low-risk areas and extremely low-risk areas.

[0113] As an example, a target area can be divided into five risk levels based on a landslide risk index, with values ​​ranging from 0 to 1. 0.8-1.0: Extremely high risk area. 0.6-0.8: High risk area. 0.4-0.6: Medium risk area. 0.2-0.4: Low risk area. 0.0-0.2: Extremely low risk area.

[0114] S104: Determine the deformed areas within the target region based on synthetic aperture radar data. Identify the corresponding hazard points in the deformed areas based on mathematical elevation model imagery.

[0115] In this embodiment, the hazard identification results include hazard points in the deformed area and the hazard risk level corresponding to the hazard points. It may also include the hazard point's number, location information, the number and location information of the sub-region to which it belongs, landslide risk level, and other related information.

[0116] In this embodiment, surface deformation can be detected based on synthetic aperture radar data using phase differential interferometry and amplitude differential interferometry to determine the deformed areas in the target region and obtain deformation data of the deformed areas.

[0117] The technical solution of S104 described above can be determined using conventional methods in this field. As an example, it can be specifically divided into the following stages:

[0118] Data preparation stage:

[0119] Acquiring Synthetic Aperture Radar (SAR) data requires collecting multiple SAR imagery images covering the target area. This data can come from different satellite sensors. The data acquisition interval must be determined based on the required timescale for monitoring surface deformation. For example, for monitoring slow crustal deformation, the interval may be months or even years. For monitoring deformation caused by rapid human engineering activities or geological disasters, the interval may be shortened to days or even hours.

[0120] Data Preprocessing: Radiometric Calibration: This converts the digital signals of SAR images into physically meaningful backscattering coefficients. Different satellite sensors have their corresponding radiometric calibration parameters to eliminate the influence of factors such as sensor gain and bias. For example, radiometric calibration of Sentinel-1 data can be performed using its provided calibration coefficient file, ensuring that image pixel values ​​accurately reflect the radar scattering characteristics of ground objects. Geometric Correction: Due to the complexity of SAR imaging geometry, geometric correction is required to transform image coordinates to a geographic coordinate system. This involves considering factors such as satellite orbital parameters and imaging geometric models. Geometric precision correction can be achieved using information such as satellite orbital state vectors and ground control points, employing methods such as polynomial fitting to ensure that the image accurately corresponds to the actual geographical location.

[0121] Steps for implementing phase difference interferometry:

[0122] Interferogram generation: A pair of preprocessed SAR images (master image and slave image) are selected, which are correlated in time and space. Generally, the master image is used as a reference, and the slave image is used for interferometric processing with the master image. An interferogram is generated by performing a difference operation on the phase information of these two images. The phase values ​​of the interferogram contain information such as surface topography and deformation.

[0123] Removing the flat-land effect: Because the Earth's surface is approximately flat, a linear phase term independent of terrain is generated in the interferogram, known as the flat-land effect. This can be removed by calculating the theoretical flat-land phase based on satellite orbital parameters and imaging geometry, and then subtracting this flat-land phase from the interferogram phase. This helps to highlight phase variations caused by topographic relief and surface deformation.

[0124] Phase unwrapping: The phase values ​​in an interferogram are principal values ​​confined to the range [-π, π], while the actual surface deformation phase may exceed this range. Phase unwrapping aims to recover the continuous true phase. Commonly used phase unwrapping methods include branching and minimum cost flow methods. For example, the minimum cost flow method transforms the phase unwrapping problem into a problem of finding the minimum cost path on a network flow graph. By constructing appropriate network node and edge weights, the optimal phase unwrapping path is found.

[0125] Deformation Calculation: Based on the unwrapped phase values, the surface deformation is calculated using the following formula: Where d is the surface deformation and λ is the radar wavelength. For example, for the Sentinel-1 C-band radar, the wavelength λ = 5.55 cm, the phase change after unwrapping... The corresponding surface deformation can then be calculated.

[0126] Steps for implementing the amplitude difference method:

[0127] Amplitude image generation: Amplitude information is extracted directly from the preprocessed SAR image to generate an amplitude image. The amplitude image reflects the backscattering intensity of ground features to radar waves. Different types of ground features (such as vegetation, water bodies, buildings, etc.) have different backscattering characteristics, which are represented by different gray values ​​in the amplitude image.

[0128] Amplitude difference calculation: Select amplitude images of two time phases and obtain amplitude change information by performing pixel-to-pixel difference operations.

[0129] Deformation Region Determination: Regions potentially prone to deformation are determined based on a threshold value for amplitude difference. Since surface deformation can alter the structure and physical properties of ground features, leading to changes in backscattering intensity, the region containing a pixel is identified as a potential deformation region when the amplitude difference exceeds a set threshold. The threshold value can be optimized through experimental analysis or by combining it with field survey data.

[0130] Deformation area identification and data integration:

[0131] Comprehensive Deformation of Deformation Regions: The results obtained from phase-difference interferometry (PDI) and amplitude-difference interferometry (FDIA) are comprehensively analyzed. Generally, PDI is more sensitive to minute deformations and can provide high-precision deformation information. FDIA can be used as an auxiliary method to detect areas where deformation may significantly alter the structure of ground features and backscattering characteristics. By setting reasonable rules, such as simultaneously satisfying a deformation threshold in PDI and being identified as a potential deformation region in FDIA, the final deformation region is determined.

[0132] Deformation data extraction: For a defined deformation area, deformation data such as the amount of deformation is extracted from the calculation results of phase difference interferometry. This data can be organized, such as by storing it in a Geographic Information System (GIS) format, including the geographic coordinates of each deformation pixel and its corresponding deformation amount. Furthermore, it can be combined with other relevant data, such as land use type and geological structure information, for further analysis and interpretation of the deformation data.

[0133] In this embodiment, the identification results of potential hazard points corresponding to the deformation area are determined based on the mathematical elevation model image, specifically including:

[0134] a: Obtain high-resolution optical remote sensing data based on mathematical elevation model images.

[0135] Deformed regions can be imaged from space by launching satellites equipped with high-resolution optical cameras. High-resolution optical remote sensing data can include spectral and spatial information with resolutions at the sub-meter level and above. Spectral information covers reflectance data across different wavelengths (such as visible light and near-infrared). Spatial information includes visual features such as the shape, size, and texture of ground objects, as well as the geographic coordinates (such as latitude and longitude) and resolution of the image.

[0136] b: Perform pre-processing on high-resolution optical remote sensing data to extract features of potential disaster sites.

[0137] The preset processing includes: filtering, edge detection, texture analysis, contrast enhancement, pattern recognition, and image segmentation.

[0138] c: Identify potential disaster sites in deformed areas based on their characteristics.

[0139] Add potential disaster sites to the disaster site directory. The directory stores the disaster site's ID, risk level, geological data (geological elements), sub-region ID, landslide risk level of the sub-region, and location information (latitude and longitude, and latitude and longitude of multiple points on the boundary) for each potential disaster site. Each entry in the directory includes the above information. The location of a potential disaster site within the target area can be determined based on the disaster site directory.

[0140] As an example, a feature library of potential disaster sites can be established to store the feature patterns of various typical potential disaster sites (such as the features of different types of landslides, the features of cracks of different sizes, etc.). Then, the features of potential disaster sites extracted from the current image are compared with the patterns in the feature library to find the matching potential disaster sites.

[0141] The location of potential disaster sites in actual geographic space can be determined based on the geographic coordinate information (latitude and longitude) of the image. This can be achieved through methods such as image geocoding and coordinate transformation, converting pixel coordinates in the image into actual latitude and longitude coordinates. Then, the identified potential disaster sites are marked on the image, their boundaries are indicated (the boundaries can be determined using the latitude and longitude information of multiple locations), and relevant information (such as number, hazard risk level, etc.) is recorded and added to the potential disaster site directory.

[0142] d: Determine the geological elements of the disaster hazard points and the corresponding weights of the geological elements.

[0143] Geological elements include, but are not limited to: deformation rate, slope, topographic relief, surface roughness, topographic curvature, and volume. Characteristics of potential disaster sites include, but are not limited to: geological disaster boundaries, fissures, landslides, and the extent of the disaster-bearing body.

[0144] e: Determine the risk level of disaster hazard points in deformation areas based on the geological elements of the hazard points and the corresponding weights of the geological elements.

[0145] In this embodiment, determining potential disaster sites requires identifying the deformed slopes in the deformed area, and potential disaster sites can be identified from the deformed slopes in the deformed area.

[0146] Methods for determining deformation slopes:

[0147] By utilizing airborne LiDAR data, the actual terrain beyond surface factors such as vegetation is revealed, resulting in high-precision DEM (Mathematical Elevation Model) images, which are then used to delineate deformable slopes (DFSL). i ), and calculate the slope deformation rate (M i The deformation slope (RA) was further calculated using point cloud data and baseline data. i ), terrain relief (SL) i ), Surface roughness (SR) i ), topographic curvature (TC) i ), volume (VL) i The data, including the parameters, are calculated using the following formulas:

[0148] DFSL i ={M i RA i SL i SR i TC i VL i};

[0149] DFSL=∑DFSL i ;

[0150] RA i =arctan(dz / dx);

[0151]

[0152]

[0153] Where i is the deformation slope number; S1 is the surface area; S2 is the horizontal surface area; angel is the angle between planes S1 and S2; S ndx, dy, and dz are the components in the X, Y, and Z directions, respectively, and dh is the thickness component of the sliding body.

[0154] Based on the deformed area and high-precision DEM imagery, high-resolution optical remote sensing data was used to extract features such as geological hazard boundaries, fissures, landslides, and the extent of the affected body through methods including filtering, edge detection, texture analysis, contrast enhancement, pattern recognition, and image segmentation. Hazard hazard point data (HDPS) was then selected through screening. This HDPS data consists of the geological elements of each hazard hazard point, and each hazard hazard point also includes the deformation rate (M). j ), slope (RA) j ), terrain relief (SL) j ), Surface roughness (SR) j ), topographic curvature (TC) j ), volume (VL) j Geological elements such as ) are calculated using the following formulas:

[0155] HDPS j =f fil (DFSL i );

[0156] HDPS j ={M j RA j SL j SR j TC j VL j};

[0157] HDPS = ∑HDPS j ;

[0158] Among them, f fil This refers to the process of screening potential disaster sites from deformed slopes; j represents the number of the potential disaster site.

[0159] Deformation rate (M) of each disaster hazard point j ), slope (RA) j ), terrain relief (SL) j ), Surface roughness (SR) j ), topographic curvature (TC) j ), volume (VL) j Geological elements such as , , and are normalized. The weight of each element relative to the risk level of the hazard is determined by the analytic hierarchy process (AHP). Finally, the disaster risk index of the hazard point is obtained by the information overlay method. And classify the risk levels of potential disaster sites.

[0160] A preset number of hazard risk levels are defined. As an example, when there are four preset hazard risk levels, the hazard risk levels from highest to lowest are: Class I Hazard Points (HDPS). Ⅰ Class II disaster hazard points (HDPS) Ⅱ Class III disaster hazard points (HDPS) Ⅲ ) and Class IV disaster hazard points (HDPS) IV When determining the risk level of hazard points in a deformation area based on their geological elements and corresponding weights, the lowest-risk Class IV hazard points (HDPS) are not considered. IV Class IV disaster hazard points (HDPS) IV This is used for subsequent adjustments to the hazard identification results. Alternatively, you can initially omit the Class IV hazard points (HDPS). IV When it is determined that the hazard identification results need to be adjusted, Class IV hazard points (HDPS) should be set. IV ).

[0161] The method for determining the risk level of a potential disaster site in a deformation area based on its geological elements and corresponding weights is as follows:

[0162]

[0163]

[0164] Among them, f NOR For the normalization function, f AHP It is the analytic hierarchy process (AHP). This represents the normalized values ​​of deformation rate, slope, topographic relief, surface roughness, topographic curvature, and volume.

[0165] S105: Based on the risk assessment results, slopes are divided to adjust the hazard identification results.

[0166] In this embodiment, when the landslide risk level includes high-risk, medium-risk, and low-risk areas, and a preset number of hazard risk levels are set, the above-mentioned S105 divides the slope according to the risk assessment results to adjust the hazard point identification results, specifically including:

[0167] The first area is divided into slope units, which are then identified as potential disaster hazard points.

[0168] In this embodiment, the first region is the region where no disaster hazard points exist among all high-risk regions. The hazard risk level corresponding to a potential disaster hazard point is the lowest among a preset number of hazard risk levels. In this embodiment, the method of dividing the first region into slope units is the same as the method of determining the deformed slopes in the deformed area described above, and will not be repeated here.

[0169] In this embodiment, the hazard point identification results are corrected based on the high-precision risk assessment results. The spatial distribution characteristics of extremely high-risk areas and high-risk areas are compared with those of hazard points. Extremely high-risk areas and high-risk areas that do not contain hazard points are divided into slope units, and these slope units are identified as potential hazard points, serving as the fourth type of hazard point (HDPS). Ⅳ Add it to the list of potential disaster sites to correct the identification results of potential disaster sites.

[0170] The newly identified disaster hazard point data includes:

[0171] In this embodiment, the first area, which contains no potential disaster sites in all high-risk areas, is divided into slope sections, and the resulting slope units are identified as potential disaster sites. This measure effectively avoids focusing only on identified disaster sites while neglecting other potentially risky areas within the high-risk region. In this way, high-risk areas are comprehensively covered, ensuring that no potentially hazardous areas are overlooked, making risk monitoring more comprehensive.

[0172] Potential disaster hazard points are assigned the lowest risk level from a pre-defined number of hazard risk levels, further refining and improving the risk level system for the entire region. Building upon the existing risk level classification based on identified hazard points, it considers potential risk areas, making the risk level classification more scientific and reasonable, and providing a more comprehensive and accurate basis for subsequent risk management.

[0173] Identifying slope units as potential disaster hazard points and assigning corresponding risk levels allows for early warning of potential disasters. Even if no obvious disaster hazards have yet been found in these areas, the proactive classification and identification, based on their high-risk regional context, enables the implementation of preventative measures to reduce the risk of future disasters.

[0174] Identifying potential disaster hazards and their risk levels helps in the rational allocation of risk management resources. For areas with different risk levels, targeted investment of human, material, and financial resources can be made for monitoring, prevention, and mitigation. Even for potential disaster hazard points with relatively low risk levels, because they are located in high-risk areas, appropriate resources can still be allocated for regular monitoring and preliminary prevention, avoiding waiting until the hazard develops into an actual disaster before taking action, thereby improving resource utilization efficiency.

[0175] Detailed risk zone delineation: The first area is divided into slope units to identify potential hazard points. Compared to simply treating the entire high-risk area as a single unit, this detailed delineation method can more accurately identify potential risk locations. Different slope units may have different geological and topographical features; considering them as independent potential hazard points allows for a more precise assessment of the risk status of each area, improving the accuracy of hazard identification.

[0176] As time passes and the environment changes, the risk status of potential hazard sites may change. This method of identifying potential hazard sites based on slope division facilitates dynamic risk assessment and adjustment. If the surrounding environment or geological conditions of a potential hazard site change, its risk level can be reassessed promptly based on the new circumstances, making the hazard identification results more consistent with the actual situation and providing more reliable support for risk management.

[0177] S106: Adjust the risk assessment results based on the results of hazard identification or the results of hazard mitigation.

[0178] In this embodiment, when the landslide risk level includes high-risk, medium-risk, and low-risk areas, and a preset number of hazard risk levels are set, the above-mentioned S106 adjusts the risk assessment result based on the hazard point identification result or the disaster hazard point treatment result, specifically including:

[0179] The landslide risk level of the second zone is adjusted based on the hazard risk level of the target hazard point. The target hazard point includes hazard points located in low-risk areas. The second zone is the sub-zone to which the target hazard point belongs.

[0180] The risk level of the standard disaster hazard points will be re-determined based on the results of their remediation.

[0181] The landslide risk level of the third area will be adjusted according to the risk level of the newly determined standard disaster hazard points.

[0182] In this embodiment, standard disaster hazard points include those located in high-risk and medium-risk areas. The third area is the sub-area to which the standard disaster hazard points belong.

[0183] In this embodiment, for target hazard points located in low-risk areas, the landslide risk level of their corresponding second area is adjusted according to their hazard risk level. This operation avoids simply generalizing low-risk areas and instead takes into account the specific circumstances of each hazard point within the area. This approach more accurately reflects the actual risk situation at different locations within low-risk areas, making the risk assessment results more realistic and providing a more accurate basis for subsequent risk management and decision-making.

[0184] For standard disaster hazard points located in high-risk and medium-risk areas, their risk levels are reassessed based on the results of their mitigation efforts, and the corresponding landslide risk level for their respective third-level areas is adjusted accordingly. This allows the risk assessment results to be dynamically updated as the mitigation status of hazard points changes. If a high-risk hazard point is effectively mitigated, its risk level decreases, and the landslide risk level of its corresponding area is adjusted accordingly, promptly reflecting the reduction in actual risk in the area, and vice versa. This dynamic adjustment mechanism greatly improves the accuracy and timeliness of risk assessment results.

[0185] By focusing on potential disaster sites within low-risk areas and adjusting the corresponding landslide risk levels, resources can be allocated more rationally. For sub-regions (secondary regions) that are generally low-risk but have certain potential hazards, resource input can be appropriately increased or decreased based on the adjusted risk level. If the risk level of a low-risk sub-region increases due to the presence of potential hazards, targeted monitoring and prevention measures can be strengthened to avoid excessive waste or underutilization of resources and improve resource utilization efficiency.

[0186] In high- and medium-risk areas, adjusting risk levels based on the remediation results of standard disaster hazard points allows for a more precise determination of resource needs. For areas with remediated hazard points and reduced risk levels (the third region), resource input can be appropriately reduced, with resources transferred to other high-risk areas or hazard points currently being remediated. Conversely, for areas with poor remediation results or newly emerging high-risk hazard points, increased resource input is necessary to ensure effective risk control in key areas and achieve optimal resource allocation.

[0187] Addressing potential disaster sites within low-risk areas helps to focus on the underlying risks in those areas. While these areas may have a low overall risk, individual sites could develop into larger risks over time and due to changes in environmental factors. Adjusting the landslide risk level of the affected area can draw attention to these potential risks, prompting the implementation of appropriate preventative measures to prevent their escalation and thus enhancing the targeted nature of risk management in low-risk areas.

[0188] For standard disaster hazard points in high-risk and medium-risk areas, adjusting the risk level and correspondingly adjusting the regional landslide risk level based on the remediation results can enhance the remediation effect. On the one hand, for hazard points that have been remediated, lowering the risk level of their respective areas can incentivize continued good remediation status. On the other hand, for hazard points that have not been effectively remediated, maintaining or raising the risk level of their respective areas can prompt increased remediation efforts and further improve the effectiveness of risk management.

[0189] The entire process, from identifying and addressing potential hazards to adjusting risk assessment results, forms a closed-loop risk management system. From identifying hazards to adjusting risk assessments based on their risk levels and remediation status, and then using the adjusted risk assessment results to guide subsequent hazard management and resource allocation, each step is interconnected and mutually influential, forming an organic whole. This closed-loop system helps to continuously improve risk management strategies and enhance the overall level of risk management.

[0190] Risk levels were adjusted for different types of areas (secondary areas for low-risk areas, and tertiary areas for high-risk and medium-risk areas) based on the specific potential hazards, promoting collaborative management across multiple areas. The risk situations in different areas were no longer viewed in isolation, but rather considered and managed comprehensively within a unified framework. This collaborative management model better integrates resources, coordinates actions, and improves the efficiency and effectiveness of risk management across the entire region.

[0191] As an example, the above solution can be implemented in the following ways:

[0192] Screening target hazard points: Based on the hazard point identification results, select the hazard points located in low-risk areas. These points are the target hazard points.

[0193] Determine the sub-region (second region): For each target hazard point, determine its sub-region (i.e., the second region) based on the established attribution relationship.

[0194] Adjusting landslide risk levels: The landslide risk levels of the second area to which these target hazard points belong will be uniformly adjusted to low-risk zones. This step is based on the assumption that low-risk hazard points have a relatively small impact on the landslide risk of their respective sub-areas.

[0195] Based on the remediation results of the standard hazard points, the corresponding hazard risk level of the standard hazard points is re-determined:

[0196] Screening of Standard Hazard Points: Based on the hazard point identification results, hazard points located in high-risk and medium-risk areas are screened out, and these points are the standard hazard points.

[0197] Assess the remediation results: For each standard hazard point, review its corresponding remediation results. The results may include whether the hazard has been remediated or not.

[0198] Reassess the risk level of potential hazards:

[0199] If the remediation result of a standard hazard point is "remediated," it means that the risk of that hazard point has been effectively controlled, and its corresponding hazard risk level is reclassified as low risk. For example, a Class III hazard point, HDPS. Ⅲ .

[0200] If the remediation result is "no remediation", then its original high-risk or medium-risk level (Class I hazard point (HDPS)) will be maintained. Ⅰ ) or Class II hazard points (HDPS) Ⅱ ))constant.

[0201] The landslide risk level for the third area will be adjusted based on the newly determined standard hazard points and their corresponding hazard risk levels.

[0202] Determine the sub-region (third region): For each standard hazard point whose risk level has been redefined, determine its sub-region (i.e., third region) according to the established attribution relationship.

[0203] Adjusting the landslide risk level:

[0204] If the risk level of the standard hazard point is redefined as high risk (Class I hazard point (HDPS)) Ⅰ The landslide risk level of the third area to which it belongs has been adjusted to a high-risk area.

[0205] If the risk level of the standard hazard point is redefined as medium risk (Class II hazard point (HDPS)) Ⅱ The landslide risk level of the third zone to which it belongs has been adjusted to a medium-risk zone.

[0206] As an example, risk assessment results are corrected based on the identification and remediation results of potential hazards. The spatial distribution characteristics of risk zones and potential hazards are compared. For potential hazards not located within risk zones, an appropriate landslide risk level is assigned to the sub-region based on the hazard risk level. For potential hazards located within risk zones, remediation data is analyzed, and a risk assessment is conducted on the remediated hazards. If the risk level decreases, the risk level of the risk zone where the hazard is located is simultaneously reduced.

[0207] In this embodiment, after an extreme rainfall event occurs, the comprehensive database is systematically updated. Specific updated data includes, but is not limited to, hydrological and meteorological data, historical case data, and socioeconomic data. Using the updated comprehensive database, a landslide hazard assessment model based on logistic regression and a vulnerability assessment model for disaster-bearing bodies are reconstructed. These two models are coupled and superimposed to obtain an updated landslide risk index, achieving iterative updating of the landslide risk index.

[0208] By utilizing ground-based monitoring data such as soil moisture content, slope deformation, and rainfall; space-based monitoring data such as Sarco satellite and meteorological satellite data; and manual inspection data such as landslide precursor data, near real-time data collection on geological structure, hydro-meteorology, geological deformation, and disaster response status of the target area can be achieved. Based on this information, the deformation rate (M) of disaster hazard points is updated synchronously. j ), slope (RA) j ), terrain relief (SL) j ), Surface roughness (SR) j ), topographic curvature (TC) j ), volume (VL) j Geological elements such as geological elements are used to determine the geological elements and their corresponding weights of potential disaster sites. Based on these elements and their corresponding weights, the risk level of potential disaster sites in the deformation area is determined, and the disaster risk index of potential disaster sites is dynamically updated. At the same time, meteorological data and information on the management of potential disaster sites are also considered to update the risk level.

[0209] This invention proposes a method for constructing a comprehensive database of rainfall-induced cluster landslides, and defines the main data types included in the database package, providing foundational data for the comprehensive identification, correction, iteration, and updating of risks and hazards related to rainfall-induced cluster landslides. A technical method for mutual feedback correction of risks and hazards in rainfall-induced cluster landslides is constructed. Based on the risk level of rainfall-induced cluster landslides, the identification results of potential hazards are improved; based on the risk level of hazard points and governance information, the risk assessment results are corrected, achieving a systematic integration of risk assessment and hazard identification. A specific workflow for the comprehensive identification method of rainfall-induced cluster landslides is defined, and a comprehensive judgment and analysis system for risks and hazards of cluster landslides is constructed, featuring dual levels of risk and hazard, dual characteristics of visible and invisible landslides, dual control at regional and slope scales, dual driving forces of instrumental monitoring and mathematical calculation, and dual mechanisms of mutual feedback correction and dynamic updating.

[0210] The overall flowchart of the method for comprehensive identification and evaluation of risk areas and hidden danger points of rain-induced mass landslides of this invention is shown in the figure below. Figure 2 As shown, this paper first proposes a data storage type for a comprehensive database of rainfall-induced cluster landslides. Using this database as input, a machine learning model is employed to achieve high-precision quantification and classification of the risk of rainfall-induced cluster landslides. In hazard identification, a technical process is constructed from deformation zone identification to hazard point classification, categorizing deforming hazards into three types based on risk level. Furthermore, a workflow for correcting hazard point identification results based on high-precision risk assessment results is established, classifying potential hazard points as a fourth type, thus improving the landslide hazard identification results. Simultaneously, risk assessment results are corrected based on hazard point categories and remediation data. Finally, a dynamic update mechanism for the risk and hazard of rainfall-induced cluster landslides, driven by extreme rainfall case data, is proposed, achieving a systematic integration of risk assessment and hazard identification.

[0211] It should be noted that the contents not described in detail in this specification are common knowledge to those skilled in the art.

[0212] This embodiment also provides a comprehensive identification and evaluation device for rainfall-induced cluster landslide risk areas and hidden danger points. A single device is used to implement the above embodiments and optional implementation methods, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0213] Figure 3 This is a schematic diagram of the structure of the comprehensive identification and evaluation device for risk areas and hidden danger points of rain-induced mass landslides according to an embodiment of the present invention.

[0214] This invention provides a comprehensive identification and evaluation device for risk zones and potential hazard points of rain-induced cluster landslides, such as... Figure 3 As shown, the comprehensive identification and evaluation device for risk areas and hazard points of rainfall-induced cluster landslides includes:

[0215] The first processing module 11 is used to update the comprehensive database when extreme rainfall events occur. When the comprehensive database is updated, the landslide hazard index and vulnerability index of each calculation unit in the target area are determined based on the updated database. The target area includes multiple sub-regions, and each sub-region includes multiple calculation units.

[0216] The second processing module 12 is used to determine the landslide risk index of each sub-region based on the landslide hazard index and the vulnerability index of the disaster-bearing body of each calculation unit under the sub-region.

[0217] The third processing module 13 is used to determine the risk assessment results based on the landslide risk index of each sub-region. The risk assessment results include: the landslide risk level corresponding to each sub-region.

[0218] The fourth processing module 14 is used to determine the deformation areas in the target region based on synthetic aperture radar data. It determines the hazard point identification results corresponding to the deformation areas based on mathematical elevation model imagery. The hazard point identification results include the hazard points in the deformation areas and the corresponding hazard risk levels.

[0219] The fifth processing module 15 is used to divide the slope according to the risk assessment results in order to adjust the hazard point identification results.

[0220] The sixth processing module 16 is used to adjust the risk assessment results based on the results of hazard identification or the results of hazard mitigation.

[0221] In one optional implementation, the fifth processing module 15 is specifically used to divide the first region into slope units when the landslide risk levels include high-risk, medium-risk, and low-risk areas, and a preset number of hazard risk levels are pre-set, and to identify the slope units as potential hazard points. The first region is the area where no hazard points exist in any of the high-risk areas. The hazard risk level corresponding to the potential hazard point is the lowest hazard risk level among the preset number of hazard risk levels.

[0222] In one optional implementation, the sixth processing module 16 is specifically used to adjust the landslide risk level of the second region according to the risk level of the target hazard point when the landslide risk level includes high-risk, medium-risk, and low-risk areas, and a preset number of hazard risk levels are set. The target hazard point includes hazard points located in low-risk areas. The second region is a sub-region to which the target hazard point belongs. The risk level of the standard hazard point is re-determined based on the treatment results of the standard hazard point. The landslide risk level of the third region is adjusted according to the re-determined risk level of the standard hazard point. The standard hazard point includes hazard points located in high-risk and medium-risk areas. The third region is a sub-region to which the standard hazard point belongs.

[0223] In one optional implementation, the fourth processing module 14 is specifically used to acquire high-resolution optical remote sensing data based on mathematical elevation model images. Pre-processing is performed on the high-resolution optical remote sensing data to extract features of potential disaster sites. Pre-processing includes: filtering, edge detection, texture analysis, contrast enhancement, pattern recognition, and image segmentation. Potential disaster sites in the deformed area are determined based on the features. Geological elements of the potential disaster sites and their corresponding weights are determined. Geological elements include: deformation rate, slope, topographic relief, surface roughness, topographic curvature, and volume. The risk level of the potential disaster sites in the deformed area is determined based on the geological elements and their corresponding weights.

[0224] In one optional implementation, the integrated database includes a baseline database and a monitoring database. The baseline database includes geological structure data, topographic data, hydrological and meteorological data, socioeconomic data, disaster management data, and historical case data. The monitoring database includes ground monitoring data, aerospace monitoring data, and manual inspection data. The monitoring database is used to determine the management results of disaster hazard points.

[0225] In one optional implementation, the first processing module 11 includes:

[0226] The first processing unit is used to determine the landslide hazard index of each calculation unit in the target area based on geological structure data, topographic data, and hydrological and meteorological data.

[0227] The second processing unit is used to determine the vulnerability index of each computing unit in the target area based on socioeconomic data and historical case data.

[0228] In one optional implementation, the first processing unit is specifically used to obtain a landslide hazard assessment model pre-constructed using logistic regression. Geological structure data, topographic data, and hydrological and meteorological data are input into the landslide hazard assessment model to obtain the landslide hazard index for each calculation unit.

[0229] In one optional implementation, the second processing unit is specifically used to obtain a vulnerability assessment model of the disaster-bearing body pre-constructed using logistic regression. Socioeconomic data and historical case data are input into the vulnerability assessment model to obtain the vulnerability index of the disaster-bearing body for each calculation unit.

[0230] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0231] This invention also provides a computer device having the above-described features. Figure 3 The device shown is a comprehensive identification and evaluation system for risk areas and potential hazards of rain-induced landslides. Please refer to [link / reference needed]. Figure 4 , Figure 4 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention, such as... Figure 4 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In an alternative implementation, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor device). Figure 4 Take a processor 10 as an example.

[0232] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0233] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0234] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store application programs required for operating the device and at least one function. The data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In an alternative embodiment, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0235] Memory 20 may include volatile memory, such as random access memory. Memory may also include non-volatile memory, such as flash memory, hard disk, or solid-state drive. Memory 20 may also include combinations of the above types of memory.

[0236] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0237] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc. Further, the storage medium may also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0238] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0239] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for comprehensive identification and evaluation of risk zones and potential hazard points of rain-induced cluster landslides, characterized in that, include: Update the comprehensive database when extreme rainfall events occur; With the comprehensive database updated, the landslide hazard index and the vulnerability index of the disaster-bearing body for each calculation unit in the target area are determined based on the updated comprehensive database. The target region includes multiple sub-regions, and each sub-region includes multiple computing units; The comprehensive database includes a baseline database and a monitoring database. The baseline database includes geological structure data, topographic data, hydrological and meteorological data, socio-economic data, disaster management data, and historical case data. The monitoring database includes ground monitoring data, aerospace monitoring data, and manual inspection data. The monitoring database is used to determine the management results of disaster hazard points. For each sub-region, the landslide risk index of the sub-region is determined based on the landslide hazard index and the vulnerability index of the disaster-bearing body of each calculation unit under the sub-region; The risk assessment results are determined based on the landslide risk index of each sub-region; the risk assessment results include: the landslide risk level corresponding to each sub-region; Deformation regions within the target area are determined based on synthetic aperture radar data; The identification results of potential hazard points in the deformed area are determined based on the mathematical elevation model imagery; the identification results of potential hazard points include the potential hazard points in the deformed area and the risk level of the potential hazard points. Slope division is performed based on the risk assessment results to adjust the hazard point identification results; The risk assessment results are adjusted based on the results of the hazard identification or the results of the mitigation of the hazard points. When the landslide risk level includes high-risk, medium-risk, and low-risk areas, and a preset number of hazard risk levels are provided, the step of dividing the slope according to the risk assessment results to adjust the hazard point identification results includes: The first region is divided into slope units, and these slope units are identified as potential disaster hazard points. The first region is an area where no disaster hazard points exist in all high-risk areas. The hazard risk level corresponding to the potential disaster hazard point is the lowest hazard risk level among a preset number of hazard risk levels. The step of adjusting the risk assessment result based on the hazard identification result or the disaster hazard mitigation result includes: The landslide risk level of the second area is adjusted according to the risk level of the target disaster hazard point; the target disaster hazard point includes disaster hazard points located in low-risk areas; the second area is the sub-area to which the target disaster hazard point belongs; The risk level of the standard disaster hazard points will be re-determined based on the treatment results of the standard disaster hazard points. The landslide risk level of the third region is adjusted according to the newly determined risk level of the standard disaster hazard points; the standard disaster hazard points include disaster hazard points located in high-risk and medium-risk areas; the third region is the sub-region to which the standard disaster hazard points belong; The step of determining the deformation region in the target area based on synthetic aperture radar data includes: Based on synthetic aperture radar data, surface deformation is detected using phase differential interferometry and amplitude differential interferometry to determine the deformed areas within the target region.

2. The method according to claim 1, characterized in that, The determination of the hazard point identification results corresponding to the deformation area based on the mathematical elevation model image includes: High-resolution optical remote sensing data are obtained from the mathematical elevation model image. The high-resolution optical remote sensing data is subjected to preset processing to extract the features of disaster hazard points; the preset processing includes: filtering, edge detection, texture analysis, contrast enhancement, pattern recognition and image segmentation; Based on the characteristics of the disaster hazard points, the disaster hazard points in the deformation area are determined; Determine the geological elements of the disaster hazard points and the corresponding weights of the geological elements; the geological elements include: deformation rate, slope, topographic relief, surface roughness, topographic curvature, and volume; The risk level of the disaster hazard points in the deformation area is determined based on the geological elements of the disaster hazard points and the weights corresponding to the geological elements.

3. The method according to claim 1, characterized in that, The process of determining the landslide hazard index and vulnerability index of each calculation unit in the target area based on the updated comprehensive database includes: The landslide hazard index of each calculation unit in the target area is determined based on the geological structure data, the topographic data, and the hydrological and meteorological data. The vulnerability index of each computing unit in the target area is determined based on the socioeconomic data and the historical case data.

4. The method according to claim 3, characterized in that, The process of determining the landslide hazard index for each calculation unit in the target area based on the geological structure data, the topographic data, and the hydrological and meteorological data includes: Obtain a landslide hazard assessment model pre-constructed using logistic regression; The geological structure data, topographic data, and hydrological and meteorological data are input into the landslide hazard assessment model to obtain the landslide hazard index for each calculation unit. The process of determining the vulnerability index of each computing unit in the target area based on the socioeconomic data and the historical case data includes: Obtain a vulnerability assessment model for disaster-bearing bodies that has been pre-constructed using logistic regression. The socioeconomic data and historical case data are input into the disaster-bearing body vulnerability assessment model to obtain the disaster-bearing body vulnerability index for each calculation unit.

5. A comprehensive identification and evaluation device for risk zones and hidden danger points of rain-induced cluster landslides, characterized in that, include: The first processing module is used to update the comprehensive database when extreme rainfall events occur; With the comprehensive database updated, the landslide hazard index and the vulnerability index of the disaster-bearing body for each calculation unit in the target area are determined based on the updated comprehensive database. The target region includes multiple sub-regions, and each sub-region includes multiple computing units; The comprehensive database includes a baseline database and a monitoring database. The baseline database includes geological structure data, topographic data, hydrological and meteorological data, socio-economic data, disaster management data, and historical case data. The monitoring database includes ground monitoring data, aerospace monitoring data, and manual inspection data. The monitoring database is used to determine the management results of disaster hazard points. The second processing module is used to determine the landslide risk index of each sub-region based on the landslide hazard index and the vulnerability index of the disaster-bearing body of each calculation unit under the sub-region. The third processing module is used to determine the risk assessment result based on the landslide risk index of each sub-region; the risk assessment result includes: the landslide risk level corresponding to each sub-region; The fourth processing module is used to determine the deformation area in the target region based on synthetic aperture radar data; and to determine the hazard point identification results corresponding to the deformation area based on mathematical elevation model imagery; the hazard point identification results include the hazard points in the deformation area and the hazard risk level corresponding to the hazard points; The fifth processing module is used to divide the slope according to the risk assessment results in order to adjust the hazard point identification results; The sixth processing module is used to adjust the risk assessment result based on the hazard identification result or the disaster hazard mitigation result; The fifth processing module is specifically used to divide the first area into slope units when the landslide risk level includes high-risk, medium-risk, and low-risk areas, and a preset number of hazard risk levels are set in advance; the slope units are then identified as potential hazard points; the first area is an area where no hazard points exist in any of the high-risk areas; the hazard risk level corresponding to the potential hazard point is the lowest hazard risk level among the preset number of hazard risk levels; The sixth processing module is specifically used to adjust the landslide risk level of the second area according to the risk level of the target disaster hazard point; the target disaster hazard point includes disaster hazard points located in low-risk areas; the second area is the sub-area to which the target disaster hazard point belongs; The risk level of the standard disaster hazard points will be re-determined based on the treatment results of the standard disaster hazard points. The landslide risk level of the third region is adjusted according to the newly determined risk level of the standard disaster hazard points; the standard disaster hazard points include disaster hazard points located in high-risk and medium-risk areas; the third region is the sub-region to which the standard disaster hazard points belong; The fourth processing module is specifically used to detect surface deformation based on synthetic aperture radar data using phase differential interferometry and amplitude differential interferometry, and to determine the deformed areas in the target region.

6. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the comprehensive identification and evaluation method for risk areas and hidden danger points of rainfall-induced cluster landslides as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the method for comprehensive identification and evaluation of risk areas and hidden danger points of rain-induced cluster landslides as described in any one of claims 1 to 4.

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