Comprehensive identification and evaluation method for rainfall type group landslide risk areas and hidden danger points

Abstract

The invention relates to the technical field of monitoring and identification, and discloses a rainfall type group landslide risk area and hidden danger point comprehensive identification and evaluation method. The method comprises the following steps: determining a landslide danger index and a disaster-bearing body vulnerability index of each calculation unit in a target area according to an updated comprehensive database; determining a risk evaluation result according to a landslide risk index of each sub-region determined by the landslide risk index and the disaster-bearing body vulnerability index, wherein the risk evaluation result comprises a landslide risk grade corresponding to each sub-region; determining a deformation area in the target area according to synthetic aperture radar data; determining a hidden danger point identification result corresponding to the deformation area, wherein the hidden danger point identification result comprises disaster hidden danger points in the deformation area and hidden danger risk levels corresponding to the disaster hidden danger points; adjusting a hidden danger point identification result according to the risk evaluation result; and adjusting a risk evaluation result according to a hidden danger point identification result and a disaster hidden danger point treatment result. According to the method, dynamic feedback and optimization of risk and hidden danger assessment can be realized, and misjudgment is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring and identification, and particularly to a comprehensive identification and evaluation method for rainfall-induced mass landslide risk areas and potential hazard points. Background Art

[0002] In the prevention and control of geological disasters, landslides are common and serious disaster forms, and landslide potential hazards and disaster risks have attracted much attention.

[0003] At present, the identification of landslide potential hazards mainly targets obvious deformed slopes at the single-slope scale. By regularly comparing high-resolution optical remote sensing images at different times to judge the morphological changes of slopes, total stations and GPS monitoring stations are set up on the ground to monitor displacement and settlement, and personnel are arranged to conduct on-site inspections to observe abnormal phenomena. Landslide risk assessment is mostly based on the regional scale, and the identification of disaster potential hazard points is mostly based on the slope scale. The evaluation results of the two are not unified in scale. The working methods of risk assessment and potential hazard identification are relatively independent and have not achieved systematic integration, resulting in a low coincidence rate between actual geological disaster points and the recorded disaster potential hazard points. Summary of the Invention

[0004] Therefore, the technical problem to be solved by the present invention is to overcome the problems in the related technologies that landslide risk assessment is mostly based on the regional scale, the identification of disaster potential hazard points is mostly based on the slope scale, and the evaluation results of the two are not unified in scale. The working methods of risk assessment and potential hazard identification are relatively independent and have not achieved systematic integration, resulting in a low coincidence rate between actual geological disaster points and the recorded disaster potential hazard points.

[0005] To solve the above technical problems, the present invention provides a comprehensive identification and evaluation method for rainfall-induced mass landslide risk areas and potential hazard points. The comprehensive identification and evaluation method for rainfall-induced mass landslide risk areas and potential hazard points includes:

[0006] When an extreme rainfall event occurs, update the comprehensive database; in the case of updating the comprehensive database, determine the landslide hazard index and the vulnerability index of the disaster-bearing body for each calculation unit in the target area according to the updated comprehensive database; the target area includes multiple sub-areas, and each sub-area includes multiple calculation units;

[0007] For each sub-area, determine the landslide risk index of the sub-area according to the landslide hazard index and the vulnerability index of the disaster-bearing body for each calculation unit under the sub-area;

[0008] Determine the risk assessment result according to the landslide risk index of each sub-area; the risk assessment result includes: the landslide risk level corresponding to each sub-area;

[0009] Determine the deformed area in the target area according to the synthetic aperture radar data;

[0010] Determine the identification result of potential hazard points corresponding to the deformed area according to the mathematical elevation model image; the identification result of potential hazard points includes the disaster potential hazard points in the deformed area and the potential hazard risk levels corresponding to the disaster potential hazard points;

[0011] Perform slope division according to the risk assessment result to adjust the identification result of potential hazard points;

[0012] Adjust the risk assessment result according to the identification result of potential hazard points or the treatment result of disaster potential hazard points.

[0013] In an alternative embodiment, when the landslide risk levels include high-risk type areas, medium-risk type areas, and low-risk type areas, and a preset number of potential hazard risk levels are preset, the performing slope division according to the risk assessment result to adjust the identification result of potential hazard points includes:

[0014] Perform slope division on the first area to determine slope units, and determine the slope units as potential disaster potential hazard points; the first area is the area where there are no disaster potential hazard points in all high-risk type areas; the potential hazard risk level corresponding to the potential disaster potential hazard points is the lowest level among the preset number of potential hazard risk levels.

[0015] In an alternative embodiment, the adjusting the risk assessment result according to the identification result of potential hazard points or the treatment result of disaster potential hazard points includes:

[0016] Adjust the landslide risk level corresponding to the second area according to the potential hazard risk level of the target disaster potential hazard point; the target disaster potential hazard point includes the disaster potential hazard points in the low-risk type area; the second area is the sub-area to which the target disaster potential hazard point belongs;

[0017] Redetermine the potential hazard risk level corresponding to the standard disaster potential hazard point according to the treatment result of the standard disaster potential hazard point;

[0018] Adjust the landslide risk level corresponding to the third area according to the redetermined potential hazard risk level corresponding to the standard disaster potential hazard point; the standard disaster potential hazard points include the disaster potential hazard points in the high-risk type area and the medium-risk type area; the third area is the sub-area to which the standard disaster potential hazard point belongs.

[0019] In an alternative embodiment, the determining the identification result of potential hazard points corresponding to the deformed area according to the mathematical elevation model image includes:

[0020] Obtain high-resolution optical remote sensing data according to the mathematical elevation model image;

[0021] Perform preset processing on the high-resolution optical remote sensing data to extract the characteristics of disaster potential points; the preset processing includes: filtering, edge detection, texture analysis, contrast enhancement, pattern recognition, and image segmentation;

[0022] Determine the disaster potential points in the deformed area according to the characteristics of the disaster potential points;

[0023] Determine the geological elements of the disaster potential points and the weights corresponding to the geological elements; the geological elements include: deformation rate, slope, terrain undulation, surface roughness, terrain curvature, and volume;

[0024] Determine the potential risk level corresponding to the disaster potential points in the deformed area according to the geological elements of the disaster potential points and the weights corresponding to the geological elements.

[0025] In an alternative embodiment, the comprehensive database includes: a background database and a monitoring database; the background database includes: geological structure data, topographic and geomorphic data, hydrological and meteorological data, social and economic data, disaster control 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 treatment results of the disaster potential points.

[0026] In an alternative embodiment, the determining the landslide hazard index and the vulnerability index of the disaster-bearing body for each calculation unit in the target area according to the updated comprehensive database includes:

[0027] Determine the landslide hazard index for each calculation unit in the target area according to the geological structure data, the topographic and geomorphic data, and the hydrological and meteorological data;

[0028] Determine the vulnerability index of the disaster-bearing body for each calculation unit in the target area according to the social and economic data and the historical case data.

[0029] In an alternative embodiment, the determining the landslide hazard index for each calculation unit in the target area according to the geological structure data, the topographic and geomorphic data, and the hydrological and meteorological data includes:

[0030] Obtain a landslide hazard assessment model constructed in advance according to the logistic regression method;

[0031] Input the geological structure data, the topographic and geomorphic data, and the hydrological and meteorological data into the landslide hazard assessment model to obtain the landslide hazard index for each calculation unit;

[0032] The determining the vulnerability index of the disaster-bearing body for each calculation unit in the target area according to the social and economic data and the historical case data includes:

[0033] Obtain a disaster-bearing body vulnerability assessment model pre-constructed according to the logistic regression method;

[0034] Input the social and economic data and the historical case data into the disaster-bearing body vulnerability assessment model to obtain the disaster-bearing body vulnerability index of each calculation unit.

[0035] In a second aspect, the present invention provides a comprehensive identification and evaluation device for rainfall-induced mass landslide risk areas and potential hazard points. The comprehensive identification and evaluation device for rainfall-induced mass landslide risk areas and potential hazard points includes:

[0036] A first processing module, configured to update the comprehensive database when an extreme rainfall event occurs; in the case of updating the comprehensive database, determine the landslide hazard index and the disaster-bearing body vulnerability index of each calculation unit in the target area according to the updated comprehensive database; the target area includes a plurality of sub-areas, and each sub-area includes a plurality of calculation units;

[0037] A second processing module, configured to determine the landslide risk index of each sub-area according to the landslide hazard index and the disaster-bearing body vulnerability index of each calculation unit under the sub-area for each sub-area;

[0038] A third processing module, configured to determine a risk evaluation result according to the landslide risk index of each sub-area; the risk evaluation result includes: the landslide risk level corresponding to each sub-area;

[0039] A fourth processing module, configured to determine the deformed area in the target area according to the synthetic aperture radar data; determine the potential hazard point identification result corresponding to the deformed area according to the digital elevation model image; the potential hazard point identification result includes the disaster potential hazard points in the deformed area and the potential hazard risk levels corresponding to the disaster potential hazard points;

[0040] A fifth processing module, configured to perform slope division according to the risk evaluation result to adjust the potential hazard point identification result;

[0041] A sixth processing module, configured to adjust the risk evaluation result according to the potential hazard point identification result or the treatment result of the disaster potential hazard points.

[0042] In a third aspect, the present invention provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the comprehensive identification and evaluation method for rainfall-induced mass landslide risk areas and potential hazard points in the first aspect or any corresponding embodiment thereof.

[0043] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the comprehensive identification and evaluation method for rainfall-induced mass landslide risk areas and potential hazard points according to the first aspect or any corresponding embodiment thereof as described above.

[0044] Fifthly, the present invention provides a computer program product including computer instructions for causing a computer to execute the comprehensive identification and evaluation method for rainfall-induced mass landslide risk areas and potential hazard points according to the first aspect or any corresponding embodiment thereof as described above.

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

[0046] The technical solution of the embodiment of the present invention updates the comprehensive database during extreme rainfall events and re-performs the comprehensive identification and evaluation of rainfall-induced mass landslide risk areas and potential hazard points based on the updated comprehensive database, so as to realize the dynamic update of potential hazard points of disasters and timely discover potential hazard points of disasters.

[0047] After determining the risk evaluation results (the landslide risk levels corresponding to each sub-region), they are associated with the deformation areas determined based on synthetic aperture radar data and the potential hazard point identification results (slope scale) determined from digital elevation model images. By adjusting the potential hazard point identification results through slope division according to the risk evaluation results, the unification of risk evaluation at the regional scale and the identification of potential hazard points of disasters at the slope scale is achieved. For example, for high-risk sub-regions, the slope division can be further refined to more accurately determine the potential hazard points of disasters within the region, avoiding the limitations of simply evaluating based on the regional scale.

[0048] Through a two-way feedback mechanism of adjusting the potential hazard point identification results according to the risk evaluation results and adjusting the risk evaluation results according to the potential hazard point identification results or the treatment results of potential hazard points of disasters. After determining the landslide risk level of a sub-region through risk evaluation, the potential hazard point identification results in the deformation area are adjusted based on this, such as determining the slope units that may be missed in high-risk sub-regions as potential disaster potential hazard points and incorporating them into the potential hazard point identification results, making the identification of potential hazard points of disasters more comprehensive and no longer limited to only determining based on the deformation and other characteristics of the region itself.

[0049] Meanwhile, when the potential hazard points of disasters are treated or their identification results change, they are timely fed back to the risk evaluation to adjust the landslide risk levels of the corresponding sub-regions.

[0050] This two-way adjustment mechanism makes the risk assessment and hidden danger identification no longer independent processes, but forms an organic whole. They influence and promote each other, greatly improving their relevance and consistency, thus solving the problem of independent working methods and low overlap in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related art, the following will briefly introduce the drawings required for the description of the specific embodiments or the related art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0052] Figure 1 It is a schematic flowchart of the comprehensive identification and evaluation method for rainfall-induced mass landslide risk areas and hidden danger points in an embodiment of the present invention;

[0053] Figure 2 It is a schematic overall flowchart of the comprehensive identification and evaluation method for rainfall-induced mass landslide risk areas and hidden danger points in an embodiment of the present invention;

[0054] Figure 3 It is a schematic structural diagram of the comprehensive identification and evaluation device for rainfall-induced mass landslide risk areas and hidden danger points in an embodiment of the present invention;

[0055] Figure 4 It is a schematic hardware structure diagram of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0057] Landslide hazards refer to slopes that already show obvious signs of deformation and are likely to become unstable, damaged, and cause disasters in the near future. Landslide risk refers to the possibility that natural slopes will become unstable, collapse, slide, and cause harm under the action of rainfall, freeze-thaw, earthquake, or engineering activities. Landslide hazard identification mainly focuses on individual slopes with historical deformation, impending deformation, and ongoing deformation, and uses methods such as remote sensing image comparison, ground instrument monitoring, and manual inspection to carry out identification work. Landslide risk assessment, on the other hand, mainly starts from disaster-causing factors, analyzes the possibility of natural slopes in the target area becoming unstable and causing disasters under rainfall triggering, and mostly uses model calculation methods to carry out evaluation work. By comparing the two, it can be seen that landslide hazard identification mostly targets obvious deformation slopes at the individual scale and uses instrument monitoring means to carry out work, while landslide risk assessment mostly targets non-obvious deformation slopes at the regional scale and uses model calculation means to carry out work. It can be found that although there are differences in the evaluation object, research scale, identification technology, etc. between rainfall-induced landslide hazard identification and risk assessment work. Based on the previous research results and the characteristics of rainfall-induced mass landslide disasters in recent years, it can be found that there are the following deficiencies in the current rainfall-induced mass landslide risk assessment and hazard identification work:

[0058] (1) Landslide hazards should include not only areas with ongoing deformation but also potentially unstable slopes. At present, the 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, rainfall-induced mass landslide risk assessment is mostly carried out at the regional scale, and disaster hazard point identification is mostly carried out at the slope scale. The evaluation results of the two do not achieve scale unity.

[0060] (3) The working methods and understanding of the results of risk assessment and hazard identification are relatively independent and have not been systematically integrated, resulting in the fact that the actual geological disaster points are located in the risk area, but the coincidence degree with the recorded disaster hazard points is relatively low.

[0061] The embodiment of the present invention provides a comprehensive identification and evaluation method for rainfall-induced mass landslide risk areas and hazard points, aiming to construct a comprehensive judgment and analysis system for mass landslide risk hazards with double levels of risk and hazards, double characteristics of obvious and non-obvious landslides, double control of regional scale and slope scale, double drive of instrument monitoring and mathematical calculation, and double mechanisms of mutual feedback correction and dynamic update, improve the identification and prediction accuracy of rainfall-induced mass landslides, and innovate the working mode of mass geological disaster risk assessment and hazard identification to solve the problems in related technologies.

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

[0063] Figure 1 It is a schematic flowchart of a comprehensive identification and evaluation method for rainfall-induced mass landslide risk areas and potential hazard points according to an embodiment of the present invention.

[0064] As Figure 1 shown, an embodiment of the present invention provides a comprehensive identification and evaluation method for rainfall-induced mass landslide risk areas and potential hazard points. The comprehensive identification and evaluation method for rainfall-induced mass landslide risk areas and potential hazard points includes:

[0065] S101: When an extreme rainfall event occurs, update the comprehensive database. In the case of updating the comprehensive database, determine the landslide hazard index and the vulnerability index of the disaster-bearing body for each calculation unit in the target area according to the updated comprehensive database.

[0066] In this embodiment, the target area includes multiple sub-areas, and each sub-area includes multiple calculation units.

[0067] In the present invention, before implementing the technical solution for the comprehensive identification and evaluation of rainfall-induced mass landslide risk areas and potential hazard points, it is necessary to pre-construct a comprehensive database, which includes: a background database and a monitoring database. It is necessary to pre-construct an initial background database and an initial monitoring database. When an extreme rainfall event occurs, systematically update the data in the comprehensive database, and re-implement the technical solution for the comprehensive identification and evaluation of rainfall-induced mass landslide risk areas and potential hazard points according to the updated comprehensive database to achieve iterative update of the disaster potential hazard points and timely determine newly emerging disaster potential hazard points.

[0068] The background database includes:

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

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

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

[0072] Socio-economic data: including but not limited to house distribution, population distribution, road distribution, and bridge distribution data.

[0073] Disaster governance data: including but not limited to the locations of registered disaster hidden danger points, the number of registered disaster hidden danger points, the basic geological data of disaster hidden danger points, and the governance data of disaster hidden danger points.

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

[0075] The monitoring database includes:

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

[0077] Aerospace monitoring data: including but not limited to optical images, Interferometric Synthetic Aperture Radar (InSar) data, airborne radar data, and meteorological satellite data.

[0078] Manual inspection data: including but not limited to landslide deformation information, landslide precursor information, avoidance information of disaster hidden danger points, and governance information of disaster hidden danger points.

[0079] The present invention does not limit the method of constructing the comprehensive database and obtaining the data in the comprehensive database, and conventional methods in the art can be adopted. For example, the above data can be obtained by means such as geological exploration, remote sensing technology, meteorological station and hydrological station observations, file access, and on-site research.

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

[0081] S1011: Determine the landslide hazard index for each calculation unit in the target area according to the geological structure data, topographic and geomorphic data, and hydro-meteorological data.

[0082] S1012: Determine the vulnerability index of the disaster-bearing body for each calculation unit in the target area according to the social and economic data and historical case data.

[0083] The above S1011 determines the landslide hazard index for each calculation unit in the target area according to the geological structure data, topographic and geomorphic data, and hydro-meteorological data, specifically including:

[0084] Obtain a landslide hazard assessment model constructed in advance according to the logistic regression method.

[0085] Input the geological structure data, topographic and geomorphic data, and hydro-meteorological data into the landslide hazard assessment model to obtain the landslide hazard index for each calculation unit.

[0086] In this embodiment, geological structure data, topographic and geomorphic data, and hydro-meteorological data (X1, X2, …, X n ) are selected from the comprehensive database to construct a landslide hazard assessment model based on a logistic regression model. The geological structure data, topographic and geomorphic data, and hydro-meteorological data of the target area are input into the landslide hazard assessment model, and the landslide hazard index (H k ) of each calculation unit is calculated. When the comprehensive database is updated, the landslide hazard assessment model is reconstructed;

[0087] In this embodiment, the calculation method of the landslide hazard index is 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 represents the logistic regression algorithm; Z H represents the sum of all control variables X1, X2, …, X n weighted; n represents the number of control variables, k represents the calculation unit number, H k represents the landslide hazard index of calculation unit k, H represents the landslide hazard index of the sub-region, and A0, A1, A2, …, A n are determined by the logistic regression algorithm. A0 represents the constant term, and A1, A2, …, A n represent the weights corresponding to the control variables respectively.

[0093] The above S1012 determines the disaster-bearing body vulnerability index of each calculation unit in the target area according to the social and economic data and historical case data, specifically including:

[0094] Obtain the disaster-bearing body vulnerability assessment model constructed in advance according to the logistic regression method.

[0095] Input the social and economic data and historical case data into the disaster-bearing body vulnerability assessment model to obtain the disaster-bearing body vulnerability index of each calculation unit.

[0096] In this embodiment, social and economic data, historical case data (Y1, Y2, …, Yn ) Construct a vulnerability assessment model for disaster-bearing bodies based on a logistic regression model, input the socio-economic data and historical case data of the target area into the vulnerability assessment model for disaster-bearing bodies, and calculate the vulnerability index (V k ) of each calculation unit. In the case of updating the comprehensive database, reconstruct the vulnerability assessment model for disaster-bearing bodies.

[0097] In this embodiment, the calculation method of the vulnerability index of the disaster-bearing body is 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] Where: Z V represents the sum of all control variables based on weights, V k represents the vulnerability index of the disaster-bearing body of calculation unit k, V represents the vulnerability index of the disaster-bearing body of the sub-region, B0, B1, B2, …, B n are determined by the logistic regression algorithm, B0 represents the constant term,

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

[0104] S102: For each sub-region, determine the landslide risk index of the sub-region according to the landslide hazard index and the vulnerability index of the disaster-bearing body of 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] Where, R k represents the landslide risk index of calculation unit k.

[0109] S103: Determine the risk assessment result according to the landslide risk index of each sub-region.

[0110] In this embodiment, the risk assessment result includes: the landslide risk level corresponding to each sub-region, and may also include relevant information such as the number and location information of each sub-region.

[0111] The landslide risk level includes high-risk type areas, medium-risk type areas, and low-risk type areas.

[0112] As an example, the landslide risk level may include five levels. Among them, the high-risk type areas include extremely high-risk areas and high-risk areas, the medium-risk type areas include risk areas, and the low-risk type areas include low-risk areas and extremely low-risk areas.

[0113] As an example, the target area can be divided into five risk levels according to the landslide risk index, and the numerical range is 0 - 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 area in the target area according to the synthetic aperture radar data. Determine the hidden danger point identification result corresponding to the deformed area according to the digital elevation model image.

[0115] In this embodiment, the hidden danger point identification result includes the disaster hidden danger points in the deformed area and the hidden danger risk level corresponding to the disaster hidden danger points, and may also include relevant information such as the number and location information of the disaster hidden danger points, the number and location information of the sub-region to which they belong, and the landslide risk level.

[0116] In this embodiment, the ground deformation can be detected according to the synthetic aperture radar data by the phase-difference interferometry and amplitude-difference method to determine the deformed area in the target area, and the deformation data of the deformed area can be obtained.

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

[0118] Data preparation stage:

[0119] Obtaining Synthetic Aperture Radar (SAR) data: Multiple scenes of SAR image data covering the target area need to be collected. These data can come from different satellite sensors. The acquisition time interval of the data should be determined according to the time scale requirements for monitoring surface deformation. For example, for monitoring slow crustal deformation, the interval may be several months or even years. For monitoring deformation caused by rapid human engineering activities or geological disasters, the time interval may be shortened to a few days or even a few hours.

[0120] Data preprocessing: Radiometric calibration: This is to convert the digital signal of the SAR image into a backscattering coefficient with physical significance. Different satellite sensors have their corresponding radiometric calibration parameters to eliminate the influence of factors such as the gain and bias of the sensor itself. For example, the radiometric calibration of Sentinel-1 data can be carried out according to the provided calibration coefficient file, so that the image pixel values can accurately reflect the radar scattering characteristics of the ground objects. Geometric correction: Due to the complexity of the SAR imaging geometry, geometric correction is required to convert the image coordinates into the geographic coordinate system. This involves considering factors such as satellite orbit parameters and imaging geometry models. The orbital state vector provided by the satellite and ground control points and other information can be used to achieve precise geometric correction through methods such as polynomial fitting, ensuring that the image can accurately correspond to the actual geographical location.

[0121] Implementation steps of phase-difference interferometry:

[0122] Interferogram generation: Select a pair of preprocessed SAR images (master image and slave image) that are related 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. By performing a differential operation on the phase information of these two images, an interferogram is generated. The phase values of the interferogram contain information such as surface topography and deformation.

[0123] Removing the flat-earth effect: Since the Earth's surface is approximately flat, a linear phase term independent of topography will be generated in the interferogram, called the flat-earth effect. The theoretical flat-earth phase can be calculated based on satellite orbit parameters and imaging geometry, and then subtracted from the interferogram phase to remove it. This helps to highlight the phase changes caused by topographic undulations and surface deformation.

[0124] Phase unwrapping: The phase values in the interferogram are the principal values limited within [-π, π], while the true surface deformation phase may exceed this range. Phase unwrapping is to recover the continuous true phase. Commonly used phase unwrapping methods include the branch-cut method, minimum-cost flow method, etc. 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 nodes 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 C-band radar of Sentinel-1, the wavelength λ = 5.55 cm, and through the unwrapped phase change the corresponding surface deformation amount can be calculated.

[0126] Implementation steps of the amplitude difference method:

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

[0128] Amplitude difference calculation: Select the amplitude images of two time phases, and obtain the amplitude change information through pixel-by-pixel difference operation.

[0129] Deformation area determination: Determine the area where deformation may occur according to the threshold of the amplitude difference. Since surface deformation may cause changes in the structure and physical properties of ground objects, resulting in changes in the backscattering intensity, when the amplitude difference exceeds the set threshold, the area where the pixel is located can be determined as a potential deformation area. The determination of the threshold can be optimized through experimental analysis or combined with field survey data.

[0130] Determination of the deformed area and data integration:

[0131] Comprehensive determination of the deformed area: The results obtained by the phase difference interferometry and the amplitude difference method are comprehensively analyzed. Generally speaking, the phase difference interferometry is more sensitive to small deformations and can provide high-precision deformation amount information. The amplitude difference method can be used as an auxiliary means to detect those areas where the structure and backscattering characteristics of ground objects may change significantly due to deformation. By setting reasonable rules, such as simultaneously satisfying that the deformation amount in the phase difference interferometry exceeds a certain threshold and the area is determined as a potential deformation area in the amplitude difference method, the final deformed area is determined.

[0132] Extraction of deformation data: For the determined deformed area, the deformation amount and other data are extracted from the calculation results of the phase difference interferometry. These data can be sorted out, such as stored in the format of a geographic information system, including the geographic coordinates of each deformed pixel point and the corresponding deformation amount. At the same time, other relevant data, such as land use type, geological structure, etc., can be combined to further analyze and interpret the deformation data.

[0133] In this embodiment, determining the identification result of potential hazard points corresponding to the deformed area according to the mathematical elevation model image specifically includes:

[0134] a: Obtaining high-resolution optical remote sensing data according to the mathematical elevation model image.

[0135] Satellites carrying high-resolution optical cameras can be launched to image the deformed area in space. The high-resolution optical remote sensing data can include spectral information and spatial information with a resolution at the sub-meter level or above. Spectral information: Reflectance data covering different bands (such as visible light band, near-infrared band, etc.). Spatial information: Includes visual features such as the shape, size, and texture of ground objects, as well as the geographic coordinate information (such as longitude and latitude) and resolution of the image.

[0136] b: Performing preset processing on the high-resolution optical remote sensing data to extract the characteristics of potential hazard points.

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

[0138] c: Determining the potential hazard points in the deformed area according to the characteristics of potential hazard points.

[0139] Adding the potential hazard points to the potential hazard point directory, and correspondingly storing the number, potential hazard risk level, potential hazard point data (geological elements), sub-region number, landslide risk level of the sub-region, and location information (longitude and latitude information, longitude and latitude information of multiple position points on the boundary) of the potential hazard points in the potential hazard point directory. Each piece of data in the directory includes the above content. According to the potential hazard point directory, the location of the potential hazard points in the target area can be determined.

[0140] As an example, a potential hazard point feature library can be established to store the feature patterns of various typical potential hazard points (such as the characteristics of different types of landslides, the characteristics of different-scale fissures, etc.). Then, compare the characteristics of potential hazard points extracted from the current image with the patterns in the feature library to find the matching potential hazard points.

[0141] The location of the potential hazard points in the actual geographical space can be determined according to the geographical coordinate information (longitude and latitude) of the image. The pixel coordinates in the image can be converted into actual longitude and latitude coordinates through methods such as image geocoding and coordinate transformation. Then, mark the determined potential hazard points on the image, mark their boundaries (the boundaries can be determined by the longitude and latitude information of multiple position points), and record their relevant information (such as number, potential hazard risk level, etc.), and add them to the potential hazard point directory.

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

[0143] The geological elements include, but are not limited to: deformation rate, slope, terrain undulation, surface roughness, terrain curvature, and volume. The characteristics of the disaster hidden danger points include, but are not limited to: geological disaster boundaries, fissures, landslide bodies, disaster-bearing body ranges, etc.

[0144] e: Determine the hidden danger risk level corresponding to the disaster hidden danger points in the deformed area according to the geological elements of the disaster hidden danger points and the weights corresponding to the geological elements.

[0145] In this embodiment, to determine the disaster hidden danger points, it is necessary to determine the deformed slopes in the deformed area, and the disaster hidden danger points can be determined from the deformed slopes in the deformed area.

[0146] The determination method of the deformed slope:

[0147] Use airborne LiDAR data to display the actual terrain conditions other than surface factors such as vegetation to obtain a high-precision DEM image (mathematical elevation model image), divide the deformed slope (DFSL i ), and calculate the slope deformation rate (M i ). Further, calculate the deformed slope gradient (RA i ), terrain undulation (SL i ), surface roughness (SR i ), terrain curvature (TC i ), volume (VL i ) and other data through point cloud data and background data. The calculation formulas for each parameter are as follows:

[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] Among them, i is the deformed slope number; S1 is the surface area; S2 is the horizontal plane area; angel is the angle between the two planes of S1 and S2; S nis the surface area component; dx, dy, and dz are the components in the X, Y, and Z directions respectively, and dh is the landslide thickness component.

[0154] Based on the deformed area and high-precision DEM images, using high-resolution optical remote sensing data, through methods such as filtering, edge detection, texture analysis, contrast enhancement, pattern recognition, and image segmentation, features such as geological disaster boundaries, fissures, landslide bodies, and the scope of disaster-bearing bodies are extracted. By screening, the disaster hidden danger point data (HDPS) is determined. The disaster hidden danger point data is the geological elements of the disaster hidden danger points. Each disaster hidden danger point also contains the deformation rate (M j ), slope (RA j ), terrain undulation degree (SL j ), surface roughness (SR j ), terrain curvature (TC j ), volume (VL j ) and other geological elements. The calculation formulas are as follows:

[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 is the process of screening disaster hidden danger points from the deformed slope; j is the number of the disaster hidden danger point.

[0159] Normalize the geological elements such as the deformation rate (M j ), slope (RA j ), terrain undulation degree (SL j ), surface roughness (SR j ), terrain curvature (TC j ), volume (VL j ) of each disaster hidden danger point. Determine the weights (C1, C2, C3, C4, C5, C6) of each element relative to the hidden danger risk level through the analytic hierarchy process. Finally, use the information superposition method to obtain the disaster risk index of the disaster hidden danger point and divide the hidden danger risk levels of the disaster hidden danger points.

[0160] There are a preset number of potential hazard risk levels pre-set. As an example, when the preset number of potential hazard risk levels is 4, the potential hazard risk levels from high to low are Class I disaster 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 potential hazard risk level corresponding to the disaster hazard point in the deformed area based on the geological elements of the disaster hazard point and the weights corresponding to the geological elements, the Class IV disaster hazard points (HDPS IV ) with the lowest risk are not considered. The Class IV disaster hazard points (HDPS IV ) are used for the subsequent process of adjusting the hazard point identification result. It is also possible not to set the Class IV disaster hazard points (HDPS IV ) first, and set the Class IV disaster hazard points (HDPS IV ) when it is determined that the hazard point identification result needs to be adjusted.

[0161] The method for determining the potential hazard risk level corresponding to the disaster hazard point in the deformed area based on the geological elements of the disaster hazard point and the weights corresponding to the geological elements is as follows:

[0162]

[0163]

[0164] where f NOR is the normalization function, f AHP is the analytic hierarchy process, represents the values after normalization of the deformation rate, slope, terrain undulation, surface roughness, terrain curvature, and volume.

[0165] S105: Divide the slope according to the risk assessment result to adjust the hazard point identification result.

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

[0167] Divide the slope of the first area to determine slope units, and determine the slope units as potential disaster hazard points.

[0168] In this embodiment, the first area is the area among all high-risk type areas where there are no potential disaster hazard points. 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. In this embodiment, the method of dividing the slope to determine the slope unit in the first area is the same as the method of determining the deformed slope in the deformed area described above, and will not be elaborated here.

[0169] In this embodiment, the hazard point identification result is corrected based on the high-precision risk assessment result. The spatial distribution characteristics of the extremely high-risk area and the high-risk area are compared with those of the disaster hazard points. The extremely high-risk area and the high-risk area without disaster hazard points are divided into slope units, and these slope units are determined as potential disaster hazard points, which are added as the fourth type of disaster hazard point (HDPS Ⅳ ) to the disaster hazard point catalog to achieve the correction of the hazard point identification result.

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

[0171] In this embodiment, the first area among all high-risk type areas where there are no disaster hazard points is divided into slopes, and the divided slope units are determined as potential disaster hazard points. This measure effectively avoids the situation of only focusing on the identified disaster hazard points and ignoring other possible risk areas in the high-risk area. In this way, the high-risk area is comprehensively covered, ensuring that no area that may cause disasters is missed, and making the risk monitoring more comprehensive.

[0172] The lowest hazard risk level among a preset number of hazard risk levels is assigned to the potential disaster hazard points, further refining and improving the risk level system of the entire area. Based on the original risk level divided according to the discovered hazard points, it takes into account the potential risk areas, making the risk level division more scientific and reasonable, and providing a more comprehensive and accurate basis for subsequent risk management.

[0173] Determining the slope unit as a potential disaster hazard point and setting the corresponding risk level can give early warnings for possible disasters. Even though no obvious disaster hazard points have been found in these areas currently, based on the background of the high-risk type areas they are in, through this forward-looking division and identification, corresponding preventive measures can be taken in advance to reduce the risk of possible disasters in the future.

[0174] By identifying potential disaster hazard points and their risk levels clearly, it helps to allocate risk management resources reasonably. For areas with different risk levels, human, material and financial resources can be targeted for monitoring, prevention and control. For these potential disaster hazard points, although the risk level is relatively low, due to being in high-risk areas, a certain amount of resources can still be appropriately allocated for regular monitoring and preliminary prevention to avoid dealing with the situation only when the disaster hazard develops into an actual disaster, thus improving the efficiency of resource utilization.

[0175] Fine division of risk areas: For the first area, slope division is carried out to determine slope units as potential disaster hazard points. Compared with simply regarding the entire high-risk area as a whole, this fine division method can more accurately identify potential risk positions. Different slope units may have different geological, topographical and other characteristics. Considering them as independent potential disaster hazard points can more precisely evaluate the risk status of each area and improve the accuracy of hazard identification.

[0176] Over time and with environmental changes, the risk status of potential disaster hazard points may change. This method of determining potential disaster hazard points based on slope division facilitates dynamic assessment and adjustment of risks. If the surrounding environment or geological conditions of a potential disaster hazard point change, its risk level can be re-evaluated in a timely manner according to the new situation, making the hazard identification results more in line with the actual situation and providing more reliable support for risk management.

[0177] S106: Adjust the risk assessment result according to the hazard point identification result or the treatment result of the disaster hazard point.

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

[0179] Adjust the landslide risk level corresponding to the second area according to the hazard risk level of the target disaster hazard point. The target disaster hazard point includes the disaster hazard points in the low-risk type area. The second area is the sub-area to which the target disaster hazard point belongs.

[0180] Re-determine the hazard risk level corresponding to the standard disaster hazard point according to the treatment result of the standard disaster hazard point.

[0181] Adjust the landslide risk level corresponding to the third area according to the re-determined hazard risk level corresponding to the standard disaster hazard point.

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

[0183] In this embodiment, for the target disaster hazard points in the low-risk type area, the landslide risk level corresponding to the second area to which they belong is adjusted according to their hazard risk levels. This operation avoids simply generalizing the low-risk areas and instead deeply considers the specific hazard points within the area. In this way, the actual risk conditions at different locations within the low-risk area can be more accurately reflected, making the risk assessment results closer to reality and providing a more accurate basis for subsequent risk management and decision-making.

[0184] For the standard disaster hazard points in the high-risk type area and the medium-risk type area, the risk levels are re-determined according to their treatment results, and accordingly, the landslide risk levels corresponding to the third area to which they belong are adjusted. This enables the risk assessment results to be dynamically updated as the treatment situation of the hazard points changes. If a high-risk hazard point is effectively treated and its risk level decreases, the landslide risk level of the corresponding area is adjusted accordingly, which can timely reflect the actual risk reduction in this area, and vice versa. This dynamic adjustment mechanism greatly improves the accuracy and timeliness of the risk assessment results.

[0185] By paying attention to the target disaster hazard points in the low-risk area and adjusting the landslide risk levels of the corresponding areas, resources can be more reasonably allocated. For the sub-areas (the second area) that are overall in the low-risk area but have certain hazard points, the resource input can be appropriately increased or decreased according to the adjusted risk levels. If the risk level of a certain low-risk sub-area is increased due to the existence of hazard points, targeted monitoring and prevention measures can be strengthened to avoid excessive waste or shortage of resources and improve resource utilization efficiency.

[0186] In the high-risk and medium-risk areas, after adjusting the risk levels according to the treatment results of the standard disaster hazard points, the resource requirements can be more accurately determined. For the areas (the third area) to which the hazard points that have been treated and have a reduced risk level belong, the resource input can be appropriately reduced, and the resources can be transferred to other high-risk areas or the hazard points being treated. For the areas where the treatment effect is not good or new high-risk hazard points appear, increase the resource input to ensure that the risks in key areas are effectively controlled and the optimal allocation of resources is achieved.

[0187] The treatment of target disaster hidden danger points in low-risk areas helps to focus on potential risks in these areas. Although the overall risk of these areas is low, individual hidden danger points may develop into greater risks over time, environmental and other factors. By adjusting the landslide risk level of the affiliated area, attention can be drawn to these potential risks, corresponding preventive measures can be taken to prevent the expansion of risks, thereby enhancing the pertinence of risk management in low-risk areas.

[0188] For standard disaster hidden danger points in high-risk and medium-risk areas, adjusting the risk level according to the treatment results and correspondingly adjusting the landslide risk level of the area can strengthen the treatment effect. On the one hand, for the treated hidden danger points, reducing the risk level of the area to which they belong can encourage maintaining a good treatment state. On the other hand, for the hidden danger points that have not been effectively treated, maintaining or increasing the risk level of the area to which they belong can prompt greater treatment efforts and further improve the effectiveness of risk management.

[0189] The whole process forms a closed-loop risk management system through hidden danger point identification, treatment, and adjustment of risk assessment results. From identifying hidden danger points to adjusting risk assessment according to their risk levels and treatment situations, and then guiding subsequent hidden danger treatment and resource allocation based on the adjusted risk assessment results, each link is interrelated and mutually influential, forming an organic whole. This closed-loop system helps to continuously improve risk management strategies and continuously enhance the overall risk management level.

[0190] Adjusting the risk level according to the situation of hidden danger points in different types of areas (the second area of low-risk areas, the third area of high-risk and medium-risk areas) promotes collaborative management among multiple areas. The risk situations of different areas are no longer viewed in isolation, but are comprehensively considered and managed within a unified framework. This collaborative management model can better integrate resources, coordinate actions, and improve the efficiency and effect of risk management in the entire area.

[0191] As an example, the ways in which the above scheme can be implemented are as follows:

[0192] Screen target hidden danger points: According to the hidden danger point identification results, screen out the disaster hidden danger points in low-risk type areas. These points are the target hidden danger points.

[0193] Determine the affiliated sub-area (the second area): For each target hidden danger point, determine the sub-area (i.e., the second area) to which it belongs based on the established attribution relationship.

[0194] Adjust the landslide risk level: Uniformly adjust the landslide risk level corresponding to the second area to which these target hidden danger points belong to the low-risk area. This step is based on the assumption that low-risk hidden danger points have little impact on the landslide risk of the sub-area where they are located.

[0195] Re - determine the hidden - danger risk level corresponding to the standard hidden - danger points according to the treatment results of the standard hidden - danger points:

[0196] Screen standard hidden - danger points: According to the hidden - danger point identification results, screen out the disaster hidden - danger points in the high - risk type areas and medium - risk type areas. These points are the standard hidden - danger points.

[0197] Evaluate the treatment results: For each standard hidden - danger point, check its corresponding treatment result information. The treatment results may include situations such as having been treated and not having been treated.

[0198] Re - determine the hidden - danger risk level:

[0199] If the treatment result of the standard hidden - danger point is "having been treated", it indicates that the risk of this hidden - danger point has been effectively controlled. Re - determine its corresponding hidden - danger risk level as low - risk. For example, the hidden - danger point HDPS of type Ⅲ Ⅲ .

[0200] If the treatment result is "not having been treated", then keep its original high - risk or medium - risk level (hidden - danger point of type Ⅰ (HDPS Ⅰ ) or hidden - danger point of type Ⅱ (HDPS Ⅱ )) unchanged.

[0201] Adjust the landslide risk level corresponding to the third area according to the hidden - danger risk level corresponding to the re - determined standard hidden - danger points:

[0202] Determine the sub - area (the third area) to which it belongs: For each standard hidden - danger point whose risk level has been re - determined, according to the established attribution relationship, clarify the sub - area (i.e., the third area) to which it belongs.

[0203] Adjust the landslide risk level:

[0204] If the risk level of the standard hidden - danger point after re - determination is high - risk (hidden - danger point of type Ⅰ (HDPS Ⅰ ), adjust the landslide risk level corresponding to its affiliated third area to a high - risk type area.

[0205] If the risk level of the standard hidden - danger point after re - determination is medium - risk (hidden - danger point of type Ⅱ (HDPS Ⅱ ), adjust the landslide risk level corresponding to its affiliated third area to a medium - risk type area.

[0206] As an example, correct the risk assessment results based on the identification results of potential hazard points or the treatment results of disaster potential hazard points. Compare the spatial distribution characteristics of the risk areas and disaster potential hazard points. For the disaster potential hazard points that do not fall within the risk areas, assign appropriate landslide risk levels to the sub-areas in combination with the potential hazard risk levels. For the disaster potential hazard points that fall within the risk areas, analyze the treatment data of the disaster potential hazard points, conduct risk assessment on the disaster potential hazard points after treatment, and if the risk level decreases, synchronously reduce the risk level of the risk area where the disaster potential hazard point is located.

[0207] In this embodiment, after an extreme rainfall event occurs, systematically update the comprehensive database. The specific updated data includes but is not limited to: hydrometeorological data, historical case data, and socioeconomic data. Using the updated comprehensive database, reconstruct the landslide hazard assessment model and the vulnerability assessment model of the disaster-bearing body based on the logistic regression model again. The two are coupled and superimposed to obtain the updated landslide risk index, realizing the iterative update of the landslide risk index.

[0208] Utilize ground monitoring such as soil moisture content, slope deformation, rainfall, and space-borne and aerial monitoring data such as Sar satellites and meteorological satellites, as well as manual inspection data such as landslide precursors to achieve near-real-time collection of information on the geological structure, hydrometeorology, deformation of geological bodies, and disaster response in the target area. Based on this information, synchronously update the deformation rate (M j ), slope (RA j ), terrain undulation degree (SL j ), surface roughness (SR j ), terrain curvature (TC j ), volume (VL j ) and other geological elements of the disaster potential hazard points. Use the determined geological elements of the disaster potential hazard points and the weights corresponding to the geological elements to determine the potential hazard risk level corresponding to the disaster potential hazard points in the deformed area, dynamically update the disaster risk index of the disaster potential hazard points, and at the same time consider meteorological data, disaster potential hazard point treatment and other information to update the potential hazard risk level.

[0209] The present invention proposes a method for constructing a comprehensive database of rainfall-induced mass landslides, and formulates the main data types included in the comprehensive database package of rainfall-induced mass landslides, providing basic data for the comprehensive identification, correction, iteration, and update of the risks and potential hazards of rainfall-induced mass landslides. A technical method for the mutual feedback and correction of the risks and potential hazards of rainfall-induced mass landslides is constructed. Based on the risk levels of rainfall-induced mass landslides, the identification results of potential hazards of rainfall-induced landslides are improved, and based on the risk levels of potential hazard points and the treatment information, the risk assessment results of rainfall-induced mass landslides are corrected, realizing the systematic integration of risk assessment and potential hazard identification work. The specific workflow of the comprehensive identification method for rainfall-induced mass landslides is formulated, and a comprehensive research and analysis system for the risks and potential hazards of mass landslides is constructed with double levels of risk and potential hazards, double characteristics of obvious and non-obvious landslides, double controls at the regional scale and slope scale, double drives of instrument monitoring and mathematical calculation, and double mechanisms of mutual feedback correction and dynamic update.

[0210] The overall process schematic diagram of the comprehensive identification and evaluation method for the risk areas and potential hazard points of rainfall-induced mass landslides in the present invention is as Figure 2 shown. First, the data storage types of the comprehensive database of rainfall-induced mass landslides are proposed. Taking this database as the data input, a machine learning model is used to achieve high-precision quantification and grading of the risks of rainfall-induced mass landslides. In the identification of potential hazards, a technical process from the identification of the deformation area to potential hazard points and then to the classification of potential hazard points is constructed. The potential hazards that are deforming are divided into three types according to the risk levels. Further, a workflow for correcting the identification results of potential hazard points based on the high-precision risk assessment results is established. The potential potential hazard points are used as the fourth type of potential hazard, improving the identification results of landslide potential hazards. At the same time, the risk assessment results are corrected based on the types of potential hazard points and the treatment data. Finally, a dynamic update mechanism for the risks and potential hazards of rainfall-induced mass landslides driven by the update of extreme rainfall case data is proposed, realizing the systematic integration of risk assessment and potential hazard identification work.

[0211] It should be noted that the content not described in detail in the specification of the present invention belongs to the well-known technology in the art.

[0212] In this embodiment, a device for comprehensive identification and evaluation of the risk areas and potential hazard points of rainfall-induced mass landslides is also provided. A single device is used to implement the above embodiments and optional implementation manners, and the ones that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0213] Figure 3 It is a schematic structural diagram of the device for comprehensive identification and evaluation of the risk areas and potential hazard points of rainfall-induced mass landslides according to the embodiment of the present invention.

[0214] The present invention provides a comprehensive identification and evaluation device for rainfall-induced mass landslide risk areas and potential hazard points, as Figure 3 shown. The comprehensive identification and evaluation device for rainfall-induced mass landslide risk areas and potential hazard points includes:

[0215] A first processing module 11, configured to update the comprehensive database when an extreme rainfall event occurs. In the case of updating the comprehensive database, determine the landslide hazard index and the vulnerability index of the disaster-bearing body for each calculation unit in the target area according to the updated comprehensive database. The target area includes a plurality of sub-areas, and each sub-area includes a plurality of calculation units.

[0216] A second processing module 12, configured to determine the landslide risk index of each sub-area according to the landslide hazard index and the vulnerability index of the disaster-bearing body of each calculation unit under the sub-area.

[0217] A third processing module 13, configured to determine the risk evaluation result according to the landslide risk index of each sub-area. The risk evaluation result includes: the landslide risk level corresponding to each sub-area.

[0218] A fourth processing module 14, configured to determine the deformed area in the target area according to the synthetic aperture radar data. Determine the potential hazard point identification result corresponding to the deformed area according to the digital elevation model image. The potential hazard point identification result includes the disaster potential hazard points in the deformed area and the potential hazard risk level corresponding to the disaster potential hazard points.

[0219] A fifth processing module 15, configured to perform slope division according to the risk evaluation result to adjust the potential hazard point identification result.

[0220] A sixth processing module 16, configured to adjust the risk evaluation result according to the potential hazard point identification result or the treatment result of the disaster potential hazard points.

[0221] In an optional implementation manner, the fifth processing module 15 is specifically configured to, when the landslide risk level includes high-risk type areas, medium-risk type areas, and low-risk type areas, and there are a preset number of potential hazard risk levels preset, perform slope division on the first area to determine slope units, and determine the slope units as potential disaster potential hazard points. The first area is the area where there are no disaster potential hazard points in all high-risk type areas. The potential hazard risk level corresponding to the potential disaster potential hazard points is the lowest potential hazard risk level among the preset number of potential hazard risk levels.

[0222] In an alternative embodiment, the sixth processing module 16 is specifically configured to adjust the landslide risk level corresponding to the second region according to the hidden danger risk level of the target disaster hidden danger point when the landslide risk level includes high-risk type areas, medium-risk type areas, and low-risk type areas, and a preset number of hidden danger risk levels are preset. The target disaster hidden danger point includes a disaster hidden danger point in the low-risk type area. The second region is a sub-region to which the target disaster hidden danger point belongs. Re-determine the hidden danger risk level corresponding to the standard disaster hidden danger point according to the treatment result of the standard disaster hidden danger point. Adjust the landslide risk level corresponding to the third region according to the re-determined hidden danger risk level corresponding to the standard disaster hidden danger point. The standard disaster hidden danger point includes a disaster hidden danger point in the high-risk type area and the medium-risk type area. The third region is a sub-region to which the standard disaster hidden danger point belongs.

[0223] In an alternative embodiment, the fourth processing module 14 is specifically configured to obtain high-resolution optical remote sensing data according to the mathematical elevation model image. Perform preset processing on the high-resolution optical remote sensing data to extract the characteristics of the disaster hidden danger points. The preset processing includes: filtering processing, edge detection, texture analysis, contrast enhancement, pattern recognition, and image segmentation. Determine the disaster hidden danger points in the deformed area according to the characteristics of the disaster hidden danger points. Determine the geological elements of the disaster hidden danger points and the weights corresponding to the geological elements. The geological elements include: deformation rate, slope, terrain undulation, surface roughness, terrain curvature, and volume. Determine the hidden danger risk level corresponding to the disaster hidden danger points in the deformed area according to the geological elements of the disaster hidden danger points and the weights corresponding to the geological elements.

[0224] In an alternative embodiment, the comprehensive database includes: a background database and a monitoring database. The background database includes: geological structure data, topographic and geomorphic data, hydrological and meteorological data, social and economic data, disaster treatment 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 treatment result of the disaster hidden danger points.

[0225] In an alternative embodiment, the first processing module 11 includes:

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

[0227] The second processing unit is configured to determine the vulnerability index of the disaster-bearing body of each calculation unit in the target area according to the social and economic data and historical case data.

[0228] In an alternative embodiment, the first processing unit is specifically configured to obtain a landslide hazard assessment model pre-constructed according to the logistic regression method. Input geological structure data, topographic and geomorphic data, and hydro-meteorological data into the landslide hazard assessment model to obtain the landslide hazard index of each calculation unit.

[0229] In an alternative embodiment, the second processing unit is specifically configured to obtain a disaster-bearing body vulnerability assessment model pre-constructed according to the logistic regression method. Input social and economic data and historical case data into the disaster-bearing body vulnerability assessment model to obtain the disaster-bearing body vulnerability index of each calculation unit.

[0230] The further functional descriptions of the above-mentioned various modules and units are the same as those in the corresponding embodiments above, and will not be elaborated here.

[0231] The embodiment of the present invention further provides a computer device having the above-mentioned Figure 3 rainfall-induced mass landslide risk area and hidden danger point comprehensive identification and evaluation device. Please refer to Figure 4 , Figure 4 which is a schematic hardware structure diagram of the computer device according to the embodiment of the present invention. As shown in Figure 4 , the computer device includes: one or more processors 10, a memory 20, and an interface for connecting various components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common main board or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In an alternative embodiment, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor device). Figure 4 Take one processor 10 as an example in

[0232] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above-mentioned hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above-mentioned programmable logic device can be a complex programmable logic device, a field programmable gate array, a general array logic, or any combination thereof.

[0233] Among them, the memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0234] The memory 20 may include a program storage area and a data storage area. Among them, the program storage area can store the operating device and application programs required for at least one function. The data storage area can store data created according to the use of the computer device and the like. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In an alternative embodiment, the memory 20 may optionally include a memory remotely provided with respect to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

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

[0236] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or communication networks.

[0237] The embodiments of the present invention also provide a computer-readable storage medium. The methods according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and to be stored in a local storage medium, so that the methods described herein can be stored as such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc. Further, the storage medium may also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0238] A part of the present invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can call or provide the methods and / or technical solutions according to the present invention through the operations of the computer. Those skilled in the art should understand that the forms of existence of computer program instructions in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways for computer program instructions to be executed by a computer include, but are not limited to: the computer directly executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Herein, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible by the computer.

[0239] Although the embodiments of the present 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 present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A comprehensive identification and evaluation method for rainfall-induced landslide risk areas and potential hazards, characterized in that: include: updating of comprehensive databases when extreme rainfall events occur; In case of updating the comprehensive database, determining the landslide hazard index and the hazard-bearing body vulnerability index of each calculation unit in the target area according to the updated comprehensive database; the target area includes a plurality of sub-areas, and each sub-area includes a plurality of calculation units; For each sub-region, determining a landslide risk index of the sub-region according to the landslide hazard index and the hazard-bearing body vulnerability index of each calculation unit under the sub-region; Determine the risk assessment results based on the landslide risk index for each sub-area; The risk assessment results include: the landslide risk level corresponding to each sub-area; determining a deformed area in the target area based on synthetic aperture radar data; Determine the hidden danger point identification result corresponding to the deformation area according to the mathematical elevation model image; the hidden danger point identification result includes the disaster hidden danger point in the deformation area and the hidden danger risk level corresponding to the disaster hidden danger point; Perform slope division according to the risk assessment result to adjust the hidden danger point identification result; The risk assessment result is adjusted according to the hidden danger point identification result or the disaster hidden danger point management result.

2. The method according to claim 1, characterized in that When the landslide risk level includes a high-risk type area, a medium-risk type area, and a low-risk type area, and a preset number of hidden danger risk levels are pre-set, the slope division is performed according to the risk assessment result to adjust the hidden danger point identification result, including: The first area is divided into slope units to determine slope units, and the slope units are determined as potential disaster risk points; the first area is an area in which no disaster risk points exist among all high-risk type areas; the risk level of the potential disaster risk point corresponding to the potential disaster risk point is the lowest risk level among a preset number of risk levels of the potential disaster risk.

3. The method according to claim 2, characterized in that The step of adjusting the risk assessment result according to the hidden danger point identification result or the disaster hidden danger point management result includes: Adjusting the landslide risk level corresponding to the second area according to the risk level of the target disaster potential point; the target disaster potential point includes a disaster potential point in a low-risk type area; the second area is a sub-area to which the target disaster potential point belongs; Re-determine the risk level of the standard disaster potential point according to the treatment results of the standard disaster potential point; The landslide risk level corresponding to the third area is adjusted according to the re-determined hazard risk level corresponding to the standard disaster hazard point; the standard disaster hazard point includes disaster hazard points in high-risk type areas and medium-risk type areas; the third area is the sub-area to which the standard disaster hazard point belongs.

4. The method according to claim 1, characterized in that: The step of determining the hidden danger point identification result corresponding to the deformation area according to the mathematical elevation model image includes: Acquiring high-resolution optical remote sensing data based on the mathematical elevation model image; Performing preset processing on the high-resolution optical remote sensing data to extract features of disaster risk points; the preset processing includes: filtering processing, edge detection, texture analysis, contrast enhancement, pattern recognition and image segmentation; Determining the disaster potential point in the deformation area according to the characteristics of the disaster potential point; Determine the geological elements of the disaster potential point and the weights corresponding to the geological elements; the geological elements include: deformation rate, slope, terrain undulation, surface roughness, terrain curvature and volume; The hazard risk level corresponding to the hazard point in the deformation area is determined according to the geological elements of the hazard point and the weights corresponding to the geological elements.

5. The method according to claim 1, characterized in that The comprehensive database includes: a background database and a monitoring database; the background 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 risk points.

6. The method according to claim 5, characterized in that The method of determining the landslide hazard index and the hazard-bearing body vulnerability index of each calculation unit in the target area according to the updated comprehensive database includes: Determine the landslide hazard index of each calculation unit in the target area according to the geological structure data, the topographic data and the hydrological and meteorological data; The disaster-prone body vulnerability index of each calculation unit in the target area is determined according to the socioeconomic data and the historical case data.

7. The method according to claim 6, characterized in that Determining the landslide hazard index of each calculation unit in the target area according to the geological structure data, the topographic data and the hydrological and meteorological data includes: Obtaining a landslide hazard assessment model pre-constructed by logistic regression; Inputting the geological structure data, the topographic data and the hydrological and meteorological data into the landslide hazard assessment model to obtain a landslide hazard index for each calculation unit; The step of determining the disaster-prone body vulnerability index of each computing unit in the target area according to the socio-economic data and the historical case data comprises: Obtaining a vulnerability assessment model of the disaster-prone body constructed in advance by means of logistic regression; The socio-economic data and the historical case data are input into the disaster-prone body vulnerability assessment model to obtain the disaster-prone body vulnerability index of each calculation unit.

8. A device for comprehensive identification and evaluation of risk areas and potential hazards of rainfall-induced group landslides, characterized in that: include: A first processing module is used to update the comprehensive database when an extreme rainfall event occurs; In case of updating the comprehensive database, determining the landslide hazard index and the hazard-bearing body vulnerability index of each calculation unit in the target area according to the updated comprehensive database; the target area includes a plurality of sub-areas, and each sub-area includes a plurality of calculation units; A second processing module is used to determine, for each sub-region, a landslide risk index of the sub-region according to the landslide hazard index and the hazard-bearing body vulnerability index of each calculation unit under the sub-region; The third processing module is used to determine the risk assessment result according to the landslide risk index of each sub-area; The risk assessment results include: the landslide risk level corresponding to each sub-area; The fourth processing module is used to determine the deformation area in the target area according to the synthetic aperture radar data; determine the hidden danger point identification result corresponding to the deformation area according to the mathematical elevation model image; the hidden danger point identification result includes the disaster hidden danger point in the deformation area and the hidden danger risk level corresponding to the disaster hidden danger point; A fifth processing module, configured to divide the slope according to the risk assessment result to adjust the hidden danger point identification result; The sixth processing module is used to adjust the risk assessment result according to the hidden danger point identification result or the disaster hidden danger point management result.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the comprehensive identification and evaluation method for rainfall-type group landslide risk areas and hidden danger points according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the comprehensive identification and evaluation method for rainfall-induced group landslide risk areas and hidden danger points according to any one of claims 1 to 7.

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