Method and device for modifying strength parameters of regional rock-soil mass

Abstract

The application relates to the technical field of geological disaster risk analysis, in particular to a regional rock-soil body strength parameter correction method and device, wherein the method comprises the following steps: collecting at least one influence factor data of a target region to construct a geographic information database of a regional landslide susceptibility evaluation model; calculating a regional landslide susceptibility index by using a frequency ratio model to determine a regional landslide occurrence probability, and obtaining a first regional landslide stability; determining a regional rock-soil body strength parameter, and calculating a regional slope stability coefficient; reducing the regional rock-soil body strength parameter to obtain a second regional slope stability, and constructing a cross-entropy loss function to quantitatively evaluate the difference of the result; and iteratively updating the rock-soil body strength parameter to obtain a final regional rock-soil body strength parameter. Therefore, the problems that, in the related art, the shear strength parameter of the slope has great uncertainty, which may lead to the increase of the calculation error of the earthquake landslide susceptibility, and the reduction of the prediction accuracy and reliability are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geological disaster risk analysis, and particularly relates to a regional rock-soil strength parameter correction method and device. BACKGROUND

[0002] In the related art, a landslide susceptibility model based on physical mechanics, in particular, a Newmark model, is widely used in post-earthquake landslide susceptibility evaluation. The Newmark model is based on the rigid body assumption. When the slope body is subjected to an earthquake acceleration exceeding the critical acceleration, sliding occurs, and when the displacement reaches a threshold value, the slope will be unstable. Because of its rigorous theory, it does not require post-earthquake landslide data and is not limited by regional geographical environment, it can realize rapid evaluation of landslide risk in the earthquake area after the earthquake.

[0003] However, in the related art, the shear strength parameters of the slope in the Newmark model are usually determined by engineering experience and historical statistical data, which has great uncertainty, and in the improved Newmark model, such as considering pore water pressure, rock slope fissure and models based on different strength criteria, there is still a problem of uncertainty of shear strength parameters, which may lead to an increase in calculation error of earthquake landslide susceptibility, and reduce the prediction accuracy and reliability, which needs to be improved. SUMMARY

[0004] The present application provides a regional rock-soil strength parameter correction method and device to solve the problems in the related art that the shear strength parameters of the slope are usually determined by engineering experience and historical statistical data, which has great uncertainty, may lead to an increase in calculation error of earthquake landslide susceptibility, and reduce the prediction accuracy and reliability.

[0005] The first aspect embodiment of the present application provides a regional rock-soil strength parameter correction method, comprising the following steps: collecting at least one influence factor data of a target region to construct a geographic information database of a regional landslide susceptibility assessment model, wherein the at least one influence factor data comprises at least one of historical landslide records, topography, historical earthquake records, and human engineering activities affecting the development of regional landslides; based on the historical landslide records of the target region and predetermined landslide influence factors, a frequency ratio model is used to construct a regional landslide susceptibility model to calculate a regional landslide susceptibility index, and the regional landslide susceptibility index is standardized to determine the regional landslide occurrence probability of the target region, and the first regional landslide stability of the target region is obtained according to the regional landslide occurrence probability; in combination with a preset regional rock-soil mechanics strength parameter, the regional rock-soil strength parameter of the target region is determined, and based on an infinite slope model and the regional rock-soil strength parameter and regional seismic fortification intensity, the regional slope stability coefficient of the target region is calculated; based on the regional slope stability coefficient, the regional rock-soil strength parameter is reduced to obtain a reduced rock-soil strength parameter, and the reduced rock-soil strength parameter is substituted into the infinite slope model to obtain the second regional slope stability of the target region, and a cross-entropy loss function is constructed to quantitatively evaluate the result difference between the first regional landslide stability and the second regional slope stability; the rock-soil strength parameter is iteratively updated until the cross-entropy loss function value reaches a preset stability condition, and the final regional rock-soil strength parameter is obtained.

[0006] Optionally, in an embodiment of the present application, based on the historical landslide records of the target region and the predetermined landslide influence factors, a frequency ratio model is used to construct a regional landslide susceptibility model to calculate a regional landslide susceptibility index, and the regional landslide susceptibility index is standardized to determine the regional landslide occurrence probability of the target region, and the first regional landslide stability of the target region is obtained according to the regional landslide occurrence probability, comprising: counting the number of landslide points and the number of grids under each group of different landslide influence factors of the at least one influence factor data to calculate the frequency ratio value of each group of landslide influence factors based on the frequency ratio model; based on the frequency ratio value of each group of landslide influence factors, the value of each landslide influence factor is assigned, and the landslide susceptibility index of the target region is calculated in combination with a landslide susceptibility index calculation formula; the landslide susceptibility index is standardized to obtain the regional landslide occurrence probability; the stable interval of the target region is matched according to the regional landslide occurrence probability to determine the first regional landslide stability.

[0007] Optionally, in an embodiment of the present application, wherein,

[0008] The expression of the frequency ratio model is:

[0009]

[0010] wherein, LA ij is the number of landslides of the i th group of the j th landslide influencing factor, FA ij is the number of grids of the i th group of the j th landslide influencing factor, FR ij is the frequency ratio value of the i th group of the j th landslide influencing factor, and n is the number of landslide influencing factors.

[0011] The calculation formula of the landslide susceptibility index is:

[0012]

[0013] wherein, LS is the landslide susceptibility index, FR ij is the frequency ratio value of the i th group of the j th landslide influencing factor, and n is the number of landslide influencing factors.

[0014] Optionally, in an embodiment of the present application, the regional rock-soil body strength parameter of the target region is determined by combining the preset regional rock-soil mechanics strength parameter, and the regional slope stability coefficient of the target region is calculated based on the infinite long slope model and the regional rock-soil body strength parameter and the regional seismic fortification intensity, which comprises: the structural surface strength cohesion and internal friction angle of each type of rock group of the target region are valued, and the thickness of the regional slope loose accumulation body is determined by combining the existing statistical data of the thickness of the superficial soil body; the slope gradient data are obtained based on the digital elevation model of the target region, and the regional slope stability coefficient is calculated based on the infinite long slope model and the rigid body limit equilibrium method by combining the regional design basic seismic acceleration.

[0015] Optionally, in an embodiment of the present application, the calculation formula of the regional slope stability coefficient is:

[0016]

[0017] wherein, FOS is the slope stability coefficient, a is the slope gradient, m is the regional design basic seismic acceleration coefficient, c is the structural surface strength cohesion, and f is the internal friction angle.

[0018] Optionally, in an embodiment of the present application, the cross-entropy loss function is:

[0019]

[0020] wherein, y is the stability of the regional unit grid judged based on the frequency ratio model, The stability of the regional unit grid is judged based on the infinite long slope model after the strength reduction.

[0021] The second aspect embodiment of the present application provides a regional rock-soil strength parameter correction device, comprising: a collection module, configured to collect at least one influence factor data of a target region to construct a geographic information database of a regional landslide susceptibility assessment model, wherein the at least one influence factor data comprises at least one of historical landslide records, topography, historical earthquake records, and human engineering activities affecting the development of regional landslides; a first calculation module, configured to construct a regional landslide susceptibility model by using a frequency ratio model based on the historical landslide records of the target region and predetermined landslide influence factors, to calculate a regional landslide susceptibility index, to standardize the regional landslide susceptibility index to determine a regional landslide occurrence probability of the target region, and to obtain a first regional landslide stability of the target region according to the regional landslide occurrence probability; a second calculation module, configured to determine a regional rock-soil strength parameter of the target region in combination with a predetermined regional rock-soil mechanics strength parameter, and to calculate a regional slope stability coefficient of the target region based on an infinite long slope model and the regional rock-soil strength parameter and a regional seismic fortification intensity; an evaluation module, configured to reduce the regional rock-soil strength parameter based on the regional slope stability coefficient to obtain a reduced rock-soil strength parameter, and to substitute the reduced rock-soil strength parameter into the infinite long slope model to obtain a second regional slope stability of the target region, and to construct a cross-entropy loss function to quantitatively evaluate the result difference between the first regional landslide stability and the second regional slope stability; and a correction module, configured to iteratively update the rock-soil strength parameter until the cross-entropy loss function value reaches a predetermined stability condition to obtain a final regional rock-soil strength parameter.

[0022] Optionally, in an embodiment of the present application, the first calculation module comprises: a first calculation unit, configured to count the number of landslide points and the number of grids under each group of different landslide influence factors of the at least one influence factor data, to calculate the frequency ratio values of each group of landslide influence factors based on a frequency ratio model; a second calculation unit, configured to assign values to the different landslide influence factors based on the frequency ratio values of each group of landslide influence factors, and to calculate the regional landslide susceptibility index of the target region in combination with a landslide susceptibility index calculation formula; a processing unit, configured to standardize the regional landslide susceptibility index to obtain the regional landslide occurrence probability; and a first determination unit, configured to match the target region to a stable interval according to the regional landslide occurrence probability to determine the first regional landslide stability.

[0023] Optionally, in an embodiment of the present application, wherein,

[0024] The expression of the frequency ratio model is:

[0025]

[0026] wherein, LA ij is the number of landslides of the i-th group of the j-th landslide influencing factor, FA ij is the number of grids of the i-th group of the j-th landslide influencing factor, FR ij is the frequency ratio value of the i-th group of the j-th landslide influencing factor, and n is the number of landslide influencing factors.

[0027] The calculation formula of the landslide susceptibility index is:

[0028]

[0029] wherein, LS is the landslide susceptibility index, FR ij is the frequency ratio value of the i-th group of the j-th landslide influencing factor, and n is the number of landslide influencing factors.

[0030] Optionally, in an embodiment of the present application, the second calculation module comprises: a second determination unit configured to value the structural surface strength cohesion and internal friction angle of each type of rock group of the target region, and determine the thickness of the regional slope loose accumulation body in combination with the existing statistical data of the thickness of the superficial soil body; and a third calculation unit configured to acquire slope gradient data based on the digital elevation model of the target region, and calculate the regional slope stability coefficient based on the infinite slope model and the rigid body limit equilibrium method in combination with the regional design basic seismic acceleration.

[0031] Optionally, in an embodiment of the present application, the calculation formula of the regional slope stability coefficient is:

[0032]

[0033] wherein, FOS is the slope stability coefficient, a is the slope gradient, m is the regional design basic seismic acceleration coefficient, c is the structural surface strength cohesion, and f is the internal friction angle.

[0034] Optionally, in an embodiment of the present application, the cross-entropy loss function is:

[0035]

[0036] wherein, y is the stability of the regional unit grid judged based on the frequency ratio model, and y' is the stability of the regional unit grid judged based on the strength-reduced infinite slope model.

[0037] The third aspect of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the regional rock-soil strength parameter correction method according to the above embodiments.

[0038] The fourth aspect of the present application provides a computer readable storage medium, which stores a computer program executable by a processor to implement the regional rock-soil strength parameter correction method according to the above embodiments.

[0039] The fifth aspect of the present application provides a computer program product, which is executable to implement the regional rock-soil strength parameter correction method according to the above embodiments.

[0040] The embodiments of the present application can construct a regional landslide susceptibility model by using a frequency ratio model based on a geographic information database, obtain a first regional landslide stability of a target region, combine a preset regional rock-soil mechanics strength parameter, calculate a regional slope stability coefficient of the target region by an infinite slope model, and then reduce the regional rock-soil strength parameter to obtain a second regional slope stability of the target region. The cross-entropy loss function is used to quantitatively evaluate the result difference between the first regional landslide stability and the second regional slope stability, so as to iteratively update the rock-soil strength parameter and accurately correct the regional rock-soil strength parameter, which helps to reduce the calculation error of the coseismic landslide susceptibility and effectively improve the accuracy and applicability of the prediction model. Thus, the problems in the related art that the shear strength parameter of the slope is usually determined by engineering experience and historical statistical data, which has great uncertainty and may cause the calculation error of the coseismic landslide susceptibility to increase and the accuracy and reliability of the prediction to decrease are solved.

[0041] Additional aspects and advantages of the present application will be made apparent by the following description and the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0042] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description, taken in conjunction with the accompanying drawings, in which:

[0043] Figure 1 A flowchart of a regional rock-soil strength parameter correction method according to an embodiment of the present application is shown in FIG. 1;

[0044] Figure 2 A principle diagram of a regional rock-soil strength parameter correction method based on data driving and physical model according to an embodiment of the present application is shown in FIG. 2;

[0045] Figure 3A landslide influencing factor distribution diagram according to an embodiment of the present application;

[0046] Figure 4 A landslide susceptibility diagram based on a frequency ratio model according to an embodiment of the present application;

[0047] Figure 5 A slope stability distribution diagram based on an infinite long slope model according to an embodiment of the present application;

[0048] Figure 6 A diagram showing changes in regional rock-soil strength parameters with iteration numbers according to an embodiment of the present application;

[0049] Figure 7 A structural diagram of a regional rock-soil strength parameter correction device according to an embodiment of the present application;

[0050] Figure 8 A structural diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0051] Embodiments of the present application are described in detail below with reference to the attached drawings, which are meant to be exemplary and not limiting.

[0052] The regional rock-soil strength parameter correction method and device of the embodiments of the present application are described below with reference to the accompanying drawings. In view of the problems in the related art that the shear strength parameters of slopes are usually determined by engineering experience and historical statistical data, which has great uncertainty and may cause the calculation error of the earthquake landslide susceptibility to increase and reduce the accuracy and reliability of the prediction, the present application provides a regional rock-soil strength parameter correction method. In the method, the regional landslide susceptibility model can be constructed based on the geographic information database by using the frequency ratio model to obtain the first regional landslide stability of the target region, and the regional slope stability coefficient of the target region can be calculated by combining the preset regional rock-soil mechanics strength parameter through the infinite slope model, and then the regional rock-soil strength parameter can be reduced to obtain the second regional slope stability of the target region, and the result difference between the first regional landslide stability and the second regional slope stability can be quantitatively evaluated by using the cross-entropy loss function, so as to iteratively update the rock-soil strength parameter and realize the accurate correction of the regional rock-soil strength parameter, which helps to reduce the calculation error of the earthquake landslide susceptibility and effectively improve the accuracy and applicability of the prediction model. Thus, the problems in the related art that the shear strength parameters of slopes are usually determined by engineering experience and historical statistical data, which has great uncertainty and may cause the calculation error of the earthquake landslide susceptibility to increase and reduce the accuracy and reliability of the prediction are solved.

[0053] Specifically, Figure 1 A flowchart of a regional rock-soil strength parameter correction method provided by the embodiments of the present application is shown in FIG. 1.

[0054] As Figure 1 shown, the regional rock-soil strength parameter correction method includes the following steps:

[0055] In step S101, at least one influence factor data of a target region is collected to construct a geographic information database of a regional landslide susceptibility evaluation model, wherein the at least one influence factor data includes at least one of historical landslide records, topography, historical earthquake records, and human engineering activities affecting the development of regional landslides.

[0056] It can be understood that the target region refers to a specific geographical region that needs to be evaluated for landslide susceptibility, and the influence factor data refers to the quantitative information of various natural and human factors that affect landslide susceptibility, including but not limited to historical landslide records, topography, historical earthquake records, and human engineering activities.

[0057] In actual execution, the geographic information database can be constructed by using the frequency ratio model, and the first regional landslide stability of the target region can be obtained by using the geographic information database. Figure 2As shown, the historical landslide records collected by the embodiments of the present application should be as complete as possible and include clear landslide location information. When the collected landslide records become a surface file, the geographic spatial location of the landslide is replaced by the centroid of the surface file, and the historical landslide records of the study area, topography, human engineering activities, and other influence factors affecting the development of regional landslides should be converted into raster files and located in the same spatial projection coordinate system. Meanwhile, the embodiments of the present application consider the engineering geological conditions of regional landslide development, and the landslide influence factors generally include regional elevation, slope gradient, slope aspect, distance from fault zone, stratum lithology, and profile curvature.

[0058] Further, in order to facilitate calculation, the determined continuous landslide influence factors should be grouped according to the specific physical meaning or natural discontinuity method of the landslide influence factors, and the determined discrete landslide influence factors should also be grouped according to the discrete values of the landslide influence factors. In addition, each landslide influence factor after grouping should be kept in the same spatial projection coordinate system, and the raster cells of each landslide influence factor should also be aligned one by one.

[0059] For example, the embodiments of the present application can collect the historical landslide records, topography, historical earthquake records, human engineering activities, and other influence factors affecting the development of regional landslides in the study area to construct a geographic information database of the regional landslide susceptibility assessment model. Taking a certain county in A province as an example, the embodiments of the present application can collect the spatial positions of 198 landslides in the study area before the earthquake, regional digital elevation model, stratum lithology distribution, and fault zone distribution data. Considering the engineering geological conditions of regional landslide development in the study area, six landslide influence factors including regional elevation, slope gradient, slope aspect, distance from fault zone, stratum lithology, and profile curvature are preliminarily selected.

[0060] Further, in combination with Figure 3As shown, the embodiment of the present application can adopt the natural break method to divide the three landslide influencing factors of regional elevation, slope gradient and profile curvature into five sub-classes respectively, wherein the regional elevation is divided into five sub-classes of <1445, 1445-2025, 2025-2563, 2563-3125 and >3125 m; the slope gradient is divided into five sub-classes of 0-14°, 14-28°, 28-38°, 38-48° and >48°; and the profile curvature is divided into five sub-classes of <-6.50, -6.05-2.34, -2.34-0.31, 0.31-5.4 and >5.4. Meanwhile, the slope aspect is divided into nine sub-classes of flat, north, northeast, east, southeast, south, southwest, west and northwest according to the actual direction, and the stratum lithology is divided into five sub-classes of granite group, diorite group, andesite group, limestone group and sandstone group according to the regional stratum lithology distribution. Further, the distance from the fault zone is divided into five sub-classes of <2, 2-4, 4-6, 6-8 and >8 km by referring to the existing research results and existing experience. Meanwhile, the embodiment of the present application can convert the distribution of all landslide influencing factors into raster data by using the ArcGIS geographic information system processing platform, so as to ensure that all raster data are in the same spatial coordinate system and all raster points are in one-to-one registration and alignment.

[0061] By integrating the multi-source data of historical landslide records, topography, historical earthquake records and human engineering activities, the embodiment of the present application can improve the comprehensiveness of information, provide an information basis for subsequent correction of regional rock and soil strength parameters, and help to improve the accuracy and reliability of prediction.

[0062] In step S102, based on the historical landslide records of the target region and the pre-determined landslide influencing factors, a regional landslide susceptibility model is constructed by using a frequency ratio model to calculate a regional landslide susceptibility index, and the regional landslide susceptibility index is standardized to determine the regional landslide occurrence probability of the target region, and the first regional landslide stability of the target region is obtained according to the regional landslide occurrence probability.

[0063] It can be understood that the pre-determined landslide influencing factors refer to the engineering geological conditions considering the development of regional landslides in the study area, and the preliminary screening of regional elevation, slope gradient, slope aspect, distance from fault zone, stratum lithology and profile curvature.

[0064] The embodiment of the application can evaluate landslide risks of different locations in a target area in detail through a frequency ratio model, help identify high-risk areas, ensure the comparability of landslide proneness indexes between different areas through standardized processing, avoid evaluation deviation caused by dimensional differences, enhance the universality and applicability of the model, and help quantify risk information through calculation of regional landslide occurrence probability, facilitate comparison of risk levels of different areas, and provide specific guidance for disaster risk management and land planning.

[0065] Optionally, in an embodiment of the application, a regional landslide proneness model is constructed based on historical landslide records of a target area and predetermined landslide influence factors by using a frequency ratio model to calculate a regional landslide proneness index, and the regional landslide proneness index is processed by standardization to determine a regional landslide occurrence probability of the target area, and a first regional landslide stability of the target area is obtained according to the regional landslide occurrence probability, including: counting the number of landslide points and the number of grids in each group of different landslide influence factors of at least one influence factor data, calculating the frequency ratio of each group of landslide influence factors based on the frequency ratio model, assigning values to each landslide influence factor based on the frequency ratio of each group of landslide influence factors, and calculating the landslide proneness index of the target area in combination with a landslide proneness index calculation formula, processing the landslide proneness index by standardization to obtain the regional landslide occurrence probability, and matching the stability interval of the target area according to the regional landslide occurrence probability to determine the first regional landslide stability.

[0066] Specifically, the embodiment of the application can count the number of landslide points and the number of grids in each group of different landslide influence factors based on the geographic information system spatial processing tool ArcGIS, and output the results to a local disk, combine the results with the landslide proneness index calculation formula, and calculate the landslide proneness index of the target area by using the Excel data processing software. Figure 2 As shown, the frequency ratio of each group of landslide influence factors is calculated by using the Excel data processing software based on the frequency ratio model, and optionally, the expression of the frequency ratio model is:

[0067]

[0068] wherein, LA ij is the number of landslides of the i th group of the j th landslide influence factor, FA ij is the number of grids of the i th group of the j th landslide influence factor, FR ij is the frequency ratio of the i th group of the j th landslide influence factor, and n is the number of landslide influence factors.

[0069] Further, the embodiment of the present application can calculate the landslide susceptibility index (LS) of the research region based on the frequency ratio of each landslide influencing factor group calculated and by using the raster calculator tool of the map algebra module in ArcGIS, by writing a corresponding conditional statement to assign values to the landslide influencing factors in ArcGIS, and by writing a landslide susceptibility index calculation formula based on the raster calculator tool of the map algebra module in ArcGIS. Optionally, in an embodiment of the present application, the calculation formula of the landslide susceptibility index is as follows:

[0070]

[0071] wherein LS is the landslide susceptibility index, FR ij is the landslide frequency ratio of the i th group of the j th landslide influencing factor, and n is the number of landslide influencing factors.

[0072] In addition, as shown in Figure 4 , the embodiment of the present application can standardize the calculated susceptibility index based on the spatial analysis function of ArcGIS to obtain the regional landslide occurrence probability (LSP), wherein the value range of LSP is [0, 1], and the calculation formula can be specifically as follows:

[0073]

[0074] wherein LSP is the regional landslide occurrence probability, and LS is the landslide susceptibility index.

[0075] Those skilled in the art should understand that the embodiment of the present application can judge the stability of the regional slope unit based on LSP, wherein when LSP>0.5, the stability of the regional slope unit is poor and may be unstable, and the unit is assigned a value of 1; when LSP<0.5, the stability of the regional slope unit is good, and the unit is assigned a value of 0.

[0076] The embodiment of the present application can group and assign values to different landslide influencing factors by using the frequency ratio model, which can more accurately identify and quantify the landslide risk of different regions in the target region, and is helpful for accurately positioning the high-risk areas. In addition, by determining the regional landslide occurrence probability and the first regional landslide stability, the present application can provide a quantitative risk assessment result for decision makers, which is helpful for reasonably allocating disaster prevention and mitigation resources, and prioritizing the regions with the highest risk or the worst stability to improve the efficiency and effectiveness of resource use.

[0077] In step S103, the regional rock-soil body strength parameters of the target region are determined in combination with the preset regional rock-soil mechanics strength parameters, and the regional slope stability coefficient of the target region is calculated based on the infinite slope model and the regional rock-soil body strength parameters and the regional seismic fortification intensity.

[0078] It can be understood that the preset regional geotechnical strength parameter refers to a geotechnical strength parameter preset based on existing research results or empirical data, including but not limited to the cohesion of the geotechnical material, the internal friction angle, and other mechanical properties that can affect the stability of the geotechnical body, such as the elastic modulus and the Poisson's ratio.

[0079] Specifically, the embodiment of the present application can determine the structure surface strength cohesion c and the internal friction angle of various rock groups in the region according to the physical and mechanical properties of the regional geotechnical body, based on the research results and experience of predecessors, and at the same time, determine the thickness t of the regional slope loose accumulation body in combination with the existing statistical data and field investigation of the shallow surface soil body, and then determine the design basic seismic acceleration of the research region and the regional slope stability safety factor by referring to the main town seismic fortification intensity and the landslide prevention design specification in China.

[0080] Further, as shown in Figure 2 the embodiment of the present application can obtain the digital elevation model (DEM) of the research region based on the open source data platform geographic information space data cloud, and at the same time, obtain the slope gradient data of the research region based on the spatial analysis function of ArcGIS with DEM as input, consider the regional design basic seismic acceleration, and calculate the regional slope stability coefficient based on the infinite long slope model and the rigid body limit equilibrium method.

[0081] Optionally, in an embodiment of the present application, the calculation formula of the regional slope stability coefficient is:

[0082]

[0083] Wherein, FOS is the slope stability coefficient, a is the slope gradient, m is the regional design basic seismic acceleration coefficient, c is the structure surface strength cohesion, and

[0084] The embodiment of the present application calculates the regional slope stability coefficient of the target region based on the infinite long slope model and the regional geotechnical strength parameter and the regional seismic fortification intensity, which is helpful for predicting the response of the slope under the action of earthquake or other external force, can continuously monitor the slope state, and is conducive to timely issuing early warning and taking emergency measures.

[0085] Optionally, in one embodiment of this application, the regional soil and rock strength parameters of the target area are determined by combining preset regional soil and rock mechanical strength parameters, and the regional slope stability coefficient of the target area is calculated based on the infinitely long slope model, the regional soil and rock strength parameters, and the regional seismic fortification intensity. This includes: taking values ​​for the structural surface strength, cohesion, and internal friction angle of various rock groups in the target area, and determining the thickness of the loose deposits on the regional slope by combining existing shallow soil thickness statistics; obtaining slope gradient data based on the digital elevation model of the target area, and calculating the regional slope stability coefficient based on the infinitely long slope model and the rigid body limit equilibrium method by combining the regional design basic seismic acceleration.

[0086] Specifically, in this application, the regional rock and soil masses can be divided into granite, diorite, andesite, limestone, and sandstone groups based on their physical and mechanical properties. Furthermore, based on previous research and experience, the structural strength, cohesion (c), and internal friction angle of each rock group in the region can be determined. The values ​​of parameters such as density are taken, and the values ​​of each parameter are shown in Table 1. Table 1 is the reference value of the strength parameters of the regional soil and rock structure surface.

[0087] Table 1

[0088] Formation lithology Andesite Diorite Granite Sandstone Limestone Specific gravity 22 22 23 19 22 Cohesion / kPa 140 140 150 50 140 Internal friction angle / ° 28 28 30 11 28

[0089] Obviously, based on existing statistical data on shallow soil thickness and field investigations, the thickness t of the loose deposits on the regional slope can be determined to be 5m. Referring to the seismic fortification intensity and landslide prevention design codes of major cities in my country, the basic design seismic acceleration for the study area can be determined to be 0.2g and the regional slope stability safety factor to be 1.2.

[0090] Furthermore, combined Figure 5 As shown, this embodiment of the application can acquire the Digital Elevation Model (DEM) of the study area based on the open-source data platform, Geographic Information Spatial Data Cloud. Simultaneously, the acquired DEM is cropped and projected into a spatial coordinate system using ArcGIS software. Using the DEM as input, the slope tool in ArcGIS's spatial analysis module can be used to calculate the slope of each slope unit in the region. Considering the basic design seismic acceleration of the region, the regional slope stability coefficient can be calculated based on an infinitely long slope model and the rigid body limit equilibrium method. The formula for calculating the regional slope stability coefficient is as follows:

[0091]

[0092] Where FOS is the slope stability coefficient, α is the slope gradient, m is the regional design basic seismic acceleration coefficient, and c is the structural surface strength cohesion. It is the internal friction angle.

[0093] Therefore, the embodiment of the present application can compare the calculated regional slope stability coefficient with the regional slope stability safety factor, wherein when the FOS of the slope unit is greater than the regional slope stability safety factor, the slope is unstable, and the grid cell value is assigned as 1; otherwise, the slope remains stable, and the grid cell value is assigned as 0.

[0094] The embodiment of the present application can provide an information basis for subsequent stability analysis by accurately determining the strength of the structural plane of the rock group, the cohesion and the internal friction angle, help to identify potential unstable layers by determining the thickness of the loose accumulation body, help to improve the accuracy of topographic analysis by using the digital elevation model to obtain slope gradient data, and effectively improve the efficiency and accuracy of calculating the stability coefficient by using the infinite long slope model and the rigid body limit equilibrium method.

[0095] In step S104, the regional rock-soil strength parameters are reduced based on the regional slope stability coefficient to obtain the reduced rock-soil strength parameters, and the reduced rock-soil strength parameters are substituted into the infinite long slope model to obtain the second regional slope stability of the target region, and a cross-entropy loss function is constructed to quantitatively evaluate the result difference between the first regional landslide stability and the second regional slope stability.

[0096] Specifically, the embodiment of the present application can perform secondary development on ArcGIS based on the open source software Python and the Arcpy module, read the generated regional landslide susceptibility and the calculated slope stability coefficient based on the infinite long slope, and read basic data such as regional stratigraphic lithology, slope gradient and rock-soil bulk density. The strength reduction method calculation program is written based on the open source software Python, and the different regional rock-soil strength parameters are reduced, wherein the reduced rock-soil strength parameter calculation formula can be as follows:

[0097] c f = c / FOS

[0098]

[0099] wherein c f and are the reduced rock-soil cohesion and internal friction angle, respectively.

[0100] Further, the embodiment of the present application can give a smaller initial value of FOS, reduce the rock-soil strength parameters by using the above formula, and bring the reduced cohesion and internal friction angle into the slope stability coefficient calculation formula to calculate the slope stability coefficient again and judge the stability of different slope units, thereby combining the slope stability coefficient with the regional landslide susceptibility to obtain the regional landslide stability. Figure 2As shown, by comparing the slope unit stability judged based on the infinite long slope model and the frequency ratio model, a cross-entropy loss function can be constructed to evaluate the difference between the two results. Optionally, in an embodiment of the present application, the cross-entropy loss function is:

[0101]

[0102] where y is the stability of the regional unit grid judged based on the frequency ratio model, is the stability of the regional unit grid judged based on the infinite long slope model after strength reduction.

[0103] By strength reduction analysis, the embodiments of the present application are helpful to evaluate the potential landslide risk. By introducing the cross-entropy loss function, the matching degree of the predicted first region landslide stability and the predicted second region stability can be improved, the overall prediction accuracy can be improved, the model can be adapted to different geological conditions and external disturbances, and the robustness of the model can be enhanced.

[0104] In step S105, the geotechnical strength parameters are iteratively updated until the cross-entropy loss function value reaches a preset stability condition, and the final regional geotechnical strength parameters are obtained.

[0105] It can be understood that the preset stability condition refers to the value of the cross-entropy loss function reaching a state of no longer significant change, that is, the function value tends to be stable.

[0106] Specifically, in combination with Figure 2 As shown, the embodiments of the present application can perform iterative calculation on the regional geotechnical strength parameters and the slope stability coefficient based on the strength reduction method. In combination with Figure 6 As shown, a smaller FOS initial value can be given, generally set to 0.1, and then the cross-entropy loss function is recalculated. When the cross-entropy loss function gradually increases, FOS=FOS+0.02 is obtained, and the cross-entropy function is calculated again. When the cross-entropy loss function gradually increases, FOS=FOS+0.02 is obtained, and the cross-entropy function is calculated again. Until the cross-entropy function remains unchanged, the regional strength reduction method corrected geotechnical strength parameters are obtained. As shown in Table 2, Table 2 is the regional strength reduction geotechnical strength parameters.

[0107] Table 2

[0108]

[0109] In addition, the embodiment of the present application can calculate the regional Newmark displacement based on the original strength parameter and the modified strength parameter respectively based on the Newmark displacement empirical formula, and divide the Newmark displacement calculated by different models into four groups (<5, 5-15, 15-30, >30 cm) based on the USGS standard, while the prediction accuracy of different models is verified by using the data after the earthquake. The average prediction accuracy of the Newmark model based on the original strength parameter is 0.67, and the average prediction accuracy of the Newmark model based on the modified strength parameter is 0.81, indicating that the present application has high accuracy and applicability.

[0110] According to the regional rock-soil strength parameter correction method proposed in the embodiment of the present application, the first regional landslide stability of the target region can be obtained by using the frequency ratio model to construct the regional landslide susceptibility model based on the geographic information database, and the regional slope stability coefficient of the target region can be calculated by combining the preset regional rock-soil mechanics strength parameter and the infinite slope model, and then the regional rock-soil strength parameter can be reduced to obtain the second regional slope stability of the target region, and the result difference between the first regional landslide stability and the second regional slope stability can be quantitatively evaluated by using the cross-entropy loss function, so as to iteratively update the rock-soil strength parameter and realize the accurate correction of the regional rock-soil strength parameter, which helps to reduce the calculation error of the co-seismic landslide susceptibility and effectively improve the accuracy and applicability of the prediction model. Thus, the problems in the related art that the shear strength parameter of the slope is usually determined by engineering experience and historical statistical data, which has great uncertainty and may lead to increased calculation error of the co-seismic landslide susceptibility and reduced prediction accuracy and reliability are solved.

[0111] Secondly, the regional rock-soil strength parameter correction device according to the embodiment of the present application is described with reference to the accompanying drawings.

[0112] Figure 7 is a block schematic diagram of the regional rock-soil strength parameter correction device of the embodiment of the present application.

[0113] As shown in Figure 7 , the regional rock-soil strength parameter correction device 10 comprises an acquisition module 100, a first calculation module 200, a second calculation module 300, an evaluation module 400 and a correction module 500.

[0114] Specifically, the acquisition module 100 is configured to acquire at least one influence factor data of a target region to construct a geographic information database of a regional landslide susceptibility evaluation model, wherein the at least one influence factor data comprises at least one of historical landslide records, topography, historical earthquake records, and human engineering activities affecting the development of regional landslides.

[0115] The first calculation module 200 is configured to construct a regional landslide susceptibility model by using a frequency ratio model based on historical landslide records of the target region and predetermined landslide influencing factors, calculate a regional landslide susceptibility index of the target region, perform standardization processing on the regional landslide susceptibility index, determine a regional landslide occurrence probability of the target region, and obtain a first regional landslide stability of the target region according to the regional landslide occurrence probability.

[0116] The second calculation module 300 is configured to determine regional rock-soil body strength parameters of the target region in combination with preset regional rock-soil mechanics strength parameters, and calculate a regional slope stability coefficient of the target region based on an infinite slope model and the regional rock-soil body strength parameters and a regional seismic fortification intensity.

[0117] The evaluation module 400 is configured to reduce the regional rock-soil body strength parameters based on the regional slope stability coefficient, obtain reduced rock-soil body strength parameters, and substitute the reduced rock-soil body strength parameters into the infinite slope model to obtain a second regional slope stability of the target region, and construct a cross-entropy loss function to quantitatively evaluate a result difference between the first regional landslide stability and the second regional slope stability.

[0118] The correction module 500 is configured to iteratively update the rock-soil body strength parameters until a preset stability condition is met, and obtain final regional rock-soil body strength parameters.

[0119] Optionally, in an embodiment of the present application, the first calculation module 200 comprises a first calculation unit, a second calculation unit, a processing unit and a first determination unit.

[0120] The first calculation unit is configured to count the number of landslide points and the number of grids in each group of different landslide influencing factors of at least one influencing factor data, and calculate a frequency ratio value of each group of landslide influencing factors based on a frequency ratio model.

[0121] The second calculation unit is configured to assign values to each group of landslide influencing factors based on the frequency ratio value of each group of landslide influencing factors, and calculate a regional landslide susceptibility index of the target region in combination with a landslide susceptibility index calculation formula.

[0122] The processing unit is configured to perform standardization processing on the regional landslide susceptibility index to obtain a regional landslide occurrence probability.

[0123] The first determination unit is configured to match a stability interval of the target region according to the regional landslide occurrence probability to determine the first regional landslide stability.

[0124] Optionally, in an embodiment of the present application, the expression of the frequency ratio model is:

[0125]

[0126] wherein, LA ij is the number of landslides of the i-th group of the j-th landslide influencing factor, FA ij is the number of grids of the i-th group of the j-th landslide influencing factor, FR ij is the frequency ratio of the i-th group of the j-th landslide influencing factor, and n is the number of landslide influencing factors.

[0127] The calculation formula of the landslide susceptibility index is:

[0128]

[0129] wherein, LS is the landslide susceptibility index, FR ij is the frequency ratio of the i-th group of the j-th landslide influencing factor, and n is the number of landslide influencing factors.

[0130] Optionally, in an embodiment of the present application, the second calculation module 300 comprises a second determination unit and a third calculation unit.

[0131] The second determination unit is configured to value the cohesion and internal friction angle of the structural surface of each type of rock group of the target region, and determine the thickness of the regional slope loose accumulation body in combination with the existing statistical data of the thickness of the superficial soil body.

[0132] The third calculation unit is configured to acquire slope gradient data based on the digital elevation model of the target region, and calculate the regional slope stability coefficient based on the infinite slope model and the rigid body limit equilibrium method in combination with the regional design basic seismic acceleration.

[0133] Optionally, in an embodiment of the present application, the calculation formula of the regional slope stability coefficient is:

[0134]

[0135] wherein, FOS is the slope stability coefficient, a is the slope gradient, m is the regional design basic seismic acceleration coefficient, c is the cohesion of the structural surface strength, is the internal friction angle.

[0136] Optionally, in an embodiment of the present application, the cross-entropy loss function is:

[0137]

[0138] wherein, y is the stability of the regional unit grid judged based on the frequency ratio model, is the stability of the regional unit grid judged based on the infinite slope model after strength reduction.

[0139] It should be noted that the foregoing explanation of the embodiment of the method for correcting the strength parameters of the regional rock-soil mass also applies to the device for correcting the strength parameters of the regional rock-soil mass, and details are not repeated here.

[0140] The device for correcting the strength parameters of the regional rock-soil mass according to the embodiments of the present application can construct a regional landslide susceptibility model by using a frequency ratio model based on a geographic information database, obtain the first regional landslide stability of the target region, and combine the preset regional rock-soil mechanics strength parameters to calculate the regional slope stability coefficient of the target region by using an infinite slope model. Then, the regional rock-soil mass strength parameters can be reduced to obtain the second regional slope stability of the target region, and the cross-entropy loss function can be used to quantitatively evaluate the result difference between the first regional landslide stability and the second regional slope stability, so as to iteratively update the rock-soil mass strength parameters and accurately correct the regional rock-soil mass strength parameters, which helps to reduce the calculation error of the coseismic landslide susceptibility and effectively improve the accuracy and applicability of the prediction model. Thus, the problems in the related art that the shear strength parameters of the slope are usually determined by engineering experience and historical statistical data, have great uncertainty, and may cause the calculation error of the coseismic landslide susceptibility to increase and the accuracy and reliability of the prediction to decrease are solved.

[0141] Figure 8 The structural schematic diagram of the electronic device provided by the embodiments of the present application is provided. The electronic device can include:

[0142] The memory 801, the processor 802, and the computer program stored in the memory 801 and executable on the processor 802.

[0143] The processor 802 implements the method for correcting the strength parameters of the regional rock-soil mass provided in the above embodiments when executing the program.

[0144] Further, the electronic device further includes:

[0145] The communication interface 803 is used for communication between the memory 801 and the processor 802.

[0146] The memory 801 is used to store the computer program executable on the processor 802.

[0147] The memory 801 can include a high-speed RAM memory, and can also include a non-volatile memory, for example, at least one disk memory.

[0148] If the memory 801, the processor 802 and the communication interface 803 are implemented independently, the communication interface 803, the memory 801 and the processor 802 can be connected with each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Figure 8 Only one thick line is used to represent the bus in the figure, but it does not mean that there is only one bus or only one type of bus.

[0149] Optionally, in a specific implementation, if the memory 801, the processor 802 and the communication interface 803 are integrated on a chip, the memory 801, the processor 802 and the communication interface 803 can complete communication between each other through an internal interface.

[0150] The processor 802 can be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0151] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the above-mentioned regional rock-soil strength parameter correction method.

[0152] The embodiments of the present application also provide a computer program product, which can run computer instructions, and the computer instructions are executed by a processor to implement the above-mentioned regional rock-soil strength parameter correction method.

[0153] In the description of the application, reference to "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that a particular feature, structure, material, or characteristic being described is included in at least one embodiment or example of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment or example. Furthermore, the described specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples. In addition, the usage of "N" means at least two, for example, two, three or the like, unless explicitly stated otherwise.

[0154] Furthermore, the terms "first", "second", or the like, are used merely as a designation of certain elements or features, and do not imply or connote relative importance or a specific order of categorization thereof. Accordingly, features described as "first" or "second" can be explicitly or implicitly included in at least one of the features. In the description of the application, the meaning of "N" is at least two, for example, two, three, etc., unless explicitly specified otherwise.

[0155] Any process or method descriptions or blocks in flow charts or otherwise described herein represent embodiments which can be managed as one or more modules, segments, or portions of code which include one or more executable instructions for implementing specific logic functions or steps, and alternate implementations are possible. In some embodiments, the processes and methods described can be executed by one or more apparatuses or devices, either directly or after conversion to another language.

[0156] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0157] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0158] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0159] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0160] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for correcting regional soil and rock mass strength parameters, characterized in that, Includes the following steps: Collect data on at least one influencing factor in the target area to construct a geographic information database for a regional landslide susceptibility assessment model. The at least one influencing factor data includes at least one of the following: historical landslide records, topography, historical earthquake records, and human engineering activities that affect the development of landslides in the region. Based on the historical landslide records and pre-determined landslide influencing factors of the target area, a regional landslide susceptibility model is constructed using a frequency ratio model to calculate a regional landslide susceptibility index. The regional landslide susceptibility index is then standardized to determine the probability of landslide occurrence in the target area. Based on the probability of landslide occurrence, the first regional landslide stability of the target area is obtained. Based on the preset regional geotechnical strength parameters, the regional geotechnical strength parameters of the target area are determined, and the regional slope stability coefficient of the target area is calculated based on the infinitely long slope model, the regional geotechnical strength parameters, and the regional seismic fortification intensity. Based on the slope stability coefficient of the region, the strength parameters of the soil and rock mass in the region are reduced to obtain the reduced soil and rock mass strength parameters. The reduced soil and rock mass strength parameters are then substituted into the infinitely long slope model to obtain the slope stability of the second region of the target area. A cross-entropy loss function is constructed to quantitatively evaluate the difference between the results of the landslide stability of the first region and the slope stability of the second region. The strength parameters of the soil and rock mass are iteratively updated until the value of the cross-entropy loss function reaches the preset stability condition, and the final strength parameters of the soil and rock mass in the region are obtained. The cross-entropy loss function is: ， Where y represents the stability of the regional cell grid determined based on the frequency ratio model. The stability of the regional cell grid is determined based on the infinitely long slope model after strength reduction.

2. The method according to claim 1, characterized in that, The process involves constructing a regional landslide susceptibility model using a frequency ratio model based on historical landslide records and pre-determined landslide influencing factors in the target area. This model calculates a regional landslide susceptibility index, standardizes the index to determine the probability of landslide occurrence in the target area, and then obtains the first regional landslide stability of the target area based on this probability. This includes: The number of landslide points and the number of grid cells in each group of different landslide influencing factors are statistically analyzed based on the at least one influencing factor data, so as to calculate the frequency ratio value of each landslide influencing factor group based on the frequency ratio model. Based on the frequency ratio of each landslide influencing factor group, each landslide influencing factor is assigned a value, and the landslide susceptibility index of the target area is calculated by combining the landslide susceptibility index calculation formula. The landslide susceptibility index is standardized to obtain the probability of landslide occurrence in the region. The stability of the first area landslide is determined by matching the stability interval of the target area with the probability of landslide occurrence in the area.

3. The method according to claim 2, characterized in that, in, The expression for the frequency ratio model is: ， in, LA ij For the first j The landslide influencing factor is the first i Number of landslides in each group FA ij For the first j The landslide influencing factor is the first i The number of grid cells in each group. FR ij For the first j The landslide influencing factor is the first i The ratio of landslide frequencies in each group n This represents the number of factors influencing landslides. The formula for calculating the landslide susceptibility index is as follows: LS = ， in, LS This is a landslide susceptibility index. FR ij For the first j The landslide influencing factor is the first i The ratio of landslide frequencies in each group n This represents the number of factors influencing landslides.

4. The method according to claim 1, characterized in that, The process involves combining preset regional soil and rock mechanical strength parameters to determine the regional soil and rock strength parameters of the target area, and calculating the regional slope stability coefficient of the target area based on an infinitely long slope model, the regional soil and rock strength parameters, and the regional seismic fortification intensity. This includes: The structural surface strength, cohesion, and internal friction angle of various rock groups in the target area are measured, and the thickness of the loose deposits on the slope in the area is determined by combining the existing statistical data on the thickness of shallow soil. Based on the digital elevation model of the target area, slope gradient data is obtained, and combined with the basic seismic acceleration of the area, the slope stability coefficient of the area is calculated based on the infinitely long slope model and the rigid body limit equilibrium method.

5. The method according to claim 4, characterized in that, The formula for calculating the slope stability coefficient of the region is as follows: ， in, FOS This is the slope stability coefficient. The slope is a gradient. m Design the basic seismic acceleration coefficient for the region. c For structural surface strength cohesion, It is the internal friction angle.

6. A regional soil and rock mass strength parameter correction device, characterized in that, include: The data acquisition module is used to collect data on at least one influencing factor in the target area to construct a geographic information database for a regional landslide susceptibility assessment model. The at least one influencing factor data includes at least one of the following: historical landslide records, topography, historical earthquake records, and human engineering activities that affect the development of landslides in the region. The first calculation module is used to construct a regional landslide susceptibility model based on the historical landslide record of the target area and the pre-determined landslide influencing factors using a frequency ratio model, to calculate the regional landslide susceptibility index, and to standardize the regional landslide susceptibility index to determine the probability of regional landslide occurrence in the target area, and to obtain the first regional landslide stability of the target area based on the probability of regional landslide occurrence. The second calculation module is used to determine the regional soil and rock strength parameters of the target area by combining the preset regional soil and rock mechanical strength parameters, and to calculate the regional slope stability coefficient of the target area based on the infinitely long slope model, the regional soil and rock strength parameters and the regional seismic fortification intensity. The evaluation module is used to reduce the strength parameters of the soil and rock mass in the region based on the slope stability coefficient of the region, obtain the reduced soil and rock mass strength parameters, and substitute the reduced soil and rock mass strength parameters into the infinitely long slope model to obtain the second regional slope stability of the target region. It also constructs a cross-entropy loss function to quantitatively evaluate the difference between the results of the landslide stability of the first region and the slope stability of the second region. The correction module is used to iteratively update the strength parameters of the soil and rock mass until the value of the cross-entropy loss function reaches the preset stability condition, so as to obtain the final strength parameters of the soil and rock mass in the region. The cross-entropy loss function is: ， Where y represents the stability of the regional cell grid determined based on the frequency ratio model. The stability of the regional cell grid is determined based on the infinitely long slope model after strength reduction.

7. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the regional soil and rock strength parameter correction method as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the regional soil and rock mass strength parameter correction method as described in any one of claims 1-5.

9. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the regional soil and rock mass strength parameter correction method as described in any one of claims 1-5.

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