A landslide hidden danger risk assessment method and system based on a three-dimensional dynamic matrix

Abstract

The application discloses a landslide hidden danger risk assessment method and system based on a three-dimensional dynamic matrix, relates to the technical field of landslide hidden danger risk assessment, and comprises the following steps: when a preset area is in a non-flood season, a hidden danger risk matrix calculation value of the preset area is calculated based on a three-dimensional dynamic matrix comprising a weight coefficient corresponding to each landslide hidden danger factor and a quantified value of each landslide hidden danger factor, and the calculated hidden danger risk matrix calculation value is taken as a final hidden danger risk matrix calculation value; and the preset area is subjected to landslide hidden danger risk assessment according to the final hidden danger risk matrix calculation value, so that a landslide hidden danger risk assessment result of the preset area is obtained. The application realizes objective and dynamic risk assessment by relying on quantitative analysis of the three-dimensional dynamic matrix and combining deformation characteristics of different stages of the landslide hidden danger, and significantly enhances the scientificity and reliability of the assessment result.

Description

Technical Field

[0001] This invention relates to the field of landslide hazard risk assessment technology, and in particular to a landslide hazard risk assessment method and system based on a three-dimensional dynamic matrix. Background Technology

[0002] In recent years, with the increasing frequency of extreme weather events due to global climate change, many regions have experienced frequent heavy rainfall, a surge in the number and scale of disasters, repeated disasters in multiple areas, and severe local losses, with landslides being the most serious. Landslides typically undergo different stages before sliding, including initial deformation, isochronous deformation, and accelerated deformation. However, because the deformation is mostly on the millimeter scale, this "hidden" characteristic makes it difficult to detect in advance using traditional manual inspection methods. Currently, nearly 80% of landslides occur outside known disaster reservoirs, but the difficulty in early identification and effective dynamic assessment of landslide risks places enormous pressure on geological disaster prevention and control during the flood season.

[0003] Currently, thanks to the significant advancements in multi-source remote sensing and in-situ measurement technologies, and relying on high-resolution optical satellite data and integrated remote sensing technologies such as InSAR, the boundaries and deformations of landslide hazards can be effectively identified. However, in assessing hazard risk, problems remain, including poor compatibility with traditional methods, strong subjectivity in risk assessment, weak fusion of multi-source data, a reliance on two-dimensional risk assessment, and insufficient dynamic data. Previously, landslide risk assessments were primarily qualitative estimates, and even quantitative assessments were often based on spatiotemporal probability statistics of past disasters. This resulted in assessment results being influenced to some extent by the experience of assessment experts, failing to leverage the quantitative advantages of multi-source integrated remote sensing technologies. Furthermore, the limited number of analytical parameters that can be included in a two-dimensional matrix and the insufficient dynamic parameters further limit the applicability and timeliness of the final results. Summary of the Invention

[0004] The technical problem to be solved by this invention is to address the shortcomings of existing technologies, specifically by providing a landslide hazard risk assessment method and system based on a three-dimensional dynamic matrix, as detailed below: 1) In a first aspect, the present invention provides a landslide hazard risk assessment method based on a three-dimensional dynamic matrix, the specific technical solution of which is as follows: When the preset area is in the non-flood season, the calculated value of the hazard risk matrix of the preset area is obtained based on the three-dimensional dynamic matrix including the weight coefficients corresponding to each landslide hazard factor and the quantitative value of each landslide hazard factor, and the calculated value of the hazard risk matrix is ​​used as the final landslide hazard risk level value. Based on the final landslide hazard risk level value, a landslide hazard risk assessment is conducted on the preset area to obtain the landslide hazard risk assessment results for the preset area.

[0005] The beneficial effects of the landslide hazard risk assessment method based on a three-dimensional dynamic matrix provided by this invention are as follows: To address the challenge of traditional methods failing to capture millimeter-level concealed deformations, this method effectively identifies subtle deformations of landslide hazards through precise calculation of weighting coefficients and quantified values ​​in a three-dimensional dynamic matrix, significantly improving the accuracy of early hazard identification. Previous risk assessments often relied on two-dimensional matrices, limiting the parameters included and lacking dynamism. The three-dimensional dynamic matrix proposed in this application integrates multi-source data, comprehensively considers landslide hazard factors, and dynamically reflects their changing trends, overcoming the limitations of two-dimensional matrices and making the assessment results more timely and applicable. Traditional assessments are often influenced by the subjectivity of expert experience, failing to fully leverage quantitative advantages. This method, relying on the quantitative analysis of a three-dimensional dynamic matrix and combining the deformation characteristics of landslide hazards at different stages, achieves objective and dynamic risk assessment, significantly enhancing the scientific rigor and reliability of the assessment results. Moreover, by dynamically updating the three-dimensional dynamic matrix and the quantitative value of each landslide hazard factor, the assessment results are ensured to have significant timeliness and can reflect the latest status of landslide hazards in real time. At the same time, it supports high-frequency data processing and rapid calculation, demonstrating superior timeliness and meeting the assessment needs of different stages during the non-flood season. In addition, by leveraging the time-dimensional extension capability of the three-dimensional matrix, this method can effectively capture the fluctuation characteristics of landslide risk and characterize the dynamic evolution trend of potential landslide hazards at different times, thereby breaking through the limitations of traditional methods in dynamic risk assessment.

[0006] Based on the above scheme, the landslide hazard risk assessment method based on a three-dimensional dynamic matrix of the present invention can be further improved as follows.

[0007] Furthermore, it also includes: correcting the calculated value of the hidden danger risk matrix based on the current situation factor, the timeliness factor, and the volatility factor of the risk assessment, and using the corrected calculated value of the hidden danger risk matrix as the final landslide hidden danger risk level value.

[0008] The beneficial effects of adopting the above-mentioned further scheme are as follows: By introducing the current situation factor, the timeliness factor, and the volatility factor of risk assessment, the calculated values ​​of the hazard risk matrix are corrected, effectively improving the accuracy and reliability of the assessment results. The current situation factor ensures that the assessment data closely matches the current geological conditions, the timeliness factor enhances the real-time capture of dynamic changes in landslide hazards, and the volatility factor smooths out short-term drastic changes in environmental factors, avoiding data fluctuations that could distort the assessment results. Using the corrected calculated values ​​of the hazard risk matrix as the final landslide hazard risk level value can more scientifically and comprehensively reflect the potential risks of landslide hazards, significantly improving the current situation, timeliness, and applicability of risk assessment, and effectively solving the problem of insufficient dynamism in two-dimensional matrix analysis using traditional methods.

[0009] Furthermore, the process of obtaining the weight coefficient corresponding to each landslide hazard factor includes: Based on on-site verification records from the historical landslide disaster database, sample data including each landslide hazard factor was obtained; Based on all sample data, a global sensitivity analysis was performed on the three-dimensional dynamic matrix to obtain the weight coefficients corresponding to each landslide hazard factor.

[0010] The beneficial effects of adopting the above-mentioned further approach are as follows: By using on-site verification records based on historical landslide disaster databases and employing global sensitivity analysis to obtain the weight coefficients corresponding to each landslide hazard factor, the traditional methods effectively solve the problems of strong subjectivity in hazard risk assessment and weak multi-source data fusion. This makes the determination of weight coefficients more scientific and objective, and can more accurately reflect the actual impact of different landslide hazard factors on landslide risk, thereby improving the accuracy and reliability of landslide hazard risk assessment. Simultaneously, utilizing historical data and global sensitivity analysis can enhance the dynamism and timeliness of the assessment results, making them more closely aligned with actual landslide hazard scenarios and providing stronger technical support for landslide disaster prevention and control.

[0011] Furthermore, all landslide hazard factors include the degree of deformation. The quantitative value of the degree of deformation is the deformation degree value, and the process of obtaining the deformation degree value includes: The deformation rate of a preset region is obtained using multi-resolution InSAR cross-interferometry and the Stacking-InSAR method. Calculate the deformation area of ​​the preset region; The deformation degree value is calculated based on the deformation rate degree value and the deformation area degree value of the preset area.

[0012] The beneficial effects of adopting the above-mentioned further scheme are as follows: by integrating multi-resolution InSAR cross-interferometry and the Stacking-InSAR method to obtain deformation rate values, and combining them with deformation area values ​​to calculate deformation degree values, the accuracy of early identification of landslide hazards is significantly improved. It can accurately capture millimeter-level weak deformations, overcome the shortcomings of traditional single-dimensional analysis, provide reliable quantitative input for three-dimensional dynamic matrices, enhance the comprehensiveness and scientific rigor of risk assessment, and help to detect landslide hazards earlier and more accurately, thus gaining valuable time for disaster prevention and control.

[0013] Furthermore, all landslide hazard factors include the hazard size, which is quantified as a hazard size level value. The process of obtaining the hazard size level value includes: Based on images of a pre-defined area collected by satellite, the boundaries of landslide hazards are delineated. The area of ​​the landslide hazard enclosed by the boundaries of the landslide hazard is not less than the minimum identifiable area, which is calculated based on the satellite spatial resolution. Based on the area of ​​potential landslide hazards, a random forest regression algorithm model is used, combined with the regional information of the preset area, to calculate the volume of potential landslide hazards; The classification level of the landslide hazard volume is determined according to the landslide scale classification standard; Based on the classification of the landslide hazard volume, and using the hierarchical risk scoring method, the hazard scale level value is determined.

[0014] The beneficial effects of adopting the above-mentioned further scheme are as follows: Landslide hazard boundaries are accurately delineated using satellite imagery; the volume of the hazard is calculated using a random forest regression algorithm combined with regional information; the volume level is determined according to landslide scale grading standards; and finally, the hazard scale level value is obtained using a hierarchical risk scoring method. This process effectively improves the accuracy and scientific rigor of landslide hazard scale assessment, achieving precise control over the entire process from spatial information to quantitative grading. It provides a reliable and unified quantitative basis for subsequent hazard risk assessment, significantly enhances the timeliness and applicability of the assessment results, and strongly supports dynamic monitoring and prevention decisions regarding landslide disasters.

[0015] Furthermore, all landslide hazard factors include the potential loss of the affected body. The quantification value of the potential loss of the affected body is the hazard loss level value. The process of obtaining the hazard loss level value includes: The potential horizontal sliding distance of landslide hazards in a preset area is fitted based on a data-driven algorithm. The potential horizontal sliding distance of the landslide hazard is normalized to obtain the hazard loss level value.

[0016] The beneficial effects of adopting the above-mentioned further scheme are as follows: by fitting the potential horizontal sliding distance of landslide hazards using a data-driven algorithm and performing normalization processing to obtain the hazard loss level value, the scientificity and objectivity of the quantitative assessment of potential losses of the disaster-bearing body are effectively improved. Normalization processing eliminates differences in sliding distances between regions, making the data more uniform and comparable, providing accurate input for three-dimensional dynamic matrix risk assessment, enhancing the timeliness and applicability of the assessment, and contributing to dynamic and comprehensive landslide hazard risk assessment.

[0017] Furthermore, it also includes: when the preset area is in the flood season, the maximum quantitative value among all landslide hazard factors is used as the final landslide hazard risk level value of the preset area.

[0018] The beneficial effects of adopting the above-mentioned further scheme are: It accurately and efficiently identifies the highest-risk hazard points, taking into account the unique characteristics of landslide hazards during the flood season. It simplifies the calculation process, ensuring that key risk areas can be quickly located in the complex and ever-changing environment of the flood season, providing strong support for emergency decision-making and resource allocation, and greatly improving the efficiency and targeted nature of landslide disaster prevention and control during the flood season.

[0019] 2) Secondly, the present invention also provides a landslide hazard risk assessment system based on a three-dimensional dynamic matrix, the specific technical solution of which is as follows: Includes a calculation module and a landslide hazard risk assessment module; The calculation module is used to: when the preset area is in the non-flood season, calculate the risk matrix of the preset area based on the three-dimensional dynamic matrix including the weight coefficients of each landslide hazard factor and the quantitative value of each landslide hazard factor, and use the calculated risk matrix of the preset area as the final landslide hazard risk level value. The landslide hazard risk assessment module is used to: conduct landslide hazard risk assessment on a preset area based on the final landslide hazard risk level value, and obtain the landslide hazard risk assessment results for the preset area.

[0020] Based on the above scheme, the landslide hazard risk assessment system based on a three-dimensional dynamic matrix of the present invention can be further improved as follows.

[0021] Furthermore, it also includes a correction module, which is used to correct the calculated value of the hidden danger risk matrix based on the current situation factor, the timeliness factor, and the volatility factor of the risk assessment, and to use the corrected calculated value of the hidden danger risk matrix as the final landslide hidden danger risk level value.

[0022] Furthermore, it also includes a weight coefficient acquisition module, which is used for: Based on on-site verification records from the historical landslide disaster database, sample data including each landslide hazard factor was obtained; Based on all sample data, a global sensitivity analysis was performed on the three-dimensional dynamic matrix to obtain the weight coefficients corresponding to each landslide hazard factor.

[0023] Furthermore, it also includes a deformation degree value acquisition module. All landslide hazard factors include deformation degree, and the quantitative value of deformation degree is the deformation degree value. The deformation degree value acquisition module is used for: The deformation rate of a preset region is obtained using multi-resolution InSAR cross-interferometry and the Stacking-InSAR method. Calculate the deformation area of ​​the preset region; The deformation degree value is calculated based on the deformation rate degree value and the deformation area degree value of the preset area.

[0024] Furthermore, it also includes: a hazard scale level value acquisition module. All landslide hazard factors include hazard scale, and the quantified value of hazard scale is the hazard scale level value. The hazard scale level value acquisition module is used for: Based on images of a pre-defined area collected by satellite, the boundaries of landslide hazards are delineated. The area of ​​the landslide hazard enclosed by the boundaries of the landslide hazard is not less than the minimum identifiable area, which is calculated based on the satellite spatial resolution. Based on the area of ​​potential landslide hazards, a random forest regression algorithm model is used, combined with the regional information of the preset area, to calculate the volume of potential landslide hazards; The classification level of the landslide hazard volume is determined according to the landslide scale classification standard; Based on the classification of the landslide hazard volume, and using the hierarchical risk scoring method, the hazard scale level value is determined.

[0025] Furthermore, it also includes a module for obtaining the risk loss level value. All landslide risk factors include the potential loss of the affected body. The quantified value of the potential loss of the affected body is the risk loss level value. The risk loss level value acquisition module is used for: The potential horizontal sliding distance of landslide hazards in a preset area is fitted based on a data-driven algorithm. The potential horizontal sliding distance of the landslide hazard is normalized to obtain the hazard loss level value.

[0026] Furthermore, it also includes a determination module, which is used to: when the preset area is in the flood season, take the maximum quantitative value among all landslide hazard factors as the final landslide hazard risk level value of the preset area.

[0027] 3) In a third aspect, the present invention also provides an electronic device, the electronic device including a processor coupled to a memory, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor, so that the electronic device implements any of the above-mentioned landslide hazard risk assessment methods based on a three-dimensional dynamic matrix.

[0028] 4) In a fourth aspect, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-mentioned landslide hazard risk assessment methods based on a three-dimensional dynamic matrix.

[0029] It should be noted that the beneficial effects of the technical solutions of the second to fourth aspects of the present invention and their corresponding possible implementations can be found in the above description of the technical effects of the first aspect and its corresponding possible implementations, and will not be repeated here. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below: Figure 1 This is a flowchart illustrating a landslide hazard risk assessment method based on a three-dimensional dynamic matrix, according to an embodiment of the present invention. Figure 2 This is a schematic diagram of a landslide hazard risk assessment system based on a three-dimensional dynamic matrix, according to an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0031] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0032] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0033] like Figure 1 As shown in the figure, a landslide hazard risk assessment method based on a three-dimensional dynamic matrix according to an embodiment of the present invention includes the following steps: S1. When the preset area is in the non-flood season, the calculated value of the hidden danger risk matrix of the preset area is obtained based on the three-dimensional dynamic matrix including the weight coefficients corresponding to each landslide hidden danger factor and the quantitative value of each landslide hidden danger factor, and the calculated value of the hidden danger risk matrix is ​​used as the final landslide hidden danger risk level value. Among them, all landslide hazard factors include: deformation degree (also known as InSAR deformation degree), hazard scale and potential loss of the disaster-bearing body. The quantitative value of deformation degree is the deformation degree value, the quantitative value of hazard scale is the hazard scale level value, and the quantitative value of potential loss of the disaster-bearing body is the hazard loss level value.

[0034] The calculated value of the hidden danger risk matrix is ​​obtained using the first formula, which is: in, Indicates: the calculated value of the hidden danger risk matrix. Indicates: the weighting coefficient corresponding to the degree of deformation. Indicates: Degree of deformation. Indicates: the weighting coefficient corresponding to the scale of the hidden danger. Indicates: Hazard scale level value, Indicates: the weighting coefficient corresponding to the potential loss of the disaster-bearing body. Indicates: Risk level value. , , and All are dimensionless values.

[0035] Furthermore, based on the InSAR deformation distribution characteristics within the landslide hazard area, it is divided into four levels: discrete, local, mostly, and overall; based on the projected area of ​​the landslide hazard on optical images, it can be divided into four levels: small, medium, large, and extra-large; based on the potential personnel and economic losses of the hazard-bearing body, it is divided into four levels: small, medium, large, and extremely large.

[0036] S2. Based on the final landslide hazard risk level value, conduct a landslide hazard risk assessment of the preset area to obtain the landslide hazard risk assessment results for the preset area.

[0037] Optionally, the above technical solution also includes: S020. Based on the current situation factor, the timeliness factor, and the volatility factor of the risk assessment, the calculated value of the hidden danger risk matrix is ​​corrected, and the corrected value of the hidden danger risk matrix is ​​used as the final landslide hidden danger risk level value.

[0038] The risk assessment of landslide hazards, in addition to comprehensively considering the degree of InSAR deformation, the size of the hazard, and the potential loss of the affected body, also needs to take into account the timeliness, accuracy, and volatility of multi-source remote sensing data to reflect the advantages of dynamic assessment results. Specifically, the corrected hazard risk matrix is ​​calculated using the second formula, which is: in, This indicates that the revised hazard risk matrix value represents the final landslide hazard risk level value. Indicates: Risk assessment timeliness factor, used to characterize the time interval between the acquisition time of multi-source remote sensing data and the current landslide hazard risk assessment time. ,in, This indicates the time interval between the acquisition time of multi-source remote sensing data and the current landslide hazard risk assessment time, expressed in months. Indicates: Risk assessment timeliness factor, used to characterize the time interval between the current landslide hazard risk assessment and the previous landslide hazard risk assessment. ,in, This indicates the time interval between the current landslide hazard risk assessment and the previous landslide hazard risk assessment, expressed in days. If this is the first time a landslide hazard risk assessment has been conducted... , Indicates: Critical interval threshold, 15 days during the flood season and 30 days during the non-flood season; This indicates the volatility factor in risk assessment, used to characterize the relative volatility of the deformation value used in the current landslide hazard risk assessment compared to historical deformation values. , The standard deviation of the deformation rate of the deformation degree value used in the current landslide hazard risk assessment and the deformation rate of the deformation degree value used in the previous current landslide hazard risk assessment is expressed in mm / a. μ represents the average deformation rate within the landslide hazard area, expressed in mm / a. , , and All are dimensionless values.

[0039] Optionally, in the above technical solution, the process of obtaining the weight coefficient corresponding to each landslide hazard factor includes: S010. Based on the on-site verification records in the historical landslide disaster database, obtain sample data including each landslide hazard factor; S011. Based on all sample data, perform a global sensitivity analysis on the three-dimensional dynamic matrix to obtain the weight coefficients corresponding to each landslide hazard factor.

[0040] To avoid the subjective influence of weighting coefficients, it is necessary to obtain on-site verification records covering deformation degree, hazard scale, and potential loss of the affected body from a historical landslide hazard database (including successfully identified geological hazard hazard points). Based on this, a sample dataset including sample data for each landslide hazard factor is constructed. Subsequently, based on all sample data, a Global Sensitivity Analysis (GSA) method is performed on the three-dimensional dynamic matrix to determine the weighting coefficients corresponding to each landslide hazard factor.

[0041] Specifically, using the GSA method and combining known landslide hazard results, the calculated values ​​of the hazard risk matrix are systematically evaluated across three dimensions—deformation degree, hazard scale, and potential loss of the affected body—within the range of weight parameter variations. The influence of spatial variability is mitigated, thus making the determination of weight coefficients more realistic. This process employs the Least Angle Regression (LARS) algorithm based on variance for efficient computation. The LARS algorithm decomposes the regression target vector into a linear combination of multiple feature vectors and aims to minimize the residual vector that is linearly independent of all features, making it particularly suitable for scenarios where the feature dimension is higher than the sample size.

[0042] Based on sample data, the variance contribution values ​​of deformation degree, hazard scale, and potential loss of the disaster-bearing body are calculated using the LARS algorithm, and the variance contribution value of deformation degree is used as the weighting coefficient. The variance contribution value of the potential hazard scale is used as the weighting coefficient. The variance contribution value of the potential loss of the disaster-bearing body is used as the weighting coefficient. Specifically, the calculation is performed using the third formula, which is: The symbols in the third formula are explained as follows: ① The calculated comprehensive sensitivity weight value represents the overall sensitivity output of the three-dimensional dynamic matrix. It is determined by the weight coefficients of three dimensions: deformation degree, hazard scale, and potential loss of the affected body. It is a dimensionless scalar value. By decomposing the variance using this value, the impact of each dimension on landslide hazard risk can be quantified.

[0043] ② For constant terms, it means The mean of , in global sensitivity analysis, represents the expected or average value of the comprehensive sensitivity weights. It is a dimensionless constant used for offset calculation.

[0044] ③ The number of sample data points represents the total number of sample data points obtained from the historical landslide disaster database (i.e., data points recorded during on-site verification), and is a positive integer. Each sample data point contains specific quantitative values ​​for the degree of deformation, the scale of the hazard, and the potential loss of the affected body.

[0045] ④ , and Both are sample index variables. Both are integers, ranging from 1 to n, used to iterate through all samples. Sample data used for indexing the degree of deformation; Used to index sample data related to the scale of potential risks; Used to index sample data related to potential losses of disaster-bearing entities.

[0046] ⑤ For the first The weighting coefficient value of the deformation degree of a sample data represents the weighting coefficient of the deformation degree dimension (dimensionless), corresponding to the deformation degree value of that sample; in global sensitivity analysis, it is used as an input variable to participate in the calculation of main effects and interaction effects.

[0047] ⑥ For the first The weighting coefficients for the hazard scale of each sample data point. These represent the dimensionless weighting coefficients for the hazard scale dimension, corresponding to the hazard scale level value for that sample, and are used as input variables in calculating the main effect and interaction effect. ⑦ For the first The potential loss weight coefficient value of each sample data represents the weight coefficient (dimensionless) of the potential loss dimension of the disaster-bearing body, which corresponds to the hidden danger loss level value of the sample and is used as an input variable to participate in the calculation of the main effect and interaction effect. ⑧ Let be the main effect function of the deformation degree dimension, indicating that only the th is considered. Weighting coefficient for the degree of deformation of each sample data The function calculates the contribution of time to the overall sensitivity weight value and outputs it as a dimensionless scalar. This function quantifies the independent influence of the deformation degree dimension.

[0048] 9 Let be the interaction function between the degree of deformation and the scale of the hidden danger, representing the simultaneous consideration of the first _____. Weighting coefficient for the degree of deformation of each sample data and the The risk scale weighting coefficient value of each sample data The function calculates the contribution of time to the overall sensitivity weight value and outputs it as a dimensionless scalar. This function quantifies the interaction between two dimensions (e.g., the coupling effect between the degree of deformation and the scale of the hazard).

[0049] ⑩ Let be the third-order interaction effect function of the degree of deformation, the scale of the hidden danger, and the potential loss of the disaster-bearing body, representing the simultaneous consideration of the third... Weighting coefficient for the degree of deformation of each sample data , No. The risk scale weighting coefficient value of each sample data , No. The potential loss weighting coefficient of each sample data point The function calculates the contribution of time to the overall sensitivity weight value and outputs it as a dimensionless scalar. This function quantifies the overall interaction effect among the three dimensions.

[0050] Optionally, in the above technical solution, all landslide hazard factors include the degree of deformation, and the quantitative value of the degree of deformation is the deformation degree value. The process of obtaining the deformation degree value includes: S10. Obtain the deformation rate value of the preset area using multi-resolution InSAR cross-interferometry and Stacking-InSAR method; The S100 and LT-1 satellites can provide five strip imaging modes and one scanning imaging mode, possessing high resolution, full polarization, and medium-to-large coverage capabilities. Using the LT-1 satellite's dual-satellite follow-fly mode, surface deformation interferometry data for a preset area were acquired. Priority was given to using its 3m resolution strip mode 1 data and 6m resolution strip mode 2 data to increase the number of interferometric image pairs and improve the accuracy of deformation feature extraction.

[0051] Multi-resolution InSAR cross-interferometry was used to process surface deformation interferometric measurement data (3m resolution strip mode 1 data and 6m resolution strip mode 2 data) for each landslide hazard within a pre-defined area. Specifically, mode 1 data at a certain moment was used as the master image. With the assistance of precise orbit and COP-DEM data, other mode 1 and mode 2 images were registered to the master image coordinate system to reduce phase aliasing. Multiple interferometric image pairs with time intervals of less than 60 days were selected to form an interferometric dataset, and differential interferometric processing was performed to generate multiple differential interferograms.

[0052] Then, each difference interferogram is processed as follows: The Goldstein-Werner filtering method was used for filtering; the minimum cost flow algorithm (MCF) was used for spatial phase unwrapping; and the low-frequency phase of the interference was removed by polynomial fitting trend surface estimation, finally obtaining multiple differential interferograms after removing the low-frequency phase of the interference.

[0053] S101. The Stacking-InSAR method is used to perform weighted stacking of all differential interferograms after removing the low-frequency phase of the interference, and the linear deformation rate is estimated. Assuming that the surface deformation is a linear process, the linear deformation rate is calculated using the fourth formula, which is: In the fourth formula, Linear deformation rate, unit: mm / a. For the first Phase of the differential interferogram after removing the low-frequency phase of the interference. For the first The time interval between each pair of interferometric images. Indicates the number of interference pairs.

[0054] To eliminate the influence of outliers, a box plot method was used to remove them. Values ​​less than Q1-1.5×IQR or Q3+1.5×IQR are used, and a probability distribution function (CDF) is introduced to calculate the deformation rate in a data-driven manner. Specifically, it is calculated using the fifth formula, which is: In the fifth formula, for The corresponding cumulative distribution function value naturally lies in the interval [0,1]. Represents: Deformation rate variable Not greater than The probability, This represents the effective number of potential landslide hazards.

[0055] S11. Calculate the deformation area value of the preset region; The quantitative values ​​of deformation characteristics that determine the risk of landslide hazards, besides the deformation rate value... There is also the value of the area of ​​deformation. Deformation area value This refers to the probability value of the existence of a deformed area within a single landslide hazard area, and the degree of deformation area. It is also calculated based on the probability distribution function, specifically: From each differential interferogram after removing the low-frequency phase interference, extract the area percentage of the deformation region within each landslide hazard range. This value is the ratio of the area of ​​the deformed region to the total area of ​​the potential landslide hazard. Then, the degree of deformation area is calculated using the sixth formula. The sixth formula is: in, express: The probability distribution function value; This indicates that the random variable S representing the proportion of the deformed region's area is less than or equal to... The probability of. Indicates: Satisfying Sample size; Deformation area value Naturally falling within the [0,1] interval, it is directly used for subsequent deformation degree values. The calculation.

[0056] S12. Based on the deformation rate value and the deformation area value of the preset area, the deformation degree value is calculated. This value can be obtained using the geometric mean method, specifically formula number seven. Formula number seven is: Optionally, in the above technical solution, all landslide hazard factors include hazard scale, and the quantitative value of hazard scale is the hazard scale level value. The process of obtaining the hazard scale level value includes: S20. Based on images of a preset area collected by satellite, the boundaries of landslide hazards are delineated. The area of ​​the landslide hazard enclosed by the boundaries of the landslide hazard is not less than the minimum identifiable area, which is calculated based on the satellite spatial resolution. InSAR deformation features provide important quantitative data on landslide risk, but the deformation area they reveal is not entirely equivalent to the landslide hazard boundary, as the deformation area may only reflect local deformation rather than the overall hazard extent. Therefore, it is necessary to combine high-resolution optical satellite imagery to comprehensively determine the boundary and scale of landslide hazards, and to accurately delineate the hazard outline through visual interpretation or machine learning algorithms.

[0057] In the process of delineating the boundaries of potential landslide hazards, the enclosed area of ​​the delineated landslide hazard boundaries must be no less than the minimum identifiable area. . The spatial resolution of the optical satellite used determines the accuracy of boundary identification to ensure that the technical feasibility requirements are met. This is specifically calculated using the eighth formula: In the eighth formula, The smallest identifiable landslide hazard area, expressed in m². This refers to the spatial resolution of optical satellites, measured in meters (m), which is the ground size corresponding to a single satellite pixel.

[0058] S21. Based on the area of ​​the landslide hazard, the random forest regression algorithm model is used, combined with the regional information of the preset area, to calculate the volume of the landslide hazard. The risk of landslide hazards is directly related to their size. Given the complex geological environments of some regions, the characteristics of landslide hazards vary significantly, mainly in terms of regional type, source material characteristics, disaster formation patterns, and source material thickness. Therefore, to accurately estimate the volume of potential landslide hazards... (Unit: m³) This application employs a random forest regression algorithm model. This model is implemented using machine learning training based on a historical landslide hazard sample database. Multiple decision trees are constructed to model the data, and the prediction results from each tree are integrated to improve the reliability of the estimation. To adapt to regional differences, two types of decision trees are independently constructed: the first is a regional zoning type decision tree, and the second is a source-disaster model decision tree. The regional zoning type decision tree is used to adapt to regional geological conditions that significantly influence landslide movement, while the source-disaster model decision tree is used to adapt to different source types (such as rock mass and loess) and disaster mechanisms (such as overall sliding and shallow sliding).

[0059] Each decision tree is trained independently using randomly selected subsamples, effectively reducing the risk of overfitting and ensuring the relative reliability of the final results. The regression calculation results are represented by the ninth formula, which is: In the ninth formula, The volume of the landslide hazard is estimated based on the area of ​​the hazard, and the unit is m. 3 , The area of ​​the potential landslide is expressed in m². For regional correction factors, For the source-disaster model coefficient, This is the regional correction index, dimensionless. The values ​​of each coefficient are shown in Tables 1 and 2.

[0060] Table 1: Table 2: S22. Determine the level of the landslide hazard volume according to the landslide scale classification standard; After obtaining the estimated volume of the landslide hazard Subsequently, the classification level of the landslide hazard volume is determined with reference to the current landslide disaster scale classification standards. The current landslide disaster scale classification standards are as follows: less than 10,000 m³ is small, 10,000 to 100,000 m³ is medium, 100,000 to 1,000,000 m³ is large, and more than 1,000,000 m³ is extra-large.

[0061] S23. Based on the classification of the landslide hazard volume and using the hierarchical risk scoring method, determine the hazard scale level value, specifically calculated using the tenth formula: In the tenth formula, The standard reference value is the value corresponding to the level of the landslide hazard volume. The standard reference value is 0 for small, 0.25 for medium, 0.5 for large, and 0.75 for extra-large. For the range of scale levels, the tenth formula takes a fixed value of 0.25. This indicates that the volume of landslide hazards is sorted from smallest to largest within the corresponding grade range, starting from 1; N is the total number of valid hazard samples within the current grade range, which needs to be dynamically obtained from the historical landslide disaster database.

[0062] Optionally, in the above technical solution, all landslide hazard factors include the potential loss of the affected body. The quantitative value of the potential loss of the affected body is the hazard loss level value, and the process of obtaining the hazard loss level value includes: S30. Fitting the potential horizontal sliding distance of landslide hazards in a preset area based on a data-driven algorithm; According to the definition of geological hazards, landslide hazard risk assessment must include potential losses, requiring a comprehensive assessment of the loss of life and property within the hazard's slope and the area affected by the landslide. Traditional methods for estimating potential landslide distances based on expert experience, while having some applicability, are highly subjective and limited in sample analysis. To address this issue, this solution employs a data-driven algorithm based on a hazard sample database, using the potential horizontal slip distance L as the response variable, and considering the landslide height... , volume of potential hazards Factors such as the average slope θ were fitted and estimated. To ensure the reliability of the data methods, three models—Artificial Neural Networks (ANN), Chi-square Automatic Interaction Detection (CHAID), and Generalized Linear Models (GLM)—were used for simultaneous simulation. Sensitivity analysis of the importance of each influencing factor in the selected data-driven models was performed using covariate permutation, and the coefficient of determination R-squared was used to determine the impact. 2 The reliability of the data-driven model results is determined by two metrics: the coefficient of determination and the mean absolute error (MAE). The final fitting formula is Formula Eleven, which is as follows: In the eleventh formula, This represents the potential horizontal distance of a landslide hazard, expressed in meters. The height of the potential landslide is in meters (m). The average slope of the potential landslide is expressed in degrees. The value of is the sliding constant, which is determined according to the type of landslide hazard, as shown in Table 3.

[0063] Table 3: S31. Normalize the potential horizontal sliding distance of the landslide hazard to obtain the hazard loss level value.

[0064] The potential horizontal sliding distance of the landslide hazard is normalized to obtain the hazard loss level value, which is specifically calculated using the twelfth formula: in, This indicates that the potential horizontal sliding distance of landslide hazards has been normalized.

[0065] Optionally, the above technical solution also includes: S021. When the preset area is in the flood season, the maximum quantitative value among all landslide hazard factors shall be taken as the final landslide hazard risk level value of the preset area.

[0066] Given the impact of global climate anomalies in recent years, extreme weather events are frequent during the flood season, with widespread and extreme rainfall in many areas, which can easily trigger landslides. While the weighted assessment method using three dimensions—InSAR deformation degree, hazard size, and potential loss of the affected body—ensures overall accuracy, it may also result in extremely high values ​​in a single dimension not being reflected in the final risk level assessment. For example, a landslide with a deformation degree rating of 0.9 might have a final hazard risk level of 0.7.

[0067] To avoid the aforementioned situations, risk assessments during the flood season should focus on the dimension of the greatest potential hazard risk, using the most unfavorable factors as the basis for assessment. The assessment model should be specifically enhanced to strengthen its sensitivity to sudden and extreme indicators. Therefore, based on the proposed "Landslide Hazard Risk Assessment Method Based on a Three-Dimensional Dynamic Matrix," an extreme value-dominated model for use during the flood season is proposed, specifically Formula Thirteen: In the thirteenth formula, The final landslide hazard risk level value when the preset area is in the flood season. Indicates taking , and The maximum quantization value in.

[0068] This paper proposes a landslide hazard risk assessment method based on a three-dimensional dynamic matrix for risk assessment in the comprehensive remote sensing identification stage of landslide hazards. Compared with the traditional two-dimensional qualitative method, the three-dimensional quantitative judgment has more advantages. Secondly, considering the dynamic changes of landslide hazards, based on the advantages of remote sensing technology, factors such as timeliness, timeliness, and volatility are introduced to further highlight the dynamic adjustment and updating of risk assessment results. Thirdly, in response to the assessment needs under extreme weather conditions during the flood season, an extreme value-dominated model is proposed based on the aforementioned methods to minimize the possibility of misjudgment of hazard risks.

[0069] Although the steps have been numbered in the above embodiments, they are only specific embodiments given by the present invention. Those skilled in the art can adjust the execution order of the steps according to the actual situation, which is also within the protection scope of the present invention. It can be understood that some embodiments may include some or all of the above embodiments.

[0070] like Figure 2 As shown, an embodiment of the present invention provides a landslide hazard risk assessment system 200 based on a three-dimensional dynamic matrix, which includes a calculation module 201 and a landslide hazard risk assessment module 202. The calculation module 201 is used to: when the preset area is in the non-flood season, calculate the risk matrix calculation value of the preset area based on the three-dimensional dynamic matrix including the weight coefficients corresponding to each landslide hazard factor and the quantitative value of each landslide hazard factor, and use the calculated risk matrix calculation value as the final landslide hazard risk level value. The landslide hazard risk assessment module 202 is used to: conduct a landslide hazard risk assessment on a preset area based on the final landslide hazard risk level value, and obtain the landslide hazard risk assessment result for the preset area.

[0071] Optionally, the above technical solution also includes a correction module, which is used to: correct the calculated value of the hidden danger risk matrix based on the current situation factor, the timeliness factor, and the volatility factor of the risk assessment, and use the corrected calculated value of the hidden danger risk matrix as the final landslide hidden danger risk level value.

[0072] Optionally, the above technical solution further includes a weight coefficient acquisition module, which is used for: Based on on-site verification records from the historical landslide disaster database, sample data including each landslide hazard factor was obtained; Based on all sample data, a global sensitivity analysis was performed on the three-dimensional dynamic matrix to obtain the weight coefficients corresponding to each landslide hazard factor.

[0073] Optionally, the above technical solution also includes a deformation degree value acquisition module. All landslide hazard factors include deformation degree, and the quantified value of deformation degree is the deformation degree value. The deformation degree value acquisition module is used for: The deformation rate of a preset region is obtained using multi-resolution InSAR cross-interferometry and the Stacking-InSAR method. Calculate the deformation area of ​​the preset region; The deformation degree value is calculated based on the deformation rate degree value and the deformation area degree value of the preset area.

[0074] Optionally, the above technical solution also includes: a hazard scale level value acquisition module, wherein all landslide hazard factors include hazard scale, the quantified value of hazard scale is the hazard scale level value, and the hazard scale level value acquisition module is used for: Based on images of a pre-defined area collected by satellite, the boundaries of landslide hazards are delineated. The area of ​​the landslide hazard enclosed by the boundaries of the landslide hazard is not less than the minimum identifiable area, which is calculated based on the satellite spatial resolution. Based on the area of ​​potential landslide hazards, a random forest regression algorithm model is used, combined with the regional information of the preset area, to calculate the volume of potential landslide hazards; The classification level of the landslide hazard volume is determined according to the landslide scale classification standard; Based on the classification of the landslide hazard volume, and using the hierarchical risk scoring method, the hazard scale level value is determined.

[0075] Optionally, the above technical solution also includes a hazard loss level value acquisition module. All landslide hazard factors include the potential loss of the affected body. The quantified value of the potential loss of the affected body is the hazard loss level value. The hazard loss level value acquisition module is used for: The potential horizontal sliding distance of landslide hazards in a preset area is fitted based on a data-driven algorithm. The potential horizontal sliding distance of the landslide hazard is normalized to obtain the hazard loss level value.

[0076] Optionally, the above technical solution also includes a determining module, which is used to: when the preset area is in the flood season, take the maximum quantitative value among all landslide hazard factors as the final landslide hazard risk level value of the preset area.

[0077] It should be noted that the beneficial effects of the landslide hazard risk assessment system 200 based on a three-dimensional dynamic matrix provided in the above embodiments are the same as those of the landslide hazard risk assessment method based on a three-dimensional dynamic matrix, and will not be repeated here. Furthermore, the system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, and will not be repeated here.

[0078] The landslide hazard risk assessment system based on a three-dimensional dynamic matrix of the present invention can be a computer program (including program code) running on a computer device. For example, the landslide hazard risk assessment system based on a three-dimensional dynamic matrix of the present invention is an application software that can be used to execute the corresponding steps in the landslide hazard risk assessment method based on a three-dimensional dynamic matrix of the present invention.

[0079] In some embodiments, the landslide hazard risk assessment system based on a three-dimensional dynamic matrix of the present invention can be implemented in a combination of hardware and software. As an example, the landslide hazard risk assessment system based on a three-dimensional dynamic matrix of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the landslide hazard risk assessment method based on a three-dimensional dynamic matrix of the present invention. For example, the processor in the form of a hardware decoding processor can be one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0080] The modules described in the embodiments of this invention can be implemented in software or hardware. The names of the modules are not, in some cases, limiting the scope of the module itself.

[0081] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned landslide hazard risk assessment methods based on a three-dimensional dynamic matrix. That is, an electronic device according to an embodiment of the present invention may include, but is not limited to: a processor and a memory; the memory is used to store the computer program; the processor is used to execute the landslide hazard risk assessment method based on a three-dimensional dynamic matrix shown in any embodiment of the present invention by calling the computer program.

[0082] In one alternative embodiment, an electronic device is provided, such as Figure 3 As shown, Figure 3 The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0083] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0084] Bus 4002 may include a path for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The bus 4002 is represented by only one thick line, but this does not mean that there is only one bus or one type of bus.

[0085] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0086] The memory 4003 stores application code (computer program) for executing the present invention, and its execution is controlled by the processor 4001. The processor 4001 executes the application code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0087] Among them, electronic devices can also be terminal devices, which can be any device that can install applications, including at least one of smartphones, tablets, laptops, desktop computers, smart speakers, smartwatches, smart TVs, and smart in-vehicle devices.

[0088] It should be noted that, Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0089] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-mentioned landslide hazard risk assessment methods based on a three-dimensional dynamic matrix.

[0090] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.

[0091] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform any of the aforementioned landslide hazard risk assessment methods based on a three-dimensional dynamic matrix.

[0092] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0093] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0094] The computer-readable storage medium provided in this invention can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EEPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0095] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.

[0096] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0097] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0098] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this invention can be specifically implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0099] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A landslide hazard risk assessment method based on a three-dimensional dynamic matrix, characterized in that, include: When the preset area is in the non-flood season, the calculated value of the hidden danger risk matrix of the preset area is obtained based on the three-dimensional dynamic matrix including the weight coefficients corresponding to each landslide hidden danger factor and the quantitative value of each landslide hidden danger factor. The calculated value of the hidden danger risk matrix is ​​used as the final landslide hidden danger risk level value. The three-dimensional dynamic matrix is ​​dynamically updated in the time dimension as the quantitative value of each hidden danger factor changes. Based on the final landslide hazard risk level value, a landslide hazard risk assessment is conducted on the preset area to obtain the landslide hazard risk assessment result for the preset area; Among them, all landslide hazard factors include: deformation degree, hazard scale, and potential loss of the disaster-bearing body. The construction process of the three-dimensional dynamic matrix includes: Based on on-site verification records in the historical landslide disaster database, sample data were obtained including three hazard factors: the degree of deformation, the scale of the hazard, and the potential loss of the disaster-bearing body. Based on all the sample data, a global sensitivity analysis was performed to obtain the weight coefficients corresponding to the degree of deformation, the scale of the hidden danger, and the potential loss of the disaster-bearing body. Based on the obtained weight coefficients corresponding to the degree of deformation, the scale of the hidden danger, and the potential loss of the disaster-bearing body, a three-dimensional dynamic matrix including the weight coefficients corresponding to each landslide hidden danger factor is constructed.

2. The landslide hazard risk assessment method based on a three-dimensional dynamic matrix according to claim 1, characterized in that, Also includes: Based on the current situation factor, the timeliness factor, and the volatility factor of the risk assessment, the calculated value of the hidden danger risk matrix is ​​corrected, and the corrected calculated value of the hidden danger risk matrix is ​​used as the final landslide hidden danger risk level value.

3. A landslide hazard risk assessment method based on a three-dimensional dynamic matrix according to claim 1 or 2, characterized in that, The quantification value of the degree of deformation is the degree of deformation value, and the process of obtaining the degree of deformation value includes: The deformation rate of the preset region is obtained using multi-resolution InSAR cross-interferometry and the Stacking-InSAR method. Calculate the deformation area value of the preset region; The deformation degree value is calculated based on the deformation rate degree value and the deformation area degree value of the preset region.

4. A landslide hazard risk assessment method based on a three-dimensional dynamic matrix according to claim 1 or 2, characterized in that, The quantitative value of the hazard scale is the hazard scale level value. The process of obtaining the hazard scale level value includes: Based on images of a pre-defined area collected by satellite, the boundaries of landslide hazards are delineated. The area of ​​the landslide hazard enclosed by the boundaries of the landslide hazard is not less than the minimum identifiable area, which is calculated based on the satellite spatial resolution. Based on the area of ​​the landslide hazard, the volume of the landslide hazard is calculated using a random forest regression algorithm model, combined with the geographical information of the preset area. The classification level of the landslide hazard volume is determined according to the landslide scale classification standard; Based on the classification of the landslide hazard volume, and using the hierarchical risk scoring method, the hazard scale level value is determined.

5. A landslide hazard risk assessment method based on a three-dimensional dynamic matrix according to claim 1 or 2, characterized in that, The quantification value of the potential loss of a disaster-bearing body is the hazard loss level value, and the process of obtaining the hazard loss level value includes: The potential horizontal sliding distance of the landslide hazard in the preset area is fitted based on a data-driven algorithm. The potential horizontal sliding distance of the landslide hazard is normalized to obtain the hazard loss level value.

6. The landslide hazard risk assessment method based on a three-dimensional dynamic matrix according to claim 1, characterized in that, Also includes: When the preset area is in the flood season, the maximum quantified value among all landslide hazard factors is taken as the final landslide hazard risk level value of the preset area.

7. A landslide hazard risk assessment system based on a three-dimensional dynamic matrix, characterized in that, Includes a calculation module and a landslide hazard risk assessment module; The calculation module is used to: when the preset area is in a non-flood season, calculate the risk matrix calculation value of the preset area based on a three-dimensional dynamic matrix including the weight coefficients corresponding to each landslide hazard factor and the quantified value of each landslide hazard factor, and use the calculated risk matrix calculation value as the final landslide hazard risk level value, and the three-dimensional dynamic matrix is ​​dynamically updated in the time dimension as the quantified value of each hazard factor changes. The landslide hazard risk assessment module is used to: assess the landslide hazard risk of the preset area based on the final landslide hazard risk level value, and obtain the landslide hazard risk assessment result of the preset area; Among them, all landslide hazard factors include: deformation degree, hazard scale, and potential loss of the disaster-bearing body. The construction process of the three-dimensional dynamic matrix includes: Based on on-site verification records in the historical landslide disaster database, sample data were obtained including three hazard factors: the degree of deformation, the scale of the hazard, and the potential loss of the disaster-bearing body. Based on all the sample data, a global sensitivity analysis was performed to obtain the weight coefficients corresponding to the degree of deformation, the scale of the hidden danger, and the potential loss of the disaster-bearing body. Based on the obtained weight coefficients corresponding to the degree of deformation, the scale of the hidden danger, and the potential loss of the disaster-bearing body, a three-dimensional dynamic matrix including the weight coefficients corresponding to each landslide hidden danger factor is constructed.

8. An electronic device, characterized in that, The invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the landslide hazard risk assessment method based on a three-dimensional dynamic matrix as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the landslide hazard risk assessment method based on a three-dimensional dynamic matrix as described in any one of claims 1 to 6.

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