Radar data driven quantitative analysis method for evolution process of landslide risk area

Abstract

The invention provides a radar data-driven quantitative analysis method for an evolution process of a landslide risk area, and the method comprises the steps: dividing a radar image through an iterative grid, and compressing the data in the radar image; establishing a quantitative relationship between the compressed radar data and the landslide risk by using a breadth-first search model, and partitioning the landslide risk in the radar image; deducing the evolution process of the risk area according to the landslide risk partition, and quantifying the catastrophe probability of the risk area by using the convergence rate and the occurrence rate; and fusing the convergence rate and the occurrence rate with catastrophe risk evaluation indexes to realize objective calculation of the catastrophe probability. According to the invention, through radar monitoring data, a potential high landslide risk area is revealed, a scientific basis is provided for prevention and response of landslide disasters, and casualties and economic losses caused by the disasters can be reduced.

Description

Technical Field

[0001] The present invention relates to the field of disaster early warning technology, and in particular to a radar data-driven quantitative analysis method for the evolution process of a landslide risk area. Background Art

[0002] Landslide is a deformation and destruction process and phenomenon of slope rock mass under the action of gravity and external factors (such as rainfall, blasting, etc.). Before the disaster occurs, the slope rock mass often exhibits abnormal mechanical behavior. The use of multi-source sensing equipment such as GNSS, radar, stress gauges, and microseismic to monitor and analyze the mechanical response of the rock mass in real time is considered to be one of the most promising methods for landslide prediction and early warning. In the slope rock mass displacement monitoring technology, traditional point monitoring technologies such as GNSS and total stations have high accuracy, but it is difficult to achieve large-scale, long-term real-time monitoring. At the same time, due to the harshness of the mining environment, the layout of equipment has also brought severe challenges. The existence of these unfavorable factors has, to a certain extent, limited the research and development of landslide risk prediction and early warning technology. As an emerging slope rock mass deformation monitoring technology in recent years, radar has broken through the limitations of traditional point monitoring methods in time and space dimensions and has gradually become the mainstream of slope monitoring. Studies have shown that landslide risk areas often occupy a small part of the radar monitoring range and may show spatial discontinuity. Accurate identification and division of risk areas is an important way to achieve landslide risk control and improve production efficiency. Summary of the invention

[0003] According to the technical problems raised above, a method for quantitative analysis of the evolution process of landslide risk areas driven by radar data is provided. The present invention mainly utilizes the method for quantitative analysis of the evolution process of landslide risk areas driven by radar data, aiming to reveal potential high landslide risk areas through radar monitoring data, provide a scientific basis for the prevention and response of landslide disasters, and help reduce casualties and economic losses caused by disasters.

[0004] The technical means adopted by the present invention are as follows:

[0005] A radar data-driven quantitative analysis method for the evolution of landslide risk areas, including:

[0006] The radar image is divided into grids iteratively to compress the data in the radar image;

[0007] Using the breadth-first search model, a quantitative relationship between compressed radar data and landslide risk is established to partition the landslide risk in radar images.

[0008] The evolution of risk areas is deduced based on landslide risk zoning, and the probability of disasters in risk areas is quantified using convergence rate and occurrence rate.

[0009] The convergence rate and occurrence rate are integrated with disaster risk assessment indicators to achieve objective calculation of disaster probability.

[0010] Furthermore, the step of iteratively gridding the radar image to compress the data in the radar image specifically includes:

[0011] Each pixel point of the radar image is regarded as an initial grid, in which the displacement monitoring value is recorded; the radar monitoring range is divided into iterative grids, and the range Δd of the displacement monitoring values ​​in all initial grids in each iterative grid is calculated respectively. The range is used to represent the degree of uniformity of rock mass deformation in the iterative grid, and the range is compared with the set threshold d c Comparison is made to determine whether the rock deformation within the initial grid is consistent.

[0012] Furthermore, the determining whether the rock mass deformation in the initial grid is tending to be consistent specifically includes:

[0013] When the range Δd>d c When , it means that the rock mass deformation in the iterative grid is inconsistent, the current iterative grid is re-divided, a second iteration is performed, and the corresponding range Δd is calculated;

[0014] When the range Δd≤d in the iteration grid c Or when the iterative grid size is less than or equal to the initial grid size, it means that the rock mass deformation in the iterative grid is consistent and the iterative grid division is completed;

[0015] The initial grid in the radar image is iterated multiple times and divided into iterative grids to achieve data compression.

[0016] Furthermore, the use of the breadth-first search model to establish a quantitative relationship between the compressed radar data and the landslide risk specifically includes:

[0017] According to the compressed radar data, the improved tangent angle is calculated:

[0018]

[0019] Among them, ΔS(i) is the displacement change, v is the deformation rate in the constant creep stage, T(i) is the vertical axis value with the same dimension as time after the change, t i is a monitoring moment, α i To improve the tangent angle;

[0020] Will improve the tangent angle α i Transformed into landslide risk value Rα t :

[0021]

[0022] Among them, αt represents the improved tangent angle at time t;

[0023] The risk attenuation coefficient β and risk enhancement coefficient θ are introduced to correct the landslide risk:

[0024]

[0025] Among them, R'α t represents the corrected landslide risk, and t represents time.

[0026] Using the eight-neighborhood breadth-first traversal search algorithm, we circle R'α t > 0, considering the situation where there are two risk areas with close distances, a risk area merging distance threshold d is set, and the initially determined risk areas are merged for the second time.

[0027] Furthermore, the quantification of the probability of disaster in the risk area by using the convergence rate and the occurrence rate specifically includes:

[0028] The risk area set is represented as Represents the collection of micro-element location information within the risk area, n t is the number of risk areas at time t, m i,t represents the number of micro-elements in the risk area at time t in area i; introduces the boundary extension index e to expand the risk area; the risk area set at time t-1 is expressed as Potential risk areas for The result after expansion using the breadth-first algorithm according to the boundary expansion index;

[0029] For those located in potential risk areas All risk areas within Development and convergence behavior and risk areas The formation of

[0030]

[0031] For risk areas All risk areas within Development and convergence as risk areas The basis of formation,

[0032]

[0033] The risk area convergence rate is expressed as Indicates potential risk areas The convergence of the inner risk area is calculated as follows:

[0034]

[0035] The development rate of the risk area is expressed as Indicates potential risk areas The development of the internal risk area is calculated as follows:

[0036]

[0037] Will and Reconstructed within the monitoring scope to represent the evolution of risk in the time dimension, with Rc t and Rp t Represents the reconstructed risk assessment matrix, and calculates the convergence rate R'c considering the risk path dependence effect t and the development rate matrix R'p t :

[0038]

[0039]

[0040] Among them, β is the risk attenuation coefficient and θ is the risk intensification coefficient.

[0041] Furthermore, the convergence rate and occurrence rate are integrated with the disaster risk assessment index to achieve objective calculation of the disaster probability, specifically including:

[0042] The convergence rate R'c t , development rate R'p t And the disaster risk assessment index R'α used to correct the inverse velocity t Fusion, to achieve objective calculation of disaster probability, the calculation method is as follows:

[0043] P t =w 1 R'α t +w 2 R'c t +w 3 R t

[0044] Among them, P t represents the probability of landslide disaster, w 1 represents the weight of landslide risk calculated based on the improved tangent angle, w 2 represents the weight of landslide risk calculated based on the convergence rate, w 3 Represents the weight used to calculate landslide risk based on development rate.

[0045] The present invention also includes a storage medium, which includes a stored program, wherein when the program is run, the radar data-driven quantitative analysis method for the evolution process of the landslide risk area is executed.

[0046] The present invention also includes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the radar data-driven quantitative analysis method for the evolution process of the landslide risk area through the computer program.

[0047] Compared with the prior art, the present invention has the following advantages:

[0048] The radar data-driven quantitative analysis method for the evolution process of landslide risk areas provided by the present invention compresses the data in the radar image by iteratively gridding the radar image; establishes a quantitative relationship between the compressed radar data and the landslide risk by using a breadth-first search model, and divides the landslide risk in the radar image into zones; deduces the evolution process of the risk zone according to the landslide risk zones, and quantifies the catastrophic probability of the risk zone by using the convergence rate and the occurrence rate; integrates the convergence rate and the occurrence rate with the catastrophic risk evaluation index to achieve an objective calculation of the catastrophic probability. The present invention reveals potential high landslide risk areas through radar monitoring data, provides a scientific basis for the prevention and response to landslide disasters, and helps to reduce casualties and economic losses caused by disasters.

[0049] Based on the above reasons, the present invention can be widely promoted in the fields of disaster warning and the like. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0051] Figure 1 The invention provides a quantitative analysis method for the evolution process of landslide risk areas driven by radar data.

[0052] Figure 2 It is a schematic diagram of the data compression method in the present invention.

[0053] Figure 3 This is a diagram showing the data compression effect in an embodiment of the present invention.

[0054] Figure 4 This is a schematic diagram of the risk area delineation method in the present invention.

[0055] Figure 5This is a risk area delineation effect diagram in an embodiment of the present invention.

[0056] Figure 6 Schematic diagram of the spatial distribution of various types of risk areas in the present invention.

[0057] Figure 7 It is the characteristics of risk area convergence, development and disaster probability evolution during the landslide incubation process in the embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0059] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0060] like Figure 1 As shown, the present invention provides a radar data-driven quantitative analysis method for the evolution process of landslide risk areas, comprising:

[0061] The radar image is divided into grids iteratively to compress the data in the radar image.

[0062] In implementation, the radar data compression method based on quadtree theory aims to reduce the amount of radar data and improve the efficiency of data processing. The specific idea is to refer to the adaptive meshing idea in finite element simulation and transform the data compression problem into a meshing problem that satisfies the deformation gradient constraint. The compression process is shown in the figure below. Figure 2 shown.

[0063] In specific implementation, as a preferred embodiment of the present invention, the radar image is divided into grids iteratively to compress the data in the radar image, specifically including:

[0064] Each pixel point of the radar image is regarded as an initial grid, in which the displacement monitoring value is recorded; the radar monitoring range is divided into four equal grids, called iterative grids, and the range Δd of the displacement monitoring values ​​in all initial grids in each iterative grid is calculated respectively. The range is used to represent the degree of uniformity of rock mass deformation in the iterative grid, and the range is compared with the set threshold d c Comparison is made to determine whether the rock deformation within the initial grid is consistent.

[0065] In specific implementation, as a preferred embodiment of the present invention, the determination of whether the rock mass deformation in the initial grid tends to be consistent specifically includes:

[0066] When the range Δd>d c When , it means that the rock mass deformation in the iterative grid is inconsistent, the current iterative grid is re-divided, a second iteration is performed, and the corresponding range Δd is calculated;

[0067] When the range Δd≤d in the iteration grid c Or when the iterative grid size is less than or equal to the initial grid size, it means that the rock mass deformation in the iterative grid is consistent and the iterative grid division is completed;

[0068] The initial grid in the radar image is iterated multiple times and divided into iterative grids to achieve data compression. When all iterative grids are divided, the data compression can be completed, and all subsequent calculations are carried out based on the compressed results. Figure 3 It is a comparison diagram of the effects before and after data compression in the embodiment.

[0069] Using the breadth-first search model, a quantitative relationship between compressed radar data and landslide risk is established to partition the landslide risk in radar images.

[0070] In specific implementation, as a preferred embodiment of the present invention, the use of a breadth-first search model to establish a quantitative relationship between compressed radar data and landslide risk specifically includes:

[0071] According to the compressed radar data, the improved tangent angle is calculated:

[0072]

[0073] Among them, ΔS(i) is the displacement change, v is the deformation rate in the constant creep stage, T(i) is the vertical axis value with the same dimension as time after the change, t i is a monitoring moment, α i To improve the tangent angle;

[0074] Will improve the tangent angle α i Transformed into landslide risk value Rαt :

[0075]

[0076] Among them, α t It represents the improved tangent angle at time t. The empirical formula is obtained through historical case statistics.

[0077] In implementation, it is considered that landslide risk has a path dependence effect, that is, the landslide risk status of a region is also crucial to the risk assessment of landslides in the future, but this importance will decrease over time. At the same time, when an area is repeatedly identified as a risk area, it means that the area has a higher landslide risk. The risk attenuation coefficient β and the risk enhancement coefficient θ are introduced to correct the landslide risk:

[0078]

[0079] Among them, R'α t represents the corrected landslide risk, t represents time;

[0080] Using the eight-neighborhood breadth-first traversal search algorithm, we circle R'α t > 0, considering the situation where there are two risk areas with close distances, a risk area merging distance threshold d is set, and the initially determined risk areas are merged for the second time. Figure 4 Schematic process of dividing risk areas for this method. Figure 5 This is the risk area delineation result in the embodiment.

[0081] According to the landslide risk zoning, the evolution process of the risk area is deduced, and the probability of disaster in the risk area is quantified using the convergence rate and occurrence rate. In implementation, the quantification of the evolution process of the risk area aims to describe the development and convergence process of the risk area, and based on this, the probability of disaster in the risk area is quantified, providing important parameters for landslide prevention and control.

[0082] In specific implementation, as a preferred embodiment of the present invention, the quantification of the probability of disaster in the risk area by using the convergence rate and the occurrence rate specifically includes:

[0083] After the risk areas are divided, in order to facilitate the description of the spatial distribution characteristics of the risk areas, the risk area set is represented as Represents the collection of micro-element location information within the risk area, n t is the number of risk areas at time t, m i,t Represents the number of micro-elements in the risk area at time t in area i.

[0084] The development and convergence of landslide risk areas are carried out in a certain area, that is, there is a potential risk area. In order to quantify this area, the boundary extension index d is introduced to expand the risk area; the risk area set at time t-1 is expressed as Figure 6 Schematic diagram of the spatial distribution of various types of risk areas, potential risk areas for The physical meaning of the expansion result using the breadth-first algorithm based on the boundary extension index is that although the initiation and development of the assumed risk zone satisfies the evolution process from disorder to order, this evolution process is different from the damage and fracture behavior of small-scale specimens such as rock. The development and convergence of the risk zone are carried out within a certain range.

[0085] For those located in potential risk areas All risk areas within Development and Convergence Behavior and Risk Zones The formation of

[0086]

[0087] For risk areas All risk areas within Development and convergence as risk areas The basis of formation,

[0088]

[0089] The risk area convergence rate is expressed as Indicates potential risk areas The convergence of the inner risk area is calculated as follows:

[0090]

[0091] The physical meaning of the risk zone convergence rate is that the large-scale convergence surface landslide disaster in the risk zone is developing from disorder to order. To a certain extent, it can also reflect the development and penetration process of the main sliding surface. The larger the convergence rate, the more consistent the disaster is and the higher the probability of landslide.

[0092] The risk zone development rate is expressed as Indicates potential risk areas The development of the internal risk area is calculated as follows:

[0093]

[0094] The physical meaning of the risk zone development rate is the process of landslide occurrence, which is often accompanied by the development of cracks. The faster the risk zone develops, to a certain extent, it indicates that the cracks expand faster and the risk of disasters is higher.

[0095] Different disaster risk assessment indicators based on the improved tangent angle, the convergence rate and development rate are attribute values ​​of the risk zone and do not change with the change of location.

[0096] Will and Reconstructed within the monitoring range, representing the evolution of risk in the time dimension. In any risk area, the two variables take the same value at any position. In the risk-free area, the two variables take zero values. To simplify the expression, Rc t and Rp t Represents the reconstructed risk assessment matrix, and calculates the convergence rate R'ct considering the risk path dependence effect 与 Development rate matrix R'p t :

[0097]

[0098]

[0099] Among them, β is the risk attenuation coefficient and θ is the risk enhancement coefficient. The introduction of the risk attenuation coefficient β indicates the importance of the landslide risk calculation results to future risk assessment. When the risk area develops rapidly, R'p t If >0, it is directly assigned to 1.

[0100] The convergence rate and occurrence rate are integrated with disaster risk assessment indicators to achieve objective calculation of disaster probability.

[0101] The rapid development and large-scale convergence of risk areas are external manifestations of the landslide process, but both are sufficient conditions for the occurrence of landslides. It is difficult to accurately judge the probability of disasters based solely on the convergence rate and development rate indicators. On the contrary, the disaster risk assessment index based on the improved tangent angle is an objective description of the risk status of each microelement within the disaster risk area, and is an inherent attribute that describes the probability of landslide disasters.

[0102] In specific implementation, as a preferred embodiment of the present invention, the convergence rate and occurrence rate are integrated with the disaster risk assessment index to achieve objective calculation of the disaster probability, which specifically includes:

[0103] The convergence rate R'c t , development rate R'p t And the disaster risk assessment index R'α used to correct the inverse velocity t Fusion, to achieve objective calculation of disaster probability, the calculation method is as follows:

[0104] Pt=w 1 R'α t +w 2 R'c t +w 3 R t

[0105] Among them, P t represents the probability of landslide disaster, w 1 represents the weight of landslide risk calculated based on the improved tangent angle, w 2 represents the weight of landslide risk calculated based on the convergence rate, w 3 Represents the weight used to calculate landslide risk based on development rate.

[0106] Using the above method, the radar data is processed to calculate the corresponding convergence rate, development rate and landslide probability. Figure 7 described.

[0107] The present invention also includes a storage medium, which includes a stored program, wherein when the program is run, the radar data-driven quantitative analysis method for the evolution process of the landslide risk area is executed.

[0108] The present invention also includes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the radar data-driven quantitative analysis method for the evolution process of the landslide risk area through the computer program.

[0109] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0110] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0111] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0112] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0113] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0114] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A radar data-driven quantitative analysis method for the evolution of landslide risk areas, characterized in that: include: The radar image is divided into grids iteratively to compress the data in the radar image; Using the breadth-first search model, a quantitative relationship between compressed radar data and landslide risk is established to partition the landslide risk in radar images. The evolution of risk areas is deduced based on landslide risk zoning, and the probability of disasters in risk areas is quantified using convergence rate and occurrence rate. The convergence rate and occurrence rate are integrated with disaster risk assessment indicators to achieve objective calculation of disaster probability.

2. The radar data-driven quantitative analysis method for landslide risk zone evolution process according to claim 1 is characterized in that: The method of dividing the radar image by iterative gridding and compressing the data in the radar image specifically includes: Each pixel point of the radar image is regarded as an initial grid, in which the displacement monitoring value is recorded; the radar monitoring range is divided into iterative grids, and the range Δd of the displacement monitoring values ​​in all initial grids in each iterative grid is calculated respectively. The range is used to represent the degree of uniformity of rock mass deformation in the iterative grid, and the range is compared with the set threshold d c Comparison is made to determine whether the rock deformation within the initial grid is consistent.

3. The radar data-driven quantitative analysis method for landslide risk zone evolution process according to claim 2 is characterized in that: The determining whether the rock mass deformation in the initial grid is tending to be consistent specifically includes: When the range Δd>d c When , it means that the rock mass deformation in the iterative grid is inconsistent, the current iterative grid is re-divided, a second iteration is performed, and the corresponding range Δd is calculated; When the range Δd≤d in the iteration grid c Or when the iterative grid size is less than or equal to the initial grid size, it means that the rock mass deformation in the iterative grid is consistent and the iterative grid division is completed; The initial grid in the radar image is iterated multiple times and divided into iterative grids to achieve data compression.

4. The radar data-driven landslide risk zone evolution quantitative analysis method according to claim 1, characterized in that: The use of the breadth-first search model to establish a quantitative relationship between the compressed radar data and the landslide risk specifically includes: According to the compressed radar data, the improved tangent angle is calculated: Among them, ΔS(i) is the displacement change, v is the deformation rate in the constant creep stage, T(i) is the vertical axis value with the same dimension as time after the change, t i is a monitoring moment, α i To improve the tangent angle; Will improve the tangent angle α i Transformed into landslide risk value Rα t : Among them, α t represents the improved tangent angle at time t; The risk attenuation coefficient β and risk enhancement coefficient θ are introduced to correct the landslide risk: Among them, R'α t represents the corrected landslide risk, t represents time; Using the eight-neighborhood breadth-first traversal search algorithm, we circle R'α t > 0, considering the situation where there are two risk areas with close distances, a risk area merging distance threshold d is set, and the initially determined risk areas are merged for the second time.

5. The radar data-driven quantitative analysis method for landslide risk zone evolution process according to claim 1, characterized in that: The method of quantifying the probability of disaster in the risk area by using the convergence rate and the occurrence rate specifically includes: The risk area set is represented as Represents the collection of micro-element location information within the risk area, n t is the number of risk areas at time t, m i,t represents the number of micro-elements in the risk area at time t in area i; introduces the boundary extension index e to expand the risk area; the risk area set at time t-1 is expressed as Potential risk areas for The result after expansion using the breadth-first algorithm according to the boundary expansion index; For those located in potential risk areas All risk areas within Development and Convergence Behavior and Risk Zones The formation of For risk areas All risk areas within Development and convergence as risk areas The basis of formation, The risk area convergence rate is expressed as Indicates potential risk areas The convergence of the inner risk area is calculated as follows: The development rate of the risk area is expressed as Indicates potential risk areas The development of the internal risk area is calculated as follows: Will and Reconstructed within the monitoring scope to represent the evolution of risk in the time dimension, with Rc t and Rp t Represents the reconstructed risk assessment matrix, and calculates the convergence rate R'c considering the risk path dependence effect t and the development rate matrix R'p t : Among them, β is the risk attenuation coefficient and θ is the risk intensification coefficient.

6. The radar data-driven quantitative analysis method for landslide risk zone evolution process according to claim 1, characterized in that: The convergence rate and occurrence rate are integrated with the disaster risk assessment index to achieve objective calculation of disaster probability, specifically including: The convergence rate R'c t , development rate R'p t And the disaster risk assessment index R'α used to correct the inverse velocity t Fusion, to achieve objective calculation of disaster probability, the calculation method is as follows: P.S t w1R'α t +w2R'c t +w3R'p t Among them, P t represents the probability of landslide disaster, w1 represents the weight of landslide risk calculated based on the improved tangent angle, w2 represents the weight of landslide risk calculated based on the convergence rate, and w3 represents the weight of landslide risk calculated based on the development rate.

7. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is run, the method according to any one of claims 1 to 6 is executed.

8. 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 method according to any one of claims 1 to 6 by running the computer program.

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