Slope disaster regional risk identification method based on multi-source data and electronic equipment

Abstract

The invention discloses a multi-source data-based slope disaster regional risk identification method and electronic equipment. The method comprises the following steps of: acquiring topographic data, soil mechanical parameter data and rainfall data of a target area and performing spatial registration processing; based on the operation type of the emergency rescue site, determining and quantifying the power disturbance on the slope, and forming an equivalent impact acceleration parameter; performing unit-by-unit safety coefficient calculation and instability risk grading in the target area through a slope stability calculation model in combination with the equivalent impact acceleration parameters, rainfall data and the soil mechanical parameters; and for the area identified as having the instability risk, according to the rainfall intensity index, calculating the probability of converting the area into the debris flow disaster after instability so as to realize differential risk identification of the landslide and the debris flow disaster. Through quantification of rescue disturbance, disaster type distinguishing is realized, and post-disaster slope secondary disaster identification precision, speed and decision pertinence are improved.

Description

Technical Field

[0001] This application relates to the field of geological hazard risk assessment technology, and in particular to a method and electronic device for identifying regional risks of slope hazards based on multi-source data. Background Technology

[0002] In the emergency rescue phase following major natural disasters such as earthquakes, large-scale landslides, or severe floods, saving lives and restoring lifelines are the primary tasks. However, the rescue areas are often prone to geological hazards and frequently face serious threats from secondary disasters, directly impacting the safety and efficiency of rescue operations. Existing technologies have significant shortcomings in this specific scenario, specifically manifested in the following ways: First, during rescue operations, the continuous vibration of heavy machinery (such as excavators and bulldozers) and construction operations such as emergency blasting will create new dynamic load impacts on the post-disaster slopes that are already in a critical state of stability, which can easily induce secondary instability of the slopes. Existing early warning models usually do not fully consider such human disturbance factors, resulting in incomplete risk assessments.

[0003] Second, post-disaster slopes often have loose soil and fractured structures, leading to a significantly accelerated rate of rainfall infiltration, rapid soil saturation, and a sharp decline in shear strength, thus exacerbating the risk of slope instability. Although existing technologies take into account the impact of rainfall, they are mostly based on normal geological conditions and fail to fully reflect the rapid deterioration of hydrological conditions on post-disaster slopes.

[0004] Third, emergency rescue has extremely high time requirements, necessitating the determination of safe rescue routes and temporary resettlement sites within a very short period. Existing sophisticated methods, such as deploying GNSS monitoring systems, while highly accurate, are time-consuming and costly to deploy, making it difficult to meet the urgent need for rapid, large-scale assessment at rescue sites.

[0005] Furthermore, existing regional rainfall early warning models often treat geological hazards such as landslides and debris flows in a general way, lacking differentiation in the mechanisms of hazard transformation. In fact, after slope instability, it may transform into a landslide dominated by block movement, or it may evolve into a debris flow involving a mixture of solid and liquid phases. The hazard characteristics and prevention strategies of the two are significantly different. Existing methods lack the ability to distinguish between these two critical aspects, which limits the targeted nature of early warning information and the effectiveness of decision-making.

[0006] Therefore, there is an urgent need for a method that can quickly identify the risk of secondary disasters on slopes in emergency rescue scenarios over a wide area. This method should take into account special conditions such as disturbances during rescue operations and post-disaster hydrological deterioration, and be able to effectively distinguish between landslide and debris flow risks, thereby improving the accuracy of disaster early warning and the scientific nature of rescue command. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this application aims to provide a method for identifying regional risks of slope disasters based on multi-source data, which can rapidly, extensively, and differentially identify the risks of secondary disasters such as landslides and debris flows.

[0008] To achieve the above objectives, this application provides a method for identifying regional risks of slope hazards based on multi-source data, including: Acquire topographic data, soil mechanical parameter data, and rainfall data of the target area and perform spatial registration processing; Based on the type of operation at the emergency rescue site, the dynamic disturbance it generates on the slope is determined and quantified, and equivalent impact acceleration parameters are formed. Combining the equivalent impact acceleration parameters, rainfall data, and soil mechanical parameters, a slope stability calculation model is used to calculate the safety factor and classify the instability risk within the target area on a unit-by-unit basis. For areas identified as having a risk of instability, the probability of them transforming into debris flow disasters after instability is calculated based on rainfall intensity indicators, so as to achieve differentiated risk identification of landslides and debris flow disasters.

[0009] Furthermore, the soil mechanical parameters include: soil cohesion, internal friction angle, bulk density, saturated water conductivity, and soil layer thickness.

[0010] Furthermore, the step of determining and quantifying the dynamic disturbance to the slope caused by the operation type at the emergency rescue site, and forming equivalent impact acceleration parameters, further includes: The types of operations at the emergency rescue site include heavy machinery operations and emergency blasting operations; Based on the type of work being performed on site, each evaluation unit is assigned an equivalent impact acceleration value.

[0011] Furthermore, the step of calculating the safety factor and classifying the instability risk on a unit-by-unit basis within the target area by combining the equivalent impact acceleration parameters, rainfall data, and soil mechanical parameters using a slope stability calculation model further includes: quantitatively characterizing the hydrodynamic process by which rainfall infiltration leads to increased pore water pressure and thus weakens the soil shear strength using soil moisture factors, with the characterization relationship being: Where h is the vertical height of the groundwater level above the sliding surface, and its value represents the pore water pressure in the soil; R is the precipitation; T is the saturated hydraulic conductivity; and sca is the catchment area per unit area. This refers to the slope of the slope.

[0012] Furthermore, the slope stability calculation model is an infinite slope stability model that couples dynamic disturbance terms and soil moisture change terms, and the calculation expression of its safety factor includes the equivalent impact acceleration parameter.

[0013] Furthermore, the formula for calculating the safety factor is as follows: Where Fs represents the safety factor, c is the soil cohesion, and Z is the soil thickness. For soil bulk density, Let g be the specific weight of water, and g be the acceleration due to gravity. For equivalent impact acceleration, The slope is the gradient of the slope. Let be the friction angle. This refers to soil moisture factors.

[0014] Furthermore, the step of calculating the probability of a landslide transforming into a debris flow disaster after instability in areas identified as having an instability risk, based on rainfall intensity indicators, to achieve differentiated risk identification between landslides and debris flow disasters, also includes: The rainfall intensity index is the rainstorm intensity index R, and its calculation formula is as follows: Where: K is the correction factor for previous rainfall, H 24 H1, H 1 / 6 The maximum rainfall amounts for 24 hours, 1 hour, and 10 minutes are respectively, H 24(D) H 1(D) H 1 / 6(D) This is the corresponding critical rainfall threshold determined based on the region's average annual rainfall.

[0015] Furthermore, the step of calculating the probability of a landslide or debris flow disaster after instability in an area identified as having an instability risk, based on the rainfall intensity index, in order to achieve differentiated risk identification of landslides and debris flow disasters, also includes: classifying different debris flow occurrence probability levels according to the value range of the rainfall intensity index R.

[0016] Furthermore, after the step of calculating the probability of a landslide turning into a debris flow disaster based on the rainfall intensity index for areas identified as having an instability risk, in order to achieve differentiated risk identification of landslides and debris flow disasters, the method also includes the step of outputting a spatial distribution map reflecting the slope stability level and a comprehensive disaster risk identification map superimposed with the probability of debris flow occurrence.

[0017] To achieve the above objectives, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor is configured to execute the computer program stored in the memory to implement the slope disaster regional risk identification method based on multi-source data as described above.

[0018] The regional risk identification method for slope disasters based on multi-source data provided in this application significantly improves the accuracy, speed, and decision-making pertinence of post-disaster secondary slope disaster identification by quantifying rescue disturbances and distinguishing disaster types, thereby enhancing the safety of emergency rescue.

[0019] Other features and advantages of this application will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing this application. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate the present application and form part of the specification. Together with the embodiments of the present application, they serve to explain the present application but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a method for identifying regional risks of slope hazards based on multi-source data according to an embodiment of this application; Figure 2 This is a schematic diagram of an electronic device structure according to an embodiment of the present invention. Detailed Implementation

[0021] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application.

[0022] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0023] The term "comprising" and its variations as used in this application are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0024] It should be noted that the terms "first" and "second" may be used in this application only to distinguish different devices, components or parts, and are not used to define the order of functions performed by these devices, components or parts or their interdependence.

[0025] It should be noted that the terms "one" and "more" used in this application are illustrative rather than restrictive, and those skilled in the art should understand that, unless explicitly stated otherwise in the context, they should be understood as "one or more". "More" should be understood as two or more.

[0026] The method for identifying regional risks of slope disasters based on multi-source data in this application includes: acquiring topographic data, soil mechanical parameter data, and rainfall data of the target area and performing spatial registration processing; determining and quantifying the dynamic disturbance to the slope caused by the operation type at the emergency rescue site, forming an equivalent impact acceleration parameter; combining the equivalent impact acceleration parameter, rainfall data, and soil mechanical parameters, calculating the unit-by-unit safety factor and classifying the instability risk within the target area using a slope stability calculation model; and calculating the probability of transforming into a debris flow disaster after instability in areas identified as having instability risk, based on rainfall intensity indicators, so as to achieve differentiated risk identification of landslides and debris flow disasters.

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0028] Example 1 Figure 1 The flowchart of the slope disaster regional risk identification method based on multi-source data according to the embodiment of this application is as follows: First, in step S1: collect data and preprocess it.

[0029] This step involves collecting and processing the basic geographic information, soil properties, and meteorological data required for the calculations, providing standardized data input for subsequent model calculations. Specifically, the collected data includes: Topographic data: Acquire digital elevation model (DEM) data for the target area. This data is used to extract topographic factors such as slope, aspect, and catchment area per unit area required for calculation. High-precision DEM data can improve the accuracy of regional assessments. DEM data is the foundation for constructing a three-dimensional terrain surface, and its resolution directly affects the accuracy of topographic factor calculations, thus impacting the reliability of subsequent stability analyses.

[0030] Soil mechanical parameter data: Acquire spatial distribution data of soil physical and mechanical parameters in the target area. These parameters are key inputs for calculating slope stability and mainly include: soil cohesion, soil internal friction angle, soil bulk density, soil saturated water conductivity, and soil layer thickness. The parameter data needs to be processed into raster data consistent with the spatial resolution and coordinate system of the DEM data. Together, they determine the inherent shear strength and seepage characteristics of the slope, and are the core physical basis for assessing whether it will become unstable under external loads (such as rainfall and vibration).

[0031] Rainfall data: Obtain real-time or forecasted rainfall data for the target area. Required data includes: Short-duration heavy rainfall data: such as maximum rainfall in 10 minutes or maximum rainfall in 1 hour. Long-duration cumulative rainfall data: such as maximum rainfall in 24 hours. The rainfall data can be interpolated data from meteorological stations, radar inversion data, or the output of numerical weather prediction products, and processed into a raster format that matches the basic geographic data (in this embodiment, the topographic data).

[0032] In the embodiments of this application, the data preprocessing steps include: performing preprocessing operations such as coordinate system unification, spatial resolution resampling, and data format conversion on the various types of data collected above, to ensure that all data layers are fully registered in space, forming a multi-source, consistent, and comprehensive database that can be used for model calculation. This step is a prerequisite for ensuring the accuracy and efficiency of subsequent rasterized parallel computing, avoiding calculation errors or boundary effects caused by data spatial mismatch.

[0033] Step S2: Estimate the impact of emergency rescue construction operations.

[0034] In this step, the additional dynamic disturbance to the slope caused by emergency rescue activities (such as mechanical vibration and blasting) is quantified and transformed into an equivalent load that can be substituted into a mechanical model. Specifically, the following is defined: This refers to the equivalent impact acceleration caused by construction operations, blasting operations, etc. Due to significant differences in on-site construction methods and varying distances from the slope, the value range is shown in Table 1 below. The unit of the value range is gravitational acceleration g (i.e., the numerical range is expressed using gravitational acceleration g as the unit of measurement). Table 1

[0035] In some exemplary embodiments, the specific rescue operation can be determined by selecting or interpolating within the range given in the table above, based on information such as the specific type of operation at the rescue site, equipment power, and distance from the slope. Values. It is understood that the above value range is the value range for heavy machinery and blasting operations in this embodiment. In other embodiments, the equivalent impact acceleration parameter value does not necessarily have to be within this range, and can be flexibly selected according to different actual conditions such as distance, amount of explosives, and mechanical operation method.

[0036] This quantification process simplifies complex dynamic disturbances that are difficult to measure precisely into a physical quantity that can be directly involved in slope stability calculations, thus realizing the modeling coupling of human activity factors.

[0037] Step S3: Slope instability risk assessment and classification.

[0038] This step is based on an improved infinite slope stability model, which couples dynamic load impact (i.e., dynamic disturbance caused by field operations, such as equivalent impact acceleration) and soil moisture changes (such as soil moisture factor) to calculate the slope safety factor (Fs) for each grid cell. Specifically, based on the infinite slope stability model proposed by Hammond et al. in 1992, the slope safety factor Fs can be used to determine whether the slope is dangerous. The calculation method for the safety factor Fs is as follows: Where c represents soil cohesion, in N / m³. 2 Z represents the vertical height of the soil (i.e., the height from the ground surface, also known as thickness), in meters (m). Soil bulk density, unit: kg / m³ 3 ; The specific gravity of water is taken as a constant of 1000 kg / m³. 3 g is the acceleration due to gravity, taken as a constant of 9.8 m / s². 2 ; Equivalent impact acceleration caused by construction operations, blasting operations, etc., in m / s². 2 This is one of the key improvements of this application, which directly introduces the artificial disturbance acceleration estimated in step S2 into the slope stability calculation, reflecting the evaluation concept of "dynamic and static load coupling". The slope is expressed in degrees (°). The friction angle is expressed in degrees (°). The soil moisture factor is calculated as follows: in: h The vertical height of the groundwater level above the sliding surface, in meters. This value represents the soil pore water pressure. As precipitation increases, the soil's shear strength decreases. R Rainfall amount, in m / d; T Here, is the saturated hydraulic conductivity, and is the saturated water conductivity. The product of the height of the soil perpendicular to the slope, in meters. 2 / d, T The expression is described as follows: ; sca The unit is the catchment area, which is the ratio of the water accumulation area to the outlet width, in meters. It is obtained from DEM data using GIS software.

[0039] Soil moisture factor The introduction of this method quantitatively characterizes the hydrodynamic process by which rainfall infiltration leads to increased pore water pressure and weakens soil shear strength, serving as a crucial bridge connecting rainfall data with slope stability.

[0040] In the embodiments of this application, based on the "Code for Investigation of Landslide Prevention Engineering GB / T 32864-2016", the slope stability state is classified as shown in Table 2: Table 2

[0041] By performing grid-by-grid calculations on the study area, a slope stability classification distribution map can be obtained, which intuitively shows the spatial distribution of unstable, understable, basically stable, and stable areas within the entire emergency rescue area.

[0042] Step S4: Debris flow risk assessment.

[0043] This step further assesses the likelihood of unstable or understability areas identified in step S3 transforming into debris flow disasters after instability, thus distinguishing between landslides and debris flows.

[0044] Debris flows are solid-liquid two-phase fluids formed by precipitation (heavy rain, snowmelt, etc.), carrying large amounts of solid materials such as mud, sand, and rocks. They exhibit viscous laminar or dilute turbulent flow patterns and are high-concentration mixed particle flows of solids and liquids. The density range of debris flows is approximately 1.2-2.4 t / m³. When the density is less than 1.2, it is more likely to be a flood carrying mud, sand, and smaller rocks; when the density is greater than 2.4, it is more likely to be a landslide with more prominent solid properties. Therefore, whether slope failure during rainfall triggers a landslide or a debris flow disaster is closely related to rainfall data.

[0045] To quantify this tendency to transform, this application introduces a rainstorm intensity index R as a criterion, the calculation formula of which is as follows: Where: K is the correction factor for previous rainfall; K=1 when there is no previous rainfall; when there is previous rainfall, K can be taken as 1.1 to 1.2 depending on the amount of rainfall and the interval between rainfall events; H 24 H1 represents the maximum 24-hour rainfall in mm; H2 represents the maximum 1-hour rainfall in mm; H... 1 / 6Maximum rainfall in mm over 10 minutes; H 24(D) H 1(D) H 1 / 6(D) The threshold rainfall values ​​for potential debris flows in this area are shown in Table 3 below: Table 3

[0046] Regions with an average annual rainfall greater than 1200 mm include Zhejiang, Fujian, Taiwan, Guangdong, Guangxi, Jiangxi, Hunan, Hubei, Anhui, western Yunnan, and southeastern Tibet. Regions with an average annual rainfall between 1200 and 800 mm include Sichuan, Guizhou, eastern and central Yunnan, southern Shaanxi, eastern Shanxi, Liaodong, Heilongjiang, Jilin, western Liaoning, and northern and western Hebei. Regions with an average annual rainfall between 800 and 500 mm include northern Shaanxi, Gansu, Inner Mongolia, Ningxia, Shanxi, parts of Xinjiang, northwestern Sichuan, and parts of Tibet. Regions with an average annual rainfall less than 500 mm include Qinghai, Xinjiang, Tibet, and parts of Gansu and Ningxia west of the Yellow River.

[0047] Based on the rainfall intensity index R, the probability of debris flow disasters can be obtained. : When R is less than 3.1, if slope failure occurs, it is a landslide disaster. ; When R is greater than or equal to 3.1, the intensity at which a debris flow may occur is: If R takes a value between 3.1 and 4.2, the probability of a debris flow is less than 0.2. If R ranges from 4.2 to 10, the probability of a debris flow is between 0.2 and 0.8. ; When R is greater than 10, the probability of a debris flow is greater than 0.8.

[0048] In some exemplary embodiments, by combining the stability grading map from step S3 and the debris flow probability from step S4, a comprehensive slope disaster risk identification map can be generated. This map not only indicates the location and stability status of high-risk instability zones, but also indicates the likelihood that these areas, once unstable, will transform into mobile debris flows, providing refined decision support for route planning, camp site selection, and differentiated prevention and control in rescue command.

[0049] The slope hazard regional risk identification method based on multi-source data proposed in this application has advantages in several aspects: (1) Quantitative Coupled Assessment Mechanism for Rescue Disturbance: This application differs from traditional static early warning models that only consider rainfall as a single cause, and innovatively constructs a dynamic and static load coupled assessment mechanism that considers the impact of construction operations at emergency rescue sites. Targeting the unique external dynamic disturbances in post-disaster rescue areas, such as heavy machinery vibration and emergency blasting, the complex dynamic effects are quantified into equivalent impact acceleration by estimating the impact of emergency rescue construction operations. This acceleration is then superimposed on the pore water pressure effect caused by rainfall infiltration and introduced into the infinite slope stability model. This improvement effectively solves the problem of underreporting caused by neglecting human disturbances and dynamic effects under extreme conditions, significantly improving the scientific rigor and practical safety of secondary risk identification for post-disaster slopes in a critical stability state.

[0050] (2) Precise Disaster Differentiation Identification: This application establishes a two-layer progressive disaster identification method based on instability discrimination and rheological transformation probability (see steps S3 to S4), which effectively overcomes the difficulty in distinguishing between landslide and debris flow risks. First, the stability coefficient is calculated using an improved infinite slope model to identify the instability source of the slope from a mechanical mechanism perspective. Then, for instability areas identified as high-risk, a rainstorm intensity index is introduced as a fluid-structure interaction criterion to further quantify the probability of loose deposits transforming into solid-liquid two-phase flow under the excitation of heavy rainfall. This hierarchical identification mechanism achieves precise differentiation between blocking disasters (landslides) and flow-impact disasters (debris flows), providing a clearer decision-making basis for the rescue command to formulate differentiated protection and emergency rescue strategies.

[0051] (3) Non-contact rapid survey capability based on multi-source data: Addressing the industry pain points of tight timeframes, difficult on-site surveys, and limited data acquisition during the golden period of post-disaster rescue, this application provides a regional rapid assessment scheme that relies entirely on publicly available and easily accessible data. The method of this application does not require the deployment of expensive on-site sensors or time-consuming manual detailed exploration; it only needs to integrate digital elevation models, regional soil survey parameters, and meteorological forecast data to achieve gridded risk calculation of large-scale rescue areas. At the same time, this method has low data acquisition thresholds and low computational time costs, and can generate dynamically updated risk distribution maps within hours, meeting the stringent requirements of timeliness and coverage in emergency rescue.

[0052] Example 2 In this embodiment of the invention, an electronic device is also provided. Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention, such as... Figure 2 As shown, the electronic device of the present invention includes a processor 201 and a memory 202, wherein the memory 202 stores a computer program, and when the computer program is read and executed by the processor 201, it executes the steps in the embodiment of the slope disaster regional risk identification method based on multi-source data as described above.

[0053] It will be understood by those skilled in the art that the above descriptions are merely preferred embodiments of this application and are not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for identifying regional risks of slope hazards based on multi-source data, characterized in that, Includes the following steps: Acquire topographic data, soil mechanical parameter data, and rainfall data of the target area and perform spatial registration processing; Based on the type of operation at the emergency rescue site, the dynamic disturbance it generates on the slope is determined and quantified, and equivalent impact acceleration parameters are formed. Combining the equivalent impact acceleration parameters, rainfall data, and soil mechanical parameters, a slope stability calculation model is used to calculate the safety factor and classify the instability risk within the target area on a unit-by-unit basis. For areas identified as having a risk of instability, the probability of them transforming into debris flow disasters after instability is calculated based on rainfall intensity indicators, so as to achieve differentiated risk identification of landslides and debris flow disasters.

2. The method for identifying regional risks of slope hazards based on multi-source data according to claim 1, characterized in that, The soil mechanical parameters include: soil cohesion, internal friction angle, bulk density, saturated water conductivity, and soil layer thickness.

3. The method for identifying regional risks of slope hazards based on multi-source data according to claim 1, characterized in that, The step of determining and quantifying the dynamic disturbance to the slope caused by the operation type at the emergency rescue site, and forming equivalent impact acceleration parameters, further includes: The types of operations at the emergency rescue site include heavy machinery operations and emergency blasting operations; Based on the type of work being performed on site, each evaluation unit is assigned an equivalent impact acceleration value.

4. The method for identifying regional risks of slope hazards based on multi-source data according to claim 1, characterized in that, The step of calculating the safety factor and classifying the instability risk on a unit-by-unit basis within the target area by combining the equivalent impact acceleration parameters, rainfall data, and soil mechanical parameters using a slope stability calculation model further includes: quantitatively characterizing the hydrodynamic process by which rainfall infiltration leads to increased pore water pressure and thus weakens the soil shear strength using soil moisture factors. The characterization relationship is as follows: Where h is the vertical height of the groundwater level above the sliding surface, and its value represents the pore water pressure in the soil; R is the precipitation; T is the saturated hydraulic conductivity; and sca is the catchment area per unit area. This refers to the slope of the slope.

5. The method for identifying regional risks of slope hazards based on multi-source data according to claim 1, characterized in that, The slope stability calculation model is an infinite slope stability model that couples dynamic disturbance terms and soil moisture change terms, and the calculation expression of its safety factor includes the equivalent impact acceleration parameter.

6. The method for identifying regional risks of slope hazards based on multi-source data according to claim 5, characterized in that, The formula for calculating the safety factor is: Where Fs represents the safety factor, c is the soil cohesion, and Z is the soil thickness. For soil bulk density, Let g be the specific weight of water, and g be the acceleration due to gravity. For equivalent impact acceleration, The slope is the gradient of the slope. Let be the friction angle. This refers to soil moisture factors.

7. The method for identifying regional risks of slope hazards based on multi-source data according to claim 1, characterized in that, The step of calculating the probability of a landslide transforming into a debris flow disaster after instability in areas identified as having an instability risk, based on rainfall intensity indicators, to achieve differentiated risk identification between landslides and debris flow disasters, further includes: The rainfall intensity index is the rainstorm intensity index R, and its calculation formula is as follows: Where: K is the correction factor for previous rainfall, H 24 H1, H 1 / 6 The maximum rainfall amounts for 24 hours, 1 hour, and 10 minutes are respectively, H 24(D) H 1(D) H 1 / 6(D) This is the corresponding critical rainfall threshold determined based on the region's average annual rainfall.

8. The method for identifying regional risks of slope hazards based on multi-source data according to claim 7, characterized in that, The step of calculating the probability of a landslide or debris flow disaster after instability in an area identified as having an instability risk, based on the rainfall intensity index, to achieve differentiated risk identification of landslides and debris flow disasters, further includes: classifying different debris flow occurrence probability levels according to the value range of the rainfall intensity index R.

9. The method for identifying regional risks of slope hazards based on multi-source data according to claim 1, characterized in that, After the step of calculating the probability of a landslide turning into a debris flow disaster based on the rainfall intensity index for areas identified as having an instability risk, in order to achieve differentiated risk identification of landslides and debris flow disasters, the method also includes the step of outputting a spatial distribution map reflecting the slope stability level and a comprehensive disaster risk identification map superimposed with the probability of debris flow occurrence.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor is used to execute the computer program stored in the memory to implement the slope disaster regional risk identification method based on multi-source data as described in any one of claims 1 to 9.

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