A mountain collapse early warning method and system based on multi-influence factor analysis

Abstract

The application discloses a mountain collapse early warning method and system based on multi-influence factor analysis, and the early warning method provided by the technical scheme is based on a multi-level fuzzy comprehensive evaluation model, a mountain collapse risk evaluation system structure model is constructed with a mountain collapse risk evaluation value as a target layer, mountain collapse influence factors as a criterion layer, and various evaluation indexes as index layers, a reference value range of the mountain collapse risk evaluation value is determined according to a calculation result of the collapse risk evaluation value, and a collapse probability value of a mountain to be evaluated is calculated according to the reference value range of the collapse risk evaluation value, scientific and comprehensive analysis can be performed according to the data of various influence factors of the mountain collapse, quantitative evaluation of the collapse risk of the mountain to be evaluated is realized, and monitoring and early warning can be performed before the mountain collapse disaster occurs, so that time for formulating disaster prevention measures and strategies is obtained.

Description

Technical Field

[0001] This invention relates to the field of geological disaster early warning technology, and in particular to an early warning method and system for landslides based on multi-influencing factor analysis. Background Technology

[0002] Landslides are a common natural disaster that can cause significant casualties and economic losses, and also have an adverse impact on social development. Therefore, studying the formation mechanism of landslides and their prevention and control measures is of great practical significance for disaster prevention and mitigation.

[0003] Current landslide early warning technologies primarily rely on displacement sensors or image recognition to monitor changes in landslide displacement. However, these methods are often only effective when a landslide is imminent, by which time the disaster has already occurred. Landslides are caused by numerous factors, including geological environmental factors, triggering factors, and human activities. Therefore, we propose a landslide early warning method and system based on multi-factor analysis to monitor and warn of landslide risks before a disaster develops, thus allowing more time for developing disaster prevention measures and strategies. Summary of the Invention

[0004] The main objective of this invention is to provide a method and system for early warning of landslides based on the analysis of multiple influencing factors, which can effectively solve the problems in the background art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A landslide early warning method based on multi-factor analysis includes the following steps:

[0007] Screening is conducted on at least one of the following factors affecting landslides: geological environment factors, inducing factors, and human factors. Evaluation indicators are determined for each of these factors. The evaluation indicators for the geological environment factors include at least one of the following: rock mass type, geological structure, mountain slope, and groundwater flow.

[0008] The triggering factors include at least one of vibration amplitude, precipitation, and snowmelt.

[0009] The human factors mentioned include at least one of the following: earthwork excavation at the toe of the slope, surcharge on the slope, and water discharge / storage volume.

[0010] Multiple evaluation index data of mountains were collected to establish a mountain collapse evaluation index database. Cluster analysis was used to screen the comparison mountains in the database that are similar to the evaluation index data of the mountain to be evaluated.

[0011] A structural model for landslide risk assessment is constructed, with the landslide risk assessment value Re as the target layer, the landslide influencing factors as the criterion layer, and the various evaluation indicators as the indicator layer.

[0012] The collapse risk assessment value Re of the mountain to be assessed and the collapse risk assessment value Rc of the control mountain were calculated using the constructed mountain collapse risk assessment system model. k Where k is the number of the reference mountain, k = 1, 2, ..., n;

[0013] Based on the calculation results of the collapse risk assessment value, the reference range [Rc] of the collapse risk assessment value is determined. n Rc m ], where m, n∈k, and Rc n <Re<Rc m The collapse probability value Pe of the mountain to be assessed is calculated based on the reference range of collapse risk assessment values. The formula for calculating the collapse probability value is as follows: Where Ne represents the number of all control mountains in the landslide risk assessment reference domain, and Nh represents the number of control mountains that have experienced landslides in the landslide risk assessment reference domain.

[0014] Furthermore, the evaluation domain of the rock mass type and geological structure evaluation indicators in the geological environmental factors is divided according to the indicator category attributes.

[0015] A landslide early warning system based on multi-factor analysis includes:

[0016] The data acquisition module is used to collect evaluation index data of the mountain, including rock mass type, geological structure, mountain slope, groundwater flow, vibration amplitude, precipitation, snow melt, earthwork excavation at the toe of the slope, load on the slope, and water discharge / storage volume.

[0017] An evaluation index database, which is communicatively connected to the data acquisition module, is used to store the evaluation index data;

[0018] The data analysis module is used to filter the evaluation index data of the same type as the evaluation index data of the mountain to be evaluated, so as to obtain the comparison mountain that is highly similar to the mountain to be evaluated.

[0019] The evaluation module is used to construct a structural model for landslide risk assessment and to calculate the landslide risk assessment value of the mountain to be assessed and the landslide risk assessment value of the control mountain based on the constructed model.

[0020] The central processing module is communicatively connected to the evaluation module. It is used to calculate the collapse probability of the mountain to be evaluated based on the calculation result of the collapse risk assessment value, obtain the collapse probability value of the mountain to be evaluated, and generate an early warning signal when the collapse probability value of the mountain to be evaluated exceeds the warning value.

[0021] The early warning module is communicatively connected to the central processing module. It is used to formulate an early warning strategy in response to the early warning signal generated by the central processing module and to transmit the early warning strategy to the smart terminal in the early warning area via radio.

[0022] The smart terminal is distributed within the warning area and is used to receive the warning strategy sent by the warning module.

[0023] Furthermore, the system includes a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0024] Furthermore, the implementation steps of this system are as follows:

[0025] Step 1) Screen the factors affecting landslides, determine the evaluation indicators for each factor, and collect the evaluation indicator data of the mountain through the data acquisition module. The evaluation indicators include rock mass type, geological structure, mountain slope, groundwater flow, vibration amplitude, precipitation, snow melt, earthwork excavation at the toe of the slope, load on the slope, and water discharge / storage. Establish a landslide evaluation indicator database and store the acquired evaluation indicator data in the evaluation indicator database.

[0026] Step 2) Using the data analysis module, cluster analysis is used to screen for control mountain evaluation index data that are similar to the evaluation index data of the mountain to be evaluated, thereby obtaining control mountains with a high degree of similarity to the evaluation index data of the mountain to be evaluated.

[0027] Step 3) Construct a landslide risk assessment system architecture model using the evaluation module. The model uses the landslide risk assessment value Re as the target layer, landslide influencing factors as the criterion layer, and the various evaluation indicators as the indicator layer. Calculate the landslide risk assessment value of the mountain to be assessed and the landslide risk assessment value of the control mountain based on the constructed model. The construction steps of the landslide risk assessment system architecture model are as follows:

[0028] S1. Design a questionnaire on the influencing factors of landslides and the relative importance of landslide risk assessment indicators under these influencing factors. Using an expert survey method and a proportional scaling method, conduct pairwise comparisons and scores of the indicators in the criterion and indicator layers of the landslide risk assessment system architecture model. Construct a judgment matrix based on the scoring results and calculate the weight matrix A of the criterion layer. i and the evaluation index matrix A in the index layer ijWhere i = 1, 2, 3; j = 1, 2, ..., n;

[0029] S2, perform a consistency check on the judgment matrix. When the consistency ratio CR of the judgment matrix is ​​less than 0.1, the consistency of the judgment matrix is ​​acceptable. When there is a case where the consistency ratio CR of the judgment matrix is ​​greater than or equal to 0.1, return to step S1.

[0030] S3, determine the evaluation level domain V = {very dangerous, somewhat dangerous, moderately safe, relatively safe, safe} five levels, and assign each level a value of V1, V2, V3, V4, V5 respectively, where 0≤V1<V2<V3, V4<V5≤100;

[0031] S4, define the value range of each level corresponding to each evaluation indicator in the indicator layer [a i b i ], where i=1,2,...,5,a i b is the minimum value of the i-th level. i The maximum value of the i-th level;

[0032] S5, based on the value range division results, obtain the membership degree of each evaluation indicator in the indicator layer, perform comprehensive analysis, and normalize to obtain the evaluation matrix R, where,

[0033] S6, based on the obtained evaluation matrix R, perform a first-level fuzzy comprehensive evaluation on each evaluation index in the index layer, and normalize the results to obtain the overall evaluation matrix B, where, B i =A ij *R i ,i=1,2,3; j=1,2...,n;

[0034] S7, Based on the obtained evaluation matrix B, perform a second-level fuzzy comprehensive evaluation on each indicator in the criterion layer, and normalize the matrix to obtain matrix C, where,

[0035]

[0036] S8. Based on the assigned values ​​of each level in step S3 and the C value obtained by normalization in step S7, calculate the total score f of the landslide risk assessment system, which is the landslide risk assessment value of the mountain, where f = V1C1 + V2C2 + V3C3 + V4C4 + V5C5.

[0037] Step 4): After obtaining the landslide risk assessment value of the mountain, the central processing module determines the reference range [Rc] of the landslide risk assessment value based on the calculation results. n Rc m], where m, n∈k, and Rc n <Re<Rc m The collapse probability value Pe of the mountain to be assessed is calculated based on the reference range of collapse risk assessment values. The formula for calculating the collapse probability value is as follows: Where Ne represents the number of all control mountains corresponding to the landslide risk assessment reference domain, and Nh represents the number of control mountains that have experienced landslides corresponding to the landslide risk assessment reference domain. The steps for determining the reference domain for the landslide risk assessment value are as follows:

[0038] Step 41) Obtain the collapse risk assessment value Re of the mountain to be assessed and the collapse risk assessment value Rc of the control mountain. k The values ​​are used to create a sample set, denoted as {Re, Rc1, Rc2, ..., Rc...}. k};

[0039] Step 42): Obtain the mean and standard deviation of the sample set, and standardize the data using the mean and standard deviation. The standardization formula is: In this formula, z is the standard parameter, σ is the variance of the sample data, and μ is the mean of the sample data;

[0040] Step 43): After standardization is completed, the standard parameters are utilized. The numerical range is adjusted to [0,1]. The collapse risk assessment value Re of the mountain to be evaluated is then used to perform a level division based on the function value of f(k). The division mechanism is as follows:

[0041] when At that time, the collapse risk assessment value Re of the mountain to be assessed was classified as Level 1;

[0042] when At that time, the collapse risk assessment value Re of the mountain to be assessed was classified as Level II;

[0043] Where f(k)min and f(k)max are the minimum and maximum values ​​of the function value of f(k), respectively, and f(k)Re is the function value of the collapse risk assessment value Re of the mountain to be assessed;

[0044] Step 44), based on the landslide risk assessment value Rc of the control mountain. k The values ​​are used to classify the collapse risk assessment values ​​to determine the reference range [Rc]. n Rc m The determining principle is as follows:

[0045] When the collapse risk assessment value Re of the mountain to be assessed is classified as Level 1, choose f(k)min≤f(k)Rc. n And f(k)Rc mThe collapse risk assessment value Rc of the control mountain corresponding to ≤(f(k)min+f(k)max) / 2 n and Rc m ;

[0046] When the collapse risk assessment value Re of the mountain to be assessed is classified as Level II, select (f(k)min+f(k)max) / 2≤f(k)Rc n And f(k)Rc m The collapse risk assessment value Rc of the control mountain corresponding to ≤(k)max n and Rc m ;

[0047] Where f(k)Rc n ,f(k)Rc m These are the landslide risk assessment values ​​Rc of the control mountain. k The function value of f(k);

[0048] Set the early warning threshold range for the probability of landslides. When the probability of landslides of the landslides to be assessed exceeds the warning value, the central processing module generates an early warning signal.

[0049] Step 5): After the central processing module generates an early warning signal, the early warning signal is sent to the early warning module. The early warning module responds to the early warning signal generated by the central processing module, formulates an early warning strategy, and sends the early warning strategy to the smart terminals in the early warning area via radio. The smart terminals receive the early warning strategy sent by the early warning module and inform the personnel in the early warning area.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] (1) The early warning method proposed in this invention is based on a multi-level fuzzy comprehensive evaluation model. By constructing a mountain collapse risk assessment system model with the mountain collapse risk assessment value as the target layer, the mountain collapse influencing factors as the criterion layer, and the various evaluation indicators as the indicator layer, the reference value range of the mountain collapse risk assessment value is determined according to the calculation results of the collapse risk assessment value, and the collapse probability value of the mountain to be assessed is calculated according to the reference value range of the collapse risk assessment value. It can conduct scientific and comprehensive analysis based on the data of various influencing factors of mountain collapse, realize the quantitative assessment of the collapse risk of the mountain to be assessed, and conduct monitoring and early warning before the mountain collapse disaster occurs, thereby gaining time for the formulation of disaster prevention measures and strategies. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating an early warning method for landslides based on multi-influencing factor analysis according to the present invention.

[0053] Figure 2This is a schematic diagram of the structure of a landslide early warning system based on multi-influencing factor analysis according to the present invention. Detailed Implementation

[0054] The present invention will be further described below with reference to specific embodiments. The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the present invention. In order to better illustrate the specific embodiments of the present invention, some parts in the drawings may be omitted, enlarged or reduced, and do not represent the actual product size.

[0055] Example 1

[0056] Geological environmental factors, precipitating factors, and human factors are used as influencing factors of landslides. The evaluation indicators of geological environmental factors include rock mass type, geological structure, mountain slope, and groundwater flow. The precipitating factors include vibration amplitude, precipitation, and snowmelt. The human factors include earthwork excavation at the toe of the slope, load on the slope, and water discharge / storage. The specific steps for constructing the structural model of the landslide risk assessment system in the technical solution of this invention are explained.

[0057] like Figure 1-2 As shown;

[0058] The construction steps of the structural model for landslide risk assessment are as follows:

[0059] S1. A questionnaire was designed to assess the relative importance of landslide influencing factors and landslide risk assessment indicators under these factors. Using an expert survey method, a proportional scaling method was employed to compare and score the indicators in the criterion and indicator layers of the landslide risk assessment system architecture model pairwise. The following table illustrates an example using the 1-9 proportional scaling method:

[0060] Table 11-9 Definition and Explanation of Scale

[0061]

[0062] Based on the scoring results from the questionnaire return statistics, a judgment matrix is ​​constructed, resulting in the following judgment matrix:

[0063] Table 2 "Landslide Risk Assessment Value" Judgment Matrix

[0064]

[0065] Table 3 "Evaluation Indicators under Geological Environmental Factors" Judgment Matrix

[0066]

[0067]

[0068] Table 4. Judgment Matrix of Evaluation Indicators under Inducing Factors

[0069]

[0070] Table 5. Judgment Matrix Table of "Evaluation Indicators under Human Factors"

[0071]

[0072] Based on the data in Tables 2-5, the weights of the geological environment factors, inducing factors, and anthropogenic factors of the criterion layer are calculated to be 0.5278, 0.3325, and 0.1396, respectively. Therefore, the weight matrix of the criterion layer is A = (A1, A2, A3) = (0.5278, 0.3325, 0.1396). The weights of the evaluation indicators of rock mass type, geological structure, mountain slope, and groundwater flow under the geological environment factors are 0.0966, 0.4520, 0.1663, and 0.2851, respectively. Therefore, A1 = (0.0966, 0.4520, 0.1663, 0.2851).

[0073] The weights of the evaluation indicators vibration amplitude, precipitation, and snowmelt amount under the inducing factors are 0.5278, 0.3325, and 0.1396, respectively, so A2 = (0.5278, 0.3325, 0.1396).

[0074] The weights of the evaluation indicators for human factors, namely, the earthwork excavation volume at the slope toe, the load on the slope, and the water discharge / storage volume, are 0.5278, 0.3325, and 0.1396, respectively, so A3 = (0.5278, 0.3325, 0.1396).

[0075] S2. Perform a consistency check on the judgment matrix. When the consistency ratios (CR) of the judgment matrix are 0.0598, 0.0516, 0.0516, and 0.0516, respectively, and are all less than 0.1, the consistency of the judgment matrix is ​​acceptable.

[0076] S3, determine the evaluation level domain V = {very dangerous, somewhat dangerous, moderately safe, relatively safe, safe} five levels, and assign each level a value of V1, V2, V3, V4, V5 respectively, where 0 ≤ V1 < V2 < V3, V4 < V5 ≤ 100. In this embodiment, V1 = 20, V2 = 40, V3 = 60, V4 = 80, V5 = 100 respectively for explanation;

[0077] S4, define the value range of each level corresponding to each evaluation indicator in the indicator layer [a i b i ], where i=1,2,...,5,a i b is the minimum value of the i-th level. iThis represents the maximum value of the i-th level. It should be noted that the rock mass type and geological structure evaluation indicators in the geological environment factors are non-quantitative evaluation indicators. In actual processing, the evaluation level domain can be divided according to the indicator category attributes. For example, regarding rock mass types, common mountain rock mass types include granite, diabase, gabbro, basalt, andesite, clastic limestone, limestone, dolomite, marl, marble, gneiss, etc. Generally, hard igneous rocks, metamorphic rocks, and sedimentary rocks such as carbonate rocks (e.g., limestone, dolomite), quartz sandstone, conglomerate, and other rocky loess with initial diagenetic characteristics, and dense loess, can form large-scale rockfalls; shale, marl, etc. Interbedded rocks and loose soil layers are often characterized by collapse and erosion. Therefore, when classifying the risk level, limestone, dolomite, quartz sandstone, and conglomerate can be classified as very dangerous based on the hardness of the rock mass. Other classifications are similar and will not be elaborated here. As for geological structures, various structural planes, such as joints, fissures, bedding planes, and faults, cut and separate the slope, providing the boundary conditions of the detached body (mountain) for the formation of landslides. The more developed the fissures in the slope, the easier it is for landslides to occur. Steeply dipping structural planes that are almost parallel to the direction of slope extension are most conducive to the formation of landslides. Therefore, the risk level can be classified based on the number of faults in the mountain or the width of fissures in the slope.

[0078] S5. Based on the value range division results, the membership degree of each evaluation indicator in the indicator layer is obtained for comprehensive analysis, thereby obtaining the evaluation matrices of each evaluation indicator under the geological environment factors, the inducing factors, and the anthropogenic factors of the mountain to be evaluated, as shown in the table below:

[0079] Table 6. Evaluation Matrix Table of Evaluation Indicators under Geological Environmental Factors

[0080]

[0081] Table 7. Evaluation Matrix Table of "Evaluation Indicators under Inducing Factors"

[0082]

[0083] Table 8 Evaluation Matrix Table of "Evaluation Indicators under Human Factors"

[0084]

[0085] The evaluation matrix R is obtained by normalization, where, have,

[0086] S6, based on the obtained evaluation matrix R, perform a first-level fuzzy comprehensive evaluation on each evaluation index in the index layer, and normalize the results to obtain the overall evaluation matrix B, where, B i =A ij *R i If i = 1, 2, 3; j = 1, 2, ..., n, then B1 = A 1j *R1=(0.0966 0.4520 0.1663 0.2851)*R1=(0.019 0.172 0.481 0.219 0.109);

[0087] B2 = A 2j *R2=(0.5278,0.3325,0.1396)*R2=(0.322 0.437 0.158 0.037 0.046);

[0088] B3 = A 3j *R3=(0.5278 0.3325 0.1396)*R3=(0.322 0.437 0.158 0.037 0.046);

[0089]

[0090] S7, Based on the obtained evaluation matrix B, perform a second-level fuzzy comprehensive evaluation on each indicator in the criterion layer, and normalize the matrix to obtain matrix C, where,

[0091]

[0092] We have C = (0.2148, 0.2971, 0.3285, 0.1331, 0.0792);

[0093] S8. Based on the assigned values ​​of each level in step S3 and the C value obtained from the normalization process in step S7, calculate the total score f of the landslide risk assessment system to be evaluated, which is the landslide risk assessment value of the mountain. Wherein, f = 20 × 0.2148 + 40 × 0.2971 + 60 × 0.3285 + 80 × 0.1331 + 100 × 0.0792 = 54.458 points. The calculated f value is between V2 and V3, close to V3. Therefore, the safety of the mountain to be evaluated is generally average.

[0094] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for early warning of landslides based on multi-factor analysis, characterized in that: Includes the following steps: Screen at least one of the following factors affecting landslides: geological environmental factors, triggering factors, and human factors; and determine the evaluation index for each of these factors. Multiple evaluation index data of mountains were collected to establish a mountain collapse evaluation index database. Cluster analysis was used to screen the comparison mountains in the database that are similar to the evaluation index data of the mountain to be evaluated. A structural model for landslide risk assessment is constructed, with the landslide risk assessment value Re as the target layer, the landslide influencing factors as the criterion layer, and the various evaluation indicators as the indicator layer. The collapse risk assessment value Re of the mountain to be assessed and the collapse risk assessment value Rc of the control mountain were calculated using the constructed mountain collapse risk assessment system model. k Where k is the number of the reference mountain, k=1,2,...,n; Based on the calculation results of the collapse risk assessment value, the reference range [Rc] of the collapse risk assessment value is determined. n Rc m ], where m, n∈k, and Rc n <Re<Rc m The collapse probability value Pe of the mountain to be assessed is calculated based on the reference range of collapse risk assessment values. The formula for calculating the collapse probability value is as follows: Where Ne is the number of all control mountains in the landslide risk assessment reference field, and Nh is the number of control mountains that have experienced landslides in the landslide risk assessment reference field. The construction steps of the landslide risk assessment system architecture model are as follows: S1. Design a questionnaire on the influencing factors of landslides and the relative importance of landslide risk assessment indicators under these influencing factors. Using an expert survey method and a proportional scaling method, conduct pairwise comparisons and scores of the indicators in the criterion and indicator layers of the landslide risk assessment system architecture model. Construct a judgment matrix based on the scoring results and calculate the weight matrix A of the criterion layer. i and the evaluation index matrix A in the index layer ij Where i = 1, 2, 3; j = 1, 2, ..., n; S2, perform a consistency check on the judgment matrix. When the consistency ratio CR of the judgment matrix is ​​less than 0.1, the consistency of the judgment matrix is ​​acceptable. When there is a case where the consistency ratio CR of the judgment matrix is ​​greater than or equal to 0.1, return to step S1. S3, determine the evaluation level domain V = {very dangerous, somewhat dangerous, moderately safe, relatively safe, safe} five levels, and assign each level a value of V1, V2, V3, V4, V5 respectively, where 0 ≤ V1 < V2 < V3, V4 < V5 ≤ 100; S4, define the value range of each level corresponding to each evaluation indicator in the indicator layer [a i b i ], where, i=1,2,...,5,a i b is the minimum value of the i-th level. i The maximum value of the i-th level; S5, based on the value range division results, obtain the membership degree of each evaluation indicator in the indicator layer, perform comprehensive analysis, and normalize to obtain the evaluation matrix R, where, ; S6, based on the obtained evaluation matrix R, perform a first-level fuzzy comprehensive evaluation on each evaluation index in the index layer, and normalize the results to obtain the overall evaluation matrix B, where, ; S7, Based on the obtained evaluation matrix B, perform a second-level fuzzy comprehensive evaluation on each indicator in the criterion layer, and normalize the matrix to obtain matrix C, where, ; S8. Based on the assigned values ​​for each level in step S3 and the C value obtained through normalization in step S7, calculate the total score f of the landslide risk assessment system, which is the landslide risk assessment value of the mountain. ; The steps for determining the reference range of the collapse risk assessment value are as follows: Step 1) Obtain the collapse risk assessment value Re of the mountain to be assessed and the collapse risk assessment value Rc of the control mountain. k The values ​​are used to create a sample set, denoted as {Re, Rc1, Rc2, ..., Rc...}. k }; Step 2), obtain the mean and standard deviation of the sample set, and standardize the data using the mean and standard deviation. The standardization formula is: In this formula, z is the standard parameter, σ is the variance of the sample data, and μ is the mean of the sample data; Step 3), after standardization is completed, the standard parameters are utilized. The numerical range is adjusted to [0,1]. The collapse risk assessment value Re of the mountain to be evaluated is then used to perform a level division based on the function value of f(k). The division mechanism is as follows: when At that time, the collapse risk assessment value Re of the mountain to be assessed was classified as Level 1; when At that time, the collapse risk assessment value Re of the mountain to be assessed was classified as Level II; Where f(k)min and f(k)max are the minimum and maximum values ​​of the function value of f(k), respectively, and f(k)Re is the function value of the collapse risk assessment value Re of the mountain to be assessed; Step 4), based on the landslide risk assessment value Rc of the reference mountain. k The values ​​are used to classify the collapse risk assessment values ​​to determine the reference range [Rc]. n Rc m The determining principle is as follows: When the collapse risk assessment value Re of the mountain to be assessed is classified as Level 1, choose f(k)min≤f(k)Rc. n And f(k)Rc m The collapse risk assessment value Rc of the control mountain corresponding to ≤(f(k)min+f(k)max) / 2 n and Rc m ; When the collapse risk assessment value Re of the mountain to be assessed is classified as Level II, choose (f(k)min+f(k)max) / 2≤f(k)Rc n And f(k)Rc m The collapse risk assessment value Rc of the control mountain corresponding to ≤(k)max n and Rc m ; Where f(k)Rc n ,f(k)Rc m These are the landslide risk assessment values ​​Rc of the control mountain. k The function value of f(k).

2. The landslide early warning method based on multi-influencing factor analysis according to claim 1, characterized in that: The evaluation indicators for the geological environmental factors include at least one of the following: rock mass type, geological structure, mountain slope, and groundwater flow. The triggering factors include at least one of vibration amplitude, precipitation, and snowmelt. The human factors mentioned include at least one of the following: earthwork excavation at the toe of the slope, load on the slope, and water discharge / storage volume.

3. A landslide early warning method based on multi-influencing factor analysis according to claim 1 or 2, characterized in that: The evaluation domain of the rock mass type and geological structure evaluation indicators in the geological environmental factors is divided according to the indicator category attributes.

4. A landslide early warning system based on multi-influence factor analysis, used to implement the landslide early warning method based on multi-influence factor analysis as described in any one of claims 1-3, characterized in that: include: The data acquisition module is used to collect evaluation index data of the mountain, including rock mass type, geological structure, mountain slope, groundwater flow, vibration amplitude, precipitation, snow melt, earthwork excavation at the toe of the slope, load on the slope, and water discharge / storage volume. An evaluation index database, which is communicatively connected to the data acquisition module, is used to store the evaluation index data; The data analysis module is used to filter out the same type of comparison mountain evaluation index data as the mountain to be evaluated, so as to obtain comparison mountains that are highly similar to the mountain to be evaluated. The evaluation module is used to construct a structural model for landslide risk assessment and to calculate the landslide risk assessment value of the mountain to be assessed and the landslide risk assessment value of the control mountain based on the constructed model. The central processing module is communicatively connected to the evaluation module. It is used to calculate the collapse probability of the mountain to be evaluated based on the calculation result of the collapse risk assessment value, obtain the collapse probability value of the mountain to be evaluated, and generate an early warning signal when the collapse probability value of the mountain to be evaluated exceeds the warning value. The early warning module is communicatively connected to the central processing module. It is used to formulate an early warning strategy in response to the early warning signal generated by the central processing module and to transmit the early warning strategy to the smart terminal in the early warning area via radio. The smart terminal is distributed within the warning area and is used to receive the warning strategy sent by the warning module.

5. The landslide early warning system based on multi-influencing factor analysis according to claim 4, characterized in that: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method according to any one of claims 1-3.

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