CN-117392809-B - Mountain collapse early warning method and system based on multi-impact factor analysis

Abstract

The invention discloses a method and a system for early warning of mountain collapse based on multi-impact factor analysis, wherein the early warning method provided by the technical scheme of the invention is based on a multi-stage fuzzy comprehensive evaluation model, by constructing a mountain collapse risk evaluation system structure model taking a mountain collapse risk evaluation value as a target layer and a mountain collapse impact factor as a criterion layer, each evaluation index as an index layer, a reference value domain of the collapse risk evaluation value is determined according to the calculation result of the collapse risk evaluation value, and the collapse probability value of a mountain to be evaluated is calculated according to the reference value domain of the collapse risk evaluation value, so that the evaluation of the collapse risk of the mountain to be evaluated can be quantitatively evaluated according to the data of various impact factors of the collapse of the mountain, and the early warning is monitored before the occurrence of the collapse disaster of the mountain, thereby taking time for making disaster prevention measures and strategies.

Inventors

• ZHANG JIAJIA
• YANG DONG
• GAO BO
• LI YUANLING

Assignees

• 中国地质科学院探矿工艺研究所

Dates

Publication Date

20260512

Application Date

20231009

Claims (5)

1. 1. A mountain collapse early warning method based on multi-influence factor analysis is characterized by comprising the following steps: Screening at least one mountain collapse influence factor including geological environment factors, induction factors and human factors, and determining an evaluation index under each influence factor; Collecting evaluation index data of multiple mountain bodies, establishing a mountain body collapse evaluation index database, and screening comparison mountain bodies similar to the evaluation index data of the mountain bodies to be evaluated in the database by adopting cluster analysis; constructing a mountain collapse risk evaluation system structure model taking a mountain collapse risk evaluation value Re as a target layer, a mountain collapse influence factor as a criterion layer and various evaluation indexes as index layers; Respectively calculating a collapse risk assessment value Re of a mountain to be assessed and a collapse risk assessment value Rc k of a comparison mountain through the constructed mountain collapse risk assessment system structure model, wherein k is the number of the comparison mountain, and k=1, 2, and n; and determining a reference value range [ Rc n ,Rc m ] of the collapse risk evaluation value according to the calculation result of the collapse risk evaluation value, wherein m, n epsilon k, rc n <Re<Rc m , and calculating a collapse probability value Pe of the mountain to be evaluated according to the reference value range of the collapse risk evaluation value, wherein the calculation formula of the collapse probability value is as follows: Where Ne is the number of all the control mountains corresponding to the collapse risk assessment value reference domain, nh is the number of the control mountains corresponding to the collapse risk assessment value reference domain in which roller coast collapse occurs; The construction steps of the mountain collapse risk evaluation system structure model are as follows: S1, designing a mountain collapse influence factor and a relatively important questionnaire of mountain collapse risk evaluation indexes under the influence factor, comparing and scoring all indexes of a criterion layer and an index layer of a mountain collapse risk evaluation system structure model in pairs by adopting a proportion scale method through an expert investigation method, constructing a judgment matrix according to a scoring result, and calculating a weight matrix A i of the criterion layer and all evaluation index matrixes A ij in the index layer, wherein i=1, 2,3, j=1, 2, n; S2, consistency test is carried out on the judgment matrix, when the consistency ratio CR of the judgment matrix is smaller than 0.1, the consistency of the judgment matrix is acceptable, and when the consistency ratio CR of the judgment matrix is larger than or equal to 0.1, the step S1 is returned; S3, determining five levels of comment level domains V= { very dangerous, general safety, safer and safer, and respectively assigning each level as V 1 ,V 2 ,V 3 ,V 4 ,V 5 , wherein V 1 ＜V 2 ＜V 3 ,V 4 ＜V 5 is more than or equal to 0 and less than or equal to 100; S4, dividing and determining value ranges [ a i ,b i ] of various grades corresponding to each evaluation index in the index layer, wherein i=1, 2, & gt, 5, a i is the minimum value of the ith grade, and b i is the maximum value of the ith grade; S5, obtaining membership degree of each evaluation index in the index layer according to the division result of the value range, carrying out comprehensive analysis, and carrying out normalization treatment to obtain an evaluation matrix R, wherein, ; S6, carrying out primary fuzzy comprehensive evaluation on each evaluation index in the index layer according to the acquired evaluation matrix R, and carrying out normalization processing to acquire a total evaluation matrix B, wherein, ; S7, according to the acquired evaluation matrix B, carrying out secondary fuzzy comprehensive evaluation on each index in the alignment layer, and carrying out normalization processing to acquire a matrix C, wherein, ; S8, calculating the total score f of the mountain collapse risk evaluation system according to the assignment of each grade in the step S3 and the C value obtained by normalization in the step S7, namely the total score f is the collapse risk evaluation value of the mountain, ; The step of determining the reference value range of the collapse risk assessment value comprises the following steps: Step 1), acquiring a collapse risk evaluation value Re of a mountain to be evaluated and a collapse risk evaluation value Rc k of a contrast mountain, and creating a sample set by using the acquired values and recording as { Re, rc 1 ,Rc 2 ,、、、,Rc k }; step 2), obtaining the mean value and standard deviation in the sample set, and standardizing the data by using the mean value and the standard deviation, wherein the standardized formula is as follows In the formula, z is a standard parameter, sigma is the variance of sample data, and mu is the mean value of the sample data; Step 3), after the standardization is completed, utilizing the standard parameters Adjusting the numerical interval to be between 0 and 1, and carrying out grade splitting on the value of the collapse risk assessment value Re of the mountain to be assessed by using the function value of f (k), wherein the splitting mechanism is as follows: When (when) The value of the collapse risk assessment value Re of the mountain to be assessed is classified into a first class; When (when) The value of the collapse risk assessment value Re of the mountain to be assessed is classified into a second class; Wherein f (k) min, f (k) max are the minimum and maximum values of the function values 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), determining a reference value range [ Rc n ,Rc m ] of the collapse risk assessment value according to the grading result of the value of the collapse risk assessment value Rc k of the comparison mountain, wherein the determination principle is as follows: When the value of the collapse risk assessment value Re of the mountain to be assessed is classified as one level, f (k) min is less than or equal to f (k) Rc n , and f (k) Rc m is less than or equal to (f (k) min+f (k) max)/2, and the collapse risk assessment values Rc n and Rc m of the comparison mountain are selected; When the value of the collapse risk assessment value Re of the mountain to be assessed is classified into two levels, selecting (f (k) min+f (k) max)/2 f (k) Rc n , and the collapse risk assessment values Rc n and Rc m of the comparison mountain corresponding to f (k) Rc m less than or equal to (k) max; wherein f (k) Rc n ,f(k)Rc m is a function value of f (k) of the collapse risk evaluation value Rc k of the control mountain, respectively.
2. 2. The mountain collapse early warning method based on multi-impact factor analysis according to claim 1, wherein the method comprises the following steps: The evaluation index of the geological environment factors comprises at least one of rock mass type, geological structure, mountain slope and groundwater flow; The induction factors comprise at least one of vibration amplitude, precipitation amount and snow melting amount; The human factors comprise at least one of the excavating amount of the earthwork of the slope toe, the stacking amount on the slope body and the water drainage/water storage amount of the waterway.
3. 3. The mountain collapse early warning method based on multi-impact factor analysis according to claim 1 or 2, wherein the rock mass type and the comment level domain of the geological structure evaluation index in the geological environment factors are divided according to the index category attribute.
4. 4. A mountain collapse early warning system based on multi-impact factor analysis, for implementing the mountain collapse early warning method based on multi-impact factor analysis as set forth in any one of claims 1 to 3, comprising: The data acquisition module is used for acquiring evaluation index data of mountain bodies, including rock mass types, geological structures, mountain gradient, groundwater flow, vibration amplitude, precipitation, snow melting amount, slope toe earthwork excavation amount, pile capacity on the slope bodies and water drainage/water storage amount; The evaluation index database is in communication connection with the data acquisition module and is used for storing the evaluation index data; The data analysis module is used for screening comparison mountain evaluation index data similar to the mountain evaluation index data to be evaluated so as to obtain comparison mountain with larger similarity to the mountain evaluation index data to be evaluated; the evaluation module is used for constructing a mountain collapse risk evaluation system structure model, and calculating a collapse risk evaluation value of the mountain to be evaluated and a collapse risk evaluation value of the contrast mountain according to the constructed model; The central processing module is in communication connection with the evaluation module and is used for calculating the collapse probability of the mountain to be evaluated according to the calculation result of the collapse risk evaluation value, acquiring the collapse probability value of the mountain to be evaluated and generating an early warning signal when the collapse probability value of the mountain to be evaluated exceeds the warning value; The early warning module is in communication connection with the central processing module and is used for responding to the early warning signal generated by the central processing module to formulate an early warning strategy and transmitting the early warning strategy to an intelligent terminal in an early warning area in a radio mode; The intelligent terminals are distributed in the early warning area and used for receiving the early warning strategies sent by the early warning module.
5. 5. The system for pre-warning of mountain collapse based on multi-impact factor analysis as claimed in claim 4, wherein the system comprises a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor performs the steps of the method as claimed in any one of claims 1-3.

Description

Mountain collapse early warning method and system based on multi-impact factor analysis Technical Field The invention relates to the technical field of geological disaster early warning, in particular to a mountain collapse early warning method and system based on multi-impact factor analysis. Background Mountain collapse is one of common natural disasters, and not only can cause serious casualties or serious economic losses, but also can have adverse effects on the development of society, so that research on the formation mechanism of collapse and the control scheme thereof has important practical significance for disaster prevention and reduction work. In the prior art, displacement sensors are arranged or displacement change of the mountain is monitored and early-warned through an image recognition technology, so that a monitoring effect can be achieved when a collapse trend exists, a disaster is formed, the reasons for the collapse of the mountain are many, the method mainly comprises geological environment factors, induction factors, human factors and the like, comprehensive and scientific analysis is performed on various influence factors, and monitoring and early-warning of the collapse risk of the mountain is performed before the disaster is formed, so that time is striven for making disaster precautionary measures and strategies. Therefore, we propose a mountain collapse early warning method and system based on multi-impact factor analysis. Disclosure of Invention The invention mainly aims to provide a mountain collapse early warning method and system based on multi-effect factor analysis, which can effectively solve the problems in the background technology. In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: A mountain collapse early warning method based on multi-influence factor analysis comprises the following steps: screening at least one mountain collapse influence factor including geological environment factors, induction factors and human factors, and determining an evaluation index under each influence factor, wherein the evaluation index of the geological environment factors comprises at least one of rock mass type, geological structure, mountain gradient and groundwater flow; The induction factors comprise at least one of vibration amplitude, precipitation amount and snow melting amount; The human factors comprise at least one of the excavating amount of the earthwork of the slope toe, the stacking amount on the slope body and the water drainage/water storage amount of the waterway; Collecting evaluation index data of multiple mountain bodies, establishing a mountain body collapse evaluation index database, and screening comparison mountain bodies similar to the evaluation index data of the mountain bodies to be evaluated in the database by adopting cluster analysis; constructing a mountain collapse risk evaluation system structure model taking a mountain collapse risk evaluation value Re as a target layer, a mountain collapse influence factor as a criterion layer and various evaluation indexes as index layers; Respectively calculating a collapse risk assessment value Re of a mountain to be assessed and a collapse risk assessment value Rc k of a comparison mountain through the constructed mountain collapse risk assessment system structure model, wherein k is the number of the comparison mountain, and k=1, 2, and n; and determining a reference value range [ Rc n,Rcm ] of the collapse risk evaluation value according to the calculation result of the collapse risk evaluation value, wherein m, n epsilon k, rc n<Re<Rcm, and calculating a collapse probability value Pe of the mountain to be evaluated according to the reference value range of the collapse risk evaluation value, wherein the calculation formula of the collapse probability value is as follows: where Ne is the number of all control mountains corresponding to the collapse risk assessment value reference domain, and Nh is the number of control mountains corresponding to the collapse risk assessment value reference domain in which roller coast collapse occurs. Further, the rock mass type and the comment level domain of the geologic structure evaluation index in the geologic environment factors are divided according to the index category attribute. A mountain collapse early warning system based on multi-impact factor analysis, comprising: The data acquisition module is used for acquiring evaluation index data of mountain bodies, including rock mass types, geological structures, mountain gradient, groundwater flow, vibration amplitude, precipitation, snow melting amount, slope toe earthwork excavation amount, pile capacity on the slope bodies and water drainage/water storage amount; The evaluation index database is in communication connection with the data acquisition module and is used for storing the evaluation index data; The data analysis module is used for screening comparison mountain