Remote thermal detection method for stripping type collapse deformation of side slope rock mass

Abstract

The invention discloses a remote thermal detection method for stripping type collapse deformation of a side slope rock mass. The method comprises the following steps: respectively calculating a rock mass lithology value, a side slope rock mass hydrogeological condition value, a fracture opening value and a side slope rock mass structure body thickness value of the side slope rock mass; according to an analytic hierarchy process, respectively determining a rock lithology weight, a slope rock hydrogeological condition weight, a fracture opening weight and a slope rock structure thickness weight; substituting the rock lithology value, the rock lithology weight, the side slope rock hydrogeological condition value, the side slope rock hydrogeological condition weight, the fracture opening value, the fracture opening weight, the side slope rock structure thickness value and the side slope rock structure thickness weight into a preset influence factor calculation formula to obtain an influence factor calculation result corresponding to the side slope rock; and determining the stability result of the slope rock mass according to the influence factor calculation result. According to the invention, the accuracy of slope rock deformation prediction can be improved.

Description

Technical Field

[0001] This invention relates to the field of geological engineering technology, and to a remote thermal detection method for slope rock mass peeling-type collapse deformation. Background Technology

[0002] Slope deformation is a key research area in geological engineering, and its detection is an important aspect of geological testing and engineering safety. Effective three-dimensional monitoring of slope deformation can provide strong protection for engineering safety, ensure personal safety, and reduce unnecessary economic losses.

[0003] Rock slope deformation, especially that of escaping cliffs, is typically caused by rock deformation resulting from daily temperature cycles. However, current technologies are not yet capable of three-dimensional monitoring of deformation and related temperature changes. At present, deformation monitoring of escaping cliffs primarily focuses on detecting changes in the mountain's deformation and slope displacement.

[0004] Close-range photogrammetry is a technique that uses photogrammetry to monitor slope deformation. It mainly uses high-precision photography equipment and professional photogrammetry software to perform non-contact photogrammetry on rock slopes, which can obtain a large amount of data. The three-dimensional coordinate data and related displacement of the slope are obtained by processing the data through multi-baseline digital close-range photogrammetry software, and then the slope deformation can be analyzed.

[0005] Three-dimensional laser scanning technology determines the spatial location of rock masses through laser ranging principles, determines the distance by the round-trip time of laser pulses, and combines horizontal and vertical scanning angles to calculate the three-dimensional coordinates of the rock masses. Finally, a three-dimensional model of the rock slope is generated, which can then be used to analyze the slope deformation. Summary of the Invention

[0006] To address the aforementioned shortcomings in existing technologies, this invention provides a remote thermal detection method for slope rock mass peeling-type collapse deformation, which solves the problem of inaccurate slope deformation prediction in existing technologies.

[0007] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: a remote thermal detection method for slope rock mass peeling-type collapse deformation, comprising the following steps:

[0008] S1. Calculate the rock mass lithology value, hydrogeological condition value, fissure aperture value, and structural thickness value of the slope rock mass using the first formula, the second formula, the third formula, and the fourth formula, respectively.

[0009] S2. Based on the analytic hierarchy process, determine the weight of rock mass lithology corresponding to the rock mass lithology value, the weight of slope rock mass hydrogeological conditions corresponding to the slope rock mass hydrogeological conditions value, the weight of fracture aperture corresponding to the fracture aperture value, and the weight of slope rock mass structure thickness corresponding to the slope rock mass structure thickness value.

[0010] S3. Substitute the rock mass lithology value, rock mass lithology weight, slope rock mass hydrogeological condition value, slope rock mass hydrogeological condition weight, fissure aperture value, fissure aperture weight, slope rock mass structure thickness value, and slope rock mass structure thickness weight into the preset influencing factor calculation formula to obtain the calculation results of the influencing factors corresponding to the slope rock mass.

[0011] S4. Based on the calculation results of the influencing factors, determine the stability result of the slope rock mass.

[0012] The beneficial effects of the above scheme are:

[0013] (1) This invention effectively monitors and analyzes the deformation behavior of rock slope peeling collapse by combining comprehensive ground laser scanning and infrared thermal imaging technology, providing an important reference for the study of potential rockfall sources.

[0014] (2) This invention uses a crack meter installed at the rock peeling edge to accurately measure and verify the accuracy of integrated ground laser scanning data. Real-time data from the crack meter is used to calibrate and evaluate the accuracy of the integrated ground laser scanning measurements, ensuring the reliability of deformation monitoring.

[0015] (3) This invention utilizes the remote detection capability of infrared thermal imagers to identify potential thermally deformable areas, providing a non-contact, large-scale monitoring method. Infrared thermal imagers can be used not only in conjunction with integrated ground laser scanning but also potentially independently. This method has significant practical value for predicting and preventing rockfalls and provides a new technical means for monitoring future hazardous areas.

[0016] Furthermore, in S1, the first formula is:

[0017] S=λ0+v*ΔT1

[0018] Where S represents the rock mass lithology value, λ0 represents the thermal conductivity at the reference temperature, v represents the temperature response coefficient related to the rock material, reflecting the change in thermal conductivity of the rock under temperature changes, and ΔT1 represents the difference between the first measured temperature and the first reference temperature.

[0019] The second formula is:

[0020] W = J n *u

[0021] Where W represents the hydrogeological condition value of the slope rock mass, Jn Indicates the number of joint groups, and u represents the interstitial water pressure;

[0022] The third formula is:

[0023] D = D0 + β * D0 * ΔT2

[0024] Where D represents the crack aperture value, D0 represents the crack aperture at the reference temperature, β represents the thermal response coefficient of the crack, reflecting the sensitivity of the crack to temperature changes, and ΔT2 represents the difference between the second measured temperature and the second reference temperature.

[0025] The fourth formula is:

[0026] M=M0+α*M0*ΔT3

[0027] Where M represents the thickness of the slope rock mass structure, M0 represents the reciprocal of the structure thickness at the reference temperature, α represents the thermal expansion coefficient of the rock mass material, and ΔT3 represents the difference between the third measured temperature and the third reference temperature.

[0028] Furthermore, prior to S1, the method also includes:

[0029] SA1. The first measured temperature of the slope rock mass was obtained using an infrared thermal imager through an indoor test method.

[0030] SA2. Using field investigation methods, the pore water pressure of slope rock masses with different soaking degrees was measured using a pore water pressure gauge;

[0031] SA3. Using field survey methods, infrared thermal imagers are used to obtain the second measured temperature of the slope rock mass, and integrated ground laser scanning is used to scan the rock fissures of the slope rock mass to obtain the fissure aperture.

[0032] SA4. An indoor test method was adopted, and an infrared thermal imager was used to obtain the third measured temperature of the slope rock mass.

[0033] Furthermore, S2 specifically includes:

[0034] S21. Determine the risk level of the rock mass lithology value, the risk level of the slope rock mass hydrogeological condition value, the risk level of the fracture aperture value, and the risk level of the slope rock mass structure thickness value, respectively.

[0035] S22. Compare the risk levels of rock mass lithology, slope rock mass hydrogeological conditions, fracture aperture, and slope rock mass structure thickness values ​​one by one to obtain a risk assessment matrix.

[0036] S23. Based on the geometric mean formula, calculate the geometric mean of each row of elements in the risk assessment judgment matrix;

[0037] S24. Normalize the geometric mean values ​​and use them as the weights of rock mass lithology, hydrogeological conditions, fracture aperture, and structural thickness, respectively.

[0038] Furthermore, the method also includes:

[0039] S25. Calculate the maximum eigenvalue of the risk assessment judgment matrix;

[0040] S26. Based on the largest eigenvalue, perform a consistency test on the risk assessment judgment matrix.

[0041] Furthermore, in S23, the geometric mean formula is:

[0042]

[0043] in, Let a represent the geometric mean of the i-th row in the risk assessment judgment matrix. ij This represents the element in the i-th row and j-th column of the risk assessment judgment matrix, where n represents the number of elements in each row.

[0044] Furthermore, in S24, the formula used to normalize the geometric mean is:

[0045]

[0046] Where, ω i Represents the geometric mean after normalization, represents the geometric mean of the i-th row in the risk assessment judgment matrix, and n represents the number of elements in each row.

[0047] Furthermore, in S25, the formula used to calculate the largest eigenvalue of the risk assessment judgment matrix is:

[0048]

[0049] Where, λ max Let represent the largest eigenvalue of the risk assessment judgment matrix, and n represent the number of elements in each row, (Aω). i Represents vector nω i The i-th element, ω i Let represent the geometric mean after normalization of the i-th . Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating a remote thermal detection method for slope rock mass peeling-type collapse deformation. Detailed Implementation

[0051] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0052] like Figure 1 As shown, a remote thermal detection method for slope rock mass peeling-type collapse deformation includes the following steps:

[0053] S1. Calculate the rock mass lithology value, hydrogeological condition value, fissure aperture value, and structural thickness value of the slope rock mass using the first formula, second formula, third formula, and fourth formula, respectively.

[0054] In this embodiment, in S1, the first formula is:

[0055] S=λ0+v*ΔT1

[0056] Where S represents the rock mass lithology value, λ0 represents the thermal conductivity at the reference temperature, v represents the temperature response coefficient related to the rock material, reflecting the change in thermal conductivity of the rock under temperature changes, and ΔT1 represents the difference between the first measured temperature and the first reference temperature.

[0057] The second formula is:

[0058] W = J n *u

[0059] Where W represents the hydrogeological condition value of the slope rock mass, J n Indicates the number of joint groups, and u represents the interstitial water pressure;

[0060] The third formula is:

[0061] D = D0 + β * D0 * ΔT2

[0062] Where D represents the crack aperture value, D0 represents the crack aperture at the reference temperature, β represents the thermal response coefficient of the crack, reflecting the sensitivity of the crack to temperature changes, and ΔT2 represents the difference between the second measured temperature and the second reference temperature.

[0063] The fourth formula is:

[0064] M=M0+α*M0*ΔT3

[0065] Where M represents the thickness of the slope rock mass structure, M0 represents the reciprocal of the structure thickness at the reference temperature, α represents the thermal expansion coefficient of the rock mass material, and ΔT3 represents the difference between the third measured temperature and the third reference temperature.

[0066] In this embodiment, prior to S1, the method further includes:

[0067] SA1. The first measured temperature of the slope rock mass was obtained using an infrared thermal imager through an indoor test method.

[0068] SA2. Using field investigation methods, the pore water pressure of slope rock masses with different soaking degrees was measured using a pore water pressure gauge;

[0069] SA3. Using field survey methods, infrared thermal imagers are used to obtain the second measured temperature of the slope rock mass, and integrated ground laser scanning is used to scan the rock fissures of the slope rock mass to obtain the fissure aperture.

[0070] SA4. An indoor test method was adopted, and an infrared thermal imager was used to obtain the third measured temperature of the slope rock mass.

[0071] For example, a growing body of research indicates that rock slope deformation, particularly that of escaping cliffs, is caused by rock deformation resulting from daily temperature cycles. Therefore, a comprehensive terrestrial laser scanning (TLS) method is used to remove anomalies and vegetation. The point cloud is processed using the master-stage scan of the original scan and aligned to a local coordinate system for three-dimensional detection of rock fissures. Simultaneously, an infrared thermal imager (IRT) is used to measure temperature changes on the rock surface. Thermal resistance sensors are inserted into the rock mass surface and within the fissures to calibrate the apparent temperature measured by the IRT camera. Long-term measurements of the observed rock mass are conducted to acquire dynamic data. Data analysis is used to find the relationship between rock fissure deformation and environmental temperature changes, gradually establishing a mathematical relationship between the two. This allows the IRT to be used independently to identify large-scale escaping collapse deformation, incorporating the effects of daily temperature cycles on rock deformation into collapse deformation prediction.

[0072] The characteristic of peeling-type collapse deformation is that the slope rock mass, under the cutting of multiple sets of structural planes, forms rectangular blocks or relatively independent "separated wedges," often exhibiting thin plate-like or sheet-like peeling. Therefore, in the process of monitoring peeling-type collapse deformation, it is necessary to obtain the properties and conditions of these structural planes and structures.

[0073] For example, using indoor testing methods, under the same room temperature conditions, an infrared thermal imager (IRT) is used to measure the temperature distribution of rock blocks of different lithologies. Referring to the actual room temperature, the variation law between the surface temperature of rock blocks of different lithologies and the actual room temperature is obtained. The rock mass lithology value S is characterized by the uniaxial compressive strength and temperature coefficient k (k is derived from multiple experiments and practical production experience). This quantitative relationship can be corrected during field investigation based on field data and production experience. Preferably, the reference benchmark values ​​for the rock mass lithology value S can be as shown in Table 1:

[0074] Table 1 Reference benchmark values ​​for rock mass lithology values

[0075]

[0076] For example, by using field investigation methods, the pore water pressure u (Pa) of rock samples with different degrees of soaking is measured using a pore water pressure gauge, and a second formula is derived based on the number of joint groups in the slope rock mass. Preferably, the reference benchmark value of the hydrogeological condition value W of the slope rock mass can be as shown in Table 2:

[0077] Table 2 Reference values ​​for hydrogeological conditions of slope rock mass

[0078]

[0079]

[0080] For example, rock slope deformation, especially that of escaping cliffs, is caused by rock deformation due to daily temperature cycles. Using a field survey method, a rock mass on a slope is selected, and a 24-hour period is used. Simultaneously, a three-dimensional inspection of rock fissures is performed using a comprehensive ground laser scanning (TLS) system, while the surface temperature of the rock mass is monitored using an infrared thermal imager (IRT). Preferably, the reference value for the fissure aperture D can be as shown in Table 3:

[0081] Table 3 Reference values ​​for fracture aperture

[0082]

[0083] Indoor experiments were conducted under identical room temperature conditions, using an infrared thermal imager (IRT) to measure the surface temperature and thermal conductivity of rock blocks of the same lithology but different thicknesses. Preferably, the reference values ​​for the thickness M of the slope rock mass structure are shown in Table 4.

[0084] Table 4 Reference values ​​for the thickness of slope rock mass structures

[0085]

[0086] S2. Based on the analytic hierarchy process, determine the rock mass lithology weight corresponding to the rock mass lithology value, the slope rock mass hydrogeological condition weight corresponding to the slope rock mass hydrogeological condition value, the fracture aperture weight corresponding to the fracture aperture value, and the slope rock mass structure thickness weight corresponding to the slope rock mass structure thickness value.

[0087] In this embodiment, S2 specifically includes:

[0088] S21. Determine the risk level of the rock mass lithology value, the risk level of the slope rock mass hydrogeological condition value, the risk level of the fracture aperture value, and the risk level of the slope rock mass structure thickness value, respectively.

[0089] S22. Compare the risk levels of rock mass lithology, slope rock mass hydrogeological conditions, fracture aperture, and slope rock mass structure thickness values ​​one by one to obtain a risk assessment matrix.

[0090] S23. Based on the geometric mean formula, calculate the geometric mean of each row of elements in the risk assessment judgment matrix;

[0091] S24. Normalize the geometric mean values ​​and use them as the weights of rock mass lithology, hydrogeological conditions, fracture aperture, and structural thickness, respectively.

[0092] In this embodiment, the method further includes:

[0093] S25. Calculate the maximum eigenvalue of the risk assessment judgment matrix;

[0094] S26. Based on the largest eigenvalue, perform a consistency test on the risk assessment judgment matrix.

[0095] In this embodiment, in S23, the geometric mean formula is:

[0096]

[0097] in, Let a represent the geometric mean of the i-th row in the risk assessment judgment matrix. ij This represents the element in the i-th row and j-th column of the risk assessment judgment matrix, where n represents the number of elements in each row.

[0098] In this embodiment, the formula used to normalize the geometric mean in S24 is:

[0099]

[0100] Where, ω i Represents the geometric mean after normalization, represents the geometric mean of the i-th row in the risk assessment judgment matrix, and n represents the number of elements in each row.

[0101] In this embodiment, in S25, the formula used to calculate the maximum eigenvalue of the risk assessment judgment matrix is:

[0102]

[0103] Where, λ max Let represent the largest eigenvalue of the risk assessment judgment matrix, and n represent the number of elements in each row, (Aω). i Represents vector nωi The i-th element, ω i Let represent the geometric mean after normalization of the i-th .

[0104] For example, four evaluation indicators are selected in this embodiment, so the judgment matrix size is 4×4. During the analysis of the slope rock mass stability evaluation indicators, it is necessary to compare each indicator pairwise and assign scores, as shown in Table 5. First, it is necessary to determine the risk level of the rock mass lithology value, the risk level of the slope rock mass hydrogeological condition value, the risk level of the fracture aperture value, and the risk level of the slope rock mass structure thickness value. The risk level can be determined based on empirical values, or it can be adjusted according to the actual situation; no specific restrictions are placed here.

[0105] The risk levels of rock mass lithology, slope rock mass hydrogeological conditions, fracture aperture, and slope rock mass structure thickness are compared pairwise, and a risk assessment judgment matrix is ​​obtained according to the scoring chart shown in Table 5.

[0106] Table 5. Risk Level Scoring Diagram

[0107]

[0108] The risk assessment judgment matrix is ​​determined based on the scoring results of the evaluation indicators, and the geometric mean of each row of the risk assessment judgment matrix is ​​calculated using the geometric mean formula. The calculated geometric mean of each row of the risk assessment judgment matrix is ​​then normalized and used as the weights for the following parameters: rock mass lithology value, slope rock mass hydrogeological condition value, fracture aperture value, and slope rock mass structure thickness value. Preferably, the risk assessment judgment matrix can be...

[0109] Calculate the maximum eigenvalue λ of the risk assessment judgment matrix. max The consistency of the risk assessment judgment matrix is ​​then checked based on the largest eigenvalue. For example, consistency test numbers CI and CR can be calculated to check the consistency of the calculation results. The CR value is compared with 0.1; if it is less than 0.1, the consistency is good; if it is greater than 0.1, the consistency is poor. Preferably, the calculation process for the consistency test numbers CI and CR can be as follows:

[0110]

[0111] Where CI and CR represent the consistency test numbers, and λ max RI represents the maximum eigenvalue, RI represents the average random consistency index, and N represents the order of the risk assessment judgment matrix.

[0112] Preferably, the weights and consistency test results of the risk assessment judgment matrix can be shown in Table 6:

[0113] Table 6. Weights and Consistency Test Results of the Risk Assessment Judgment Matrix

[0114]

[0115] Where X1 represents the rock mass lithology, X2 represents the hydrogeological conditions of the slope rock mass, X3 represents the fissure aperture, and X4 represents the thickness of the slope rock mass structure.

[0116] The higher the CI value, the worse the consistency of the risk assessment judgment matrix; conversely, the lower the CI value, the better the consistency of the judgment matrix.

[0117] Preferably, the corresponding reference values ​​for the RI value and the order of the risk assessment judgment matrix are shown in Table 7:

[0118] Table 7. Corresponding Reference Values ​​for RI Values ​​and the Order of the Risk Assessment Judgment Matrix

[0119]

[0120] S3. Substitute the rock mass lithology value, rock mass lithology weight, slope rock mass hydrogeological condition value, slope rock mass hydrogeological condition weight, fissure aperture value, fissure aperture weight, slope rock mass structure thickness value, and slope rock mass structure thickness weight into the preset influencing factor calculation formula to obtain the calculation results of the influencing factors corresponding to the slope rock mass.

[0121] For example, the preset formula for calculating influencing factors is:

[0122] X(S,W,D,M)=ω1S+ω2W+ω3D+ω4M

[0123] Where X(S,W,D,M) represents the calculation results of the influencing factors corresponding to the slope rock mass, ω1 represents the weight of the rock mass lithology value, ω2 represents the weight of the slope rock mass hydrogeological condition value, ω3 represents the weight of the fissure aperture value, and ω4 represents the weight of the slope rock mass structure thickness value.

[0124] S4. Based on the calculation results of the influencing factors, determine the stability result of the slope rock mass.

[0125] Preferably, the correspondence between the calculated results of the influencing factors of the slope rock mass and the stability results of the slope rock mass can be shown in Table 8:

[0126] Table 8. Correspondence between the calculation results of influencing factors and the stability results of the slope rock mass.

[0127]

[0128] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of the invention.

Claims

1. A remote thermal detection method for slope rock mass peeling-type collapse deformation, characterized in that, The method includes: S1. Calculate the rock mass lithology value, hydrogeological condition value, fissure aperture value, and structural thickness value of the slope rock mass using the first formula, the second formula, the third formula, and the fourth formula, respectively. S2. Based on the analytic hierarchy process, determine the rock mass lithology weight corresponding to the rock mass lithology value, the slope rock mass hydrogeological condition weight corresponding to the slope rock mass hydrogeological condition value, the fracture aperture weight corresponding to the fracture aperture value, and the slope rock mass structure thickness weight corresponding to the slope rock mass structure thickness value. S3. Substitute the rock mass lithology value, the rock mass lithology weight, the slope rock mass hydrogeological condition value, the slope rock mass hydrogeological condition weight, the fissure aperture value, the fissure aperture weight, the slope rock mass structure thickness value, and the slope rock mass structure thickness weight into the preset influencing factor calculation formula to obtain the influencing factor calculation results corresponding to the slope rock mass. S4. Based on the calculation results of the influencing factors, determine the stability result of the slope rock mass.

2. The method according to claim 1, characterized in that, In S1, the first formula is: S=λ0+v*ΔT1 Where S represents the rock mass lithology value, λ0 represents the thermal conductivity at the reference temperature, v represents the temperature response coefficient related to the rock material, reflecting the change in thermal conductivity of the rock under temperature changes, and ΔT1 represents the difference between the first measured temperature and the first reference temperature. The second formula is: W=J n *u Where W represents the hydrogeological condition value of the slope rock mass, J n Indicates the number of joint groups, and u represents the interstitial water pressure; The third formula is: D = D0 + β * D0 * ΔT2 Where D represents the crack aperture value, D0 represents the crack aperture at the reference temperature, β represents the thermal response coefficient of the crack, reflecting the sensitivity of the crack to temperature changes, and ΔT2 represents the difference between the second measured temperature and the second reference temperature. The fourth formula is: M=M0+α*M0*ΔT3 Where M represents the thickness of the slope rock mass structure, M0 represents the reciprocal of the structure thickness at the reference temperature, α represents the thermal expansion coefficient of the rock mass material, and ΔT3 represents the difference between the third measured temperature and the third reference temperature.

3. The method according to claim 1, characterized in that, Before step S1, the method further includes: SA1. The first measured temperature of the slope rock mass was obtained using an infrared thermal imager through an indoor test method. SA2. Using field investigation methods, the pore water pressure of slope rock masses with different soaking degrees was measured using a pore water pressure gauge; SA3. Using field survey methods, infrared thermal imagers are used to obtain the second measured temperature of the slope rock mass, and integrated ground laser scanning is used to scan the rock fissures of the slope rock mass to obtain the fissure aperture. SA4. An indoor test method was adopted, and an infrared thermal imager was used to obtain the third measured temperature of the slope rock mass.

4. The method according to claim 1, characterized in that, S2 specifically includes: S21. Determine the risk level of the rock mass lithology value, the risk level of the slope rock mass hydrogeological condition value, the risk level of the fracture aperture value, and the risk level of the slope rock mass structure thickness value, respectively. S22. Compare the risk levels of the rock mass lithology value, the hydrogeological condition value of the slope rock mass, the fissure aperture value, and the thickness value of the slope rock mass structure in pairs to obtain a risk assessment judgment matrix. S23. Based on the geometric mean formula, calculate the geometric mean of each row of elements in the risk assessment judgment matrix; S24. Normalize the geometric mean values ​​and use them as the rock mass lithology weights corresponding to the rock mass lithology values, the slope rock mass hydrogeological condition weights corresponding to the slope rock mass hydrogeological condition values, the fracture aperture weights corresponding to the fracture aperture values, and the slope rock mass structure thickness weights corresponding to the slope rock mass structure thickness values.

5. The method according to claim 4, characterized in that, The method further includes: S25. Calculate the maximum eigenvalue of the risk assessment judgment matrix; S26. Perform a consistency check on the risk assessment judgment matrix based on the maximum eigenvalue.

6. The method according to claim 4, characterized in that, In S23, the geometric mean formula is: in, Let a represent the geometric mean of the i-th row in the risk assessment judgment matrix. ij This represents the element in the i-th row and j-th column of the risk assessment judgment matrix, where n represents the number of elements in each row.

7. The method according to claim 4, characterized in that, In step S24, the formula used to normalize the geometric mean is: Where, ω i Represents the geometric mean after normalization, represents the geometric mean of the i-th row in the risk assessment judgment matrix, and n represents the number of elements in each row.

8. The method according to claim 5, characterized in that, In step S25, the formula used to calculate the maximum eigenvalue of the risk assessment judgment matrix is: Where, λ max Let represent the largest eigenvalue of the risk assessment judgment matrix, and n represent the number of elements in each row, (Aω). i Represents vector nω i The i-th element, ω i Let represent the geometric mean after normalization of the i-th .

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