Foundation pit evaluation method and device based on fuzzy hierarchy and set pair analysis and medium

Abstract

The invention provides a foundation pit evaluation method and device based on fuzzy hierarchy and set pair analysis and a medium. The method comprises the following steps: constructing a foundation pit safety risk evaluation index system comprising a plurality of subsystems; based on any subsystem, a plurality of score groups, a plurality of triangular fuzzy reciprocal judgment matrixes, a plurality of conversion judgment matrixes and a set pair judgment matrix are sequentially obtained based on all the evaluation indexes of the subsystem, and index weights corresponding to all the evaluation indexes are determined; establishing a five-element connection number calculation formula, and determining a connection number main value based on the obtained real-time monitoring value, the index weight, the five-element connection number calculation formula and a preset safety risk grade standard; and determining a safety evaluation grade of the subsystem and a safety evaluation result of the foundation pit engineering based on all the contact number main values and a safety risk grade standard. According to the technical scheme, the construction safety risk of the foundation pit engineering is evaluated through the fuzzy analytic hierarchy process and the set pair analysis method, and the reliability of the safety evaluation result of the foundation pit engineering is improved.

Description

Technical Field

[0001] This invention relates to the field of foundation pit monitoring technology, and in particular to a foundation pit evaluation method, device and medium based on fuzzy hierarchy and set pair analysis. Background Technology

[0002] In recent years, foundation pit engineering has shown a trend towards greater depth and area, leading to increasingly higher construction risks. To ensure the safety of foundation pit construction, it is necessary to track safety monitoring data at the construction site and conduct safety assessments based on this data.

[0003] Foundation pit engineering is a complex system involving multiple factors, and it exhibits significant regional characteristics. Currently, safety assessments of foundation pit projects rely on the experience of expert groups. However, the individual safety assessment results of different experts are somewhat biased, making it impossible to fully uncover all the uncertainties in foundation pit engineering. In other words, the reliability of safety assessment results obtained from existing technologies for foundation pit engineering is low. Summary of the Invention

[0004] This invention aims to address at least one of the technical problems existing in the prior art. To this end, this invention proposes a method, apparatus, and medium for foundation pit evaluation based on fuzzy hierarchical analysis and set pair analysis. It integrates fuzzy hierarchical analysis with set pair analysis to comprehensively evaluate the construction safety risks of foundation pit projects and fully explore the various uncertainties of foundation pit projects, thereby improving the reliability of the safety evaluation results for foundation pit projects.

[0005] In a first aspect, embodiments of the present invention provide a method for evaluating foundation pits based on fuzzy hierarchy and set pair analysis, including:

[0006] A foundation pit safety risk assessment index system is constructed, wherein the foundation pit safety risk assessment index system includes multiple subsystems, and each subsystem includes at least one assessment index;

[0007] Based on any of the subsystems, multiple score groups are obtained based on all the evaluation indicators of the subsystem. Multiple triangular fuzzy reciprocal judgment matrices are obtained based on all the score groups. Multiple transformation judgment matrices are obtained based on all the triangular fuzzy reciprocal judgment matrices. Set pair judgment matrices are obtained based on all the transformation judgment matrices. A compatibility judgment matrix is ​​established based on the set pair judgment matrix. The index weights corresponding to all the evaluation indicators of the subsystem are determined based on the compatibility judgment matrix.

[0008] A preset safety risk level standard and multiple real-time monitoring values ​​are obtained. The safety evaluation level of the safety risk level standard and the evaluation index are paired. A five-element connection coefficient calculation formula is established based on the pair. The principal value of the connection coefficient of all the evaluation indicators is determined based on all the real-time monitoring values, all the indicator weights, the five-element connection coefficient calculation formula and the safety risk level standard. The real-time monitoring value is the real-time value of the evaluation indicator, and the safety risk level standard includes multiple safety evaluation levels.

[0009] The safety evaluation level of the subsystem is determined based on the principal values ​​of the contact numbers corresponding to all the evaluation indicators of any subsystem and the safety risk level standard, and the safety evaluation result of the foundation pit project is determined based on all the safety evaluation levels.

[0010] According to some embodiments of the present invention, multiple score groups are obtained based on all the evaluation indicators of the subsystem, multiple triangular fuzzy reciprocal judgment matrices are obtained based on all the score groups respectively, and multiple transformation judgment matrices are obtained based on all the triangular fuzzy reciprocal judgment matrices respectively, including:

[0011] All evaluation indicators of the subsystem are obtained, and multiple score groups are obtained by having multiple experts score all evaluation indicators based on pairwise relative importance, wherein the number of score groups is equal to the number of experts;

[0012] Based on any score group, the triangular fuzzy reciprocal judgment matrix is ​​constructed, wherein the expression of the triangular fuzzy reciprocal judgment matrix is: X = (x ij ) n×n Let X be the triangular fuzzy reciprocal judgment matrix of order n, and let x be the triangular fuzzy reciprocal judgment matrix. ij Let i be the element in the i-th row and j-th column of the triangular fuzzy reciprocal judgment matrix, and n be the number of evaluation indicators of the subsystem, where i, j, and n are all positive integers.

[0013] Based on any of the aforementioned triangular fuzzy reciprocal judgment matrices, the transformation judgment matrix is ​​obtained based on the triangular fuzzy reciprocal judgment matrix and the first formula, where x ij The expression is: x ij =(e ij ,f ij ,g ij ), and g ij ≥e ij ≥f ij >1, e ij f is the lowest evaluation value for the relative importance of indicator i to indicator j. ijLet g be a possible evaluation value for the relative importance of indicator i to indicator j. ij Let i be the highest evaluation value for the relative importance of indicator i to indicator j. The score group includes the lowest evaluation value, the possible evaluation value, and the highest evaluation value. The first formula is: The expression for the transformation judgment matrix is: Y = (y ij ) n×n Y is the transformation judgment matrix, y ij The element in the i-th row and j-th column of the transformation judgment matrix.

[0014] According to some embodiments of the present invention, after obtaining multiple transformation judgment matrices based on all the said triangular fuzzy reciprocal judgment matrices, the method further includes:

[0015] Based on any of the transformation judgment matrices, multiple normalized feature elements are obtained using the second formula based on the transformation judgment matrix. A normalized feature vector is obtained based on all the normalized feature elements. The approximate value of the maximum eigenvalue is obtained based on the third formula, the transformation judgment matrix, and the normalized feature vector. The consistency ratio of the transformation judgment matrix is ​​obtained based on the fourth formula and the approximate value of the maximum eigenvalue. The consistency ratio is used to perform a consistency check on the transformation judgment matrix.

[0016] A comprehensive judgment matrix is ​​obtained based on the fifth formula and r transformation judgment matrices that pass the consistency test. A consistency test is then performed on the comprehensive judgment matrix, where r is the number of experts.

[0017] If the comprehensive judgment matrix fails the consistency test, all evaluation indicators of the subsystem are re-scored based on pairwise relative importance.

[0018] Wherein, the second formula is v i For the i-th normalized feature element, the third formula is: λ max Let (Yv) be the approximate value of the largest eigenvalue. i The fourth formula is the i-th element obtained by multiplying the transformation judgment matrix and the normalized eigenvector. CR is the consistency ratio, RI is the random consistency index in the preset random consistency index value table, and the fifth formula is... Let be the element in the i-th row and j-th column of the k-th transformation judgment matrix, where k is a positive integer, less than or equal to r. The expression for the comprehensive judgment matrix is: Z = (z ij ) n×n Z is the comprehensive judgment matrix, z ijis the element in the i-th row and j-th column of the comprehensive judgment matrix.

[0019] According to some embodiments of the present invention, a set pair judgment matrix is ​​obtained based on all of the said transformation judgment matrices, including:

[0020] An identity matrix is ​​constructed based on r transformation judgment matrices that pass the consistency test and the sixth formula; a difference matrix is ​​constructed based on all the transformation judgment matrices and the seventh formula; and a set pair judgment matrix is ​​constructed based on the identity matrix and the difference matrix.

[0021] The sixth formula is: Let be the minimum value among the elements in the i-th row and j-th column of the r transformation judgment matrices. Let the maximum value among the elements in the i-th row and j-th column of the r transformation judgment matrices be the identity matrix, and the expression for the identity matrix is: A is the identity matrix, a ij For the element in the i-th row and j-th column of the identity matrix, the seventh formula is: The expression for the difference matrix is: B is the difference matrix, b ij Let be the element in the i-th row and j-th column of the dissimilarity matrix. The expression for the set pair judgment matrix is: U = A + αB, where U is the set pair judgment matrix, α is the dissimilarity coefficient, and α ∈ [0, 1].

[0022] According to some embodiments of the present invention, a compatible judgment matrix is ​​established based on the set-pair judgment matrix, and the index weights corresponding to all the evaluation indicators of the subsystem are determined based on the compatible judgment matrix, including:

[0023] The compatibility judgment matrix is ​​obtained based on the eighth formula and the set pair judgment matrix, wherein the eighth formula is: n is the total number of all the evaluation indicators of the subsystem, u ip Let u be the element in the i-th row and p-th column of the judgment matrix of the set. pj Let be the element in the p-th row and j-th column of the set-p judgment matrix, where p is a positive integer, less than or equal to n. The expression for the compatibility judgment matrix is: D = (d ij ) n×n D is the compatibility judgment matrix, d ij The element in the i-th row and j-th column of the compatibility judgment matrix;

[0024] Multiple index weights are obtained based on the ninth formula and the compatibility judgment matrix, wherein the ninth formula is: w sThe index weight is the s-th evaluation index of the subsystem, where s is a positive integer and s is less than or equal to n.

[0025] According to some embodiments of the present invention, a five-element connection coefficient calculation formula is established based on the set pairs, and the principal values ​​of the connection coefficients of all the evaluation indicators are determined based on all the real-time monitoring values, all the indicator weights, the five-element connection coefficient calculation formula, and the safety risk level standard, including:

[0026] The formula for calculating the five-element connection coefficient is established to obtain multiple real-time monitoring values. Each subsystem includes a first-level subsystem, at least one second-level subsystem, and at least one third-level subsystem. Each first-level, second-level, and third-level subsystem includes at least one evaluation index. The evaluation index located in the first-level subsystem is a first-level index, the evaluation index located in the second-level subsystem is a second-level index, and the evaluation index located in the third-level subsystem is a third-level index. The first-level index corresponds to the second-level subsystem, the second-level index corresponds to the third-level subsystem, and the real-time monitoring value is the real-time value of the third-level index.

[0027] Based on any of the real-time monitoring values, multiple third-level connection number components are determined based on the real-time monitoring values ​​and the five-element connection number calculation formula; multiple second-level connection number components are determined based on all the third-level connection number components; multiple first-level connection number components are determined based on all the second-level connection number components; and the total system connection number component is determined based on all the first-level connection number components.

[0028] The third-level contact number components are determined by the five-element contact number calculation formula. The principal value of the second-level contact number is determined based on all the second-level contact number components. The principal value of the first-level contact number is determined based on all the first-level contact number components. The principal value of the total system contact number is determined based on the total system contact number components. The principal value of the contact number includes the principal value of the total system contact number, the principal value of the first-level contact number, the principal value of the second-level contact number, and the principal value of the third-level contact number.

[0029] The expression for the formula for calculating the five-element connection coefficient is as follows: μ mtlLet c be the principal value of the third-level correlation coefficient of the third-level subsystem corresponding to the t-th second-level indicator of the second-level subsystem corresponding to the m-th first-level indicator of the third-level subsystem. Here, m represents the m-th first-level indicator of the first-level subsystem, t represents the t-th second-level indicator of the second-level subsystem, and l represents the l-th third-level indicator of the third-level subsystem. m, t, and l are positive integers, where m is less than or equal to the number of first-level indicators of the first-level subsystem, t is less than or equal to the number of second-level indicators of the second-level subsystem, and l is less than or equal to the number of third-level indicators of the third-level subsystem. mtl S is the real-time monitoring value corresponding to the l-th tertiary indicator of the tertiary subsystem corresponding to the t-th secondary indicator of the second-level subsystem corresponding to the m-th primary indicator of the primary subsystem. (x,x+1)l The boundary value between the xth and x+1th levels of the lth level indicator of the three-level subsystem is defined based on a preset safety risk level standard and threshold table. i1, i2 and i3 are all difference coefficients, and j1 is the opposing indicator.

[0030] According to some embodiments of the present invention, multiple tertiary connection number components are determined based on the real-time monitoring values ​​and the five-element connection number calculation formula; multiple secondary connection number components are determined based on all the tertiary connection number components; multiple primary connection number components are determined based on all the secondary connection number components; and a total system connection number component is determined based on all the primary connection number components, including:

[0031] Based on the real-time monitoring values ​​and the boundary values, a corresponding interval is determined. Based on the corresponding interval, a formula for calculating the comprehensive evaluation connection number of the three-level subsystem is determined. Based on the formula for calculating the comprehensive evaluation connection number of the three-level subsystem, multiple components of the three-level connection number are determined. The expression for the formula for calculating the comprehensive evaluation connection number of the three-level subsystem is: μ mtl =r mtl1 +r mtl2 i1+r mtl3 i2+r mtl4 i3+r mtl5 j1, r mtl1 r mtl2 r mtl3 r mtl4 and r mpl5 All are components of the third-level connection number, r mtl1 r mtl2 r mtl3 r mtl4 and r mpl5 The values ​​of r are all in the range [0,1], and r mtl1 +r mtl2 i1+r mtl3i2+r mtl4 i3+r mtl5 j1 = 1;

[0032] Based on all the aforementioned third-level connection components, multiple second-level connection components are determined, wherein the expressions for the second-level connection components are: r mtl For the m-th primary indicator of the primary subsystem, r is the q-th secondary correlation component of the t-th secondary indicator of the secondary subsystem, where q is a positive integer, greater than or equal to 1 and less than or equal to 5. mtlq Let w be the number of the three-level connection components, L be the total number of the three-level indicators of the three-level subsystem, and w be the number of the three-level connections components. mtl r is the indicator weight of the l-th tertiary indicator of the tertiary subsystem corresponding to the t-th secondary indicator of the second-level subsystem corresponding to the m-th primary indicator of the primary subsystem. mtlq The m-th primary indicator of the primary subsystem corresponds to the t-th secondary indicator of the secondary subsystem, which in turn corresponds to the l-th tertiary indicator of the tertiary subsystem;

[0033] Multiple first-level connection components are determined based on all the second-level connection components, wherein the expressions for the first-level connection components are: r mq Let w be the q-th primary connection component corresponding to the m-th primary indicator of the primary subsystem, T be the total number of all secondary indicators of the secondary subsystem, and w be the number of primary indicators corresponding to the m-th primary indicator of the primary subsystem. mt The index weight of the t-th secondary index of the secondary subsystem corresponding to the m-th primary index of the primary subsystem;

[0034] The total system connection number component is determined based on all the first-level connection number components, wherein the expression for the total system connection number component is: r q Let w be the q-th component of the total system connection count, M be the total number of all the first-level indicators of the first-level subsystem, and w be the total number of the first-level indicators of the first-level subsystem. m The index weight is the m-th primary index of the primary subsystem.

[0035] According to some embodiments of the present invention, the third-level connection number components are determined by the five-element connection number calculation formula, the principal value of the second-level connection number is determined based on all the second-level connection number components, the principal value of the first-level connection number is determined based on all the first-level connection number components, and the principal value of the total system connection number is determined based on the total system connection number components, including:

[0036] The principal value of the third-level connection number is determined based on all the components of the third-level connection number and the calculation formula of the comprehensive evaluation connection number of the third-level subsystem;

[0037] The principal value of the second-level connection number is determined based on all the components of the second-level connection number and the formula for calculating the comprehensive evaluation connection number of the second-level subsystem. The expression for the formula for calculating the comprehensive evaluation connection number of the second-level subsystem is: μ mt =r mt1 +r mt2 i1+r mt3 i2+r mt4 i3+r mt5 j1, μ mt r is the principal value of the secondary correlation coefficient of the t-th secondary indicator of the secondary subsystem corresponding to the m-th primary indicator of the primary subsystem. mt1 r mt2 r mt3 r mt4 and r mt5 All of these are components of the second-level connection number;

[0038] The principal value of the first-level connection number is determined based on all the components of the first-level connection number and the calculation formula for the comprehensive evaluation connection number of the first-level subsystem. The expression for the calculation formula for the comprehensive evaluation connection number of the first-level subsystem is: μ m =r m1 +r m2 i1+r m3 i2+r m4 i3+r m5 j1, μ m r is the principal value of the first-level connection coefficient of the m-th first-level indicator of the first-level subsystem. m1 r m2 r m3 r m4 and r m5 All of these are components of the first-level contact number;

[0039] The principal value of the total system connection number is determined based on the components of the total system connection number and the formula for calculating the total system comprehensive evaluation connection number. The expression for the principal value of the total system connection number is: μ = r1 + r2i1 + r3i2 + r4i3 + r5j1, where μ is the principal value of the total system connection number, and r1, r2, r3, r4 and r5 are all components of the total system connection number.

[0040] Secondly, embodiments of the present invention provide a foundation pit evaluation device based on fuzzy hierarchy and set pair analysis, including at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which are executed by the at least one control processor to enable the at least one control processor to perform the foundation pit evaluation method based on fuzzy hierarchy and set pair analysis as described in the first aspect above.

[0041] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions for performing the foundation pit evaluation method based on fuzzy hierarchy and set pair analysis as described in the first aspect above.

[0042] The foundation pit evaluation method based on fuzzy hierarchical and set pair analysis according to embodiments of the present invention has at least the following beneficial effects: Constructing a foundation pit safety risk evaluation index system, wherein the foundation pit safety risk evaluation index system includes multiple subsystems, and each subsystem includes at least one evaluation index; based on any subsystem, obtaining multiple score groups based on all the evaluation indices of the subsystem, obtaining multiple triangular fuzzy reciprocal judgment matrices based on all the score groups, obtaining multiple transformation judgment matrices based on all the triangular fuzzy reciprocal judgment matrices, obtaining set pair judgment matrices based on all the transformation judgment matrices, establishing a compatibility judgment matrix based on the set pair judgment matrix, and determining the index weights corresponding to all the evaluation indices of the subsystem based on the compatibility judgment matrix. The process involves: acquiring a preset safety risk level standard and multiple real-time monitoring values; forming set pairs between the safety evaluation levels of the safety risk level standard and the evaluation indicators; establishing a five-element relationship coefficient calculation formula based on the set pairs; determining the principal values ​​of the relationship coefficients of all the evaluation indicators based on all the real-time monitoring values, all the indicator weights, the five-element relationship coefficient calculation formula, and the safety risk level standard; determining the safety evaluation level of the subsystem based on the principal values ​​of the relationship coefficients corresponding to all the evaluation indicators of any subsystem and the safety risk level standard; and determining the safety evaluation result of the foundation pit project based on all the safety evaluation levels. According to the technical solution of this embodiment, by establishing a foundation pit safety risk evaluation indicator system including multiple subsystems, each subsystem including at least one evaluation, the various uncertainties of foundation pit projects are fully explored. By using fuzzy hierarchical analysis and set pair analysis, the bias of individual expert evaluations and the reliability of expert group evaluations are fully considered, and reasonable weighting of evaluation indicators is achieved, thereby improving the reliability of the safety evaluation result of the foundation pit project determined based on real-time monitoring values. Attached Figure Description

[0043] Figure 1 This is a flowchart of a foundation pit evaluation method based on fuzzy hierarchy and set pair analysis provided in an embodiment of the present invention;

[0044] Figure 2 This is a structural diagram of the foundation pit safety risk assessment index system provided in another embodiment of the present invention;

[0045] Figure 3 This is a trend change diagram of the principal values ​​of the total system connection number of a foundation pit project provided in another embodiment of the present invention;

[0046] Figure 4 This is a structural diagram of a foundation pit evaluation device based on fuzzy hierarchy and set pair analysis provided in another embodiment of the present invention. Detailed Implementation

[0047] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0048] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0049] In the description of this invention, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0050] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0051] The foundation pit evaluation method based on fuzzy hierarchical and set pair analysis according to embodiments of the present invention has at least the following beneficial effects: Constructing a foundation pit safety risk evaluation index system, wherein the foundation pit safety risk evaluation index system includes multiple subsystems, and each subsystem includes at least one evaluation index; based on any subsystem, obtaining multiple score groups based on all the evaluation indices of the subsystem, obtaining multiple triangular fuzzy reciprocal judgment matrices based on all the score groups, obtaining multiple transformation judgment matrices based on all the triangular fuzzy reciprocal judgment matrices, obtaining set pair judgment matrices based on all the transformation judgment matrices, establishing a compatibility judgment matrix based on the set pair judgment matrix, and determining the index weights corresponding to all the evaluation indices of the subsystem based on the compatibility judgment matrix. The process involves: acquiring a preset safety risk level standard and multiple real-time monitoring values; forming set pairs between the safety evaluation levels of the safety risk level standard and the evaluation indicators; establishing a five-element relationship coefficient calculation formula based on the set pairs; determining the principal values ​​of the relationship coefficients of all the evaluation indicators based on all the real-time monitoring values, all the indicator weights, the five-element relationship coefficient calculation formula, and the safety risk level standard; determining the safety evaluation level of the subsystem based on the principal values ​​of the relationship coefficients corresponding to all the evaluation indicators of any subsystem and the safety risk level standard; and determining the safety evaluation result of the foundation pit project based on all the safety evaluation levels. According to the technical solution of this embodiment, by establishing a foundation pit safety risk evaluation indicator system including multiple subsystems, each subsystem including at least one evaluation, the various uncertainties of foundation pit projects are fully explored. By using fuzzy hierarchical analysis and set pair analysis, the bias of individual expert evaluations and the reliability of expert group evaluations are fully considered, and reasonable weighting of evaluation indicators is achieved, thereby improving the reliability of the safety evaluation result of the foundation pit project determined based on real-time monitoring values.

[0052] The technical solutions of the embodiments of the present invention will be further illustrated in the following figures.

[0053] Reference Figure 1 , Figure 1 This is a flowchart of a foundation pit evaluation method based on fuzzy hierarchy and set pair analysis provided in an embodiment of the present invention. The foundation pit evaluation method based on fuzzy hierarchy and set pair analysis includes, but is not limited to, the following steps:

[0054] S10, Construct a safety risk assessment index system for foundation pits, wherein the safety risk assessment index system for foundation pits includes multiple subsystems, and each subsystem includes at least one assessment index;

[0055] S20. Based on any subsystem, multiple score groups are obtained based on all evaluation indicators of the subsystem. Multiple triangular fuzzy reciprocal judgment matrices are obtained based on all score groups. Multiple transformation judgment matrices are obtained based on all triangular fuzzy reciprocal judgment matrices. Set pair judgment matrix is ​​obtained based on all transformation judgment matrices. Compatibility judgment matrix is ​​established based on set pair judgment matrix. Index weights corresponding to all evaluation indicators of the subsystem are determined based on compatibility judgment matrix.

[0056] S30: Obtain the preset safety risk level standard and multiple real-time monitoring values, form a set pair between the safety evaluation level and evaluation indicators of the safety risk level standard, establish a five-element connection number calculation formula based on the set pair, and determine the main value of the connection number of all evaluation indicators based on all real-time monitoring values, all indicator weights, the five-element connection number calculation formula and the safety risk level standard. Among them, the real-time monitoring value is the real-time value of the evaluation indicator, and the safety risk level standard includes multiple safety evaluation levels.

[0057] S40. Based on the principal values ​​of the connection numbers corresponding to all evaluation indicators of any subsystem and the safety risk level standard, determine the safety evaluation level of the subsystem, and determine the safety evaluation result of the foundation pit project based on all safety evaluation levels.

[0058] It should be noted that, based on the characteristics of the retaining structure, the surrounding environment, and the site conditions of the foundation pit project, and with reference to relevant standards and experience in foundation pit engineering, safety status evaluation indicators related to the foundation pit are selected from the construction monitoring projects of the foundation pit project as evaluation indicators. With reference to similar project experience, and through expert consultation and the requirements of relevant technical specifications, a hierarchical structure system composed of evaluation indicators is constructed to establish a foundation pit safety risk evaluation indicator system.

[0059] It should be noted that the foundation pit safety risk assessment index system includes multiple subsystems. All subsystems include a first-level subsystem, at least one second-level subsystem, and at least one third-level subsystem. Each first-level, second-level, and third-level subsystem includes at least one evaluation index. Evaluation indices located in the first-level subsystem are first-level indicators, evaluation indices located in the second-level subsystem are second-level indicators, and evaluation indices located in the third-level subsystem are third-level indicators. First-level indicators correspond to second-level subsystems, and second-level indicators correspond to third-level subsystems.

[0060] It should be noted that, see Figure 2 And Table 1, Figure 2 This is a structural diagram of the foundation pit safety risk assessment index system provided in another embodiment of the present invention. Table 1 is a schematic diagram of the subsystems and hierarchical structure of the foundation pit safety risk assessment index system. Figure 2This application indicates that the overall system of the foundation pit safety risk assessment index system is the comprehensive safety status level of the foundation pit. The comprehensive safety status level corresponds to a first-level subsystem, which includes two first-level indicators: the foundation pit support structure and the surrounding environment. The foundation pit support structure corresponds to a second-level subsystem, which includes five second-level indicators: deep horizontal displacement of the retaining structure, horizontal displacement of the capping beam, vertical displacement of the capping beam, axial force of the support, and settlement of the columns. These second-level indicators are the monitoring items at the construction site of the foundation pit project. The deep horizontal displacement of the retaining structure corresponds to a third-level subsystem, which includes two third-level indicators: cumulative value and rate of change. In the table, through I... mtl The l-th tertiary indicator characterizing the t-th secondary indicator of the secondary subsystem corresponding to the m-th primary indicator, for example, I. 111 The cumulative value of the deep horizontal displacement corresponding to the tertiary subsystem is used to characterize the retaining results of the secondary subsystem of the foundation pit support structure. Specific evaluation indicators for each subsystem are detailed in the text of Table 1, as shown below:

[0061]

[0062] Table 1. Schematic diagram of subsystems and hierarchical levels of the foundation pit safety risk assessment index system.

[0063] It should be noted that a foundation pit safety risk assessment index system is constructed through multiple subsystems and all evaluation indicators within those subsystems, enabling a comprehensive safety evaluation of the foundation pit construction site. This application uses the Fuzzy Analytic Hierarchy Process (FAHP) and Set Pair Analysis (SPA) to reasonably assign weights to the evaluation indicators. Since foundation pit engineering is a high-risk project, to comprehensively consider multiple opinions and ensure the efficiency of group decision-making, multiple score groups are obtained by having multiple experts score all evaluation indicators of all subsystems within the foundation pit safety risk assessment index system. Based on these score groups and FAHP, a triangular fuzzy reciprocal judgment matrix and a transformation judgment matrix are obtained. The similarity-difference model of SPA is introduced to consider the differences in opinions within the expert group. Based on the transformation judgment matrix, a set pair judgment matrix is ​​obtained. The introduction of SPA enables the reasonable weighting of all evaluation indicators in the foundation pit safety risk assessment index system, allowing the determination of indicator weights based on the set pair judgment matrix. Each indicator weight corresponds one-to-one with the evaluation indicator.

[0064] It should be noted that this application sets safety risk levels and evaluation indicators into pairs, and uses the same-different inverse hierarchical method to establish a five-element connection number calculation formula for the evaluation indicators. The different parts in the five-element connection number calculation formula are described in more detail, and the comprehensive weighted method is used to obtain the principal values ​​of the connection numbers of all subsystems and the total system of the foundation pit safety risk evaluation indicator system.

[0065] It should be noted that Table 2 is the standard for safety risk levels. Through literature review and engineering analogies, and based on the cumulative values ​​and rate of change alarm values ​​of monitoring data specified in relevant standards or design documents, a commonly used five-level classification method was used to establish a standard for classifying safety risk levels. This standard includes the assessment of safety risks in foundation pit construction, acceptance criteria, countermeasures, and the classification standards for foundation pit safety status levels, along with the assessment and countermeasures taken. The preset safety risk level standard consists of five safety assessment levels, setting the foundation pit safety risk level, the risk status corresponding to each level, the countermeasures corresponding to each level, the monitoring items and control values ​​corresponding to each level, and the risk assessment and acceptance criteria. The boundary values ​​between the five foundation pit safety risk levels are determined based on the control values ​​of the monitoring items. The control values ​​of the monitoring items are determined by standards and design documents, and the boundary values ​​between the five foundation pit safety risk levels are the relevant proportions of the control values.

[0066] Table 2 is shown below:

[0067]

[0068] Table 2 Safety Risk Level Standards

[0069] It should be noted that, according to the "equal division principle," the interval [-1,1] is divided into five sub-intervals: (0.6,1), (0.2,0.6), (-0.2,0.2), (-0.6,-0.2), and (-1,-0.6), corresponding to safety evaluation levels I, II, III, IV, and V, respectively. The larger the principal value of the correlation coefficient, the lower the corresponding safety risk level, indicating a safer foundation pit system. By comparing the principal values ​​of the correlation coefficients of all evaluation indicators with the five sub-intervals, the safety risk level of the foundation pit for each subsystem can be obtained. This allows for different levels of safety risk assessment of the foundation pit, resulting in the safety evaluation results of the foundation pit project.

[0070] It should be noted that the system obtains real-time monitoring values, obtains the principal values ​​of the correlation coefficients of each evaluation indicator based on the real-time monitoring values, determines the real-time safety risk level of each evaluation indicator at the construction site of the foundation pit project by using the principal values ​​of the correlation coefficients and the preset safety risk level standards, determines the real-time safety evaluation result of the foundation pit project based on all safety risk levels, and can output the corresponding risk status, countermeasures, risk assessment and acceptance criteria based on the real-time safety evaluation result.

[0071] It should be noted that, referring to Figure 3 , Figure 3 This is another embodiment of the present invention, which provides a trend change graph of the principal values ​​of the total system of foundation pit engineering. After obtaining all real-time monitoring values ​​at two time points, the curve graph of the principal values ​​of the principal values ​​of all evaluation indicators can be determined based on the real-time monitoring values ​​at the two time points, thereby obtaining the changing trend of the safety evaluation results of the foundation pit engineering construction site; by combining construction logs and records, the causes can be found and analyzed, and targeted engineering measures can be taken.

[0072] It should be noted that this application introduces set pair analysis into the safety evaluation process of foundation pit engineering, fully characterizing the dialectical relationship between the certainty and uncertainty of foundation pit construction risk factors. It leverages the decision-making advantages of expert groups and considers the similarities and differences in the individual safety evaluations of expert groups. Combining fuzzy hierarchical analysis and analytic hierarchy process, a fuzzy hierarchical and set pair analysis (FAHP-SPA) coupled model for comprehensive safety risk evaluation of foundation pit construction is established based on real-time monitoring data of the foundation pit construction site, making the safety evaluation results of foundation pit engineering more scientific and reasonable.

[0073] It should be noted that this application integrates fuzzy hierarchical analysis with set pair analysis to comprehensively evaluate the construction safety risks of foundation pit engineering and fully explore the various uncertainties of foundation pit engineering, thereby improving the reliability of the safety evaluation results of foundation pit engineering.

[0074] It should be noted that to ensure the safety of the construction process of foundation pit projects, a safety assessment is required to obtain the assessment results, and then targeted protective measures can be taken based on these results. However, due to the distinct regional characteristics of foundation pit projects, the different environments of each project, and the insufficient historical data on safety risks at the construction site, the current safety rating process for foundation pit projects relies entirely on the experience of expert groups. Furthermore, foundation pit projects exhibit various uncertainties, including randomness, gray areas, and fuzziness, while existing assessment methods can only analyze some of these uncertainties. The individual safety assessment results from different experts are subjective and biased, failing to fully exploit the uncertainties within the foundation pit safety risk system, resulting in low reliability of the obtained safety assessment results.

[0075] It should be noted that this application combines fuzzy hierarchical analysis (AHP) and set pair analysis to conduct a safety assessment of the construction site of foundation pit engineering. A comprehensive safety risk assessment system for foundation pits is established through multiple subsystems and all evaluation indicators of each subsystem, fully considering the uncertainty of individual expert evaluations and the reliability of expert group decisions. The AHP method yields a triangular fuzzy reciprocal judgment matrix and a transformation judgment matrix based on all value groups. Set pair analysis is introduced to consider the similarities and differences among all value groups. Based on the transformation judgment matrix, a set pair judgment matrix is ​​obtained, and based on the set pair judgment matrix, the weights of each evaluation indicator are determined, thus achieving reasonable weighting of all evaluation indicators. Therefore, by combining fuzzy hierarchical analysis and set pair analysis, various uncertainties in foundation pit engineering are fully explored, reducing the subjectivity and bias of individual expert safety evaluations and considering the reliability and consistency of individual expert safety evaluations, thereby improving the reliability of the safety assessment results.

[0076] Additionally, in one embodiment, in Figure 1 In step S20 shown, multiple score groups are obtained based on all evaluation indicators of the subsystem, multiple triangular fuzzy reciprocal judgment matrices are obtained based on all score groups, and multiple transformation judgment matrices are obtained based on all triangular fuzzy reciprocal judgment matrices, including but not limited to the following steps:

[0077] S21. Obtain all evaluation indicators of the subsystem. Multiple experts score all evaluation indicators based on pairwise relative importance to obtain multiple score groups, where the number of score groups is equal to the number of experts.

[0078] S22, Based on any score group, construct a triangular fuzzy reciprocal judgment matrix, where the expression for the triangular fuzzy reciprocal judgment matrix is: X = (x ij ) n×n Let X be an n-order triangular fuzzy reciprocal judgment matrix, and x be a triangular fuzzy reciprocal judgment matrix. ij Let i be the element in the i-th row and j-th column of the triangular fuzzy reciprocal judgment matrix, and n be the number of evaluation indicators of the subsystem, where i, j, and n are all positive integers.

[0079] S23, based on any triangular fuzzy reciprocal judgment matrix, the transformation judgment matrix is ​​obtained based on the triangular fuzzy reciprocal judgment matrix and the first formula, where x ij The expression is: x ij =(e ij ,f ij ,g ij ), and g ij ≥e ij ≥f ij >1, e ijf represents the minimum evaluation value for the relative importance of indicator i to indicator j. ij Let g be a possible evaluation value for the relative importance of indicator i to indicator j. ij Let be the highest evaluation value for the relative importance of indicator i to indicator j. The score group includes the lowest evaluation value, the possible evaluation value, and the highest evaluation value. The first formula is: The expression for the transformation judgment matrix is: Y = (y ij ) n×n Y is the transformation judgment matrix, y ij This is to transform the elements in the i-th row and j-th column of the judgment matrix.

[0080] It should be noted that, based on any subsystem, multiple score groups are obtained by having multiple experts score all evaluation indicators of that subsystem according to pairwise relative importance. Each expert determines one score group based on one subsystem, meaning the number of score groups equals the number of experts. For example, when the number of experts is 5, i.e., r = 5, 5 score groups are obtained, and 5 triangular fuzzy reciprocal judgment matrices are obtained based on these 5 score groups: X (1) X (2) X (3) X (4) and X (5) Five transformation judgment matrices are obtained based on five triangular fuzzy reciprocal judgment matrices: Y (1) Y (2) Y (3) Y (4) and Y (5) .

[0081] It should be noted that for the elements of the triangular fuzzy judgment matrix, xii = (1,1,1), meaning the relative importance of index i to index i is always 1; and there is

[0082] It should be noted that, based on any score group, a triangular fuzzy reciprocal judgment matrix is ​​constructed; based on any triangular fuzzy reciprocal judgment matrix, a transformation judgment matrix is ​​obtained. That is, the number of experts is equal to the number of score groups, the number of triangular fuzzy reciprocal judgment matrices, and the number of transformation judgment matrices.

[0083] It should be noted that the importance judgment scale criterion is used to judge and compare the relative importance of each pair of evaluation indicators, thereby establishing a triangular fuzzy reciprocal judgment matrix.

[0084] It should be noted that, referring to Table 3, which presents the importance judgment criteria of this application, the traditional 1-9 scale importance judgment criteria mainly divide the degree of importance into five levels: equally important, slightly important, significantly important, much more important, and extremely important, and quantified using 1, 3, 5, 7, and 9 respectively. 2, 4, 6, and 8 are used to represent the degree of importance between the above adjacent importance levels. However, the importance scale values ​​of the traditional 1-9 scale importance judgment criteria are discontinuous, and this limits the engineer's choice during use. Therefore, without violating the basic principles of the traditional 1-9 scale criteria, to ensure the continuity of the importance scale values, this application expands the selection range of importance scale values ​​and improves the traditional 1-9 scale criteria. When the degree of importance is between the aforementioned importance level, real numbers between adjacent importance level scales are used for quantification. Table 3 is shown below:

[0085]

[0086] Table 3. Criteria for Determining the Scale Importance of this Application

[0087] For example, the foundation pit safety risk assessment index system includes multiple subsystems. Each subsystem includes a primary subsystem, at least one secondary subsystem, and at least one tertiary subsystem. Each primary, secondary, and tertiary subsystem includes at least one evaluation index. Evaluation indices located in the primary subsystem are primary indicators, those in the secondary subsystem are secondary indicators, and those in the tertiary subsystem are tertiary indicators. Primary indicators correspond to secondary subsystems, and secondary indicators correspond to tertiary subsystems. The primary indicators of the primary subsystem include the foundation pit support structure. The secondary subsystem corresponding to the foundation pit support structure has five secondary indicators: deep horizontal displacement of the retaining structure, horizontal displacement of the capping beam, vertical displacement of the capping beam, axial force of the support, and settlement of the columns. One expert ranked all secondary subsystems within the secondary subsystem of the foundation pit support structure based on pairwise relative importance. The result was: importance of deep horizontal displacement of the retaining structure > importance of horizontal displacement of the capping beam > importance of axial force of the support > importance of column settlement > importance of vertical displacement of the capping beam. This ranking based on importance and the expert's score group yielded the expert's corresponding triangular fuzzy judgment matrix.

[0088] It should be noted that for the elements of the triangular fuzzy judgment matrix, xii = (1,1,1), that is, the triangular fuzzy reciprocal judgment matrix is ​​obtained by multiple score groups based on multiple experts for index i. Since the score groups obtained by experts are one-sided, in order to consider the reliability and consistency of the safety evaluation of multiple experts, it is necessary to perform a consistency check on the triangular fuzzy reciprocal judgment matrix. Therefore, it is necessary to transform the triangular fuzzy reciprocal judgment matrix and obtain the transformed judgment matrix based on the triangular fuzzy reciprocal judgment matrix.

[0089] In another embodiment, after obtaining multiple transformation judgment matrices based on all triangular fuzzy reciprocal judgment matrices in step S20, the steps include, but are not limited to, the following:

[0090] S201: Based on any transformation judgment matrix, multiple normalized feature elements are obtained from the transformation judgment matrix using the second formula. A normalized feature vector is obtained based on all normalized feature elements. The approximate value of the maximum eigenvalue is obtained based on the third formula, the transformation judgment matrix, and the normalized feature vector. The consistency ratio of the transformation judgment matrix is ​​obtained based on the fourth formula and the approximate value of the maximum eigenvalue. The consistency ratio is used to perform a consistency check on the transformation judgment matrix.

[0091] S202, based on the fifth formula and r transformation judgment matrices that have passed the consistency test, a comprehensive judgment matrix is ​​obtained, and a consistency test is performed on the comprehensive judgment matrix, where r is the number of experts;

[0092] S203, when the comprehensive judgment matrix fails the consistency test, all evaluation indicators of the subsystem are re-scored based on pairwise relative importance.

[0093] The second formula is: v i For the i-th normalized feature element, the third formula is: λ max (Yv) is the approximate value of the largest eigenvalue. i The fourth formula is to obtain the i-th element by multiplying the transformation judgment matrix and the normalized eigenvector. CR represents the consistency ratio, RI represents the random consistency index from the preset random consistency index value table, and the fifth formula is... Let be the element in the i-th row and j-th column of the k-th transformation judgment matrix, where k is a positive integer, less than or equal to r. The expression for the comprehensive judgment matrix is: Z = (z ij ) n×n Z is the comprehensive judgment matrix, z ij This is the element in the i-th row and j-th column of the comprehensive judgment matrix.

[0094] It should be noted that, in order to ensure the global logical consistency among multiple experts, the transformation judgment matrix needs to be checked for consistency. The smaller the consistency ratio obtained from the transformation judgment matrix, the higher the consistency of the transformation judgment matrix. When the consistency ratio is equal to zero, it means that it is completely consistent. When the consistency ratio is less than the preset threshold, the transformation judgment matrix meets the consistency requirements.

[0095] It should be noted that, referring to Table 4, which is a table of random consistency index values, the order of the transformation judgment matrix is ​​selected as the corresponding consistency index in the random consistency index value table and substituted into the fourth formula to obtain the consistency ratio of the transformation judgment matrix. Table 4 is shown below:

[0096] order RI order RI 1 / 6 1.25 2 / 7 1.35 3 0.52 8 1.42 4 0.89 9 1.46 5 1.12 10 1.49

[0097] Table 4. Random Consistency Index Values

[0098] It should be noted that, in order to ensure that the importance ranking of all evaluation indicators of the subsystem by all experts meets the consistency requirement within a certain range, thereby achieving the local and overall unity of the opinions of all experts, after the transformation judgment matrix corresponding to all experts passes the consistency test, a comprehensive judgment matrix is ​​obtained based on the complete transformation judgment matrix, and then the comprehensive judgment matrix is ​​subjected to a consistency test.

[0099] It should be noted that if any transformation judgment matrix fails the consistency test, the experts corresponding to the transformation judgment matrix need to re-score all evaluation indicators of the subsystem to obtain a new score group, and then obtain the transformation judgment matrix based on the new score group and perform the consistency test again.

[0100] It should be noted that if the comprehensive judgment matrix fails the consistency check, all experts need to re-score all evaluation indicators of the subsystem to obtain multiple new score groups, and then obtain multiple transformation judgment matrices based on all the new score groups and perform consistency checks. Finally, the comprehensive judgment matrix obtained based on all the transformation judgment matrices will be subjected to consistency checks.

[0101] For example, based on the above-mentioned triangular fuzzy reciprocal judgment matrix X (1) Obtain the corresponding transformation judgment matrix And the transformation judgment matrix Y (1) A consistency check is performed; in this application, when the consistency ratio is less than 0.1, the transformation judgment matrix meets the consistency requirements, and the consistency ratio CR is obtained. (1)=0.057 < 0.1, meaning the transformation judgment matrix meets the consistency requirement. When the number of experts is 5, the remaining transformation judgment matrices are sequentially tested for consistency, and the consistency ratios of the remaining transformation judgment matrices are 0.040, 0.043, 0.049, and 0.004, respectively. Based on all transformation judgment matrices, the comprehensive judgment matrix is ​​obtained: The consistency ratio of the comprehensive judgment matrix was calculated to be 0.037 < 0.1, indicating that the comprehensive judgment matrix meets the consistency requirement and represents that the internal opinions of all experts are consistent.

[0102] In another embodiment, in step S20, the set pair judgment matrix is ​​obtained based on all transformation judgment matrices, including but not limited to the following steps:

[0103] S24, construct an identity matrix based on r transformation judgment matrices that pass the consistency test and the sixth formula, construct a difference matrix based on all transformation judgment matrices and the seventh formula, and construct a set pair judgment matrix based on the identity matrix and the difference matrix;

[0104] Among them, the sixth formula is Let be the minimum value among the elements in the i-th row and j-th column of the r transformation judgment matrices. Let the maximum value among the elements in the i-th row and j-th column of the r transformation judgment matrices be the expression for the identity matrix: A is a identity matrix, a ij Let be the element in the i-th row and j-th column of the identity matrix. The seventh formula is... The expression for the difference matrix is: B is the difference matrix, b ij Let be the element in the i-th row and j-th column of the dissimilarity matrix. The expression for the set pair judgment matrix is: U = A + αB, where U is the set pair judgment matrix, α is the dissimilarity coefficient, and α∈[0,1].

[0105] It should be noted that when the number of experts is 5, i.e., r=5, obtain the 5 transformation judgment matrices Y corresponding to each expert. (1) Y (2) Y (3) Y (4) and Y (5) ; Obtain the element in the i-th row and j-th column of the 5 transformation judgment matrices, i.e. and Since all transformation judgment matrices have passed the consistency check, the element in the i-th row and j-th column of each of the different transformation judgment matrices must be greater than 1, less than 1, or equal to 1. That is, if one of the elements in the i-th row and j-th column of the five transformation judgment matrices is equal to 1, then the elements in the i-th row and j-th column of the other four transformation judgment matrices are also equal to 1. That is, when y ij When a = 1, a ij =1; similarly, when y ij When b = 1, ij =0.

[0106] It should be noted that α is the coefficient of difference, which measures the magnitude of differences in understanding within the expert group; the larger the coefficient of difference, the greater the difference. The identity matrix represents the evaluation values ​​that all experts find acceptable regarding the relative importance of two indicators, while the difference matrix represents the differences in the evaluation values ​​of the relative importance of two indicators among all experts.

[0107] For example, based on the above 5 transformation judgment matrices Y (1) Y (2) Y (3) Y (4) and Y (5) The set-pair judgment matrix is ​​obtained as follows:

[0108] In another embodiment, in step S20, a compatibility judgment matrix is ​​established based on the set pair judgment matrix, and the index weights corresponding to all evaluation indicators of the subsystem are determined based on the compatibility judgment matrix, including but not limited to the following steps:

[0109] S25, based on the eighth formula and the set pair judgment matrix, the compatibility judgment matrix is ​​obtained, where the eighth formula is: n is the total number of evaluation indicators for the subsystem, u ip Let u be the element in the i-th row and p-th column of the set-p judgment matrix. pj Let D be the elements in the p-th row and j-th column of the set-p judgment matrix, where p is a positive integer less than or equal to n. The expression for the compatible judgment matrix is: D = (d ij ) n×n D is the compatibility judgment matrix, d ij Let be the element in the i-th row and j-th column of the compatibility judgment matrix;

[0110] S26, based on the ninth formula and the compatibility judgment matrix, obtains the weights of multiple indicators, where the ninth formula is: w s Let be the weight of the s-th evaluation index of the subsystem, where s is a positive integer and s is less than or equal to n.

[0111] It should be noted that since the set-pair judgment matrix is ​​generally not consistent, the compatibility judgment matrix is ​​obtained based on the set-pair judgment matrix by formula 8, and the index weights of all evaluation indicators of the corresponding subsystem are determined based on formula 9 and the compatibility judgment matrix.

[0112] For example, the compatibility judgment matrix obtained based on the above set pair judgment matrix is ​​as follows: The evaluation indicators for the first-level subsystem include the foundation pit support structure. Based on the compatibility judgment matrix, the weights of all indicators for the second-level subsystem corresponding to the foundation pit support structure are obtained, w1 = (w 11 ,w 12 ,w 13 ,w 14 ,w 15 ) T =(0.4055,0.2857,0.0533,0.1757,0.0533) T Similarly, the weights of all evaluation indicators in the foundation pit safety risk assessment indicator system are obtained. Table 5 shows the weight allocation table for all evaluation indicators:

[0113]

[0114] Table 5 Weight Allocation of Safety Risk Assessment Indicators for Foundation Pit

[0115] In another embodiment, in step S30, a formula for calculating the five-element connection coefficient is established based on set pairs. The principal values ​​of the connection coefficients of all evaluation indicators are determined based on all real-time monitoring values, all indicator weights, the formula for calculating the five-element connection coefficient, and the safety risk level standard. This includes, but is not limited to, the following steps:

[0116] S31, establish a five-element connection number calculation formula and obtain multiple real-time monitoring values. Among them, all subsystems include a first-level subsystem, at least one second-level subsystem and at least one third-level subsystem. Each first-level subsystem, second-level subsystem and third-level subsystem includes at least one evaluation index. The evaluation index located in the first-level subsystem is the first-level index, the evaluation index located in the second-level subsystem is the second-level index, and the evaluation index located in the third-level subsystem is the third-level index. The first-level index corresponds to the second-level subsystem, the second-level index corresponds to the third-level subsystem, and the real-time monitoring value is the real-time value of the third-level index.

[0117] S32, based on any real-time monitoring value, determine multiple third-level connection number components based on the real-time monitoring value and the five-element connection number calculation formula, determine multiple second-level connection number components based on all third-level connection number components, determine multiple first-level connection number components based on all second-level connection number components, and determine the total system connection number components based on all first-level connection number components.

[0118] S33, the third-level connection number components are determined by the five-element connection number calculation formula. The principal value of the second-level connection number is determined based on all second-level connection number components, the principal value of the first-level connection number is determined based on all first-level connection number components, and the principal value of the total system connection number is determined based on the total system connection number components. The principal value of the connection number includes the principal value of the total system connection number, the principal value of the first-level connection number, the principal value of the second-level connection number, and the principal value of the third-level connection number.

[0119] The expression for the formula of the five-element connection number is: μ mtl Let c be the principal value of the tertiary relationship coefficient, which corresponds to the m-th primary indicator of the tertiary subsystem and the t-th secondary indicator of the tertiary subsystem. Here, m represents the m-th primary indicator of the primary subsystem, t represents the t-th secondary indicator of the tertiary subsystem, and l represents the l-th tertiary indicator of the tertiary subsystem. m, t, and l are positive integers, where m is less than or equal to the number of primary indicators in the primary subsystem, t is less than or equal to the number of secondary indicators in the tertiary subsystem, and l is less than or equal to the number of tertiary indicators in the tertiary subsystem. mtl S is the real-time monitoring value corresponding to the l-th tertiary indicator of the tertiary subsystem, which corresponds to the t-th secondary indicator of the second-level subsystem corresponding to the m-th primary indicator of the primary subsystem. (x,x+1)l The boundary value between level x and level x+1 of the l-th level indicator of the three-level subsystem is determined based on the preset safety risk level standard and threshold table. i1, i2 and i3 are all difference coefficients, and j1 is the opposing indicator.

[0120] It should be noted that, based on any real-time monitoring value, the real-time monitoring value is compared with the boundary values ​​of five intervals to determine the corresponding interval of the real-time monitoring value. Based on the five-element connection number calculation formula and the corresponding interval, the comprehensive evaluation connection number calculation formula for the three-level subsystem is determined, thereby determining the three-level connection number components. Based on all three-level connection number components, all second-level connection number components, all first-level connection number components, and the total system connection number components are determined sequentially. Thus, a principal value of the connection number is determined based on all connection number components. This application uses a five-element connection number calculation formula; therefore, the number of connection number components is five, and a principal value of the connection number is determined through these five components.

[0121] It should be noted that all evaluation indicators in the foundation pit safety risk assessment indicator system are positive indicators, and the safety risk level increases with the increase of real-time monitoring values.

[0122] It should be noted that i1, i2 and i3 are all difference coefficients. According to the "equal distribution principle", let i1 equal 0.5, i2 equal 0 and i3 equal -0.5. j1 is the opposite index, let j1 equal -1.

[0123] It should be noted that, referring to Table 6, which is a table of safety risk level standards and thresholds, the real-time monitoring values ​​of the three-level indicators are compared with the corresponding boundary values ​​of each safety evaluation level in the table to determine the corresponding range of the real-time monitoring values. Based on the five-element connection coefficient calculation formula, the comprehensive evaluation connection coefficient calculation formula for the three-level subsystem is then determined, resulting in multiple components of the three-level connection coefficient. Table 6 is shown below:

[0124]

[0125]

[0126] Table 6 Safety Risk Level Standards and Thresholds

[0127] In another embodiment, step S32 includes, but is not limited to, the following steps:

[0128] S321, based on real-time monitoring values ​​and limit values, determine the corresponding intervals; based on the corresponding intervals, determine the calculation formula for the comprehensive evaluation connection number of the three-level subsystem; based on the calculation formula for the comprehensive evaluation connection number of the three-level subsystem, determine multiple components of the three-level connection number. The expression for the calculation formula for the comprehensive evaluation connection number of the three-level subsystem is: μ mtl =r mtl1 +r mtl2 i1+r mtl3 i2+r mtk4 i3+r mtl5 j1, r mtl1 r mtl2 r mtl3 r mtl4 and r mpl5 All are third-order connection components, r mtl1 r mtl2 r mtl3 r mtl4 and r mpl5 The values ​​of r are all in the range [0,1], and r mtl1 +r mtl2 i1+r mtl3 i2+r mtl4 i3+r mtl5 j1 = 1;

[0129] S322, Based on all tertiary connection number components, determine multiple secondary connection number components, wherein the expression for the secondary connection number components is: r mtl Let r be the q-th secondary correlation component of the t-th secondary indicator of the second-level subsystem corresponding to the m-th primary indicator of the primary subsystem, where q is a positive integer, greater than or equal to 1 and less than or equal to 5. mtlq Let L be the number of all third-level indicators in the third-level subsystem, and w be the number of third-level connections. mtlLet r be the indicator weight of the l-th tertiary indicator of the tertiary subsystem corresponding to the t-th secondary indicator of the second-level subsystem corresponding to the m-th primary indicator of the primary subsystem. mtlq The m-th primary indicator of the primary subsystem corresponds to the t-th secondary indicator of the secondary subsystem, which in turn corresponds to the l-th tertiary indicator of the tertiary subsystem.

[0130] S323, Based on all second-level connection number components, determine multiple first-level connection number components, wherein the expression for the first-level connection number components is: r mq Let w be the q-th primary correlation component corresponding to the m-th primary indicator of the primary subsystem, T be the total number of secondary indicators of the secondary subsystem, and w be the number of secondary indicators of the secondary subsystem. mt The indicator weight is the weight of the t-th secondary indicator in the secondary subsystem corresponding to the m-th primary indicator of the primary subsystem.

[0131] S324, determine the total system connection number components based on all first-level connection number components, where the expression for the total system connection number components is: r q Let w be the q-th component of the total system connection, M be the total number of first-level indicators of the first-level subsystem, and w be the total number of first-level indicators of the first-level subsystem. m The index weight is the m-th primary index of the primary subsystem.

[0132] It should be noted that the expressions for the second-level connection components, the first-level connection components, and the total system connection components can be determined. The second-level connection components are calculated based on the third-level connection components, the first-level connection components are calculated based on the second-level connection components, and the total system connection components are calculated based on the first-level connection components.

[0133] For example, the deep horizontal displacement of the retaining structure is a secondary index of the secondary subsystem of the foundation pit support structure of the primary subsystem. The cumulative value of the deep horizontal displacement of the retaining structure is the first tertiary index of the tertiary subsystem corresponding to the secondary index deep horizontal displacement of the retaining structure. When the real-time monitoring value of the cumulative value of the deep horizontal displacement of the retaining structure is between zero and the boundary value between the first and second levels, that is, the real-time monitoring value is at the first level, the formula for calculating the corresponding tertiary subsystem comprehensive evaluation correlation coefficient is obtained: μ mtl =r mtl1 +r mtl2 i1+r mtl3 i2+r mtl4 i3+r mtl5 j1, where r mtl1 =1, r mtl2＝0 r mtl3 =0, r mtl4 =0, r mtl5=0, thus determining all tertiary connection number components of the comprehensive evaluation connection number calculation formula for the tertiary subsystem based on real-time monitoring values ​​and the five-element connection number calculation formula. Based on the expressions of the secondary connection number components and all tertiary connection number components, all secondary connection number components are determined, thereby determining the primary connection number components based on the secondary connection number components, and finally determining the total system connection number components based on the primary connection number components. The secondary indicators of the secondary subsystem, and the tertiary subsystem corresponding to the deep horizontal displacement of the retaining structure, include two tertiary indicators, therefore L = 2.

[0134] In another embodiment, step S33 includes, but is not limited to, the following steps:

[0135] S331, the principal value of the third-level connection number is determined based on all components of the third-level connection number and the calculation formula of the connection number of the comprehensive evaluation of the third-level subsystem;

[0136] S332, the principal value of the second-level connection number is determined based on all components of the second-level connection number and the formula for calculating the comprehensive evaluation connection number of the second-level subsystem. The expression for the formula for calculating the comprehensive evaluation connection number of the second-level subsystem is: μ mt =r mt1 +r mt2 i1+r mt3 i2+r mt4 i3+r mt5 j1, μ mt Let r be the principal value of the secondary relationship coefficient of the t-th secondary indicator of the secondary subsystem corresponding to the m-th primary indicator of the primary subsystem. mt1 r mt2 r mt3 r mt4 and r mt5 All are second-order component numbers;

[0137] S333, the principal value of the first-level connection number is determined based on all first-level connection number components and the calculation formula for the comprehensive evaluation connection number of the first-level subsystem. The expression for the calculation formula for the comprehensive evaluation connection number of the first-level subsystem is: μ m =r m1 +r m2 i1+r m3 i2+r m4 i3+r m5 j1, μ m Let r be the principal value of the primary correlation coefficient of the m-th primary indicator of the primary subsystem. m1 r m2 r m3 r m4 and r m5 All are first-order connection components;

[0138] S334. The principal value of the total system connection number is determined based on the components of the total system connection number and the formula for calculating the total system comprehensive evaluation connection number. The expression of the principal value of the total system connection number is: μ=r1+r2i1+r3i2+r4i3+r5j1, where μ is the principal value of the total system connection number, and r1, r2, r3, r4 and r5 are all components of the total system connection number.

[0139] It should be noted that, based on the real-time monitoring values ​​of the three-level indicators, all corresponding three-level contact number components are determined. All three-level contact number components are substituted into the comprehensive evaluation contact number calculation formula of the three-level subsystem. According to the "equal distribution principle", i1 = 0.5, i2 = 0, i3 = -0.5 and j1 = -1, thus obtaining the principal value of the contact number of the three-level indicator.

[0140] It should be noted that when i1 = 0.5, i2 = 0, i3 = -0.5, and j1 = -1, multiple second-level connection components are obtained based on all third-level connection components. These second-level connection components are then substituted into the formula for calculating the comprehensive evaluation connection number of the second-level subsystem to determine the principal value of the second-level connection number. Similarly, the principal value of the first-level connection number is determined based on all first-level connection components and the formula for calculating the comprehensive evaluation connection number of the first-level subsystem, and the principal value of the total system connection number is determined based on the total system connection components and the formula for calculating the comprehensive evaluation connection number of the total system.

[0141] It should be noted that after obtaining the principal values ​​of the correlation coefficients of all evaluation indicators in the foundation pit safety risk assessment index system, the principal values ​​of the correlation coefficients are compared with the safety risk level standards in turn to determine the safety risk level of each evaluation indicator, thereby determining the safety risk level of the overall system, all first-level subsystems, all second-level subsystems, and all third-level subsystems. Based on the total safety risk level, the safety assessment results of the construction process of the foundation pit project are determined.

[0142] For ease of understanding, the following complete implementation example is provided:

[0143] S501, Construct a safety risk assessment index system for foundation pits. The safety risk assessment index system for foundation pits includes a first-level subsystem, at least one second-level subsystem, and at least one third-level subsystem. Each first-level subsystem, second-level subsystem, and third-level subsystem includes at least one evaluation index. Evaluation indices located in the first-level subsystem are first-level indicators, evaluation indices located in the second-level subsystem are second-level indicators, and evaluation indices located in the third-level subsystem are third-level indicators. First-level indicators correspond to second-level subsystems, and second-level indicators correspond to third-level subsystems.

[0144] S502, based on any subsystem, multiple score groups are obtained based on all evaluation indicators of the subsystem, multiple triangular fuzzy reciprocal judgment matrices are obtained based on all score groups, and multiple transformation judgment matrices are obtained based on all triangular fuzzy reciprocal judgment matrices.

[0145] S503: Based on any transformation judgment matrix, calculate the consistency ratio of the transformation judgment matrix, perform a consistency check on the transformation judgment matrix based on the consistency ratio, obtain a comprehensive judgment matrix based on all transformation judgment matrices that have passed the consistency check, and perform a consistency check on the comprehensive judgment matrix.

[0146] S504. Based on all the comprehensive judgment matrices that have passed the consistency test and the corresponding comprehensive judgment matrices that have passed the consistency transformation judgment matrix, a set-pair judgment matrix is ​​obtained. Based on the set-pair judgment matrix, the index weights corresponding to all evaluation indicators of the subsystem are determined. The number of transformation judgment matrices is equal to the number of score groups, and all transformation judgment matrices have passed the consistency test. The comprehensive judgment matrix based on all transformation judgment matrices has passed the consistency test.

[0147] S505, Obtain the preset safety risk level standard, combine the safety evaluation level and evaluation index of the safety risk level standard into a set pair, and establish a five-element connection number calculation formula based on the set pair;

[0148] S506, obtain real-time monitoring values, and based on any real-time monitoring value, determine multiple third-level connection number components based on the real-time monitoring value and the five-element connection number calculation formula, determine multiple second-level connection number components based on all third-level connection number components, determine multiple first-level connection number components based on all second-level connection number components, and determine the total system connection number components based on all first-level connection number components.

[0149] S507, the principal value of the third-level connection number is determined based on all third-level connection number components, the principal value of the second-level connection number is determined based on all second-level connection number components, the principal value of the first-level connection number is determined based on all first-level connection number components, and the principal value of the total system connection number is determined based on the total system connection number components. The principal value of the connection number includes the principal value of the total system connection number, the principal value of the first-level connection number, the principal value of the second-level connection number, and the principal value of the third-level connection number.

[0150] S508, based on the principal value of the total system connection number, the safety evaluation level of the total system is determined; the principal value of the first-level connection number is determined; the principal value of the second-level connection number is determined; the principal value of the third-level connection number is determined; and the safety evaluation result of the foundation pit project is determined based on all the safety evaluation levels. The safety evaluation result of the foundation pit project includes risk assessment, acceptance criteria, and countermeasures.

[0151] This application integrates fuzzy hierarchical analysis (AHP) and set pair analysis to comprehensively evaluate the safety risks of foundation pit construction, thereby obtaining a safety evaluation result. This approach fully explores the various uncertainties inherent in deep foundation pit engineering, such as randomness, grayness, and fuzziness. By constructing a foundation pit safety risk evaluation index system that includes multiple evaluation indicators, and using monitoring items as evaluation indicators, a comprehensive foundation pit safety risk evaluation system is established, fully considering the uncertainty of individual expert evaluations and the reliability of expert group decisions. The introduction of set pair analysis considers the similarities and differences in individual expert opinions, achieving reasonable weighting of each evaluation indicator. Using monitoring alarm values ​​as a reference for foundation pit safety risk classification, and using real-time monitoring values ​​as analytical data, combined with automated monitoring information technology, a real-time comprehensive evaluation of the safety risks of foundation pit construction can be achieved, which can, to a certain extent, avoid false alarms in foundation pit construction monitoring.

[0152] like Figure 4 As shown, Figure 4 This is a structural diagram of a foundation pit evaluation device based on fuzzy hierarchical analysis and set pair analysis provided in one embodiment of the present invention. The present invention also provides a foundation pit evaluation device based on fuzzy hierarchical analysis and set pair analysis, comprising:

[0153] The processor 601 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0154] The memory 602 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 602 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 602 and is called and executed by the processor 601 to implement the pit evaluation method based on fuzzy hierarchy and set pair analysis of the embodiments of this application.

[0155] The input / output interface 603 is used to implement information input and output;

[0156] The communication interface 604 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0157] Bus 605 transmits information between various components of the device (e.g., processor 601, memory 602, input / output interface 603, and communication interface 604);

[0158] The processor 601, memory 602, input / output interface 603, and communication interface 604 are connected to each other within the device via bus 605.

[0159] This application also provides an electronic device, including the foundation pit evaluation device based on fuzzy hierarchy and set pair analysis as described above.

[0160] This application embodiment also provides a storage medium, which is a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the above-described pit evaluation method based on fuzzy hierarchy and set pair analysis.

[0161] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof. The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate, and may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0162] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0163] The above provides a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A method for evaluating foundation pits based on fuzzy hierarchical and set pair analysis, characterized in that, include: A foundation pit safety risk assessment index system is constructed, wherein the foundation pit safety risk assessment index system includes multiple subsystems, and each subsystem includes at least one assessment index; Based on any of the subsystems, multiple score groups are obtained based on all the evaluation indicators of the subsystem. Multiple triangular fuzzy reciprocal judgment matrices are obtained based on all the score groups. Multiple transformation judgment matrices are obtained based on all the triangular fuzzy reciprocal judgment matrices. Set pair judgment matrices are obtained based on all the transformation judgment matrices. A compatibility judgment matrix is ​​established based on the set pair judgment matrix. The index weights corresponding to all the evaluation indicators of the subsystem are determined based on the compatibility judgment matrix. A preset safety risk level standard and multiple real-time monitoring values ​​are obtained. The safety evaluation level of the safety risk level standard and the evaluation index are paired. A five-element connection coefficient calculation formula is established based on the pair. The principal value of the connection coefficient of all the evaluation indicators is determined based on all the real-time monitoring values, all the indicator weights, the five-element connection coefficient calculation formula and the safety risk level standard. The real-time monitoring value is the real-time value of the evaluation indicator, and the safety risk level standard includes multiple safety evaluation levels. The safety evaluation level of the subsystem is determined based on the principal values ​​of the contact numbers corresponding to all the evaluation indicators of any subsystem and the safety risk level standard, and the safety evaluation result of the foundation pit project is determined based on all the safety evaluation levels.

2. The foundation pit evaluation method based on fuzzy hierarchical and set pair analysis according to claim 1, characterized in that, Based on all the evaluation indicators of the subsystem, multiple score groups are obtained; based on all the score groups, multiple triangular fuzzy reciprocal judgment matrices are obtained; and based on all the triangular fuzzy reciprocal judgment matrices, multiple transformation judgment matrices are obtained, including: All evaluation indicators of the subsystem are obtained, and multiple score groups are obtained by having multiple experts score all evaluation indicators based on pairwise relative importance, wherein the number of score groups is equal to the number of experts. Based on any score group, the triangular fuzzy reciprocal judgment matrix is ​​constructed, wherein the expression of the triangular fuzzy reciprocal judgment matrix is: X = (x ij ) n×n Let X be the triangular fuzzy reciprocal judgment matrix of order n, and let x be the triangular fuzzy reciprocal judgment matrix. ij Let i be the element in the i-th row and j-th column of the triangular fuzzy reciprocal judgment matrix, and n be the number of evaluation indicators of the subsystem, where i, j, and n are all positive integers. Based on any of the aforementioned triangular fuzzy reciprocal judgment matrices, the transformation judgment matrix is ​​obtained based on the triangular fuzzy reciprocal judgment matrix and the first formula, where x ij The expression is: x ij =(e ij ,f ij ,g ij ), and g ij ≥e ij ≥f ij >1, e ij f is the lowest evaluation value for the relative importance of indicator i to indicator j. ij Let g be a possible evaluation value for the relative importance of indicator i to indicator j. ij Let i be the highest evaluation value for the relative importance of indicator i to indicator j. The score group includes the lowest evaluation value, the possible evaluation value, and the highest evaluation value. The first formula is: The expression for the transformation judgment matrix is: Y = (y ij ) n×n Y is the transformation judgment matrix, y ij The element in the i-th row and j-th column of the transformation judgment matrix.

3. The foundation pit evaluation method based on fuzzy hierarchical and set pair analysis according to claim 2, characterized in that, After obtaining multiple transformation judgment matrices based on all the aforementioned triangular fuzzy reciprocal judgment matrices, the process further includes: Based on any of the transformation judgment matrices, multiple normalized feature elements are obtained using the second formula based on the transformation judgment matrix. A normalized feature vector is obtained based on all the normalized feature elements. The approximate value of the maximum eigenvalue is obtained based on the third formula, the transformation judgment matrix, and the normalized feature vector. The consistency ratio of the transformation judgment matrix is ​​obtained based on the fourth formula and the approximate value of the maximum eigenvalue. The consistency ratio is used to perform a consistency check on the transformation judgment matrix. A comprehensive judgment matrix is ​​obtained based on the fifth formula and r transformation judgment matrices that pass the consistency test. A consistency test is then performed on the comprehensive judgment matrix, where r is the number of experts. If the comprehensive judgment matrix fails the consistency test, all evaluation indicators of the subsystem are re-scored based on pairwise relative importance. Wherein, the second formula is v i For the i-th normalized feature element, the third formula is: λ max Let (Yv) be the approximate value of the largest eigenvalue. i The fourth formula is the i-th element obtained by multiplying the transformation judgment matrix and the normalized eigenvector. CR is the consistency ratio, RI is the random consistency index in the preset random consistency index value table, and the fifth formula is... Let be the element in the i-th row and j-th column of the k-th transformation judgment matrix, where k is a positive integer, less than or equal to r. The expression for the comprehensive judgment matrix is: Z = (z ij ) n×n Z is the comprehensive judgment matrix, z ij is the element in the i-th row and j-th column of the comprehensive judgment matrix.

4. The foundation pit evaluation method based on fuzzy hierarchical and set pair analysis according to claim 3, characterized in that, Based on all the aforementioned transformation judgment matrices, a set pair judgment matrix is ​​obtained, including: An identity matrix is ​​constructed based on r transformation judgment matrices that pass the consistency test and the sixth formula; a difference matrix is ​​constructed based on all the transformation judgment matrices and the seventh formula; and a set pair judgment matrix is ​​constructed based on the identity matrix and the difference matrix. The sixth formula is: Let be the minimum value among the elements in the i-th row and j-th column of the r transformation judgment matrices. Let the maximum value among the elements in the i-th row and j-th column of the r transformation judgment matrices be the identity matrix, and the expression for the identity matrix is: A is the identity matrix, a ij For the element in the i-th row and j-th column of the identity matrix, the seventh formula is: The expression for the difference matrix is: B is the difference matrix, b iij Let be the element in the i-th row and j-th column of the dissimilarity matrix. The expression for the set pair judgment matrix is: U = A + αB, where U is the set pair judgment matrix, α is the dissimilarity coefficient, and α ∈ [0, 1].

5. The foundation pit evaluation method based on fuzzy hierarchical and set pair analysis according to claim 1, characterized in that, Based on the set-pair judgment matrix, a compatible judgment matrix is ​​established. Based on the compatible judgment matrix, the index weights corresponding to all the evaluation indicators of the subsystem are determined, including: The compatibility judgment matrix is ​​obtained based on the eighth formula and the set pair judgment matrix, wherein the eighth formula is: n is the total number of all the evaluation indicators of the subsystem, u ip Let u be the element in the i-th row and p-th column of the judgment matrix of the set. pj Let be the element in the p-th row and j-th column of the set-p judgment matrix, where p is a positive integer, less than or equal to n. The expression for the compatibility judgment matrix is: D = (d ij ) n×n D is the compatibility judgment matrix, d ij The element in the i-th row and j-th column of the compatibility judgment matrix; Multiple index weights are obtained based on the ninth formula and the compatibility judgment matrix, wherein the ninth formula is: w s The index weight is the s-th evaluation index of the subsystem, where s is a positive integer and s is less than or equal to n.

6. The foundation pit evaluation method based on fuzzy hierarchical and set pair analysis according to claim 1, characterized in that, Based on the aforementioned set pairs, a five-element connection coefficient calculation formula is established. Based on all the aforementioned real-time monitoring values, all the aforementioned indicator weights, the aforementioned five-element connection coefficient calculation formula, and the aforementioned safety risk level standards, the principal values ​​of the connection coefficients for all the aforementioned evaluation indicators are determined, including: The formula for calculating the five-element connection coefficient is established to obtain multiple real-time monitoring values. Each subsystem includes a first-level subsystem, at least one second-level subsystem, and at least one third-level subsystem. Each first-level, second-level, and third-level subsystem includes at least one evaluation index. The evaluation index located in the first-level subsystem is a first-level index, the evaluation index located in the second-level subsystem is a second-level index, and the evaluation index located in the third-level subsystem is a third-level index. The first-level index corresponds to the second-level subsystem, the second-level index corresponds to the third-level subsystem, and the real-time monitoring value is the real-time value of the third-level index. Based on any of the real-time monitoring values, multiple third-level connection number components are determined based on the real-time monitoring values ​​and the five-element connection number calculation formula; multiple second-level connection number components are determined based on all the third-level connection number components; multiple first-level connection number components are determined based on all the second-level connection number components; and the total system connection number component is determined based on all the first-level connection number components. The third-level contact number components are determined by the five-element contact number calculation formula. The principal value of the second-level contact number is determined based on all the second-level contact number components. The principal value of the first-level contact number is determined based on all the first-level contact number components. The principal value of the total system contact number is determined based on the total system contact number components. The principal value of the contact number includes the principal value of the total system contact number, the principal value of the first-level contact number, the principal value of the second-level contact number, and the principal value of the third-level contact number. The expression for the formula for calculating the five-element connection coefficient is as follows: μ mtl Let c be the principal value of the third-level correlation coefficient of the third-level subsystem corresponding to the t-th second-level indicator of the second-level subsystem corresponding to the m-th first-level indicator of the third-level subsystem. Here, m represents the m-th first-level indicator of the first-level subsystem, t represents the t-th second-level indicator of the second-level subsystem, and l represents the l-th third-level indicator of the third-level subsystem. m, t, and l are positive integers, where m is less than or equal to the number of first-level indicators of the first-level subsystem, t is less than or equal to the number of second-level indicators of the second-level subsystem, and l is less than or equal to the number of third-level indicators of the third-level subsystem. mtl S is the real-time monitoring value corresponding to the l-th tertiary indicator of the tertiary subsystem corresponding to the t-th secondary indicator of the second-level subsystem corresponding to the m-th primary indicator of the primary subsystem. (x,x+1)l The boundary value between the xth and x+1th levels of the lth level indicator of the three-level subsystem is defined based on a preset safety risk level standard and threshold table. i1, i2 and i3 are all difference coefficients, and j1 is the opposing indicator.

7. The foundation pit evaluation method based on fuzzy hierarchical and set pair analysis according to claim 6, characterized in that, Based on the real-time monitoring values ​​and the five-element connection number calculation formula, multiple third-level connection number components are determined; based on all the third-level connection number components, multiple second-level connection number components are determined; based on all the second-level connection number components, multiple first-level connection number components are determined; and based on all the first-level connection number components, the total system connection number components are determined, including: Based on the real-time monitoring values ​​and the boundary values, a corresponding interval is determined. Based on the corresponding interval, a formula for calculating the comprehensive evaluation connection number of the three-level subsystem is determined. Based on the formula for calculating the comprehensive evaluation connection number of the three-level subsystem, multiple components of the three-level connection number are determined. The expression for the formula for calculating the comprehensive evaluation connection number of the three-level subsystem is: μ mtl =r mtl1 +r mtl2 i1+r mtl3 i2+r mtl4 i3+r mtl5 j1, r mtl1 r mtl2 r mtl3 r mtl4 and r mpl5 All are components of the third-level connection number, r mtl1 r mtl2 r mtl3 r mtl4 and r mpl5 The values ​​of r are all in the range [0,1], and r mtl1 +r mtl2 i1+r mtl3 i2+r mtl4 i3+r mtl5 j1 = 1; Based on all the aforementioned third-level connection components, multiple second-level connection components are determined, wherein the expressions for the second-level connection components are: r mtl For the m-th primary indicator of the primary subsystem, r is the q-th secondary correlation component of the t-th secondary indicator of the secondary subsystem, where q is a positive integer, greater than or equal to 1 and less than or equal to 5. mtlq Let w be the number of the three-level connection components, L be the total number of the three-level indicators of the three-level subsystem, and w be the number of the three-level connections components. mtl r is the indicator weight of the l-th tertiary indicator of the tertiary subsystem corresponding to the t-th secondary indicator of the second-level subsystem corresponding to the m-th primary indicator of the primary subsystem. mtlq The m-th primary indicator of the primary subsystem corresponds to the t-th secondary indicator of the secondary subsystem, which in turn corresponds to the l-th tertiary indicator of the tertiary subsystem. Multiple first-level connection components are determined based on all the second-level connection components, wherein the expressions for the first-level connection components are: r mq Let w be the q-th primary connection component corresponding to the m-th primary indicator of the primary subsystem, T be the total number of all secondary indicators of the secondary subsystem, and w be the number of primary indicators corresponding to the m-th primary indicator of the primary subsystem. mt The index weight of the t-th secondary index of the secondary subsystem corresponding to the m-th primary index of the primary subsystem; The total system connection number component is determined based on all the first-level connection number components, wherein the expression for the total system connection number component is: r q Let w be the q-th component of the total system connection count, M be the total number of all the first-level indicators of the first-level subsystem, and w be the total number of the first-level indicators of the first-level subsystem. m The index weight is the m-th primary index of the primary subsystem.

8. The foundation pit evaluation method based on fuzzy hierarchical and set pair analysis according to claim 7, characterized in that, The third-level connection components are determined by the five-element connection formula. The principal values ​​of the second-level connection numbers are determined based on all the second-level connection components. The principal values ​​of the first-level connection numbers are determined based on all the first-level connection components. The principal value of the total system connection number is determined based on the total system connection components, including: The principal value of the third-level connection number is determined based on all the components of the third-level connection number and the calculation formula of the comprehensive evaluation connection number of the third-level subsystem; The principal value of the second-level connection number is determined based on all the components of the second-level connection number and the formula for calculating the comprehensive evaluation connection number of the second-level subsystem. The expression for the formula for calculating the comprehensive evaluation connection number of the second-level subsystem is: μ mt =r mt1 +r mt2 i1+r mt3 i2+r mt4 i3+r mt5 j1, μ mt r is the principal value of the secondary correlation coefficient of the t-th secondary indicator of the secondary subsystem corresponding to the m-th primary indicator of the primary subsystem. mt1 r mt2 r mt3 r mt4 and r mt5 All of these are components of the second-level connection number; The principal value of the first-level connection number is determined based on all the components of the first-level connection number and the calculation formula for the comprehensive evaluation connection number of the first-level subsystem. The expression for the calculation formula for the comprehensive evaluation connection number of the first-level subsystem is: μ m =r m1 +r m2 i1+r m3 i2+r m4 i3+r m5 j1, μ m r is the principal value of the first-level connection coefficient of the m-th first-level indicator of the first-level subsystem. m1 r m2 r m3 r m4 and r m5 All of these are components of the first-level contact number; The principal value of the total system connection number is determined based on the components of the total system connection number and the formula for calculating the total system comprehensive evaluation connection number. The expression for the principal value of the total system connection number is: μ = r1 + r2i1 + r3i2 + r4i3 + r5j1, where μ is the principal value of the total system connection number, and r1, r2, r3, r4 and r5 are all components of the total system connection number.

9. A foundation pit evaluation device based on fuzzy hierarchical and set pair analysis, characterized in that, It includes at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform the pit evaluation method based on fuzzy hierarchy and set pair analysis as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform the foundation pit evaluation method based on fuzzy hierarchy and set pair analysis as described in any one of claims 1 to 7.

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