Comprehensive evaluation method, medium and equipment for geological environment of abandoned mine

Abstract

The invention relates to the technical field of environment evaluation, in particular to an abandoned mine geological environment comprehensive evaluation method, a medium and equipment. The method comprises the following steps: constructing an abandoned mine geological environment comprehensive evaluation index system; determining the optimal subjective weight of the evaluation index based on a fuzzy analytic hierarchy process and an IRMO algorithm; determining objective weights of evaluation indexes based on an entropy weight method; based on the game theory, determining the comprehensive weight of the evaluation index; and dividing evaluation grades, calculating the membership degree of the evaluation object to each evaluation grade based on a fuzzy comprehensive evaluation method, and determining the evaluation grade of the evaluation object according to a maximum membership degree principle. Subjective and objective coordination of evaluation index weights, geological conditions, mine resource damage and loss degrees and geological environment problems are considered; the IRMO algorithm is introduced, the subjective weight obtaining method of the fuzzy analytic hierarchy process is optimized, the influence of the expert level on the judgment matrix in the existing analysis method is weakened, the credibility of the weight is improved, and the final evaluation result is more reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental assessment, and in particular to a method, medium and equipment for comprehensive assessment of the geological environment of abandoned mines. Background Art

[0002] Abandoned open-pit mines, a legacy of mining development, present numerous adverse geological conditions, such as environmental pollution and geological disasters, which have had a serious negative impact on people's lives and livelihoods. In recent years, targeted efforts have been made to remediate abandoned open-pit mines, minimizing the negative impacts of open-pit mining. It is necessary to evaluate the geological environment of abandoned open-pit mines to provide a reference for restoration and remediation efforts.

[0003] Currently, commonly used methods for evaluating the geological environment of mines include GIS evaluation, neural network analysis, analytic hierarchy process (AHP), entropy weighting, and fuzzy comprehensive evaluation. GIS and neural network methods accurately assess the geological environment of mines based on extensive field data. However, the large amount of data required makes them difficult to apply widely. The AHP-fuzzy comprehensive evaluation method has lower application requirements, but is significantly influenced by expert experience and lacks objectivity. The entropy weighting method objectively reflects the impact of individual evaluation indicators, but a unified standard has not yet been established within the industry, making its sole use prone to deviations from reality. Furthermore, when calculating weights using the AHP method, adjustments to achieve full consistency are difficult when the judgment matrix does not meet the full consistency condition.

[0004] Therefore, there is an urgent need to propose a more simplified and accurate comprehensive evaluation method for the geological environment of abandoned mines to solve the problems existing in the existing technology. Summary of the Invention

[0005] The present invention aims to provide a method, medium and equipment for comprehensive evaluation of the geological environment of abandoned mines. The specific technical solutions are as follows: A comprehensive evaluation method for the geological environment of abandoned mines comprises the following steps: S1: Construct a comprehensive evaluation index system for the geological environment of abandoned mines; S2: Determine the optimal subjective weights of evaluation indicators based on fuzzy analytic hierarchy process and IRMO algorithm; S3: Determine the objective weights of evaluation indicators based on the entropy weight method; S4: Based on game theory, determine the comprehensive weight of evaluation indicators; S5: Divide the evaluation levels, calculate the membership of the evaluation object to each evaluation level based on the fuzzy comprehensive evaluation method, and determine the evaluation level of the evaluation object according to the maximum membership principle.

[0006] Preferably, the S1 specifically includes: The geological background, resource destruction and geological environmental problems are selected as the first-level evaluation indicators of the comprehensive evaluation index system of the geological environment of abandoned mines; The average annual rainfall, earthquake intensity, lithology and topography are selected as the secondary evaluation indicators of the geological background; the destruction of topography and landscape, land damage and aquifer damage are selected as the secondary evaluation indicators of resource damage; geological disasters, slope structure, water and soil pollution and soil erosion are selected as the secondary evaluation indicators of geological environment problems; The evaluation level of the impact of each evaluation indicator on the geological environment is divided into three levels: slight, relatively serious and serious.

[0007] Preferably, the S2 specifically includes: S2.1: Construct a fuzzy judgment matrix and calculate the subjective weight of each evaluation indicator through the fuzzy judgment matrix that meets the complete consistency condition; S2.2: Construct the fitness function of the IRMO algorithm and use the IRMO algorithm to solve the optimal subjective weight of the evaluation index.

[0008] Preferably, the S2.1 specifically includes: Construct a fuzzy judgment matrix through subjective judgment or expert scoring A , used to express the relative importance of different evaluation indicators, the fuzzy judgment matrix is ​​as follows: 9); Then the fuzzy judgment matrix is ​​tested for consistency. Only those that pass the consistency test can further determine the weights of each evaluation index. Otherwise, the elements in the fuzzy judgment matrix are adjusted until a fuzzy judgment matrix that meets the complete consistency condition is obtained. When it meets the complete consistency condition, the elements in the matrix are Satisfies the following formula: 10); Where: and j The values ​​are 1, 2, ..., m ; m is the number of evaluation indicators; and is the subjective weight of the evaluation index; p ≥( m- 1) / 2, indicating the importance attached to the differences between different evaluation indicators, p The smaller it is, the more emphasis is placed on differences; According to formula 10), when the fuzzy judgment matrix has complete consistency, it satisfies the following formula: 11); Based on the complete consistency condition, the optimization function is constructed as follows: 12); Where: CR is the consistency indicator; st For supplementary conditions; is the adjusted matrix element; is the difference between the first row element and the adjusted matrix element; is the first row element of the original matrix, which represents the confident judgment scale value of a certain evaluation index compared with other evaluation indicators; The first row element of the original matrix and the The average of the differences between row elements; CIC(m) is the consistency coefficient. When it is less than a certain critical value, the matrix is ​​considered to have satisfactory consistency, and the subjective weights of the evaluation indicators calculated by it are acceptable. The optimized vector is obtained from Equation 12) q =( , , … , , , … , , , … , , … , ),for dimensional vector.

[0009] Preferably, the S2.2 specifically includes: Select Equation 12) as the fitness function of the IRMO algorithm , the function is as follows: 13); According to the fuzzy judgment matrix obtained by expert scoring, a nop The initial particle population of particles, each particle represents a solution vector, the particle population [X] The information is shown in formula 14): 14); Calculate the fitness of each particle in the initial particle population, compare and select the initial optimal particle as the global optimal particle, then update the particle population and select the contemporary optimal particle, compare it with the current global optimal particle fitness, and select the better one as the global optimal particle. Perform iterative search according to the above steps until the maximum number of iterations or the target fitness size is reached. The final global optimal particle is the optimal subjective weight of the evaluation index.

[0010] Preferably, the S3 specifically includes: Create the original matrix X as follows: 15); Where: Matrix X =( ) k×m , Indicates the u The first evaluation object v Evaluation index values, u =1,2,…, k ; v =1,2,…, m , k is the number of evaluation objects; The values ​​of each evaluation index are standardized and divided into positive evaluation index and negative evaluation index. There are different processing methods for the two evaluation indexes. The standardized value is ; 16); 17); Where: Indicates the v The maximum value of the evaluation index, Indicates the v The minimum value of the evaluation index; Calculate the entropy value of each evaluation index , the expression is as follows: 18); 19); Where: =0, take ln =0; Calculate the v The objective weight of the evaluation index is expressed as follows: 20); According to the above formula, the objective weight vector is obtained =( , ,…, ).

[0011] Preferably, the S4 specifically includes: Establish a linear combination vector, remember to use L The evaluation index weight vector set obtained by this method is : twenty one); Where: Indicates the Method m The weight of each indicator; The L The linear combination of weight vectors is expressed as: twenty two); Where: is the combined weight vector; is transposed; is the linear combination coefficient; Solve the optimal linear combination coefficients and minimize the combination weight vector based on game theory principles Evaluation index weight vector set The deviation is expressed as follows: twenty three); According to the matrix differential properties, the linear equations satisfying the optimal first-order derivative condition of Equation 23) are obtained as shown below: twenty four); Solve the linear equations to obtain the optimal linear combination coefficients =[ , ,…, ]; Before calculating the combination weight, the linear combination coefficients need to be normalized to obtain new linear combination coefficients. as follows: 25); Calculate the combined weight vector W , the expression is as follows: 26); The optimal subjective weight obtained by S2 The objective weight obtained with S3 Perform the combined weighting calculation and further obtain the combined weight vector as follows: 27).

[0012] Preferably, the S5 specifically includes: Establish the evaluation factor set, which is a collection of various evaluation indicators. U express, U ={ U 1 ,U 2 ,…,U m}; Establish an evaluation level set. The evaluation level set is a collection of possible evaluation levels that the evaluator may make on the evaluation object. The evaluation level set is set according to the actual evaluation needs. V express, V ={ V 1 ,V 2 ,…,V n}, n is the evaluation level number; Determine the evaluation index weight set. The weight is an evaluation index that measures the degree of influence of each evaluation index on the evaluation result, which is recorded as W ={ w 1 ,w 2 ,…,w m}; Construct a membership function. The membership function is used to calculate the degree to which the evaluation index belongs to a certain evaluation level. The trapezoidal distribution function is used as the membership function. The formula is as follows: 1); 2); 3); in: 4); 5); Where: 、 、 Respectively represent the degree of membership of the evaluation index to be mild, relatively serious and serious; 、 、 They are the standard values ​​for the three evaluation levels respectively; 、 is the upper limit of the concentrated transition interval; is the interval transition coefficient; Single factor fuzzy evaluation, each evaluation index is evaluated using the evaluation level set, and the fuzzy evaluation set is obtained as follows: 6); in: For the m The evaluation index is n The membership value of each evaluation level; Comprehensive evaluation, based on single-factor fuzzy evaluation, conducts fuzzy comprehensive evaluation; the comprehensive evaluation membership is calculated using the combined weight and fuzzy evaluation set, and the calculation formula is as follows: 7); Where: B is the upper membership matrix; According to the principle of maximum membership, the evaluation level with the maximum membership is the comprehensive evaluation level of the abandoned mine geological environment; The system score is calculated according to the following formula F , sort multiple evaluation objects: 8); Where: is the fraction of the evaluation set.

[0013] The present invention also provides a readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implements the above-mentioned method for comprehensive evaluation of the geological environment of abandoned mines.

[0014] The present invention also provides an electronic device comprising: at least one processor, at least one memory and computer program instructions stored in the memory, when the computer program instructions are executed by the processor, the above-mentioned comprehensive evaluation method for the geological environment of abandoned mines is performed.

[0015] The application of the technical solution of the present invention has the following beneficial effects: A comprehensive evaluation method for the geological environment of abandoned mines includes the following steps: constructing a comprehensive evaluation index system for the geological environment of abandoned mines; determining the optimal subjective weights of the evaluation indicators based on the fuzzy analytic hierarchy process and the IRMO algorithm; determining the objective weights of the evaluation indicators based on the entropy weight method; determining the comprehensive weights of the evaluation indicators based on game theory; dividing the evaluation levels, calculating the membership of the evaluation object to each evaluation level based on the fuzzy comprehensive evaluation method, and determining the evaluation level of the evaluation object based on the maximum membership principle. The present invention takes into account the coordination of the subjectivity and objectivity of the evaluation index weights, as well as geological conditions, the degree of damage and loss of mine resources, and geological environmental issues; by introducing the IRMO algorithm, the subjective weight acquisition method of the fuzzy analytic hierarchy process is optimized, the influence of the expert level on the judgment matrix in the existing analysis method is weakened, the credibility of the weights is improved, and the final evaluation results are more reliable.

[0016] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings: Figure 1 A schematic flow chart of a method for comprehensive evaluation of the geological environment of abandoned mines according to an embodiment of the present invention; Figure 2 IRMO algorithm flow chart in the embodiment; Figure 3 This is a graph showing the combined weight values ​​of the evaluation indicators in the embodiment; Figure 4 4 is a comparison chart of subjective weight, objective weight and combined weight in the embodiment. DETAILED DESCRIPTION

[0018] The embodiments of the present invention are described in detail below with reference to the accompanying drawings. However, the present invention can be implemented in many different ways as defined and covered by the claims.

[0019] In one embodiment, see Figure 1 A comprehensive evaluation method for the geological environment of abandoned mines comprises the following steps: S1: Constructing a comprehensive evaluation index system for the geological environment of abandoned mines, which specifically includes: S1.1 Evaluation system evaluation indicators: This embodiment selects comprehensive evaluation indicators of the geological environment of open-pit mines based on the five principles that should be followed in the selection of evaluation indicators: scientific principle, easy access principle, practical principle, applicability principle, and independence principle of evaluation indicators. The selection of evaluation indicators is shown in Table 1.

[0020] Table 1 Comprehensive evaluation indicators of geological environment of abandoned open-pit mines

[0021] The geological environment of abandoned mines is evaluated comprehensively based on three aspects: geological conditions, resource damage caused by mining, and geological environmental problems. Geological condition evaluation indicators include average annual rainfall, earthquake intensity, lithology, and topography. These four evaluation indicators induce geological hazards to varying degrees and reflect the stability of the regional geological environment. Resource damage evaluation indicators include surface landscape damage, land damage, and aquifer damage, which measure the degree of damage to the geological environment of abandoned mines. Geological environmental problem evaluation indicators include geological hazards, soil and water pollution, and soil erosion, which can be used to measure the difficulty of subsequent mine remediation and restoration.

[0022] S1.2 Evaluation index grading standards This example, with reference to the "Detailed Implementation Rules for the National Technical Requirements for Mine Geological Environmental Surveys," categorizes the impact of each evaluation indicator on the geological environment into three levels: minor, relatively severe, and severe. This grading is also used in the final comprehensive geological environment evaluation. Based on relevant national standards and specifications, such as the "Specifications for Mine Geological Environmental Surveys and Evaluations," the "Specifications for Land Damage Assessment Due to Mining," and the "Standards for Classifying the Hazard of Soil and Water Loss," a subjective analysis of each evaluation indicator was performed, resulting in the grading shown in Table 2. The impact of each evaluation indicator on the geological environment was categorized into three levels: minor, relatively severe, and severe.

[0023] Table 2 Classification of comprehensive evaluation indicators of geological environment of abandoned open-pit mines

[0024] Note: ① 01 cultivated land, 02 garden land, 03 forest land, 04 grassland, 06 mining land, damage to basic farmland, regardless of the area, is serious; ② Pz is the comprehensive water and soil pollution index, calculated with reference to Appendix F.4 of the "Code for Investigation and Evaluation of Mining Geological Environment".

[0025] S2: Based on the fuzzy analytic hierarchy process (FAHP) and IRMO algorithm, determine the optimal subjective weights of the evaluation indicators, including: S2.1: Construct a fuzzy judgment matrix and calculate the subjective weight of each evaluation indicator through the fuzzy judgment matrix that meets the complete consistency condition, including: Construct a fuzzy judgment matrix through subjective judgment or expert scoringA , used to represent the relative importance of different evaluation indicators, using the 0.1-0.9 scale method to characterize the degree of importance, as shown in Table 3. The fuzzy judgment matrix is ​​as follows: 9); Fuzzy judgment matrix A Has the following properties: ① =0.5, i = 1,2,…, m ;② = , j = 1,2,…, m ③ = – +0.5, k =1,2,…, m ; Table 3 0.1~0.9 scaling method

[0026] Then the fuzzy judgment matrix is ​​tested for consistency. Only those that pass the consistency test can further determine the weights of each evaluation index. Otherwise, the elements in the fuzzy judgment matrix are adjusted until a fuzzy judgment matrix that meets the complete consistency condition is obtained. When it meets the complete consistency condition, the elements in the matrix are Satisfies the following formula: 10); Where: m is the number of evaluation indicators; For the The subjective weight of each evaluation indicator, For the The subjective weight of each evaluation indicator; p ≥( m-1 ) / 2, indicating the importance attached to the differences between different evaluation indicators, p The smaller it is, the more emphasis is placed on differences; in actual situations, there are too many evaluation indicators and the relationships between them are complex.

[0027] According to formula 10), when the fuzzy judgment matrix has complete consistency, it satisfies the following formula: 11); As the number of evaluation indicators increases, the matrix dimension also increases. This makes it difficult to adjust the initial fuzzy judgment matrix to meet the full consistency condition when it does not meet the full consistency condition. This embodiment introduces the IRMO algorithm to search for the best fuzzy consistency matrix and the subjective weight vector of the evaluation indicators. wsub =( w 1 ,w 2 ,…,w m ).

[0028] Based on the complete consistency condition, the optimization function is constructed as follows: 12); Where: CR is the consistency indicator; st For supplementary conditions; is the adjusted matrix element; is the difference between the first row element and the adjusted matrix element; is the first row element of the original matrix, which represents the confident judgment scale value of a certain evaluation index compared with other evaluation indicators; The first row element of the original matrix and the The average of the differences between row elements; CIC ( m ) is the consistency coefficient. When it is less than a certain critical value, the matrix is ​​considered to have satisfactory consistency, and the subjective weights of the evaluation indicators calculated by it are acceptable. In this embodiment, the critical value is taken as 0.10 based on the existing research, that is, CIC ( m )<0.10.

[0029] The optimized vector is obtained from Equation 12) q =( , , … , , , … , , , … , , … , ),for dimensional vector.

[0030] S2.2: Construct the fitness function of the IRMO algorithm and use the IRMO algorithm to solve the optimal subjective weight of the evaluation indicators, including: This embodiment applies the IRMO algorithm to the subjective weight solution of the fuzzy analytic hierarchy process, using Equation 12) as the fitness function. By calculating the fitness of each generation of particles and comparing and selecting the particle with the best fitness, it is the contemporary optimal solution. After the iteration is completed, the solution space is compressed to a point, which is the global optimal solution.

[0031] Select Equation 12) as the fitness function of the IRMO algorithm , the function is as follows: 13); According to the fuzzy judgment matrix obtained by expert scoring, a nop The initial particle population of particles, each particle represents a solution vector, the particle population [X] The information is shown in formula 14): 14); Calculate the fitness of each particle in the initial particle population, compare and select the initial optimal particle as the global optimal particle, then update the particle population and select the contemporary optimal particle, compare it with the current global optimal particle fitness, and select the better one as the global optimal particle. Perform iterative search according to the above steps until the maximum number of iterations or the target fitness size is reached. The final global optimal particle is the optimal subjective weight of the evaluation index.

[0032] Let the maximum number of iterations be G , the current number of iterations is k , Rbest 、 Gbest are the contemporary optimal particle and the global optimal particle respectively, f is the fitness value. In this embodiment, the number of particles in the population is set to nop = 100, particle dimension nod = m The IRMO-FAHP solution process is as follows: Figure 2 shown.

[0033] S3: Based on the entropy weight method (EWM), the objective weights of the evaluation indicators are determined. The weights obtained by the fuzzy analytic hierarchy process are highly subjective and lack objective authenticity. Therefore, the entropy weight method is introduced to calculate the objective weights. The steps for calculating the weights using the entropy weight method are as follows: S3.1. Establish the original matrix X as follows: 15); Where: Matrix X =( ) k×m , Indicates the u The first evaluation object v Evaluation index values, u=1,2,…, k ; v =1,2,…, m , k is the number of evaluation objects; S3.2. Standardize the values ​​of each evaluation index and divide the evaluation index into positive evaluation index (the larger the value, the better) and negative evaluation index (the smaller the value, the better). There are different processing methods for the two evaluation indicators. The standardized value is recorded as ; 16); 17); Where: Indicates the v The maximum value of the evaluation index, Indicates the v The minimum value of the evaluation index; S3.3. Calculate the entropy value of each evaluation index , the expression is as follows: 18); 19); Where: =0, take ln =0; S3.4, calculate the v The objective weight of the evaluation index is expressed as follows: 20); According to the above formula, the objective weight vector is obtained =( , ,…, ).

[0034] S4: Based on game theory, determine the comprehensive weight of the evaluation indicators. In order to take into account the advantages of both subjective and objective weighting methods, a combined weighting method based on game theory is used to combine the subjective and objective weights to obtain the final weight. Specifically, it includes: S4.1 establishes a linear combination vector, using L The evaluation index weight vector set obtained by this method is : twenty one); Where: Indicates the Method m The weight of each indicator; The L The linear combination of weight vectors is expressed as: twenty two; Where: is the combined weight vector; is transposed; is the linear combination coefficient; S4.2 solves the optimal linear combination coefficients based on the principles of game theory and minimizes the combination weight vector Evaluation index weight vector set The deviation is expressed as follows: twenty three); According to the matrix differential properties, the linear equations satisfying the optimal first-order derivative condition of Equation 23) are obtained as shown below: twenty four); Solve the linear equations to obtain the optimal linear combination coefficients =[ , ,…, ]; S4.3 Combination weight calculation: Before calculating the combination weight, the linear combination coefficients need to be normalized to obtain new linear combination coefficients. as follows: 25); Calculate the combined weight vector W , the expression is as follows: 26); The optimal subjective weight obtained by S2 The objective weight obtained with S3 Perform the combined weighting calculation and further obtain the combined weight vector as follows: 27).

[0035] S5: Divide the evaluation levels, calculate the membership of the evaluation object to each evaluation level based on the fuzzy comprehensive evaluation method, and determine the evaluation level of the evaluation object according to the maximum membership principle, specifically including: S5.1 Establish the evaluation factor set, which is a collection of various evaluation indicators. U express, U ={ U 1 ,U 2 ,…, Um}; The factors set in this embodiment are as follows: U ={geological conditions A, resource damage B, geological environment problems C}, U1=A={annual average rainfall A1, earthquake intensity A2, lithology A3, topography A4}, U2=B={topography and landscape damage B1, land damage B2, aquifer damage B3}, U3=C={geological disasters C1, slope structure C2, water and soil pollution C3, soil erosion C4}, in this embodiment, m=11.

[0036] S5.2 Establish an evaluation level set. The evaluation level set is a collection of possible evaluation levels that the evaluator may make on the evaluation object. The evaluation level set is set according to the actual evaluation needs. V express, V ={ V 1 ,V 2 ,…,V n}, n is the evaluation grade number; this embodiment divides the evaluation grade into three grades according to the evaluation grade of the evaluation index, namely V ={Grade I (better), Grade II (poor), Grade III (bad)}.

[0037] S5.3 Determine the weight set of evaluation indicators. The weight is an evaluation indicator that measures the degree of influence of each evaluation indicator on the evaluation result, which is recorded as W ={ w 1 ,w 2 ,…,w m}; S5.4 Construct a membership function. The membership function is used to calculate the degree to which the evaluation index belongs to a certain evaluation level. The most commonly used method to determine the membership function is the assignment method. The assignment forms include rectangular distribution function, trapezoidal distribution function, etc. In geological environment evaluation, the trapezoidal distribution function is mostly used as the membership function. The formula is as follows: 1); 2); 3); in: 4); 5); Where: 、 、 Respectively represent the degree of membership of the evaluation index to be mild, relatively serious and serious; 、 、 They are the standard values ​​for the three evaluation levels respectively; 、 is the upper limit of the concentrated transition interval; is the interval transition coefficient, which is 0.5 in this embodiment.

[0038] Single factor fuzzy evaluation, each evaluation index is evaluated using the evaluation level set, and the fuzzy evaluation set is obtained as follows: 6); Comprehensive evaluation, based on single-factor fuzzy evaluation, conducts fuzzy comprehensive evaluation; the comprehensive evaluation membership is calculated using the combined weight and fuzzy evaluation set, and the calculation formula is as follows: 7); Where: B is the upper membership matrix; According to the principle of maximum membership, the evaluation level with the maximum membership is the comprehensive evaluation level of the abandoned mine geological environment; Then calculate the system score according to the following formula F , sort multiple evaluation objects: 8); Where: is the fraction of the evaluation set, which is assigned based on experience.

[0039] In this embodiment, Quarry A, Quarry B, and Quarry C in a certain city are selected as evaluation objects. The city has a warm temperate continental semi-arid climate, and the annual precipitation is less than the evaporation. The winter is dry and cold with little snow, the spring is rainy and windy, and the climate is dry. The summer is hot and dry with many rainstorms, and the temperature drops rapidly in autumn with continuous cloudy and rainy weather. Since 2021, the city has had more rainfall overall, with a total precipitation of more than 1,100 mm. The characteristic period of the earthquake reflection spectrum in the region is 0.40s, the peak acceleration value of the seismic motion is 0.15g, the corresponding basic earthquake intensity is VII, the seismic fortification intensity is 7, and the comprehensive horizontal seismic coefficient is 0.035. The three project sites are all former abandoned open-pit mines with prominent ecological and environmental problems, serious land occupation and vegetation excavation damage, large exposed areas of the mine surface, serious visual pollution, and the development of geological disasters such as collapse. The specific conditions of each evaluation indicator in the evaluation system are shown in Table 4.

[0040] Table 4 Evaluation indicators of each project site

[0041] Determine the weight of evaluation indicators: The subjective weights of IRMO-FAHP were determined, and the evaluation indicators were compared and evaluated using the 0.1-0.9 scale method through expert scoring. The scoring results are shown in Tables 5-8.

[0042] Table 5 Evaluation index layer U judgment matrix

[0043] Table 6 Evaluation index layer A judgment matrix

[0044] Table 7 Evaluation index layer B judgment matrix

[0045] Table 8 Evaluation index layer C judgment matrix

[0046] After constructing the judgment matrix of each evaluation index layer, the optimal subjective weight wsub was calculated by IRMO-fuzzy hierarchical analysis method. The results are shown in Table 9. The consistency coefficient CIC of each fuzzy judgment matrix is ​​less than 0.10, which shows satisfactory consistency.

[0047] Table 9 IRMO-Fuzzy AHP Subjective Weights

[0048] Determine the objective weight of the entropy weight method EWM: In order to facilitate calculation, the qualitative evaluation indicators are quantified, with the values ​​of mild, relatively serious, and severe being 1, 2, and 3 respectively. The evaluation indicator A3 is specially set as: I=1, II=1.5, III=2, IV=2.5, V=3. The quantitative values ​​of each evaluation indicator and the objective weight w obj As shown in Table 10.

[0049] Table 10 Quantitative values ​​and objective weights of evaluation indicators

[0050] The model uses IRMO-fuzzy analytic hierarchy process and entropy weight method (EWM) to determine subjective and objective weights, and then uses game theory to combine weights to obtain the combined weight coefficients of the comprehensive evaluation of the geological environment of abandoned open-pit mines, which are: =0.7877, =0.3112, after normalization we get =0.7168, =0.2832, the calculated combined weight result is as follows Figure 3 shown.

[0051] Compare and analyze the subjective weight, objective weight and combined weight, such as Figure 4 As shown in the figure, the subjective weights vary significantly, while the objective weights for multiple evaluation indicators are the same. This is because objective weighting only evaluates the evaluation indicators from a mathematical perspective. When the evaluation samples have only a single data point for a certain evaluation indicator, the weights will be the same. In comparison, subjective weighting often relies on expert experience, and the evaluation results vary from person to person. The combined weight lies between subjective and objective weights, comprehensively considering subjective factors and objective data, making the evaluation results more reasonable and reliable.

[0052] Fuzzy comprehensive evaluation of geological environment of abandoned open-pit mines: Construct a fuzzy evaluation set, calculate the membership degree of the evaluation indicators to different evaluation levels, and use it as the fuzzy evaluation set. The calculation results are shown in Table 11-13.

[0053] Table 11 Membership values ​​of evaluation indicators for Quarry A

[0054] Table 12 Membership values ​​of evaluation indicators of Quarry B

[0055] Table 13 Membership values ​​of evaluation indicators for Quarry C

[0056] The fuzzy relationship matrix can be obtained from Table 11-13: ; Secondary fuzzy comprehensive evaluation: Geological condition evaluation, according to formula 7), the weight vector of the secondary evaluation index under geological conditions is multiplied by the corresponding position matrix of the fuzzy relationship matrix and normalized to obtain BA 甲 =BA 乙 =BA 丙 =[0,0.7502,0.2498], the results show that the impact of geological conditions on the geological environment in each study area is “relatively serious”.

[0057] Resource destruction, B 甲 B=[0.2249,0.2647,0.5104]; B 乙 B=[0.4042,0.0854,0.5104]; B 丙 B=[0.4613,0.5387,0]. The results show that the resource damage of Quarry A and Quarry B has a "serious" impact on the regional geological environment, while the resource damage of Quarry C has a "relatively serious" impact.

[0058] Geological and environmental issues, B 甲 C=[0.1687,0.3381,0.4932];B 乙 C=B 丙 C=[0.1687,0,0.8313]. The results show that the geological environmental problems caused by the three quarries all have a "serious" impact on the geological environment.

[0059] First-level fuzzy comprehensive evaluation: According to formula 7) calculate: B 甲 =w×R 甲 =[0.1413,0.4236,0.4351];B 乙 =w×R 乙 =[0.1930,0.2190,0.5880];B 丙 =w×R 丙 =[0.2095,0.3499,0.4406].

[0060] According to the principle of maximum membership, the geological environment membership evaluation levels of the three quarries are all "Level III (poor)", indicating that the geological environment is in poor condition and governance and restoration measures are urgently needed.

[0061] Then calculate the evaluation system score according to formula 8): F 甲 =2.2938, F 乙 =2.3950, F 丙 =2.2311. The results show that the geological environment is from best to worst: Quarry C > Quarry A > Quarry B.

[0062] This embodiment also includes a readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the above-mentioned comprehensive evaluation method for the geological environment of abandoned mines is implemented.

[0063] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0064] This embodiment also includes an electronic device, including: at least one processor, at least one memory, and computer program instructions stored in the memory. When the computer program instructions are executed by the processor, the above-mentioned comprehensive evaluation method for the geological environment of abandoned mines is performed.

[0065] The electronic device may be a computing device such as a mobile phone, desktop computer, laptop, PDA, or cloud server. The electronic device may include, but is not limited to, a processor and memory. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0066] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A comprehensive evaluation method for the geological environment of abandoned mines, characterized in that: The steps include: S1: Construct a comprehensive evaluation index system for the geological environment of abandoned mines; S2: Determine the optimal subjective weights of evaluation indicators based on fuzzy analytic hierarchy process and IRMO algorithm; S3: Determine the objective weights of evaluation indicators based on the entropy weight method; S4: Based on game theory, determine the comprehensive weight of evaluation indicators; S5: Divide the evaluation levels, calculate the membership of the evaluation object to each evaluation level based on the fuzzy comprehensive evaluation method, and determine the evaluation level of the evaluation object according to the maximum membership principle.

2. A comprehensive evaluation method for the geological environment of abandoned mines according to claim 1, characterized in that: Said S1 specifically includes: The geological background, resource destruction and geological environmental problems are selected as the first-level evaluation indicators of the comprehensive evaluation index system of the geological environment of abandoned mines; The average annual rainfall, earthquake intensity, lithology and topography are selected as the secondary evaluation indicators of the geological background; the destruction of topography and landscape, land damage and aquifer damage are selected as the secondary evaluation indicators of resource damage; geological disasters, slope structure, water and soil pollution and soil erosion are selected as the secondary evaluation indicators of geological environment problems; The evaluation level of the impact of each evaluation indicator on the geological environment is divided into three levels: slight, relatively serious and serious.

3. A comprehensive evaluation method for the geological environment of abandoned mines according to claim 2, characterized in that: The S2 specifically includes: S2.1: Construct a fuzzy judgment matrix and calculate the subjective weight of each evaluation indicator through the fuzzy judgment matrix that meets the complete consistency condition; S2.2: Construct the fitness function of the IRMO algorithm and use the IRMO algorithm to solve the optimal subjective weight of the evaluation index.

4. A comprehensive evaluation method for the geological environment of abandoned mines according to claim 3, characterized in that: The S2.1 specifically includes: Construct a fuzzy judgment matrix through subjective judgment or expert scoring A , used to express the relative importance of different evaluation indicators, the fuzzy judgment matrix is ​​as follows: 9)； Then the fuzzy judgment matrix is ​​tested for consistency. Only those that pass the consistency test can further determine the weights of each evaluation index. Otherwise, the elements in the fuzzy judgment matrix are adjusted until a fuzzy judgment matrix that meets the complete consistency condition is obtained. When it meets the complete consistency condition, the elements in the matrix are Satisfies the following formula: 10)； Where: and j The values ​​are 1, 2, ..., m ; m is the number of evaluation indicators; and is the subjective weight of the evaluation index; p ≥( m- 1) / 2, indicating the importance attached to the differences between different evaluation indicators, p The smaller it is, the more emphasis is placed on differences; According to formula 10), when the fuzzy judgment matrix has complete consistency, it satisfies the following formula: 11)； Based on the complete consistency condition, the optimization function is constructed as follows: 12)； Where: CR is the consistency indicator; st For supplementary conditions; is the adjusted matrix element; is the difference between the first row element and the adjusted matrix element; is the first row element of the original matrix, which represents the confident judgment scale value of a certain evaluation index compared with other evaluation indicators; The first row element of the original matrix and the The average of the differences between row elements; CIC(m) is the consistency coefficient. When it is less than a certain critical value, the matrix is ​​considered to have satisfactory consistency, and the subjective weights of the evaluation indicators calculated by it are acceptable. The optimized vector is obtained from Equation 12) q =( , , … , , , … , , , … , , … , ),for dimensional vector.

5. A comprehensive evaluation method for the geological environment of abandoned mines according to claim 4, characterized in that: Said S2.2 specifically includes: Select Equation 12) as the fitness function of the IRMO algorithm , the function is as follows: 13)； According to the fuzzy judgment matrix obtained by expert scoring, a nop The initial particle population of particles, each particle represents a solution vector, the particle population [X] The information is shown in formula 14): 14)； Calculate the fitness of each particle in the initial particle population, compare and select the initial optimal particle as the global optimal particle, then update the particle population and select the contemporary optimal particle, compare it with the current global optimal particle fitness, and select the better one as the global optimal particle. Perform iterative search according to the above steps until the maximum number of iterations or the target fitness size is reached. The final global optimal particle is the optimal subjective weight of the evaluation index.

6. A comprehensive evaluation method for the geological environment of abandoned mines according to claim 5, characterized in that: The S3 specifically includes: Create the original matrix X as follows: 15)； Where: Matrix X =( ) k×m , Indicates the u The first evaluation object v Evaluation index values, u =1,2,…, k ; v =1,2,…, m , k is the number of evaluation objects; The values ​​of each evaluation index are standardized and divided into positive evaluation index and negative evaluation index. There are different processing methods for the two evaluation indexes. The standardized value is ; 16)； 17)； Where: Indicates the v The maximum value of the evaluation index, Indicates the v The minimum value of the evaluation index; Calculate the entropy value of each evaluation index , the expression is as follows: 18)； 19)； Where: =0, take ln =0; Calculate the v The objective weight of the evaluation index is expressed as follows: 20)； According to the above formula, the objective weight vector is obtained =( , ,…, ) .

7. A comprehensive evaluation method for the geological environment of abandoned mines according to claim 6, characterized in that: The S4 specifically includes: Establish a linear combination vector, remember to use L The evaluation index weight vector set obtained by this method is : 21)； Where: Indicates the Method m The weight of each indicator; The L The linear combination of weight vectors is expressed as: 22)； Where: is the combined weight vector; is transposed; is the linear combination coefficient; Solve the optimal linear combination coefficients and minimize the combination weight vector based on game theory principles and the evaluation index weight vector set The deviation is expressed as follows: 23)； According to the matrix differential properties, the linear equations satisfying the optimal first-order derivative condition of Equation 23) are obtained as shown below: 24)； Solve the linear equations to obtain the optimal linear combination coefficients =[ , ,…, ] ; Before calculating the combination weight, the linear combination coefficients need to be normalized to obtain new linear combination coefficients. as follows: 25)； Calculate the combined weight vector W , the expression is as follows: 26)； The optimal subjective weight obtained by S2 The objective weight obtained with S3 Perform the combined weighting calculation and further obtain the combined weight vector as follows: 27)。 8. A comprehensive evaluation method for the geological environment of abandoned mines according to claim 7, characterized in that: The S5 specifically includes: Establish the evaluation factor set, which is a collection of various evaluation indicators. U express, U ={ U 1 ,U 2 ,…,U m }; Establish an evaluation level set. The evaluation level set is a collection of possible evaluation levels that the evaluator may make on the evaluation object. The evaluation level set is set according to the actual evaluation needs. V express, V ={ V 1 ,V 2 ,…,V n }, n is the evaluation level number; Determine the evaluation index weight set. The weight is an evaluation index that measures the degree of influence of each evaluation index on the evaluation result, which is recorded as W ={ w 1 ,w 2 ,…,w m }; Construct a membership function. The membership function is used to calculate the degree to which the evaluation index belongs to a certain evaluation level. The trapezoidal distribution function is used as the membership function. The formula is as follows: 1)； 2)； 3)； in: 4)； 5)； Where: 、 、 Respectively represent the degree of membership of the evaluation index to be mild, relatively serious and serious; 、 、 They are the standard values ​​for the three evaluation levels respectively; 、 is the upper limit of the concentrated transition interval; is the interval transition coefficient; Single factor fuzzy evaluation, each evaluation index is evaluated using the evaluation level set, and the fuzzy evaluation set is obtained as follows: 6)； in: For the m The evaluation index is n The membership value of each evaluation level; Comprehensive evaluation, based on single-factor fuzzy evaluation, conducts fuzzy comprehensive evaluation; the comprehensive evaluation membership is calculated using the combined weight and fuzzy evaluation set, and the calculation formula is as follows: 7)； Where: B is the upper membership matrix; According to the principle of maximum membership, the evaluation level with the maximum membership is the comprehensive evaluation level of the abandoned mine geological environment; The system score is calculated according to the following formula F , sort multiple evaluation objects: 8)； Where: is the fraction of the evaluation set.

9. A readable storage medium, characterized in that Computer program instructions are stored thereon, and when the computer program instructions are executed by a processor, the method for comprehensive evaluation of the geological environment of abandoned mines as described in any one of claims 1 to 8 is implemented.

10. An electronic device, characterized in that: include: At least one processor, at least one memory and computer program instructions stored in the memory, when the computer program instructions are executed by the processor, it is a comprehensive evaluation method for the geological environment of abandoned mines as described in any one of claims 1 to 8.

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