Mine underground space suitability evaluation method based on geological multi-parameter fusion

Abstract

The invention relates to the field of mine engineering evaluation, in particular to a geological multi-parameter fusion mine underground space suitability evaluation method. The invention discloses a geological multi-parameter fusion mine underground space suitability evaluation method. The method comprises the following steps: collecting secondary indexes; calculating a secondary weight; calculating a first-level index score; and calculating a first-level weight, and further calculating a suitability score of the underground space of the target mine. According to the method, multiple secondary indexes of a target mine underground space are collected, the linear correlation degree and the non-linear correlation degree of each secondary index and the suitability of the mine underground space are calculated and obtained based on a mine reference sample, and then the secondary weight of each secondary index is calculated and obtained; according to the method, the influence of various geological factors on the suitability of the mine underground space is comprehensively considered, the suitability of the mine underground space can be evaluated more comprehensively and accurately, and a scientific basis is provided for development and utilization of the mine underground space.

Description

Technical Field

[0001] The present invention relates to the field of mine engineering evaluation, and particularly to a method for evaluating the suitability of underground mine space by fusing multiple geological parameters. Background Art

[0002] With the global energy structure transformation towards cleaner and lower-carbon, large-scale underground energy storage technologies such as compressed air energy storage and hydrogen energy storage have become key solutions to balance the volatility of new energy power generation and achieve flexible power grid peak shaving; due to its natural enclosure, spatial scale advantage and wide geographical distribution, underground mine space is regarded as a potential carrier for clean energy storage; however, not all underground mine spaces can meet the requirements for the construction of energy storage facilities: geological instability may lead to the leakage of energy storage media, deterioration of hydrogeological conditions may corrode the storage structure, and too high surface ecological sensitivity or intensive socio-economic activities will significantly increase the engineering risks and costs; how to scientifically screen underground mine spaces with strong adaptability, high safety factor and excellent economy as energy storage bases has become a key technical challenge for the large-scale development of new energy and the circular utilization of mining area resources.

[0003] Existing evaluation technologies for the suitability of underground mine space mostly focus on the fields of mine safe mining or disaster prevention and control, and have significant limitations in the application of clean energy storage scenarios: traditional evaluation methods lack detailed analysis of the sealing and dynamic stability indicators that are crucial for energy storage sites, and do not fully consider key parameters affecting energy storage economy such as the degree of green power enrichment and the access conditions of adjacent energy networks, resulting in evaluation results that are difficult to match the actual needs of energy storage facilities; on the other hand, existing technologies mostly fuse multiple parameters through expert experience method or fixed weight superposition, and their weight allocation mechanism lacks adaptability to the multi-objective collaborative optimization of "stability - economy - environmental compatibility"; this is likely to lead to sub-optimal decisions such as "good geological conditions but too high power transmission costs" or "low construction costs but prominent ecological risks" in the screening of energy storage sites, restricting the comprehensive benefits of the recycling of mining area resources.

[0004] In view of the above problems, the present invention proposes a method for evaluating the suitability of underground mine space by fusing multiple geological parameters, which can provide a scientific basis for the transformation of abandoned mines and the siting of new energy storage bases, and help the in-depth integration of the new energy industry and the ecological restoration of mining areas. Summary of the Invention

[0005] The present invention collects multiple secondary indicators of the target underground mine space, and calculates the linear correlation degree and non-linear correlation degree between each secondary indicator and the suitability of the underground mine space based on the mine reference samples, and then calculates the secondary weights of each secondary indicator; this method comprehensively considers the influence of various geological factors on the suitability of underground mine space, can more comprehensively and accurately evaluate the suitability of underground mine space, and provides a scientific basis for the development and utilization of underground mine space.

[0006] A method for evaluating the suitability of underground space in mines by integrating multiple geological parameters, comprising:

[0007] Collect a number of secondary indicators of the underground space of the target mine, including ground stress, seismic intensity, groundwater system distribution, peak ground acceleration of seismic motion, susceptibility to geological disasters, lithology of overlying strata, fault density, permeability of roof rock strata, population density, ground conditions, proportion of sensitive protected areas, enrichment degree of green electricity, terrain slope angle, and number of abandoned mine caves;

[0008] Obtain a number of mine reference samples, calculate the linear correlation degree and non-linear correlation degree between each secondary indicator and the suitability of the mine underground space based on the mine reference samples, and then calculate the secondary weights of each secondary indicator;

[0009] Based on the number of secondary indicators of the underground space of the target mine collected and the corresponding secondary weights, calculate the scores of each primary indicator of the underground space of the target mine. The primary indicators include stability, sealing performance, social environment, and economic benefits; the secondary indicators corresponding to stability are ground stress, seismic intensity, groundwater system distribution, peak ground acceleration of seismic motion, and susceptibility to geological disasters; the secondary indicators corresponding to sealing performance are lithology of overlying strata, fault density, and permeability of roof rock strata; the secondary indicators corresponding to the social environment are population density, ground conditions, and proportion of sensitive protected areas; the secondary indicators corresponding to economic benefits are enrichment degree of green electricity, terrain slope angle, and number of abandoned mine caves;

[0010] Use the secondary weights of each secondary indicator to calculate the primary weights of the primary indicators, and then calculate the suitability score of the underground space of the target mine.

[0011] Preferably, calculate the linear correlation degree between each secondary indicator and the suitability of the mine underground space based on the mine reference samples. The specific operation is as follows:

[0012] Obtain mine reference samples, each of which contains an underground space of a mine with a known suitability score and all the secondary indicators corresponding to the underground space of the mine;

[0013] Record the secondary indicator as , = 1, 2,..., 14; To correspond to ground stress, seismic intensity, groundwater system distribution, peak ground acceleration of seismic motion, susceptibility to geological disasters, lithology of overlying strata, fault density, permeability of roof rock strata, population density, ground conditions, proportion of sensitive protected areas, enrichment degree of green electricity, terrain slope angle, and number of abandoned mine caves in turn;

[0014] Based on The secondary indicators included in a mine reference sample and the suitability score , = 1, 2, …, ; Using the formula Calculate to obtain the linear correlation degree between the secondary indicator and the suitability of the mine underground space , where is the average value of ; is the average value of .

[0015] Preferably, calculate the non - linear correlation degree between each secondary indicator and the suitability of the mine underground space based on the mine reference sample. The specific operations are as follows:

[0016] Based on the secondary indicators included in a mine reference sample and the suitability score and respectively use the formulas and to calculate and obtain the standardized secondary indicator and the standardized suitability score , where and represent the maximum and minimum values among the secondary indicators respectively; and

[0017] represent the maximum and minimum values among the suitability scores respectively; Divide the interval into a uniform grid of ; For each of the mine reference samples, determine the grid position where it is located; Use the formula to calculate and obtain the joint probability distribution , where = 1, 2, …, 10; = 1, 2, …, 10; and ;

[0018] Subsequently, the formula is used

[0019] to calculate and obtain the secondary indicators and the non - linear correlation degree with the suitability of underground mine space .

[0020] Preferably, calculate and obtain the secondary weights of each secondary indicator. The specific operations are as follows:

[0021] Use the formula to normalize the non - linear correlation degree between the secondary indicator and the suitability of underground mine space ;

[0022] Use the formula to calculate and obtain the comprehensive correlation degree of the secondary indicator , where and are weight coefficients ; and are obtained through the swarm optimization algorithm;

[0023] For any secondary indicator , if , then based on the comprehensive correlation degree of the current secondary indicator , use the formula to calculate and obtain the enhanced comprehensive correlation degree of the current secondary indicator , where is the enhancement coefficient used to control the amplification range of the comprehensive correlation degree; if , then the enhanced comprehensive correlation degree of the current secondary indicator ;

[0024] Finally, use the formula to calculate and obtain the secondary weights of each secondary indicator .

[0025] Preferably, based on a number of secondary indicators and their corresponding secondary weights of the target mine underground space collected, calculate the scores of each primary indicator of the target mine underground space. The specific operations are as follows:

[0026] Based on all the secondary indicators of the target mine underground space collected , for in - situ stress , seismic intensity , groundwater system distribution , peak ground acceleration of earthquake 、 Geological disaster susceptibility 、 Fault density 、 Roof rock permeability 、 Proportion of sensitive protected areas 、 Terrain slope angle and the number of abandoned mine shafts , use the formula to calculate and obtain the secondary scores of the above secondary indicators , where and and are respectively the specified maximum and minimum values of each secondary indicator;

[0027] For the overlying strata lithology 、 Population density 、 Ground conditions and the enrichment degree of green electricity , use the formula to calculate and obtain the secondary scores of the above secondary indicators ; ;

[0028] Use the formula to calculate and obtain the stability score ; Use the formula to calculate and obtain the sealing score ; Use the formula to calculate and obtain the social environment score ; Use the formula to calculate and obtain the economic benefit score .

[0029] Preferably, use the secondary weights of each secondary indicator to calculate and obtain the primary weight of the primary indicator, and then calculate and obtain the suitability score of the underground space of the target mine. The specific operations are as follows:

[0030] Use the formula to calculate and obtain the stability weight ; Use the formula to calculate and obtain the sealing weight ; Use the formula to calculate and obtain the social environment weight ; Use the formula to calculate and obtain the economic benefit weight ;

[0031] Finally, use the formula to calculate and obtain the suitability score of the underground space of the target mine .

[0032] Preferably, obtain and The population optimization algorithm for the value of

[0033] Preferably, the genetic algorithm is applied to obtain and The specific operations for the value of

[0034] Step 1: According to the preset scale parameter, generate a population containing a number of initial candidate solutions, and each candidate solution is a two-dimensional weight coefficient combination that satisfies the constraint conditions ; Set the maximum number of iterations and the cross and mutation probability parameters of the genetic algorithm;

[0035] Step 2: Take each candidate weight combination as an individual. For any individual, calculate the accuracy index of the prediction of the suitability of the underground space of the mine obtained by applying the candidate weight combination corresponding to this individual, and map it to the fitness value of this individual;

[0036] Step 3: Select the individual with the top 1 fitness ranking in each generation of the population as the elite individual and directly retain it to the next generation;

[0037] For the remaining individuals, adopt the roulette wheel strategy and allocate the selection probability according to the proportion of the fitness value; the individual with a higher fitness has a higher probability of being selected, and select a specified number of parent individuals to form an optimized group;

[0038] Step 4: For the parent individuals in the optimized group, randomly pair them according to the preset crossover probability and exchange gene segments to generate a new set of offspring individuals; randomly select some individuals in the new set of offspring individuals and impose a small random perturbation on their weight parameters with the preset mutation probability to form an improved set of candidate solutions;

[0039] Step 5: Combine the elite individuals, the new set of offspring individuals generated by crossover, and the improved set of candidate solutions after mutation adjustment into a mixed candidate set; calculate the fitness of all individuals in the mixed candidate set, and form a new generation of population with the individuals with high fitness in the front and the elite individuals to complete a single evolutionary iteration;

[0040] Step 6: Repeat Steps 3 to 5 until the preset maximum number of iterations is reached; output the weight coefficient combination with the optimal fitness in the final generation of the population as the solution after algorithm optimization.

[0041] The present invention has the following advantages:

[0042] 1. The present invention collects multiple secondary indicators of the underground space of the target mine, calculates the linear correlation degree and non-linear correlation degree between each secondary indicator and the suitability of the mine underground space based on the mine reference samples, and then calculates the secondary weights of each secondary indicator; this method comprehensively considers the influence of various geological factors on the suitability of the mine underground space, can more comprehensively and accurately evaluate the suitability of the mine underground space, and provides a scientific basis for the development and utilization of the mine underground space.

[0043] 2. The present invention uses the secondary weights of each secondary indicator to calculate the primary weights of the primary indicators, and then calculates the suitability score of the target mine underground space; in this way, the complex multi-index evaluation problem can be transformed into a simple weight calculation problem, which simplifies the evaluation process and improves the evaluation efficiency; at the same time, the present invention also uses population optimization algorithms such as genetic algorithms to obtain the weight coefficients, further improving the accuracy and reliability of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic flow chart of the method for evaluating the suitability of mine underground space by fusing multiple geological parameters adopted in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0046] Embodiment, a method for evaluating the suitability of mine underground space by fusing multiple geological parameters, as Figure 1 shown, includes:

[0047] Collect several secondary indicators of the underground space of the target mine, including in-situ stress, seismic intensity, groundwater system distribution, peak ground acceleration of seismic motion, geological disaster susceptibility, lithology of overlying strata, fault density, permeability of roof rock strata, population density, ground conditions, proportion of sensitive protection areas, enrichment degree of green electricity, terrain slope angle, and number of abandoned mine caves; The collection of these indicators is crucial for accurately evaluating the suitability of the mine underground space. The in-situ stress indicator reflects the stress state of the location where the underground space is located. Seismic intensity and peak ground acceleration of seismic motion reflect the impact degree of earthquakes on the underground space and are important bases for measuring the seismic performance of the underground space. The distribution of the groundwater system affects the hydrogeological conditions of the underground space and is crucial for evaluating its waterproof and drainage capabilities. The geological disaster susceptibility indicator reflects the possibility of geological disasters occurring in the surrounding areas of the underground space. Indicators such as the lithology of overlying strata, fault density, and permeability of roof rock strata are directly related to the sealing and stability of the underground space; The population density indicator reflects the personnel distribution in the surrounding areas of the underground space and is of great significance for evaluating the impact on surrounding residents during the project construction and operation process. Ground conditions and the proportion of sensitive protection areas reflect the current land use situation and ecological environment sensitivity of the area where the underground space is located and play an important role in ensuring the rationality and sustainability of project construction. Indicators such as the enrichment degree of green electricity, terrain slope angle, and number of abandoned mine caves are closely related to the economic benefits of the underground space. The enrichment degree of green electricity reflects the development potential of renewable energy in the area. The terrain slope angle affects the construction and operation costs of the underground space. The number of abandoned mine caves provides more options and possibilities for the development and utilization of the underground space; By collecting and analyzing these secondary indicators, comprehensive and accurate basic data can be provided for the evaluation of the suitability of the mine underground space, thereby providing a scientific basis for the rational development and utilization of the mine underground space;

[0048] Obtain several mine reference samples, calculate the linear correlation degree and non-linear correlation degree between each secondary indicator and the suitability of the mine underground space based on the mine reference samples, and then calculate the secondary weights of each secondary indicator; Through the analysis of these samples, the linear correlation degree and non-linear correlation degree between each secondary indicator and the suitability of the mine underground space can be calculated. The linear correlation degree reflects the linear relationship between the secondary indicator and the suitability and can reveal the direct correlation between the two. The non-linear correlation degree further considers the complex and non-linear relationship between the two, such as curve relationship, threshold effect, etc. The combined application of this can more comprehensively reveal the internal connection between the secondary indicator and the suitability of the mine underground space, avoid the deviation and omission that may be brought about by only considering the linear relationship, and thus more accurately determine the secondary weights of each secondary indicator, providing a more scientific and reasonable basis for the subsequent first-level indicator scoring and the final suitability scoring, effectively improving the accuracy and reliability of the evaluation results;

[0049] Based on a number of secondary indicators and their corresponding secondary weights of the target mine's underground space obtained through collection, calculate the scores of each primary indicator of the target mine's underground space. The primary indicators include stability, sealing performance, social environment, and economic benefits. The secondary indicators corresponding to stability are ground stress, seismic intensity, groundwater system distribution, peak ground acceleration of seismic motion, and susceptibility to geological disasters. The secondary indicators corresponding to sealing performance are lithology of overlying strata, fault density, and permeability of roof rock strata. The secondary indicators corresponding to the social environment are population density, ground conditions, and proportion of sensitive protection areas. The secondary indicators corresponding to economic benefits are enrichment degree of green electricity, terrain slope angle, and number of abandoned mine caves. This process not only makes the evaluation results more scientific and reasonable but also provides a solid foundation for subsequent overall suitability scoring.

[0050] Using the secondary weights of each secondary indicator, calculate the primary weights of the primary indicators, and then calculate the suitability score of the target mine's underground space.

[0051] When evaluating the suitability of a mine's underground space, calculating the primary weights of the primary indicators is a crucial step. This helps ensure that the final suitability score can comprehensively reflect the actual performance of the mine's underground space in each key aspect. By calculating the primary weights of the primary indicators, the importance of different evaluation dimensions in the overall suitability evaluation can be reflected, so that the contribution of each dimension can be reasonably reflected in the final suitability score. For example, stability may be more critical in the evaluation of some mine's underground spaces, while in other mines, social environment factors may have a decisive impact. Therefore, by weighting the primary indicators, it can be ensured that when calculating the final suitability score, the contribution of each dimension matches its importance in the overall evaluation. This means that even if all the scores of the secondary indicators under a certain primary indicator are very high, but if the primary weight of this primary indicator is low, then its contribution to the final suitability score will be relatively small. To sum up, distinguishing and calculating the weights of each primary indicator not only helps to deeply understand the impact of different evaluation dimensions on the suitability of the mine's underground space but also makes the final evaluation results more scientific, reasonable, and practically guiding. This method avoids the biases that may be brought by simple averaging or subjective judgment and provides a more accurate and reliable evaluation tool for the development and utilization of the mine's underground space.

[0052] Based on the mine reference samples, calculate the linear correlation degree between each secondary indicator and the suitability of the mine's underground space. The specific operation is as follows:

[0053] Obtain mine reference samples, and each mine reference sample contains a mine's underground space with a known suitability score and all the secondary indicators corresponding to this mine's underground space.

[0054] Denote the secondary indicator as , = 1, 2, …, 14; to correspond to in turn in-situ stress, seismic intensity, groundwater system distribution, peak ground acceleration of seismic motion, geological disaster susceptibility, lithology of overlying strata, fault density, roof rock permeability, population density, ground conditions, proportion of sensitive protected areas, enrichment degree of green electricity, topographic slope angle, and number of abandoned mine caves;

[0055] Based on secondary indicators included in a number of mine reference samples , = 1, 2, …, ; Using the formula calculate and obtain the linear correlation degree between the secondary indicator and the suitability of mine underground space where is the average value of ; is the average value of ;

[0056] Based on the mine reference samples, calculate and obtain the non-linear correlation degree between each secondary indicator and the suitability of mine underground space. The specific operations are as follows:

[0057] Based on secondary indicators included in a number of mine reference samples and the suitability scores and calculate and obtain the standardized secondary indicator and the standardized suitability score after standardization respectively, where and represent respectively the maximum and minimum values among secondary indicators and represent respectively the maximum and minimum values among suitability scores

[0058] Divide the interval into uniform grids. For each of the mine reference samples, determine the grid position where it is located; Use the formula to calculate and obtain the joint probability distribution where ​​​= 1, 2, …, 10; = 1, 2, …, 10; represents the number of samples falling into the grid among samples; Subsequently, the marginal probability distributions and are calculated and obtained by using the formulas and ;

[0059] Subsequently, the non - linear correlation degree

[0060] between the secondary index and the suitability of the underground space of the mine is calculated by using the formula .

[0061] The secondary weights of each secondary index are calculated as follows:

[0062] The non - linear correlation degree between the secondary index and the suitability of the underground space of the mine is normalized by using the formula ;

[0063] The comprehensive correlation degree of the secondary index is calculated by using the formula , where and are weight coefficients, ; and are obtained by the swarm optimization algorithm;

[0064] For any secondary index , if , then based on the comprehensive correlation degree of the current secondary index , the enhanced comprehensive correlation degree of the current secondary index is calculated by using the formula , where is the enhancement coefficient used to control the amplification range of the comprehensive correlation degree; if , then the enhanced comprehensive correlation degree of the current secondary index ;

[0065] Finally, the secondary weights of each secondary index are calculated by using the formula .

[0066] Based on a number of secondary indicators of the target mine underground space and their corresponding secondary weights collected, calculate the scores of each primary indicator of the target mine underground space. The specific operations are as follows:

[0067] Based on all the secondary indicators of the target mine underground space collected , for in-situ stress , seismic intensity , groundwater system distribution , peak ground acceleration , geological hazard susceptibility , fault density , roof rock permeability , proportion of sensitive protected areas , terrain slope angle and the number of abandoned mine shafts , use the formula to calculate the secondary scores of the above secondary indicators , where and and are respectively the specified maximum value and the specified minimum value of each secondary indicator. The specified maximum value and the specified minimum value are safety thresholds preset based on the specific application scenarios of the suitability assessment of the mine underground space and expert experience. The setting of these values follows national or industry technical specifications and engineering standards. For example, for stability indicators such as in-situ stress and seismic intensity, the specified values are set according to the critical stress thresholds and seismic fortification intensity limits defined in the Code for Geotechnical Investigation and the Technical Code for Seismic Safety Evaluation of Engineering Sites; the specified values of the secondary indicators related to sealing (such as roof rock permeability) are cited from the allowable range of rock mass penetration parameters in the Technical Code for Waterproofing of Underground Engineering; the geological hazard susceptibility indicators are determined according to the high and low risk thresholds divided in the Code for Risk Assessment of Geological Hazards; the social and economic indicators (such as the proportion of sensitive protected areas) refer to the regional development limits in the Guidelines for Demarcating the Ecological Protection Red Line; the specified values of technical and economic indicators such as the enrichment degree of green electricity are determined by referring to the lowest feasible values in the new energy grid-connected economic standard;

[0068] For the lithology of the overlying strata , population density , ground conditions and the enrichment degree of green electricity , use the formula to calculate the secondary scores of the above secondary indicators , ;

[0069] The selection of the above two secondary scoring calculation formulas depends on the positive or negative impact characteristics of the secondary indicators on the suitability score. For indicators such as the lithology of the overlying strata and the degree of green power enrichment, their values are positively correlated with suitability (e.g., the higher the lithology grade, the better the sealing performance), so is used to directly reflect the positive contribution of the current value within the specification range; for indicators such as the susceptibility of geological disasters and the fault density, their values are negatively correlated with suitability (i.e., the larger the value, the higher the risk), so is used, which conforms to the logic of "the lower value is better" in actual engineering;

[0070] The stability score is calculated using the formula; The sealing score is calculated using the formula; The social environment score is calculated using the formula; The economic benefit score is calculated using the formula; .

[0071] Using the secondary weights of each secondary indicator, the primary weight of the primary indicator is calculated, and then the suitability score of the underground space of the target mine is calculated. The specific operations are as follows:

[0072] The stability weight is calculated using the formula; The sealing weight is calculated using the formula; The social environment weight is calculated using the formula; The economic benefit weight is calculated using the formula; ;

[0073] Finally, the suitability score of the underground space of the target mine is calculated using the formula; .

[0074] To obtain and The population optimization algorithm for the value of is the genetic algorithm. The genetic algorithm is an optimization algorithm based on the theory of biological evolution, inspired by the biological evolution process in nature, such as natural selection, genetic variation, and survival of the fittest, etc. This algorithm encodes the possible solutions of the problem as chromosomes, randomly generates multiple chromosomes in the initial population, and then through operations such as selection, crossover, and mutation, simulates the genetic and natural selection mechanisms in the biological evolution process, continuously optimizes the population to find the optimal solution or satisfactory solution of the problem. Its specific steps include: initializing the population to generate random individuals; evaluating the quality of individuals through the fitness function; selecting according to the fitness, making it more likely for excellent individuals to be selected for reproduction; performing crossover operations to exchange the genetic information of parental individuals to generate new offspring individuals; applying mutation operations to change part of the genes of individuals with a certain probability to increase the diversity of the population; repeating the above process iteratively until the termination condition is met, such as reaching the maximum number of iterations or finding a satisfactory solution.

[0075] Obtain using the genetic algorithm and The specific operations for the value of are as follows:

[0076] Step 1: According to the preset scale parameter, generate a population containing several initial candidate solutions, and each candidate solution is a two-dimensional weight coefficient combination that satisfies the constraint conditions ; Set the maximum number of iterations of the genetic algorithm and the crossover and mutation probability parameters;

[0077] Step 2: Take each candidate weight combination as an individual. For any one individual, calculate the accuracy index of the prediction of the suitability of the underground space of the mine obtained by applying the candidate weight combination corresponding to this individual, and map it to the fitness value of this individual;

[0078] Step 3: Select the individual with the top 1 fitness ranking in each generation of the population as the elite individual and directly retain it to the next generation;

[0079] For the remaining individuals, adopt the roulette wheel strategy and allocate the selection probability according to the proportion of the fitness values; individuals with higher fitness have a higher probability of being selected, and select a specified number of parental individuals to form an optimized group;

[0080] Step 4: For the parental individuals in the optimized group, randomly pair them according to the preset crossover probability and exchange gene segments to generate a new set of offspring individuals; randomly select some individuals in the new set of offspring individuals and apply a small random perturbation to their weight parameters with the preset mutation probability to form an improved set of candidate solutions;

[0081] Step 5: Combine the elite individuals, the new individual set generated by crossover, and the improved candidate solution set after mutation adjustment into a mixed candidate set; calculate the fitness of all individuals in the mixed candidate set, and form a new generation of population with the individuals with high fitness in the front and the elite individuals to complete a single evolutionary iteration;

[0082] Step 6: Repeat Steps 3 to 5 until the preset maximum number of iterations is reached; output the weight coefficient combination with the best fitness in the final generation of population as the solution after algorithm optimization.

[0083] It should be understood that those of ordinary skill in the art can make improvements or transformations according to the above description, and all such improvements and transformations should fall within the protection scope of the appended claims of the present invention. The parts not described in detail in this specification belong to the prior art well known to those skilled in the art.

Claims

1. A mine underground space suitability assessment method based on geological multi-parameter fusion, characterized in that: include: Collect several secondary indicators of the underground space of the target mine, including ground stress, earthquake intensity, groundwater distribution, peak seismic acceleration, susceptibility to geological disasters, overlying strata lithology, fault density, roof rock permeability, population density, ground conditions, proportion of sensitive protection areas, enrichment of green electricity, terrain slope angle and number of abandoned mine caves; Obtain several mine reference samples, calculate the linear correlation and nonlinear correlation between each secondary indicator and the suitability of the mine underground space based on the mine reference samples, and then calculate the secondary weight of each secondary indicator; Based on the collected secondary indicators of the underground space of the target mine and the corresponding secondary weights, the scores of the primary indicators of the underground space of the target mine are calculated. The primary indicators include stability, sealing, social environment and economic benefits; the secondary indicators corresponding to stability are ground stress, earthquake intensity, groundwater distribution, peak acceleration of seismic motion and susceptibility to geological disasters; the secondary indicators corresponding to sealing are the lithology of the overlying strata, fault density and permeability of the roof rock layer; the secondary indicators corresponding to the social environment are population density, ground conditions and the proportion of sensitive protection areas; the secondary indicators corresponding to economic benefits are the enrichment of green electricity, terrain slope angle and the number of abandoned mine caves; The secondary weights of each secondary indicator are used to calculate the primary weight of the primary indicator, and then the suitability score of the underground space of the target mine is calculated.

2. The mine underground space suitability assessment method based on geological multi-parameter fusion according to claim 1 is characterized in that: Based on the mine reference sample, the linear correlation between each secondary index and the suitability of the mine underground space is calculated. The specific operation is as follows: Get Mine reference samples, each of which contains an underground mine space with a known suitability score and all secondary indicators corresponding to the underground mine space; Label the secondary indicator as , =1, 2, …, 14; to They correspond in turn to ground stress, earthquake intensity, groundwater distribution, peak seismic acceleration, susceptibility to geological disasters, overlying strata lithology, fault density, roof rock permeability, population density, ground conditions, proportion of sensitive protected areas, green electricity enrichment, terrain slope angle and number of abandoned mines; based on Secondary indicators included in the mine reference sample and suitability score , =1, 2, …, ; Using the formula Calculate and obtain secondary indicators Linear correlation with suitability of underground space in mines ,in, for indivual The average value of for indivual The average value of .

3. The mine underground space suitability assessment method based on geological multi-parameter fusion according to claim 2 is characterized in that: Based on the mine reference sample, the nonlinear correlation between each secondary index and the suitability of the mine underground space is calculated. The specific operation is as follows: based on Secondary indicators included in the mine reference sample and suitability score , respectively using the formula and Calculate and obtain the standardized secondary index after standardization and standardized suitability scores ,in, and Respectively Secondary indicators The maximum and minimum values ​​in ; and Respectively Suitability Rating The maximum and minimum values ​​in ; Will The interval is divided into For each mine reference sample , determine the grid location; use the formula Calculate the joint probability distribution ,in, =1, 2, …, 10; =1, 2, …, 10; Indicated in samples fall into the grid The number of samples; then use the formula and Calculate the marginal probability distribution and ; Then use the formula Calculate and obtain secondary indicators Nonlinear correlation with suitability of underground space in mines .

4. The mine underground space suitability assessment method based on geological multi-parameter fusion according to claim 3 is characterized in that: Calculate and obtain the secondary weight of each secondary indicator. The specific operations are as follows: Using the formula For secondary indicators Nonlinear correlation with suitability of underground space in mines Perform normalization; Using the formula Calculate and obtain secondary indicators The comprehensive correlation ,in, and is the weight coefficient, ; and The value of is obtained through the group optimization algorithm; For any secondary indicator ,like , then in the current secondary index The comprehensive correlation Based on the formula Calculate and obtain the current secondary index Enhanced comprehensive correlation ,in, is the enhancement coefficient, which is used to control the amplification of the comprehensive correlation; if , then the current secondary index Enhanced comprehensive correlation ; Finally, using the formula Calculate and obtain each secondary index Secondary weight .

5. The method for evaluating the suitability of underground space in a mine by integrating geological multi-parameters according to claim 4, characterized in that: Based on the collected secondary indicators of the target mine underground space and the corresponding secondary weights, the scores of each primary indicator of the target mine underground space are calculated. The specific operations are as follows: All secondary indicators of the underground space of the target mine based on the acquisition , for ground stress ,earthquake intensity , Distribution of groundwater system , peak ground acceleration , Susceptibility to geological disasters , fault density , roof rock permeability , Proportion of sensitive protected areas , terrain slope angle and the number of abandoned mines , using the formula Calculate and obtain the above secondary indicators Second level rating ,in and They are respectively the preset maximum and minimum values ​​of each secondary indicator; Based on the lithology of the overlying strata , population density , Ground conditions and the enrichment of green electricity , using the formula Calculate and obtain the above secondary indicators Second level rating ; Using the formula Calculate the stability score ; Using the formula Calculate the sealing score ; Using the formula Calculate and obtain social environment score ; Using the formula Calculate and obtain economic benefit score .

6. A mine underground space suitability assessment method based on geological multi-parameter fusion according to claim 5, characterized in that: The secondary weights of each secondary indicator are used to calculate the primary weight of the primary indicator, and then the suitability score of the underground space of the target mine is calculated. The specific operations are as follows: Using the formula Calculate the stability weight ; Using the formula Calculate the sealing weight ; Using the formula Calculate and obtain social environment weight ; Using the formula Calculate the weight of economic benefits ; Finally, using the formula Calculate the suitability score of the underground space of the target mine .

7. A mine underground space suitability assessment method based on geological multi-parameter fusion according to claim 6, characterized in that: Get and The population optimization algorithm for the value of is a genetic algorithm.

8. The mine underground space suitability assessment method based on geological multi-parameter fusion according to claim 7 is characterized in that: Apply genetic algorithm to obtain and The specific operations for obtaining the value of are as follows: Step 1: Generate a population containing several initial candidate solutions according to the preset scale parameters. Each candidate solution is a two-dimensional weight coefficient combination that satisfies the constraints. ; Set the maximum number of iterations and crossover and mutation probability parameters of the genetic algorithm; Step 2: Take each candidate weight combination as an individual, and for any individual, calculate the accuracy index of the mine underground space suitability prediction obtained by applying the candidate weight combination corresponding to the individual, and map it to the fitness value of the individual; Step 3: Select the individuals with the highest fitness in each generation as elite individuals and keep them directly to the next generation; A roulette strategy is used for the remaining individuals to allocate selection probabilities according to the proportion of fitness values; individuals with higher fitness have a higher probability of being selected, and a specified number of parent individuals are screened out to form a preferred group; Step 4: For the parent individuals of the preferred group, randomly pair and exchange gene fragments according to the preset crossover probability to generate a new set of offspring individuals; randomly select some individuals from the new set of offspring individuals, and apply small random perturbations to their weight parameters with the preset mutation probability to form an improved candidate solution set; Step 5: Merge the elite individuals, the new offspring individuals generated by crossover, and the improved candidate solutions adjusted by mutation into a mixed candidate set; calculate the fitness of all individuals in the mixed candidate set, and form a new generation population according to the top high fitness individuals and elite individuals to complete a single evolutionary iteration; Step 6: Repeat steps 3 to 5 until the preset maximum number of iterations is reached; Output the weight coefficient combination with the best fitness in the final generation population As the solution after algorithm optimization.

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