A method and system for optimizing the layout of coal mining subsidence areas
By constructing a multi-dimensional water and soil coupling suitability evaluation system and modular design, the problem of inaccurate suitability evaluation in the layout of coal mining subsidence areas has been solved, achieving synergistic optimization and long-term stability of ecology and economy, and adapting to dynamic changes.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANDONG AGRICULTURAL UNIVERSITY
- Filing Date
- 2026-04-27
- Publication Date
- 2026-07-31
AI Technical Summary
Existing technologies for the layout of coal mining subsidence areas suffer from problems such as inaccurate suitability assessment, lack of dynamic adjustment, unquantified ecological benefits, serious homogenization of schemes, and inability to cope with dynamic changes, resulting in insufficient synergistic optimization of ecology and economy.
A multi-dimensional water and soil coupling suitability evaluation system was constructed, which includes soil, subsidence risk, dynamic evolution, and biological adaptability. The weights of the indicators were determined by the mutual information-variance coefficient coupling weighting method. The system was combined with modular design and ecological connectivity structure, and carbon sink benefit assessment was incorporated. Dynamic optimization was carried out through Internet of Things monitoring.
It improves the accuracy of regional suitability classification, reduces engineering costs, enhances ecological adaptability, maximizes the synergistic effect of ecological, economic and social value, and ensures the long-term stability of the plan.
Smart Images
Figure CN122490155A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of land consolidation engineering technology, specifically relating to a method and system for optimizing the layout of coal mining subsidence areas. Background Technology
[0002] Coal mining has given rise to coal mining subsidence areas. These areas generally face problems such as fragmented terrain, damaged soil structure, imbalance of water and soil resources, and degradation of the ecological environment. They seriously hinder agricultural production and sustainable land use, and can also trigger a chain reaction of soil erosion and increased risk of geological disasters, thus posing a dual constraint on regional ecological security and economic and social development.
[0003] Current technologies for the layout and remediation of coal mining subsidence areas suffer from the following deficiencies: suitability assessments often focus on single factors, failing to construct a coupled systemic evaluation framework. They rely solely on static indicators and human experience for weighting, lacking consideration for the dynamic evolution of subsidence and biological adaptability, leading to a disconnect between evaluation results and actual needs. Field remediation plans are highly homogenized, failing to incorporate subsidence subtypes (waterlogged / drought-prone / fragmented) and local resource differentiation, lacking modular design and ecological connectivity considerations, resulting in insufficient adaptability and economic viability. Soil erosion calculations do not comprehensively consider multiple influencing factors, and benefit assessments focus only on a single dimension, failing to quantify core ecological benefits such as carbon sequestration and ecosystem service value, making it difficult to achieve synergistic optimization of ecology, economy, and production. The system lacks real-time monitoring and dynamic adjustment mechanisms, unable to cope with dynamic changes such as continuous subsidence in the subsidence area, exhibiting insufficient long-term stability and failing to meet the needs of precise remediation and sustainable utilization. Summary of the Invention
[0004] To achieve the above objectives, the present invention employs the following technical solution: This invention provides a method for optimizing the layout of coal mining subsidence areas, comprising the following steps: S1. Obtain basic data for the target area; S2. A water-soil coupling suitability evaluation system is constructed based on five indicators: soil type, distance from the subsidence area, topographic slope, subsidence dynamic evolution characteristics, and biological adaptability score. The coupling weights of each indicator in the target area are determined by the mutual information-variance coefficient coupling weighting method. S3. Calculate the comprehensive suitability score based on the coupling weights of each indicator and divide the suitable areas. Allocate the schemes according to the type of suitable area and optimize the target area. S4. The annual average soil erosion per unit area of the optimized target area is calculated using the modified general soil loss model. S5. The optimized target area is evaluated using a hierarchical ideal solution-dual distance proximity assessment model to obtain the evaluation results.
[0005] Further, step S1 specifically includes: collecting regional scope data, ground elevation data, soil type data, subsidence monitoring data, hydrological and water resources data, and power planning data of the target area; the regional scope data includes the subsidence area governance boundary and permanent basic farmland control boundary determined by the land and space planning.
[0006] Furthermore, in step S2, the weights of each indicator are determined using the mutual information-coefficient of variation coupled weighting method: The scores of each indicator are processed using a positive standardization method to obtain standardized scores; the coefficient of variation of each indicator is calculated based on the mean and standard deviation of the standardized indicators. The formula is expressed as follows: , in, The coefficient of variation for the j-th indicator is represented by . This represents the standard deviation of the j-th indicator after standardization. Represent the standardized mean of the j-th indicator; calculate the mutual information value of the indicators. The formula is expressed as follows: , in, This represents the marginal probability of the standardized value of the j-th index; This represents the joint probability of the j-th and k-th indicators; Indicates the number of samples; Indicates the total number of indicators; The marginal probability of the standardized value of the k-th indicator; the coefficient of variation of the indicator Mutual information value of indicators After normalization, the index coupling weights are obtained, as shown in the following formula: , , , in, The normalized index coefficient of variation; Represents the normalized mutual information value of the index; Indicates the weighted index coupling, satisfying .
[0007] Further, in step S3, the standardized scores of each indicator are weighted and summed with the corresponding weights of each indicator to obtain the comprehensive suitability score; based on the comprehensive suitability score, the region with a comprehensive suitability score greater than or equal to 0.8 is divided into a high suitability region, the region with a comprehensive suitability score between 0.5 and 0.8 is divided into a medium suitability region, and the region with a comprehensive suitability score less than 0.5 is divided into a low suitability region.
[0008] Furthermore, in step S4, the formula for the average annual soil erosion per unit area is expressed as follows: , in, This represents the average annual soil erosion per unit area. Indicates the erosivity factor of rainfall; Indicates soil erodibility factor; Indicates the slope length and slope factor; Indicates vegetation cover factor; This indicates the factors related to soil and water conservation measures.
[0009] Furthermore, step S5 specifically includes: S51. Determine the evaluation indicators, including annual average soil erosion per unit area, economic cost, ecological benefits, production suitability, carbon sink benefits, and ecosystem service value (ESV); the evaluation indicators are divided into negative indicators and positive indicators; the negative indicators include economic cost and soil erosion; the positive indicators include ecological benefits, production suitability, carbon sink benefits, and ecosystem service value (ESV); standardize the evaluation indicators. S52. The optimized target area is comprehensively evaluated using the hierarchical ideal solution-dual distance proximity evaluation model to obtain the comprehensive benefit evaluation results.
[0010] S521. Determine the objective weights of each evaluation indicator using the mutual information-variance coefficient coupling weighting method; S522. Weight each evaluation indicator to obtain a weighted standardized matrix. , where a is the layout scheme for the a-th plot and b is the evaluation indicator for the b-th plot; S523. The indicators are divided into a core indicator layer and a secondary indicator layer according to their weight ratios. The weight of the core indicator layer is greater than or equal to 0.15, including soil erosion, economic cost, ecological benefits and production suitability. The weight of the secondary indicator layer is less than 0.15, including carbon sink benefits and ecosystem service value (ESV). S524. Constructing a positive ideal solution , , This represents the positive ideal solution of the first core indicator. Both core and secondary indicators take... At the same time, construct the negative ideal solution , Both core and secondary indicators are taken , Represent the negative ideal solution of the first core indicator; calculate the grey relational degree of the core indicator. This leads to the core indicator, grey relational distance. and the weighted Euclidean distance of all indicators The formula is expressed as follows: , , , in, This indicates that the minimum value is taken for the solution dimension; This indicates taking the minimum value for each indicator dimension; This indicates taking the maximum value for the solution dimension; This indicates taking the maximum value for the indicator dimension; This means first finding the minimum according to the plan, then finding the minimum according to the indicator, to obtain the global minimum deviation. This means first finding the maximum according to the plan, then finding the maximum according to the indicator, to obtain the global maximum deviation; Represents the resolution coefficient; This represents the positive ideal solution for the b-th evaluation index; This indicates the number of core indicators; here, m1=6. This represents the weight of the b-th core indicator; similarly, the grey relational distance of the core indicator used to obtain the negative ideal solution is... Weighted Euclidean distance of all indicators ; S525. Calculate the fusion proximity based on the core indicator grey relational distance and the weighted Euclidean distance of all indicators: The formula is as follows: , ; Excellent overall benefits; The overall benefits are good; The overall benefits are not good.
[0011] This invention provides a system for optimizing the layout of coal mining subsidence areas, which implements the aforementioned method for optimizing the layout of coal mining subsidence areas, including: Data acquisition module: used to acquire basic data for the target area; Weighting Calculation Module: Based on five indicators—soil type, distance from the subsidence area, topographic slope, subsidence dynamic evolution characteristics, and biological adaptability score—a water-soil coupling suitability evaluation system is constructed. The coupling weights of each indicator in the target area are determined by the mutual information-variance coefficient coupling weighting method. Suitable Area Division Module: Calculates the comprehensive suitability score based on the coupling weights of each indicator and divides suitable areas, allocates schemes according to the type of suitable area, and optimizes the target area; Soil erosion calculation module: The optimized annual soil erosion per unit area of the target area is calculated using a modified general soil loss model; Evaluation module: The hierarchical ideal solution-dual distance proximity evaluation model is used to conduct a comprehensive benefit evaluation of the optimized target area and obtain the evaluation results.
[0012] The advantages of this invention are: This invention constructs a multi-dimensional soil-water coupling suitability evaluation system encompassing "soil-collapse risk-dynamic evolution-biological adaptation," combined with an objective data-calibrated mutual information-variance coefficient coupling weighting method. This enhances the accuracy and foresight of regional suitability classification, mitigating the secondary collapse risk caused by traditional static evaluations. Employing a "3+N" modular design and localized sub-solution combinations, it adapts to different collapse subclasses and resource conditions, reducing engineering costs while improving ecological adaptability. The newly added ecological connectivity structure further strengthens the stability of the regional ecosystem. It innovatively integrates quantitative assessments of carbon sink benefits and ecosystem service value, forming a comprehensive benefit evaluation system with existing multi-dimensional indicators, maximizing the synergistic effect of ecological, economic, and social value. Through IoT monitoring and adaptive optimization modules, it constructs a closed-loop system covering the entire lifecycle of "evaluation-design-assessment-optimization," effectively addressing dynamic changes in subsidence areas and ensuring long-term stability of the solution. The system's modular design is logically clear and highly operable, adapting to the remediation needs of coal mining subsidence areas in different regions. It effectively solves problems such as one-sided evaluation, homogeneous solutions, singular benefits, and lack of dynamic adjustment in existing technologies, providing scientific and feasible technical support for the sustainable utilization of coal mining subsidence areas. Attached Figure Description
[0013] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.
[0014] Figure 1 This is a flowchart of the steps of the method of the present invention. Detailed Implementation
[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0016] Example 1 In this embodiment, as Figure 1 As shown, this invention provides a method for optimizing the layout of coal mining subsidence areas, the specific steps of which include: S1. Obtain basic data of the target area; collect regional range data, ground elevation data, soil type data, subsidence monitoring data, hydrological and water resources data, and power planning data of the target area; the regional range data includes the subsidence area governance boundary and permanent basic farmland control boundary determined by the land and space planning.
[0017] S2. A water-soil coupling suitability evaluation system was constructed based on five indicators: soil type, distance from the subsidence area, topographic slope, subsidence dynamic evolution characteristics, and biocompatibility score. The coupling weights of each indicator in the target area were determined using the mutual information-variance coefficient coupling weighting method. The scoring rules for each influencing factor are as follows: Soil type: loam 1.0 point, clay 0.8 point, sandy soil 0.3 point, gravelly soil 0.0 point; Distance from the collapse zone: 1.0 point for distances greater than 500 meters, 0.6 points for distances between 300 and 500 meters, and 0.0 point for distances less than 300 meters. Topographic slope: less than 10 degrees 1.0 minute, 10-20 degrees 0.6 minute, 20-25 degrees 0.3 minute, greater than 25 degrees 0.0 minute; Collapse stability coefficient: Stable (less than 0.05 m² / year) 2 / a) 1.0 point, slightly unstable (0.05-0.1 square meters per year) 2 / a) 0.6 points, severely unstable (greater than 0.1 square meters per year) 2 / a) 0.0 points; The collapse stability coefficient is the product of the measured collapse rate in the past 5 years and the predicted settlement in the next 3 years; Biological suitability score: 1 point for perfect fit, 0.7 points for basic fit, 0.3 points for slight fit, and 0.1 points for poor fit; the biological suitability score is calculated based on the matching degree of "soil parameters - native biological suitability".
[0018] Specifically, a positive standardization method is used to process the scores of each indicator to obtain standardized scores; the coefficient of variation of each indicator is calculated based on the mean and standard deviation of the standardized indicators. The formula is expressed as follows: , in, The coefficient of variation for the j-th indicator is represented by . This represents the standard deviation of the j-th indicator after standardization. Represent the standardized mean of the j-th indicator; calculate the mutual information value of the indicators. The formula is expressed as follows: , in, This represents the marginal probability of the standardized value of the j-th index; This represents the joint probability of the j-th and k-th indicators; Indicates the number of samples; Indicates the total number of indicators; The marginal probability of the standardized value of the k-th indicator; the coefficient of variation of the indicator Mutual information value of indicators After normalization, the index coupling weights are obtained, as shown in the following formula: , , , in, The normalized index coefficient of variation; Represents the normalized mutual information value of the index; Indicates the weighted index coupling, satisfying .
[0019] S3. Calculate the comprehensive suitability score based on the coupling weights of each indicator and divide the suitable areas. Allocate the schemes according to the type of suitable area and optimize the target area. Specifically, the standardized scores of each indicator are weighted and summed with the corresponding weights of each indicator to obtain the comprehensive suitability score. Based on the comprehensive suitability score, the areas with a comprehensive suitability score greater than or equal to 0.8 are classified as high suitability areas, the areas with a comprehensive suitability score between 0.5 and 0.8 are classified as medium suitability areas, and the areas with a comprehensive suitability score less than 0.5 are classified as low suitability areas.
[0020] Specifically, a farmland distribution map is drawn: a farmland distribution map is drawn by combining the regional scope and ground elevation undulations to provide a spatial benchmark for field design, facility support, benefit assessment and Internet of Things monitoring; Field size division: According to the "Specifications for the Construction of High-Standard Farmland", the size of the fields is determined in combination with the type of suitable area. Fields in high-suitability areas are 200-800 meters long and 100-300 meters wide, with a ground elevation difference of ≤0.5m; fields in medium-suitability areas are 100-200 meters long and 50-100 meters wide, with a ground elevation difference of ≤1.0m; ecological units in low-suitability areas are 50-150 meters long and 30-80 meters wide, with an internal elevation difference of ≤2.0m, and the boundaries are aligned with contour lines. Estimate earthwork volume: The DEM raster mean estimation method for coal mining subsidence areas was adopted. First, high, medium and low suitable areas were used as the calculation zones, and the original elevation data, design elevation and actual treatment area of the DEM raster of each zone were extracted. Calculate the difference between the original mean elevation of the zone and the design elevation to obtain the average elevation difference of the zone; The earthwork volume of a zone is calculated as follows: zone average elevation difference × actual treatment area of the zone. The total earthwork volume is obtained by summing the earthwork volumes of each zone, and a simple cut-fill balance check is performed on the excavation and fill volumes.
[0021] Specifically, differentiated field design and high-standard farmland facilities. Differentiated field designs were implemented using three core modules—terrain reshaping, irrigation and drainage systems, and vegetation configuration—and N localized sub-schemes, categorized into three types based on subsidence depth: In areas with a collapse depth of 1-3 meters, a terraced modular scheme is adopted. The plots are 150-200 meters long and 100-150 meters wide, with an edge slope of ≤12 degrees and an internal slope of ≤5 degrees. The slope aspect is set in combination with sunlight and prevailing wind direction. For flat and low-lying areas, a modular solution for plains and low-lying land is adopted, with plots 400-900 meters long and 180-350 meters wide, equipped with a composite drainage system consisting of main drainage channels, secondary drainage channels, and sediment interception facilities; For areas with a collapse depth greater than 3 meters, a deep-water artificial wetland ecological restoration scheme is adopted. The core design is "core deep-water area + shallow water area on the bank + ecological slope + slope toe buffer zone". An ecological connectivity structure with a width of 5-8m, a slope of 1:3, and a permeable gravel layer at the bottom is added. The localization sub-solutions for the three core modules are as follows: The terrain reshaping module includes sub-solutions for concrete grids (1.5-2.0m deep), native gabion nets, and ecological concrete grids; the irrigation and drainage system module includes sub-solutions for fish ponds / conventional ditches, ecological infiltration ponds, and ecological gravel blind ditches; the vegetation configuration module includes sub-solutions for staple crops, nitrogen-fixing crops + native herbs, and mixed trees, shrubs and grasses. The supporting high-standard farmland facilities are as follows: Irrigation and drainage facilities: The irrigation system adopts low-pressure pipeline irrigation with an irrigation guarantee rate of ≥90%; the drainage system is laid out in a "main-secondary-branch" hierarchical manner, and can drain field water within 3 days during the rainy season, with ecological drainage facilities provided in medium and low-lying suitable areas; field road facilities: configured according to the subsidence of suitable areas.
[0022] In highly suitable areas, the following approach is adopted: "two-layer bidirectional geogrid + graded crushed stone flexible base course + concrete / asphalt surface course". The geogrid has a tensile strength ≥80kN / m and an elongation ≤10%. The graded crushed stone thickness is 20-25 cm. The surface course thickness is ≥18 cm for concrete and ≥8 cm for asphalt. In suitable areas, the "rubber particle-asphalt mixture base course + permeable asphalt surface course" is adopted. The base course thickness is 15-20 cm, the rubber particle content is 15%-20%, the surface course thickness is 6-8 cm, the road cross slope is 2%-3%, and the deformation resistance is ≥30 mm. In low-suitability areas, the following approach is adopted: "Geocell + graded sand and gravel filling + steel-plastic composite geogrid + ceramsite-rubber mixture base layer + ecological permeable concrete surface layer". The geocell height is 15 cm, the anchor spacing is 1.5 m, the depth is ≥50 cm, the settlement deformation resistance is ≥50 mm, and a 20-30 cm flexible buffer zone is reserved at the road edge. Anti-collapse flexible facilities: Flexible culverts are selected according to suitable areas.
[0023] HDPE double-wall corrugated pipe culverts (diameter 300-800 mm, wall thickness 12-25 mm, joint hot-melt welding) are used in high / medium suitable areas. In low-suitability areas, steel-plastic composite flexible corrugated pipe culverts (pipe diameter 500-1200 mm, corrugation height ≥25 mm) are used, and flexible rubber culverts (pipe diameter 200-500 mm, flange connection) are used at irrigation canal intersections. All culvert foundations are laid with 10-15 cm thick medium-coarse sand + bidirectional tensile strength ≥60kN / m geogrid, and the top is backfilled with graded sand and gravel in layers and compacted. In low suitable areas, culverts are provided with settlement compensation sections with a diameter increased by 10% and a length of 50 cm every 5-8 m. Farmland protection and ecological environment protection facilities: Install ecological slope protection and anti-slide piles to prevent soil erosion and slope collapse, and configure ecological buffer zones, wetland buffer zones and ecological corridors to enhance biodiversity; Farmland power transmission and distribution facilities: Transformers and power supply lines are configured according to production and facility operation needs, covering the power needs of irrigation pumping stations and monitoring equipment, and leakage protection devices are installed to ensure power supply safety.
[0024] S4. The annual average soil erosion per unit area of the optimized target area is calculated using the modified general soil loss model. Specifically, the formula for the average annual soil erosion per unit area is as follows: , in, This represents the average annual soil erosion per unit area. Indicates the erosivity factor of rainfall; Indicates soil erodibility factor; Indicates the slope length and slope factor; Indicates vegetation cover factor; This indicates the factors related to soil and water conservation measures.
[0025] Among them, the R factor is derived from the daily rainfall and rainstorm intensity observation data of the meteorological department in the target area over the past 10-30 years, and is calculated using the Wischmeier modified formula. When no measured data is available, the default value of the industry standard is used. The K factor is based on the particle composition and organic matter content of the 0-20cm topsoil sample, and is calculated using the EPIC model formula. When no measured data is available, the corresponding value of the industry standard is consulted. The LS factor is calculated by extracting the actual slope length and slope of the field from DEM data and GIS software, and then substituting it into the RUSLE model recommended formula. The C factor is determined based on the vegetation configuration plan and the actual coverage, with reference to the "Classification and Grading Standards for Soil Erosion". The P factor is determined based on the farmland protection facility plan, with reference to the "Technical Specifications for Comprehensive Soil and Water Conservation".
[0026] S5. The optimized target area is evaluated using a hierarchical ideal solution-dual distance proximity assessment model to obtain the evaluation results.
[0027] Specifically, S51, determine the assessment indicators, including the annual soil erosion per unit area (t / (hm²)). a) Economic cost (ten thousand yuan / hm²), ecological benefits (dimensionless, 0-1 points), production suitability (dimensionless, 0-1 points), carbon sequestration benefits (tC / (hm²)). a)) and Ecosystem Service Value (ESV) (ten thousand yuan / (hm²) a)); The evaluation indicators are divided into negative indicators and positive indicators; the negative indicators include economic costs and soil erosion; the positive indicators include ecological benefits, production adaptability, carbon sink benefits, and ecosystem service value (ESV); the evaluation indicators are standardized. The specific quantification methods for each evaluation indicator are as follows: Economic cost: It adopts a full life cycle quantitative logic, including the initial construction cost and the long-term operation and maintenance cost; The initial construction cost is the sum of earthwork costs, facility construction costs, and land reclamation and vegetation planting costs. Earthwork costs are calculated by multiplying the earthwork volume by the regional market price per unit of earthwork. Facility construction costs are calculated by summing the costs of each facility item. Land reclamation and vegetation planting costs are calculated by multiplying the reclamation area by the unit reclamation price, plus seedling and planting labor costs. Operation and maintenance costs are accrued at 3%-5% of the total initial construction cost. The total economic cost over the entire life cycle = initial construction cost + operation and maintenance cost × project design operating years (20-30 years), as shown in the formula: , , , in, The total economic cost over the entire life cycle (ten thousand yuan / hm²). The initial construction cost (ten thousand yuan / hm²); η represents the annual operation and maintenance cost (RMB 10,000 / hm²); η represents the operation and maintenance cost accrual ratio (3%~5%); n represents the project's designed operating life (20~30 years). The cost of earthwork (ten thousand yuan / hm²); The cost of facility construction (ten thousand yuan / hm²); Cost of land consolidation and vegetation planting (ten thousand yuan / hm²).
[0028] Ecological benefits: Calculated by “quantitative improvement + value transformation”, the soil erosion control benefit = erosion reduction × 15-25 yuan / t, carbon sink benefit = (vegetation biomass × carbon content coefficient - construction and operation carbon emissions) × 50-80 yuan / tCO2, with the crop carbon content coefficient taken as 0.45 and trees and shrubs as 0.5; Benefits of soil erosion control: , in, Benefits of soil erosion control (yuan / (hm²)) a)); The difference between the annual soil erosion after and before remediation (erosion reduction, t / (hm²)) a)); Economic loss per unit of soil loss (15-25 yuan / t).
[0029] Carbon sink benefit formula: , , in, Carbon sequestration benefits (yuan / (hm²)) a)); Vegetation biomass (t / hm²); The vegetation carbon content coefficient is 0.45 for crops and 0.5 for trees and shrubs, dimensionless. Carbon emissions for project construction and operation (tC / (hm²)) a)); This is the benchmark price for the domestic carbon market (50-80 yuan / t CO2). Net carbon sequestration (tC / (hm²)) a)).
[0030] Ecosystem Service Value (ESV): Calculated using the equivalent factor method, based on field area × vegetation ecosystem service value equivalent × regional unit equivalent value. Four core services were selected: water conservation, air purification, biological habitat, and soil and fertilizer conservation. The coefficients are referenced from the "Table of Ecosystem Service Value Equivalent per Unit Area of Terrestrial Ecosystems in China". The formula is as follows: , , in, To optimize the value of ecosystem services (ten thousand yuan / (hm²)) a)); S is the area of the field (hm²); The equivalent of the ecosystem service value of the kth type of vegetation (dimensionless). The equivalent value of regional unit ecosystem services (ten thousand yuan / hm², converted based on the average regional GDP); n is the number of vegetation species. Value added for ESV (ten thousand yuan / (hm²)) a)); Ecosystem service value of the original subsidence area (ten thousand yuan / (hm²)) a)).
[0031] S52. The optimized target area is comprehensively evaluated using the hierarchical ideal solution-dual distance proximity evaluation model to obtain the comprehensive benefit evaluation results.
[0032] S521. Determine the objective weights of each evaluation indicator using the mutual information-variance coefficient coupling weighting method; S522. Weight each evaluation indicator to obtain a weighted standardized matrix. , where a is the layout scheme for the a-th plot and b is the evaluation indicator for the b-th plot; S523. The indicators are divided into a core indicator layer and a secondary indicator layer according to their weight ratios. The weight of the core indicator layer is greater than or equal to 0.15, including soil erosion, economic cost, ecological benefits and production suitability. The weight of the secondary indicator layer is less than 0.15, including carbon sink benefits and ecosystem service value (ESV). S524. Constructing a positive ideal solution , , This represents the positive ideal solution of the first core indicator. Both core and secondary indicators take... At the same time, construct the negative ideal solution , Both core and secondary indicators are taken , Represent the negative ideal solution of the first core indicator; calculate the grey relational degree of the core indicator. This leads to the core indicator, grey relational distance. and the weighted Euclidean distance of all indicators The formula is expressed as follows: , , , in, This indicates that the minimum value is taken for the solution dimension; This indicates taking the minimum value for each indicator dimension; This indicates taking the maximum value for the solution dimension; This indicates taking the maximum value for the indicator dimension; This means first finding the minimum according to the plan, then finding the minimum according to the indicator, to obtain the global minimum deviation. This means first finding the maximum according to the plan, then finding the maximum according to the indicator, to obtain the global maximum deviation; This represents the resolution coefficient, with a value of 0.5. This represents the positive ideal solution for the b-th evaluation index; This indicates the number of core indicators; here, m1=6. This represents the weight of the b-th core indicator; similarly, the grey relational distance of the core indicator used to obtain the negative ideal solution is... Weighted Euclidean distance of all indicators ; S525. Calculate the fusion proximity based on the core indicator grey relational distance and the weighted Euclidean distance of all indicators: The formula is as follows: , ; The overall benefits are excellent, and the solution can be implemented directly. To achieve good overall benefits, the plan needs minor adjustments. The overall benefits are not good, so the plan needs to be redesigned.
[0033] The closer the value is to 1, the closer the plan is to the optimal state in terms of core indicators (soil erosion, economic cost, etc.) and all indicators, and the better the overall benefits. The closer the value is to 0, the closer the solution is to the worst-case scenario, and the worse the overall benefit. Meanwhile, The value directly serves as the core basis for determining the comprehensive benefit level (≥0.8 is excellent and can be implemented, 0.6-0.8 is good and needs fine-tuning, <0.6 needs to be redesigned), allowing multi-dimensional and heterogeneous evaluation indicators (economic, ecological, production, etc.) to form unified and comparable results, providing clear and scientific quantitative support for the decision-making and implementation of field optimization plans.
[0034] Example 2 This embodiment provides a system for optimizing the layout of coal mining subsidence areas, which implements the method for optimizing the layout of coal mining subsidence areas described in Embodiment 1, including: Data acquisition module: used to acquire basic data for the target area; Weighting Calculation Module: Based on five indicators—soil type, distance from the subsidence area, topographic slope, subsidence dynamic evolution characteristics, and biological adaptability score—a water-soil coupling suitability evaluation system is constructed. The coupling weights of each indicator in the target area are determined by the mutual information-variance coefficient coupling weighting method. Suitable Area Division Module: Calculates the comprehensive suitability score based on the coupling weights of each indicator and divides suitable areas, allocates schemes according to the type of suitable area, and optimizes the target area; Soil erosion calculation module: The optimized annual soil erosion per unit area of the target area is calculated using a modified general soil loss model; Evaluation module: The hierarchical ideal solution-dual distance proximity evaluation model is used to conduct a comprehensive benefit evaluation of the optimized target area and obtain the evaluation results.
[0035] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A method for optimizing the layout of coal mining subsidence areas, characterized in that, Includes the following steps: S1. Obtain basic data for the target area; S2. A water-soil coupling suitability evaluation system is constructed based on five indicators: soil type, distance from the subsidence area, topographic slope, subsidence dynamic evolution characteristics, and biological adaptability score. The coupling weights of each indicator in the target area are determined by the mutual information-variance coefficient coupling weighting method. S3. Calculate the comprehensive suitability score based on the coupling weights of each indicator and divide the suitable areas. Allocate the schemes according to the type of suitable area and optimize the target area. S4. The annual average soil erosion per unit area of the optimized target area is calculated using the modified general soil loss model. S5. The optimized target area is evaluated using a hierarchical ideal solution-dual distance proximity assessment model to obtain the evaluation results.
2. The method for optimizing the layout of coal mining subsidence areas according to claim 1, characterized in that, Step S1 specifically includes: collecting regional range data, ground elevation data, soil type data, subsidence monitoring data, hydrological and water resources data, and power planning data of the target area; the regional range data includes the subsidence area governance boundary and permanent basic farmland control boundary determined by the land and space planning.
3. The method for optimizing the layout of coal mining subsidence areas according to claim 1, characterized in that, In step S2, the weights of each indicator are determined using the mutual information-coefficient of variation coupled weighting method: The scores of each indicator are processed using a positive standardization method to obtain standardized scores; the coefficient of variation of each indicator is calculated based on the mean and standard deviation of the standardized indicators. The formula is expressed as follows: ， in, The coefficient of variation for the j-th indicator is represented by . This represents the standard deviation of the j-th indicator after standardization. Represent the standardized mean of the j-th indicator; calculate the mutual information value of the indicators. The formula is expressed as follows: ， in, This represents the marginal probability of the standardized value of the j-th index; Let represent the joint probability of the j-th and k-th indicators; Indicates the number of samples; Indicates the total number of indicators; The marginal probability of the standardized value of the k-th indicator; the coefficient of variation of the indicator Mutual information value of indicators After normalization, the index coupling weights are obtained, as shown in the following formula: ， ， ， in, The normalized index coefficient of variation; This represents the normalized mutual information value of the index; Indicates the weighted index coupling, satisfying .
4. The method for optimizing the layout of coal mining subsidence areas according to claim 1, characterized in that, In step S3, the standardized scores of each indicator are weighted and summed with the corresponding weights of each indicator to obtain the comprehensive suitability score. Based on the comprehensive suitability score, the region with a comprehensive suitability score greater than or equal to 0.8 is designated as the high suitability region, the region with a comprehensive suitability score between 0.5 and 0.8 is designated as the medium suitability region, and the region with a comprehensive suitability score less than 0.5 is designated as the low suitability region.
5. The method for optimizing the layout of coal mining subsidence areas according to claim 1, characterized in that, In step S4, the formula for the average annual soil erosion per unit area is as follows: ， in, This represents the average annual soil erosion per unit area. Indicates the erosivity factor of rainfall; Indicates soil erodibility factor; Indicates the slope length and slope factor; Indicates vegetation cover factor; This indicates the factors related to soil and water conservation measures.
6. The method for optimizing the layout of coal mining subsidence areas according to claim 1, characterized in that, Step S5 specifically includes: S51. Determine the evaluation indicators, including annual average soil erosion per unit area, economic cost, ecological benefits, production suitability, carbon sink benefits, and ecosystem service value (ESV); the evaluation indicators are divided into negative indicators and positive indicators; the negative indicators include economic cost and soil erosion; the positive indicators include ecological benefits, production suitability, carbon sink benefits, and ecosystem service value (ESV); standardize the evaluation indicators. S52. The optimized target area is comprehensively evaluated using the hierarchical ideal solution-dual distance proximity evaluation model to obtain the comprehensive benefit evaluation results.
7. The method for optimizing the layout of coal mining subsidence areas according to claim 6, characterized in that, A hierarchical ideal solution-dual distance proximity evaluation model is used to comprehensively evaluate the optimized target region: S521. Determine the objective weights of each evaluation indicator using the mutual information-variance coefficient coupling weighting method; S522. Weight each evaluation indicator to obtain a weighted standardized matrix. , where a is the layout scheme for the a-th plot and b is the evaluation indicator for the b-th plot; S523. The indicators are divided into a core indicator layer and a secondary indicator layer according to their weight ratios. The weight of the core indicator layer is greater than or equal to 0.15, including soil erosion, economic cost, ecological benefits and production suitability. The weight of the secondary indicator layer is less than 0.15, including carbon sink benefits and ecosystem service value (ESV). S524. Constructing a positive ideal solution , , This represents the positive ideal solution of the first core indicator. Both core and secondary indicators take... At the same time, construct the negative ideal solution , Both core and secondary indicators are taken , This represents the negative ideal solution for the first core indicator; Calculate the grey relational degree of the core indicators This leads to the core indicator, grey relational distance. and the weighted Euclidean distance of all indicators The formula is expressed as follows: ， ， ， in, This indicates that the minimum value is taken for the solution dimension; This indicates taking the minimum value for each indicator dimension; This indicates taking the maximum value for the solution dimension; This indicates taking the maximum value for the indicator dimension; This means first finding the minimum according to the plan, then finding the minimum according to the indicator, to obtain the global minimum deviation. This means first finding the maximum according to the plan, then finding the maximum according to the indicator, to obtain the global maximum deviation; This represents the resolution coefficient, with a value of 0.
5. This represents the positive ideal solution for the b-th evaluation index; This indicates the number of core indicators, m1=6; This indicates the weight of the b-th core indicator; Similarly, the core indicator for obtaining the negative ideal solution is the grey relational distance. Weighted Euclidean distance of all indicators ; S525. Calculate the fusion proximity based on the core indicator grey relational distance and the weighted Euclidean distance of all indicators: The formula is as follows: , ; Excellent overall benefits; The overall benefits are good; The overall benefits are not good.
8. A system for optimizing the layout of coal mining subsidence areas, comprising implementing a method for optimizing the layout of coal mining subsidence areas as described in any one of claims 1-7, characterized in that, include: Data acquisition module: used to acquire basic data for the target area; Weighting Calculation Module: Based on five indicators—soil type, distance from the subsidence area, topographic slope, subsidence dynamic evolution characteristics, and biological adaptability score—a water-soil coupling suitability evaluation system is constructed. The coupling weights of each indicator in the target area are determined by the mutual information-variance coefficient coupling weighting method. Suitable Area Division Module: Calculates the comprehensive suitability score based on the coupling weights of each indicator and divides suitable areas, allocates schemes according to the type of suitable area, and optimizes the target area; Soil erosion calculation module: The optimized annual soil erosion per unit area of the target area is calculated using a modified general soil loss model; Evaluation module: The hierarchical ideal solution-dual distance proximity evaluation model is used to conduct a comprehensive benefit evaluation of the optimized target area and obtain the evaluation results.