A method and system for ecological restoration of soil in a coal mining subsidence area

Abstract

The present application provides a kind of soil ecological restoration method and system of coal mining subsidence area, it is related to soil remediation field, method includes: according to topographic feature, coal mining subsidence area is segmented, obtains several subsidence areas;According to topographic information, combine rainfall condition, respectively, carrier material is migrated and diffused analysis, according to the results of diffusion analysis, respectively, to initial repair parameter is compensated once;According to soil humidity, respectively, to function microorganism is active fluctuation analysis, according to the results of fluctuation analysis, secondary compensation is carried out, obtains several optimization repair parameters, executes the soil remediation of several subsidence areas.The technical problem that the present application aims at solving is that traditional microbial remediation method does not fully consider the migration and diffusion of microorganism and the change of microbial activity in soil remediation process, which leads to insufficient accuracy and stability of soil remediation;The accuracy and reliability of microbial remediation parameter setting can be significantly improved, and the effect of effectively degrading pollutants and restoring soil fertility can be achieved.

Description

Technical Field

[0001] This invention relates to the field of soil remediation, and in particular to a method and system for ecological restoration of soil in coal mining subsidence areas. Background Technology

[0002] In recent years, microbial-based soil remediation technology has been widely used in the remediation of various contaminated soils, especially in the degradation of organic pollutants and heavy metal pollutants, showing good prospects. Microbial remediation utilizes specific microbial strains to degrade pollutants in the soil, and is a relatively environmentally friendly and low-cost remediation method.

[0003] However, traditional microbial remediation methods still have certain limitations. First, soil microorganisms may migrate and spread unevenly during the remediation process, resulting in excessively high microbial concentrations in some areas and excessively low microbial concentrations in others. This uneven distribution makes the remediation effect unstable, especially in subsidence areas with complex topographic features. Second, factors such as soil humidity directly affect the activity of microorganisms. Traditional methods often ignore the influence of these dynamic factors, resulting in large fluctuations in the degradation efficiency of microorganisms, which in turn affects the remediation effect. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for soil ecological restoration in coal mining subsidence areas, addressing the technical problem that traditional microbial remediation methods fail to adequately consider the migration and diffusion of microorganisms and changes in microbial activity during the soil remediation process, resulting in insufficient accuracy and stability in soil remediation. The method includes:

[0005] In a first aspect, the present invention provides a method for soil ecological restoration of coal mining subsidence areas, comprising: detecting and acquiring pollutant types and concentrations in the coal mining subsidence area; using a microbial remediation database to match functional microorganisms and carrier materials to construct initial microbial remediation parameters; dividing the coal mining subsidence area according to topographic features to obtain several subsidence regions; performing migration and diffusion analysis on the carrier materials based on the topographic information of the several subsidence regions and in conjunction with rainfall conditions; performing a first compensation on the initial microbial remediation parameters based on the diffusion analysis results to obtain several compensated remediation parameters; performing activity fluctuation analysis on the functional microorganisms based on the soil moisture of the several subsidence regions; performing a second compensation on the several compensated remediation parameters based on the fluctuation analysis results to obtain several optimized remediation parameters; and performing soil ecological restoration of the several subsidence regions according to the several optimized remediation parameters.

[0006] Preferably, the method for soil ecological restoration in coal mining subsidence areas further includes: detecting and acquiring the soil acid-base characteristics of the coal mining subsidence area; using a microbial remediation database, performing functional microbial matching based on the soil acid-base characteristics, pollutant types, and pollutant concentrations to determine several microbial types and concentrations; performing carrier material matching based on the several microbial types and concentrations to determine multiple carrier material types and concentrations; and constructing initial microbial remediation parameters based on the several microbial types, concentrations, and carrier material types and concentrations.

[0007] Preferably, the method for soil ecological restoration of coal mining subsidence areas further includes: in a simulated space, starting from the lowest point of the coal mining subsidence area, simulating water irrigation into the coal mining subsidence area, and calculating the height difference and slope difference between the current position and the lowest point; if the height difference is greater than a preset height threshold or the slope difference is greater than a preset slope threshold, then a dividing line is set at the current position, and simulating water irrigation into the coal mining subsidence area continues from the current position until the highest point of the coal mining subsidence area is reached, resulting in multiple dividing lines; the coal mining subsidence area is divided according to the multiple dividing lines to obtain several subsidence areas, wherein each subsidence area is marked with an area slope and an area height.

[0008] Preferably, the method for soil ecological restoration of coal mining subsidence areas further includes: obtaining the average annual rainfall and average annual rainfall intensity of the coal mining subsidence area within a preset historical time range, and setting them as rainfall conditions; randomly selecting a first subsidence area from the plurality of subsidence areas, and obtaining the first area slope and first area height of the first subsidence area; randomly selecting a first carrier material type from a plurality of carrier material types; performing migration and diffusion analysis on the first carrier material type based on the average annual rainfall, average annual rainfall intensity, first area slope, and first area height, and outputting a first diffusion ratio; sequentially analyzing and obtaining multiple diffusion ratios of multiple carrier material types in the first subsidence area, and compensating the microbial concentration and carrier material concentration in the initial microbial remediation parameters according to the multiple diffusion ratios, outputting a first compensated remediation parameter, and adding it to the plurality of compensated remediation parameters.

[0009] Preferably, the method for soil ecological restoration in coal mining subsidence areas further includes: using the first carrier material type as a search condition, collecting a set of sample annual average rainfall, a set of sample annual average rainfall intensity, a set of sample area slope, and a set of sample area height based on historical microbial restoration records of coal mining subsidence areas, and obtaining the annual diffusion ratio of the first carrier material type under different sample annual average rainfall, sample annual average rainfall intensity, sample area slope, and sample area height, constructing a sample diffusion ratio set, wherein the annual diffusion ratio is a positive or negative value; using the sample annual average rainfall, sample annual average rainfall intensity, sample area slope, and sample area height set as input, and using the sample diffusion ratio set as supervision, training a BP neural network until the model converges to obtain a material diffusion analyzer; using the material diffusion analyzer, performing migration and diffusion analysis based on the annual average rainfall, annual average rainfall intensity, first area slope, and first area height, and outputting a first diffusion ratio.

[0010] Preferably, the method for soil ecological restoration in a coal mining subsidence area further includes: subtracting the plurality of diffusion ratios from 1 to obtain a plurality of primary compensation coefficients; multiplying the plurality of primary compensation coefficients by the carrier material concentration of the corresponding carrier material type and the corresponding microbial concentration of the corresponding carrier material type in the initial microbial restoration parameters to output a first compensation restoration parameter.

[0011] Preferably, the method for soil ecological restoration of coal mining subsidence areas further includes: randomly selecting a first subsidence area, obtaining the average soil moisture value of the first subsidence area in the first year, and a first compensation restoration parameter; randomly selecting a first microbial type from several microbial types; predicting the first reproduction rate of the first microbial type based on the average soil moisture value in the first year, wherein a first activity fluctuation analyzer is constructed based on a BP neural network to predict the reproduction rate; setting the ratio of the first expected reproduction rate of the first microbial type to the first reproduction rate as a secondary compensation coefficient, and sequentially analyzing and obtaining several secondary compensation coefficients, wherein the first expected reproduction rate is the reproduction rate of the first microbial type under standard soil moisture; multiplying the several secondary compensation coefficients by the corresponding microbial concentration in the first compensation restoration parameter and the corresponding carrier material concentration of the corresponding microbial concentration to obtain a first optimized restoration parameter, and adding it to the several optimized restoration parameters.

[0012] Secondly, the present invention also provides a soil ecological restoration system for coal mining subsidence areas, used to execute a soil ecological restoration method for coal mining subsidence areas as described in the first aspect, comprising: an initial restoration parameter construction module, used to detect and obtain the pollutant types and concentrations in the coal mining subsidence area, and to construct initial microbial restoration parameters by matching functional microorganisms and carrier materials using a microbial restoration database; a coal mining subsidence area segmentation module, used to segment the coal mining subsidence area according to topographic features to obtain several subsidence areas; a migration and diffusion analysis module, used to perform migration and diffusion analysis on the carrier materials according to the topographic information of the several subsidence areas and combined with rainfall conditions, and to perform a first compensation on the initial microbial restoration parameters according to the diffusion analysis results to obtain several compensated restoration parameters; an activity fluctuation analysis module, used to perform activity fluctuation analysis on the functional microorganisms according to the soil moisture of the several subsidence areas, and to perform a second compensation on the several compensated restoration parameters according to the fluctuation analysis results to obtain several optimized restoration parameters; and a soil ecological restoration module, used to execute soil ecological restoration of the several subsidence areas according to the several optimized restoration parameters.

[0013] Thirdly, the present invention also provides an electronic device, comprising:

[0014] At least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the method described in any one of the first aspects above.

[0015] Fourthly, a computer-readable storage medium storing a computer program that, when executed, implements the steps of the method described in any one of the first aspects above.

[0016] The embodiments of the present invention have the following advantages:

[0017] By detecting and obtaining the types and concentrations of pollutants in the coal mining subsidence area, and using a microbial remediation database to match functional microorganisms with carrier materials, initial microbial remediation parameters are constructed. Next, the coal mining subsidence area is segmented according to topographic features to obtain several subsidence zones. Further, based on the topographic information of these subsidence zones and combined with rainfall conditions, migration and diffusion analysis is performed on the carrier materials for each zone. Based on the diffusion analysis results, the initial microbial remediation parameters are compensated once, resulting in several compensated remediation parameters. Then, based on the topographic information of these subsidence zones and combined with rainfall conditions, migration and diffusion analysis is performed on the carrier materials for each zone. Based on the diffusion analysis results, the initial microbial remediation parameters are compensated once, resulting in several compensated remediation parameters. Finally, soil ecological remediation is performed in the subsidence zones according to these optimized remediation parameters. In other words, by dynamically adjusting the microbial remediation parameters based on the topographic features, climate characteristics, and soil environmental factors of the coal mining subsidence area, the accuracy and reliability of the microbial remediation parameter settings can be significantly improved, achieving the goal of efficient and stable soil remediation, thereby effectively degrading pollutants and restoring soil fertility. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the steps of a method for soil ecological restoration in coal mining subsidence areas according to the present invention.

[0019] Figure 2 This is a schematic diagram of the structure of a soil ecological restoration system for coal mining subsidence areas according to the present invention;

[0020] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention;

[0021] Figure 4 This is a schematic diagram of the structure of a computer-readable storage medium provided by the present invention.

[0022] Explanation of reference numerals in the attached figures:

[0023] The system includes an initial repair parameter construction module 11, a coal mining subsidence area segmentation module 12, a migration and diffusion analysis module 13, an activity fluctuation analysis module 14, a soil ecological restoration module 15, an electronic device 500, a memory 510, a processor 520, a first computer program 511, a computer-readable storage medium 600, and a second computer program 611. Detailed Implementation

[0024] This invention provides a method and system for soil ecological restoration in coal mining subsidence areas, solving the technical problem that traditional microbial remediation methods fail to adequately consider the migration, diffusion, and changes in microbial activity during the soil remediation process, resulting in insufficient accuracy and stability in soil remediation. By dynamically adjusting the microbial remediation parameters based on the topographic features, climate characteristics, and soil environmental factors of the coal mining subsidence area, the accuracy and reliability of the microbial remediation parameter settings can be significantly improved, achieving the goal of efficient and stable soil remediation, thereby effectively degrading pollutants and restoring soil fertility.

[0025] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all of them.

[0026] Example 1, please refer to the appendix. Figure 1 This invention provides a method for soil ecological restoration in coal mining subsidence areas, applied to a soil ecological restoration system for coal mining subsidence areas, specifically including the following steps:

[0027] S10: Detect and obtain the types and concentrations of pollutants in the coal mining subsidence area, and use the microbial remediation database to match functional microorganisms and carrier materials to construct initial microbial remediation parameters.

[0028] Furthermore, step S10 of the present invention further includes:

[0029] S11: Detect and obtain the soil acid-base characteristics of the coal mining subsidence area; S12: Utilize the microbial remediation database to perform functional microbial matching based on the soil acid-base characteristics, pollutant types, and pollutant concentrations, and determine several microbial types and concentrations; S13: Based on the several microbial types and concentrations, perform carrier material matching to determine multiple carrier material types and concentrations; S14: Construct initial microbial remediation parameters based on the several microbial types, concentrations, carrier material types, and concentrations.

[0030] Specifically, firstly, based on the topographical characteristics of the coal mining subsidence area (such as low-lying areas, sloping areas, and flat areas), multiple sampling points are determined. These sampling points should cover the entire remediation area to ensure comprehensive pollutant distribution. Next, different sampling depths are selected based on the potential depth of pollutant distribution. Generally, sampling depths can be divided into topsoil (0 to 20 cm), middle soil (20 to 40 cm), and deep soil (40 cm and above). Then, depending on the area size, ensure a sufficient number of sampling points, typically collecting at least 3 to 5 samples per area, and sampling should be conducted in different seasons to assess the dynamic changes of pollutants. Further, pollutant analysis methods are used to test the sampled soil. Commonly used pollutant analysis methods include gas chromatography, liquid chromatography, and atomic absorption spectrometry to obtain the types and concentrations of pollutants in the coal mining subsidence area. Pollutant types include organic pollutants (such as petroleum hydrocarbons), heavy metal pollutants (such as lead, cadmium, arsenic, chromium, and mercury), and nutrient pollutants (such as excess nitrogen and phosphorus).

[0031] Next, the soil acidity and alkalinity characteristics of the coal mining subsidence area were obtained through testing, such as using a pH meter to determine the soil's acidity or alkalinity based on the measured pH value. A pH value less than 7 indicates acidic soil, greater than 7 indicates alkaline soil, and a pH value close to 7 indicates neutral soil. Then, a microbial remediation database was constructed. This database, based on historical data, contains a large amount of information on microorganisms related to soil remediation, including the functional characteristics of the microorganisms, their adapted soil environments, their ability to degrade pollutants, and their optimal growth conditions.

[0032] Further utilizing the microbial remediation database, functional microorganisms are matched based on the soil's pH characteristics, pollutant type, and pollutant concentration. Specifically, based on the soil's pH value, the database can recommend microorganisms suitable for that pH range. For example, if the soil is acidic, the database will recommend acid-tolerant bacteria or fungi; if the soil is alkaline, it will recommend alkali-tolerant microorganisms. Based on the type of soil pollutant (e.g., heavy metals, organic pollutants), the database will recommend microorganisms with targeted degradation functions. For example, if the soil pollutant is oil or organic solvent, microbial strains capable of degrading petroleum hydrocarbons can be selected; if the soil pollutant is heavy metals (e.g., lead, cadmium), microorganisms with heavy metal removal or reduction functions will be selected. Based on the pollutant concentration, the database can suggest appropriate microbial concentrations. For low-concentration pollution, lower microbial concentrations are recommended; for high-concentration pollution, higher microbial concentrations are recommended to ensure sufficient degradation capacity. Based on the above matching results, suitable microbial strains and their concentrations are determined, resulting in several microbial types and concentrations.

[0033] Then, carrier materials are matched based on the aforementioned microbial types and concentrations. This involves selecting suitable carrier materials according to the microbial type and soil environment characteristics. Common carrier material types include biochar, bentonite, and natural organic matter (such as humic acid and compost). Different types of carrier materials are suitable for different microbial types and scenarios. For example, biochar is a carbon-based material with a highly porous structure, providing a habitat for microorganisms while also having the ability to adsorb pollutants. Biochar can increase the organic matter content of soil and improve soil structure, making it suitable for the remediation of soils contaminated with organic pollutants and heavy metals. It can be matched with microorganisms that can grow in a carbon-based environment (such as organic degrading bacteria and heavy metal reducing bacteria). Bentonite is a natural clay mineral with high adsorption capacity. It can adsorb water and microorganisms, which helps microorganisms survive in the soil. Bentonite also has a good soil improvement effect, which can improve the water retention and aeration of the soil. It is suitable for the remediation of moist, low-acid soils, especially when the pollutants in the soil that need to be remediated are organic pollutants or heavy metals. It is suitable for matching with microorganisms that are tolerant to moisture and clay environments (such as certain denitrifying bacteria, nitrifying bacteria, etc.).

[0034] Finally, the aforementioned microbial types, concentrations, carrier material types, and concentrations are combined to construct initial microbial remediation parameters. By comprehensively considering multiple factors such as microbial type, concentration, and carrier material type and concentration, the remediation effect of microorganisms in soil can be maximized.

[0035] S20: Divide the coal mining subsidence area according to the terrain features to obtain several subsidence areas.

[0036] Furthermore, step S20 of the present invention also includes:

[0037] S21: In the simulation space, starting from the lowest point of the coal mining subsidence area, simulate water injection into the coal mining subsidence area, and calculate the height difference and slope difference between the current position and the lowest point; S22: If the height difference is greater than a preset height threshold or the slope difference is greater than a preset slope threshold, then set a dividing line at the current position, and continue to simulate water injection into the coal mining subsidence area starting from the current position until the highest point of the coal mining subsidence area is reached, obtaining multiple dividing lines. Divide the coal mining subsidence area according to the multiple dividing lines to obtain several subsidence areas, wherein each subsidence area is marked with area slope and area height.

[0038] Specifically, firstly, a simulation space is constructed, which can be based on a digital elevation model or a 3D topographic map to accurately represent the terrain undulations of the subsidence area. Next, within the simulation space, starting from the lowest point of the coal mining subsidence area, water is simulated to flow from the lowest point and gradually expand to various parts of the subsidence area. In each simulation step, the height difference between the current position and the lowest point is calculated, i.e., the difference between the elevation of the current position and the elevation of the lowest point. Based on the changes in terrain, the difference between the slope of the current position and the slope of the lowest point is calculated. The slope is the rate of change of ground undulation, usually expressed as the slope of the terrain. The height difference and slope difference between the current position and the lowest point are thus obtained.

[0039] Next, preset height thresholds and preset slope thresholds are configured. These thresholds can be set according to the segmentation accuracy and the area of ​​the coal mining subsidence zone. For example, the preset height threshold can be set to 1 meter and the preset slope threshold to 2 degrees. If the height difference between the current position and the lowest point is greater than the preset height threshold, it indicates that the terrain at the current position has changed significantly, and segmentation is required at the current position. If the slope difference between the current position and the lowest point is greater than the threshold, it indicates that the slope at the current position has changed significantly, and segmentation is also required at the current position. That is, if the height difference is greater than the preset height threshold or the slope difference is greater than the preset slope threshold, a segmentation line is set at the current position. Then, starting from the current position, simulated water injection continues into the coal mining subsidence zone, repeating the above steps (calculating the height difference and slope difference, and determining whether segmentation is required) until the simulated water flow reaches the highest point of the subsidence zone, resulting in multiple segmentation lines.

[0040] The coal mining subsidence area is further divided according to the multiple dividing lines. Each subsidence area is confined between two adjacent dividing lines, forming a separate region. Each region has specific topographic features, including its regional height and regional slope. The height of each subsidence area is set by default to the median value of all topographic heights within the region, i.e., the median value of the maximum and minimum heights within the region. The slope of each region is set by default to the median value of the slopes within the region, i.e., the average or median value of all slopes within the region. After the division, the coal mining subsidence area is divided into several subsidence areas, and each subsidence area is identified by its regional slope and regional height. These values ​​can provide accurate regional division and data support for ecological restoration, effectively improving the accuracy and targeting of soil remediation, thereby achieving a more efficient restoration effect.

[0041] S30: Based on the topographic information of the several subsidence areas and combined with rainfall conditions, the migration and diffusion analysis of the carrier material is performed respectively. Based on the diffusion analysis results, the initial microbial remediation parameters are compensated once to obtain several compensated remediation parameters.

[0042] Furthermore, step S30 of the present invention also includes:

[0043] S31: Obtain the average annual rainfall and average annual rainfall intensity of the coal mining subsidence area within a preset historical time range, and set them as rainfall conditions; S32: Randomly select a first subsidence area from the several subsidence areas, and obtain the first area slope and first area height of the first subsidence area; S33: Randomly select a first carrier material type from multiple carrier material types.

[0044] Specifically, firstly, the average annual rainfall and average annual rainfall intensity of the coal mining subsidence area within a preset historical time range (e.g., the last 5 years) are obtained. This involves acquiring rainfall data from the coal mining subsidence area within the preset historical time range, calculating the total annual precipitation, and obtaining the average value. The average annual rainfall intensity within the same historical time range is also calculated, where rainfall intensity is the intensity of precipitation per unit time. The average annual rainfall and average annual rainfall intensity are then set as rainfall conditions. Next, any subsidence area is randomly selected from the several subsidence areas as the first subsidence area, and the first area slope and first area height of the first subsidence area are obtained. On the other hand, a first carrier material type, such as biochar or bentonite, is randomly selected from multiple carrier material types.

[0045] S34: Based on the average annual rainfall, average annual rainfall intensity, slope of the first region, and height of the first region, perform migration and diffusion analysis on the first carrier material type and output the first diffusion ratio.

[0046] Furthermore, step S34 of the present invention also includes:

[0047] S341: Using the first carrier material type as the search condition, based on the microbial remediation records of historical coal mining subsidence areas, collect the sample annual average rainfall set, sample annual average rainfall intensity set, sample area slope set, and sample area height set, and obtain the annual diffusion ratio of the first carrier material type under different sample annual average rainfall, sample annual average rainfall intensity, sample area slope, and sample area height, constructing a sample diffusion ratio set, where the annual diffusion ratio is a positive or negative value; S342: Using the sample annual average rainfall set, sample annual average rainfall intensity set, sample area slope set, and sample area height set as input, and using the sample diffusion ratio set as supervision, train a BP neural network until the model converges to obtain a material diffusion analyzer; S343: Using the material diffusion analyzer, perform migration and diffusion analysis based on the annual average rainfall, annual average rainfall intensity, first area slope, and first area height, and output the first diffusion ratio.

[0048] Specifically, firstly, using the type of the primary carrier material as the search criterion, based on historical microbial remediation records of coal mining subsidence areas, relevant historical remediation cases and experimental records were retrieved according to the selected carrier material type (such as biochar, bentonite, etc.). The carrier material type significantly affects the migration and diffusion characteristics of microorganisms; therefore, the selected carrier material directly influences the microbial remediation effect. Sets of average annual rainfall, average annual rainfall intensity, slope of the sample area, and height of the sample area were collected. Next, the annual diffusion ratio of the primary carrier material type was obtained under different average annual rainfall, average annual rainfall intensity, slope of the sample area, and height of the sample area. The annual diffusion ratio refers to the change in the migration and diffusion degree of the carrier material and microorganisms, reflecting the activity and stability of microorganisms in the soil, and indicating the changes in the migration and diffusion of microorganisms under different topographic and climatic conditions within a given year. The annual diffusion ratio can be positive or negative; a positive value indicates the increase in the total amount of microorganisms, and a negative value indicates the decrease in the total amount of microorganisms. A set of sample diffusion ratios was then constructed.

[0049] Next, the set of sample annual average rainfall, sample annual average rainfall intensity, sample area slope, sample area height, and sample diffusion ratio are used as training data. The sample annual average rainfall, sample annual average rainfall intensity, sample area slope, and sample area height are used as inputs, and the sample diffusion ratio is used as supervision to train a BP neural network. The BP neural network includes an input layer, multiple hidden layers, and an output layer. The input layer receives four input features: annual average rainfall, annual average rainfall intensity, area slope, and area height. The output layer has one neuron to output the annual diffusion ratio (positive or negative). To improve the training efficiency of the BP neural network, the input data needs to be standardized or normalized, such as normalizing the annual average rainfall, annual average rainfall intensity, area slope, and area height to the interval [0, 1] to avoid the differences in the dimensions of different features affecting network training. During training, the network's output is calculated via forward propagation. The input data passes through each layer's computation, ultimately yielding the network's prediction. Next, a loss function (such as mean squared error) is used to measure the difference between the network's prediction and the true label. The network parameters are updated by calculating the gradient of the loss function with respect to each parameter (weights and biases) to reduce the loss. These steps (forward propagation, loss calculation, backpropagation, and parameter update) are iterated multiple times on the training set. In each iteration, the neural network adjusts the weights and biases to minimize the loss function until the loss converges or the maximum number of iterations is reached, resulting in a trained material diffusion analyzer.

[0050] Finally, the average annual rainfall, average annual rainfall intensity, slope of the first area, and height of the first area are input into the material diffusion analyzer for migration and diffusion analysis, and the first diffusion ratio is output. Through the migration and diffusion analysis of the material diffusion analyzer, the microbial diffusion ratio in a specific area can be accurately predicted. This result provides data support for practical soil remediation schemes, allowing for fine-tuning of parameters to ensure the efficiency and stability of microbial remediation.

[0051] S35: Sequentially analyze and obtain multiple diffusion ratios of multiple carrier material types in the first subsidence area, compensate the microbial concentration and carrier material concentration in the initial microbial remediation parameters according to the multiple diffusion ratios, output the first compensation remediation parameter, and add it to the multiple compensation remediation parameters.

[0052] Furthermore, step S35 of the present invention also includes:

[0053] S351: Subtract the plurality of diffusion ratios from 1 to obtain a plurality of primary compensation coefficients; S352: Multiply the plurality of primary compensation coefficients by the carrier material concentration of the corresponding carrier material type and the corresponding microbial concentration of the corresponding carrier material type in the initial microbial remediation parameters to output the first compensation remediation parameter.

[0054] Specifically, using the same method as obtaining the first diffusion ratio, multiple diffusion ratios for multiple carrier material types in the first subsidence area are sequentially analyzed and obtained. Then, each of the multiple diffusion ratios is subtracted from 1, and the difference is used as a primary compensation coefficient, resulting in multiple primary compensation coefficients. These primary compensation coefficients are then multiplied by the carrier material concentration of the corresponding carrier material type in the initial microbial remediation parameters, and the product is used as the carrier material concentration after primary compensation. Finally, each of the multiple primary compensation coefficients is multiplied by the corresponding microbial concentration of the corresponding carrier material type, and the product is used as the microbial concentration after primary compensation. This yields the first compensation remediation parameter, representing the required microbial and carrier material concentrations considering microbial diffusion. This process allows the remediation scheme to more accurately adapt to different regional conditions, taking into account the impact of microbial diffusion, thereby improving the remediation effect and stability.

[0055] Then, using the same method as obtaining the first compensation and repair parameters, several compensation and repair parameters for the several subsidence areas are obtained sequentially through analysis.

[0056] S40: Based on the soil moisture of the several subsidence areas, the activity fluctuation analysis of the functional microorganisms is performed respectively. Based on the fluctuation analysis results, the several compensation and remediation parameters are compensated a second time to obtain several optimized remediation parameters.

[0057] Furthermore, step S40 of the present invention further includes:

[0058] S41: Randomly select a first subsidence area, obtain the average soil moisture value of the first subsidence area in the first year, and the first compensation and remediation parameter; S42: Randomly select a first microbial type from several microbial types; S43: Based on the average soil moisture value in the first year, predict the first reproduction rate of the first microbial type, wherein a first activity fluctuation analyzer is constructed based on a BP neural network to predict the reproduction rate; S44: Set the ratio of the first expected reproduction rate of the first microbial type to the first reproduction rate as a secondary compensation coefficient, and sequentially analyze and obtain several secondary compensation coefficients, wherein the first expected reproduction rate is the reproduction rate of the first microbial type under standard soil moisture; S45: Based on the several secondary compensation coefficients, multiply them by the corresponding microbial concentration in the first compensation and remediation parameter and the corresponding carrier material concentration of the corresponding microbial concentration to obtain the first optimized remediation parameter, and add it to the several optimized remediation parameters.

[0059] Specifically, firstly, any one of the several subsidence areas is randomly selected as the first subsidence area, and the average soil moisture value for the first year and the first compensation and remediation parameters for the first subsidence area are obtained. For example, the average soil moisture value for the first year is obtained by calculating the average of the moisture data for the first subsidence area over the past 12 months. Next, any one of several microbial types is randomly selected as the first microbial type, such as heavy metal reducing bacteria or petroleum degrading bacteria.

[0060] Next, a first activity fluctuation analyzer is constructed based on a BP neural network. This first activity fluctuation analyzer is a BP neural network model that can be iteratively optimized in machine learning. It is used to predict the reproduction rate of a first microbial type (e.g., organic matter-decomposing fungi) under different soil moisture conditions. It includes an input layer, multiple hidden layers, and an output layer. The input data for the input layer is soil moisture, and the output data for the output layer is the reproduction rate of the first microbial type. Then, using the first microbial type as a constraint, sample soil moisture sets and sample reproduction rate sets are collected. These sets are used for supervised training of the first activity fluctuation analyzer. During training, firstly, a suitable loss function (e.g., mean squared error) is selected to measure the error between the network's predicted and actual values, with the goal of minimizing this error and adjusting the network weights. Next, the input data passes through the network to obtain the predicted output, and the error between the predicted and actual outputs is calculated using the loss function. Then, the network weights are adjusted based on the error to reduce the prediction error, and the gradient descent algorithm is used to minimize the loss function and optimize the network weights. The network undergoes multiple iterations during training, with each iteration updating the weights and biases to improve prediction accuracy. During training, the network's error gradually decreases until the model converges, resulting in the first activity fluctuation analyzer after training. Through supervised learning, the BP neural network gradually learns the relationship between soil moisture and microbial reproduction rate. After training, the neural network can predict the microbial reproduction rate based on given soil moisture conditions, effectively improving the accuracy and efficiency of microbial reproduction rate prediction.

[0061] Then, the average soil moisture value of the first year is input into the first activity fluctuation analyzer to predict and obtain the first reproduction rate of the first microbial type; further, the ratio of the first expected reproduction rate of the first microbial type to the first reproduction rate is set as the secondary compensation coefficient, wherein the first expected reproduction rate is the reproduction rate of the first microbial type under standard soil moisture; then, using the same method, several secondary compensation coefficients of several microbial types in the first subsidence area are analyzed and obtained in sequence.

[0062] Then, the plurality of secondary compensation coefficients are multiplied by the corresponding microbial concentration in the first compensation and repair parameters, and the product of the two is taken as the microbial concentration after secondary compensation; the plurality of secondary compensation coefficients are multiplied by the corresponding carrier material concentration of the corresponding microbial concentration, and the product of the two is taken as the carrier material concentration after secondary compensation, so as to obtain the first optimized repair parameters of the first subsidence area, and the same method is used to analyze and obtain the plurality of optimized repair parameters of the plurality of subsidence areas in sequence.

[0063] S50: Perform soil ecological restoration of the subsidence areas according to the aforementioned optimized restoration parameters.

[0064] Specifically, according to the aforementioned optimized remediation parameters, the required microbial types, carrier materials, and other relevant remediation materials are prepared. For example, based on the selected microbial types and concentrations, as well as the selected carrier materials and their concentrations, corresponding remediation agents are prepared according to the different requirements of the region. Then, the microbial remediation agents are evenly applied to the soil surface or deep layers through spraying, broadcasting, tilling, or other methods. By implementing soil ecological remediation in coal mining subsidence areas according to the optimized remediation parameters, the accuracy and effectiveness of the remediation can be significantly improved. This remediation method can dynamically adjust the application amount and concentration of the microbial remediation agent according to the specific conditions of different subsidence areas (such as soil moisture, pollutant types, topographic features, etc.), ensuring that the microbial activity and remediation effect reach the optimal level during the remediation process, ultimately achieving pollutant degradation and soil fertility restoration.

[0065] In summary, the soil ecological restoration method for coal mining subsidence areas provided by this invention has the following technical effects:

[0066] By detecting and obtaining the types and concentrations of pollutants in the coal mining subsidence area, and using a microbial remediation database to match functional microorganisms with carrier materials, initial microbial remediation parameters are constructed. Next, the coal mining subsidence area is segmented according to topographic features to obtain several subsidence zones. Further, based on the topographic information of these subsidence zones and combined with rainfall conditions, migration and diffusion analysis is performed on the carrier materials for each zone. Based on the diffusion analysis results, the initial microbial remediation parameters are compensated once, resulting in several compensated remediation parameters. Then, based on the topographic information of these subsidence zones and combined with rainfall conditions, migration and diffusion analysis is performed on the carrier materials for each zone. Based on the diffusion analysis results, the initial microbial remediation parameters are compensated once, resulting in several compensated remediation parameters. Finally, soil ecological remediation is performed in the subsidence zones according to these optimized remediation parameters. In other words, by dynamically adjusting the microbial remediation parameters based on the topographic features, climate characteristics, and soil environmental factors of the coal mining subsidence area, the accuracy and reliability of the microbial remediation parameter settings can be significantly improved, achieving the goal of efficient and stable soil remediation, thereby effectively degrading pollutants and restoring soil fertility.

[0067] Example 2: Based on the same inventive concept as the soil ecological restoration method for coal mining subsidence areas described in the previous examples, this invention also provides a soil ecological restoration system for coal mining subsidence areas. Please refer to the appendix. Figure 2The system includes: an initial remediation parameter construction module 11, used to detect and obtain the pollutant types and concentrations in the coal mining subsidence area, and to construct initial microbial remediation parameters by matching functional microorganisms and carrier materials using a microbial remediation database; a coal mining subsidence area segmentation module 12, used to segment the coal mining subsidence area according to topographic features to obtain several subsidence areas; a migration and diffusion analysis module 13, used to perform migration and diffusion analysis on the carrier materials based on the topographic information of the several subsidence areas and combined with rainfall conditions, and to perform a first compensation on the initial microbial remediation parameters based on the diffusion analysis results to obtain several compensated remediation parameters; an activity fluctuation analysis module 14, used to perform activity fluctuation analysis on the functional microorganisms based on the soil moisture of the several subsidence areas, and to perform a second compensation on the several compensated remediation parameters based on the fluctuation analysis results to obtain several optimized remediation parameters; and a soil ecological remediation module 15, used to perform soil ecological remediation on the several subsidence areas according to the several optimized remediation parameters.

[0068] Furthermore, the soil ecological restoration system for coal mining subsidence areas is also used for: detecting and acquiring the soil acid-base characteristics of the coal mining subsidence area; using a microbial remediation database, performing functional microbial matching based on the soil acid-base characteristics, pollutant types, and pollutant concentrations to determine several microbial types and concentrations; performing carrier material matching based on the several microbial types and concentrations to determine multiple carrier material types and concentrations; and constructing initial microbial remediation parameters based on the several microbial types, concentrations, and carrier material types and concentrations.

[0069] Furthermore, the soil ecological restoration system for coal mining subsidence areas is also used for: in a simulated space, starting from the lowest point of the coal mining subsidence area, simulating water irrigation into the coal mining subsidence area, and calculating the height difference and slope difference between the current position and the lowest point; if the height difference is greater than a preset height threshold or the slope difference is greater than a preset slope threshold, then a dividing line is set at the current position, and simulating water irrigation into the coal mining subsidence area continues from the current position until the highest point of the coal mining subsidence area is reached, resulting in multiple dividing lines; the coal mining subsidence area is divided according to the multiple dividing lines to obtain several subsidence areas, wherein each subsidence area is marked with an area slope and an area height.

[0070] Furthermore, the soil ecological restoration system for coal mining subsidence areas is also used for: obtaining the average annual rainfall and average annual rainfall intensity of the coal mining subsidence area within a preset historical time range, setting them as rainfall conditions; randomly selecting a first subsidence area from among several subsidence areas, and obtaining the first area slope and first area height of the first subsidence area; randomly selecting a first carrier material type from among multiple carrier material types; performing migration and diffusion analysis on the first carrier material type based on the average annual rainfall, average annual rainfall intensity, first area slope, and first area height, and outputting a first diffusion ratio; sequentially analyzing and obtaining multiple diffusion ratios of multiple carrier material types in the first subsidence area, compensating the microbial concentration and carrier material concentration in the initial microbial remediation parameters according to the multiple diffusion ratios, outputting a first compensated remediation parameter, and adding it to the several compensated remediation parameters.

[0071] Furthermore, the soil ecological restoration system for coal mining subsidence areas is also used for: using the first carrier material type as a search condition, collecting a set of sample annual average rainfall, a set of sample annual average rainfall intensity, a set of sample area slope, and a set of sample area height based on historical microbial restoration records of coal mining subsidence areas, and obtaining the annual diffusion ratio of the first carrier material type under different sample annual average rainfall, sample annual average rainfall intensity, sample area slope, and sample area height, constructing a sample diffusion ratio set, wherein the annual diffusion ratio is a positive or negative value; using the sample annual average rainfall, sample annual average rainfall intensity, sample area slope, and sample area height set as input, and using the sample diffusion ratio set as supervision, training a BP neural network until the model converges to obtain a material diffusion analyzer; using the material diffusion analyzer, performing migration and diffusion analysis based on the annual average rainfall, annual average rainfall intensity, first area slope, and first area height, and outputting the first diffusion ratio.

[0072] Furthermore, the soil ecological restoration system for coal mining subsidence areas is also used to: subtract the plurality of diffusion ratios from 1 to obtain a plurality of primary compensation coefficients; multiply the plurality of primary compensation coefficients by the carrier material concentration of the corresponding carrier material type and the corresponding microbial concentration of the corresponding carrier material type in the initial microbial restoration parameters to output the first compensation restoration parameter.

[0073] Furthermore, the soil ecological restoration system for coal mining subsidence areas is also used for: randomly selecting a first subsidence area, obtaining the average soil moisture value of the first subsidence area in the first year, and a first compensation restoration parameter; randomly selecting a first microbial type from several microbial types; predicting the first reproduction rate of the first microbial type based on the average soil moisture value in the first year, wherein a first activity fluctuation analyzer is constructed based on a BP neural network to predict the reproduction rate; setting the ratio of the first expected reproduction rate of the first microbial type to the first reproduction rate as a secondary compensation coefficient, and sequentially analyzing and obtaining several secondary compensation coefficients, wherein the first expected reproduction rate is the reproduction rate of the first microbial type under standard soil moisture; multiplying the several secondary compensation coefficients by the corresponding microbial concentration in the first compensation restoration parameter and the corresponding carrier material concentration of the corresponding microbial concentration to obtain a first optimized restoration parameter, and adding it to the several optimized restoration parameters.

[0074] Example 3, please refer to Figure 3 , Figure 3 This is a schematic diagram illustrating an embodiment of the electronic device provided in this invention. For example... Figure 3 As shown, this embodiment of the invention provides an electronic device 500, including a memory 510, a processor 520, and a first computer program 511 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the first computer program 511, it performs the following steps: detecting and acquiring the pollutant type and concentration in a coal mining subsidence area; using a microbial remediation database, matching functional microorganisms and carrier materials to construct initial microbial remediation parameters; dividing the coal mining subsidence area according to topographic features to obtain several subsidence areas; based on the topographic information of the several subsidence areas and combined with rainfall conditions, performing migration and diffusion analysis on the carrier materials respectively; and compensating the initial microbial remediation parameters once based on the diffusion analysis results to obtain several compensated remediation parameters; performing activity fluctuation analysis on the functional microorganisms respectively based on the soil moisture of the several subsidence areas; and compensating the several compensated remediation parameters a second time based on the fluctuation analysis results to obtain several optimized remediation parameters; and performing soil ecological remediation in the several subsidence areas according to the several optimized remediation parameters.

[0075] Example 4, please refer to Figure 4 , Figure 4 This is a schematic diagram illustrating an embodiment of a computer-readable storage medium provided by an embodiment of the present invention. For example... Figure 4As shown, this embodiment provides a computer-readable storage medium 600, on which a second computer program 611 is stored. When the second computer program 611 is executed by a processor, it performs the following steps: detecting and acquiring the pollutant type and concentration in the coal mining subsidence area; using a microbial remediation database, matching functional microorganisms and carrier materials to construct initial microbial remediation parameters; dividing the coal mining subsidence area according to topographic features to obtain several subsidence areas; based on the topographic information of the several subsidence areas and combined with rainfall conditions, performing migration and diffusion analysis on the carrier materials respectively; and compensating the initial microbial remediation parameters once based on the diffusion analysis results to obtain several compensated remediation parameters; performing activity fluctuation analysis on the functional microorganisms respectively based on the soil moisture of the several subsidence areas; and compensating the several compensated remediation parameters a second time based on the fluctuation analysis results to obtain several optimized remediation parameters; and performing soil ecological remediation in the several subsidence areas according to the several optimized remediation parameters.

[0076] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0077] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0079] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0080] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0081] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Clearly, those skilled in the art can make various alterations and variations to the invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the invention and its equivalents, the invention is also intended to include these modifications and variations.

Claims

1. A method for soil ecological restoration in coal mining subsidence areas, characterized in that, The methods include: The types and concentrations of pollutants in the coal mining subsidence area were detected and obtained. Using a microbial remediation database, functional microorganisms and carrier materials were matched to construct initial microbial remediation parameters. The coal mining subsidence area is divided according to the terrain features to obtain several subsidence areas; Based on the topographic information of the several subsidence areas and combined with rainfall conditions, migration and diffusion analysis was performed on the carrier material. Based on the diffusion analysis results, the initial microbial remediation parameters were compensated once to obtain several compensated remediation parameters. Based on the soil moisture in the aforementioned subsidence areas, the activity fluctuation analysis of the functional microorganisms was performed. Based on the fluctuation analysis results, the aforementioned compensation and remediation parameters were compensated a second time to obtain several optimized remediation parameters. Soil ecological restoration of the subsidence areas is carried out according to the aforementioned optimized restoration parameters.

2. The method for soil ecological restoration in coal mining subsidence areas according to claim 1, characterized in that, Using a microbial remediation database, functional microorganisms and carrier materials were matched to construct initial microbial remediation parameters, including: Detect and obtain soil acid-base characteristics in coal mining subsidence areas; Using a microbial remediation database, functional microorganisms are matched based on the soil pH characteristics, pollutant types, and pollutant concentrations to determine several microbial types and concentrations. Based on the aforementioned microbial types and concentrations, carrier materials are matched to determine multiple carrier material types and concentrations. Initial microbial remediation parameters are constructed based on the aforementioned microbial types, microbial concentrations, multiple carrier material types, and multiple carrier material concentrations.

3. The method for soil ecological restoration in coal mining subsidence areas according to claim 2, characterized in that, The coal mining subsidence area is divided according to topographic features to obtain several subsidence zones, including: In the simulation space, starting from the lowest point of the coal mining subsidence area, simulated water injection is carried out into the coal mining subsidence area, and the height difference and slope difference between the current position and the lowest point are calculated and obtained. If the height difference is greater than a preset height threshold or the slope difference is greater than a preset slope threshold, a dividing line is set at the current position, and simulated water injection continues into the coal mining subsidence area from the current position until the highest point of the coal mining subsidence area is reached, resulting in multiple dividing lines. The coal mining subsidence area is then divided according to these multiple dividing lines to obtain several subsidence areas, each of which is marked with a regional slope and a regional height.

4. A method for soil ecological restoration in coal mining subsidence areas according to claim 3, characterized in that, Based on the topographic information of the aforementioned subsidence areas and combined with rainfall conditions, migration and diffusion analysis was performed on the carrier material. Based on the diffusion analysis results, the initial microbial remediation parameters were compensated once, resulting in several compensated remediation parameters, including: The average annual rainfall and average annual rainfall intensity of the coal mining subsidence area within a preset historical time range are obtained and set as rainfall conditions; A first subsidence area is randomly selected from the plurality of subsidence areas, and the first area slope and first area height of the first subsidence area are obtained; Randomly select the first carrier material type from multiple carrier material types; Based on the average annual rainfall, average annual rainfall intensity, slope of the first region, and height of the first region, a migration and diffusion analysis is performed on the first carrier material type, and a first diffusion ratio is output. The diffusion ratios of multiple carrier material types in the first subsidence area are analyzed sequentially. The microbial concentration and carrier material concentration in the initial microbial remediation parameters are compensated according to the multiple diffusion ratios. The first compensated remediation parameter is output and added to the multiple compensated remediation parameters.

5. A method for soil ecological restoration in coal mining subsidence areas according to claim 4, characterized in that, Based on the average annual rainfall, average annual rainfall intensity, slope of the first region, and height of the first region, a migration and diffusion analysis is performed on the first carrier material type to output a first diffusion ratio, including: Using the first carrier material type as the search condition, based on the microbial remediation records of historical coal mining subsidence areas, a set of sample annual average rainfall, a set of sample annual average rainfall intensity, a set of sample area slope, and a set of sample area height are collected. The annual diffusion ratio of the first carrier material type under different sample annual average rainfall, sample annual average rainfall intensity, sample area slope, and sample area height is obtained, and a sample diffusion ratio set is constructed, wherein the annual diffusion ratio is a positive or negative value. Using the sample annual average rainfall set, sample annual average rainfall intensity set, sample area slope set, and sample area height set as inputs, and using the sample diffusion ratio set as supervision, a BP neural network is trained until the model converges to obtain a material diffusion analyzer. Using the material diffusion analyzer, migration and diffusion analysis is performed based on the average annual rainfall, average annual rainfall intensity, slope of the first region, and height of the first region, and the first diffusion ratio is output.

6. A method for soil ecological restoration in coal mining subsidence areas according to claim 4, characterized in that, The initial microbial remediation parameters, including microbial concentration and carrier material concentration, are compensated according to the multiple diffusion ratios to output the first compensated remediation parameters, including: By subtracting the multiple diffusion ratios from 1, multiple primary compensation coefficients are obtained; The first compensation and repair parameter is output by multiplying the multiple primary compensation coefficients by the carrier material concentration of the corresponding carrier material type and the corresponding microbial concentration of the corresponding carrier material type in the initial microbial remediation parameters.

7. A method for soil ecological restoration in coal mining subsidence areas according to claim 1, characterized in that, Based on the soil moisture in the aforementioned subsidence areas, the activity fluctuation of the functional microorganisms was analyzed. Based on the fluctuation analysis results, secondary compensation was performed on the aforementioned compensation and remediation parameters to obtain several optimized remediation parameters, including: A first subsidence area is randomly selected, and the average soil moisture value of the first subsidence area in the first year and the first compensation and repair parameters are obtained. The first microbial type is randomly selected from several microbial types; Based on the average soil moisture in the first year, the first reproduction rate of the first microbial type is predicted and obtained, wherein a first activity fluctuation analyzer is constructed based on a BP neural network to predict the reproduction rate. The ratio of the first expected reproduction rate of the first microbial type to the first reproduction rate is set as the secondary compensation coefficient. Several secondary compensation coefficients are obtained by sequential analysis. The first expected reproduction rate is the reproduction rate of the first microbial type under standard soil moisture. Based on the plurality of secondary compensation coefficients, the corresponding microbial concentration in the first compensation and repair parameters and the corresponding carrier material concentration of the corresponding microbial concentration are multiplied respectively to obtain the first optimized repair parameters, which are then added to the plurality of optimized repair parameters.

8. A soil ecological restoration system for coal mining subsidence areas, characterized in that, The steps for implementing a soil ecological restoration method for a coal mining subsidence area according to any one of claims 1 to 7 include: The initial remediation parameter construction module is used to detect and obtain the pollutant types and concentrations in the coal mining subsidence area, and to construct initial microbial remediation parameters by matching functional microorganisms and carrier materials using a microbial remediation database. The coal mining subsidence area segmentation module is used to segment the coal mining subsidence area according to the terrain features and obtain several subsidence areas; The migration and diffusion analysis module is used to perform migration and diffusion analysis on the carrier material based on the topographic information of the several subsidence areas and the rainfall conditions, and to compensate the initial microbial remediation parameters once based on the diffusion analysis results to obtain several compensated remediation parameters. The activity fluctuation analysis module is used to perform activity fluctuation analysis on the functional microorganisms according to the soil moisture of the several subsidence areas, and to perform secondary compensation on the several compensation and remediation parameters according to the fluctuation analysis results to obtain several optimized remediation parameters. The soil ecological restoration module is used to perform soil ecological restoration of the several subsidence areas according to the several optimized restoration parameters.

9. An electronic device, characterized in that, include: Memory, used to store computer software programs; A processor is configured to read and execute the computer software program, thereby implementing the steps of the soil ecological restoration method for coal mining subsidence areas as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that, The storage medium stores a computer software program, which, when executed by a processor, implements the steps of a soil ecological restoration method for coal mining subsidence areas as described in any one of claims 1 to 7.

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