Mining area low-cost ecological reconstruction method and system based on big data

Abstract

The invention relates to the technical field of ecological reconstruction, and discloses a mining area low-cost ecological reconstruction method and system based on big data, and the method comprises the steps: obtaining the ecological information and spatial information of each sub-area in a mining area, carrying out the calculation according to the ecological information, obtaining a soil ecological value, a land ecological value and a vegetation ecological value, and carrying out the calculation. The method comprises the following steps: weighting according to a soil ecological value, a land ecological value and a vegetation ecological value to obtain sub-region ecological values, carrying out mean value calculation according to all the sub-region ecological values to obtain an overall ecological value of a mining area, constructing a binary tree structure according to spatial information, and carrying out classification to determine node values of each layer, and calculating mining area spatial values of the sub-regions according to node values of each layer and corresponding node weight coefficients, summarizing the mining area spatial values of all the sub-regions and carrying out mean value calculation to obtain a mining area overall spatial value, and carrying out calculation according to the mining area overall ecological value and the mining area overall spatial value to obtain a mining area reconstruction value. The method can realize quantitative calculation of ecological data.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological restoration, and particularly to a low-cost ecological restoration method and system for mining areas based on big data. Background Art

[0002] At present, the destruction of the ecological environment in mining areas has become a global environmental issue. Although the exploitation of mineral resources plays an important role in promoting economic development, its large-scale development has led to a series of ecological problems such as soil degradation, vegetation damage, and soil erosion, seriously affecting regional ecological security and sustainable development. Therefore, carrying out ecological restoration work in mining areas is of great significance for the rational utilization of resources, ecological environment protection, and the sustainable development of the economy and society.

[0003] The existing ecological restoration technologies for mining areas mainly rely on empirical judgment and qualitative analysis, lacking scientific and quantitative data support and systematic methodology. For example, the ecological restoration plans for mining areas often only consider a single ecological factor, such as vegetation restoration and soil improvement, and fail to fully integrate multi-dimensional data such as soil, vegetation, and spatial information. In addition, the evaluation of the ecological restoration priority is mostly carried out through simple regional division, which cannot effectively reflect the difficulty and potential of ecological restoration between different sub-regions. This limitation leads to low accuracy in ecological restoration in mining areas, unreasonable resource allocation, and it is difficult to maximize ecological benefits.

[0004] In summary, the current ecological restoration technologies for mining areas are difficult to achieve efficient integration and quantitative calculation of ecological data in the analysis of multi-regional ecological differences, resulting in low accuracy of ecological restoration in mining areas and inability to maximize the effectiveness of ecological restoration. Summary of the Invention

[0005] The present invention provides a low-cost ecological restoration method and system for mining areas based on big data to solve the problems in the prior art that it is difficult to achieve efficient integration and quantitative calculation of ecological data, resulting in low accuracy of ecological restoration in mining areas and inability to maximize the effectiveness of ecological restoration.

[0006] In a first aspect, to solve the above technical problems, the present invention provides a low-cost ecological restoration method for mining areas based on big data, including:

[0007] Obtaining the ecological information and spatial information of each sub-region in the mining area;

[0008] Calculating respectively according to the ecological information to obtain a soil ecological value, a land ecological value, and a vegetation ecological value;

[0009] Performing weighting according to the soil ecological value, the land ecological value, and the vegetation ecological value to obtain a sub-region ecological value, and performing mean calculation according to all the sub-region ecological values to obtain an overall ecological value of the mining area;

[0010] Construct a binary tree structure for classification according to the spatial information to determine the node values of each layer, and calculate the mining area spatial value of the sub-region according to the node values of each layer and the corresponding node weight coefficients;

[0011] Summarize the mining area spatial values of all sub-regions and calculate the average value to obtain the overall mining area spatial value;

[0012] Calculate according to the overall ecological value of the mining area and the overall spatial value of the mining area to obtain the mining area reconstruction value;

[0013] Sort according to the magnitude of the mining area reconstruction value to obtain the regional reconstruction priority, and output the regional reconstruction priority.

[0014] Preferably, the ecological information includes: soil organic matter content, soil pH value, soil water content, soil density, land use type, land use intensity, precipitation, soil thickness, vegetation coverage, vegetation biomass, vegetation type and vegetation density;

[0015] The spatial information includes: land slope, altitude, land slope aspect and land undulation degree.

[0016] Preferably, the calculation according to the ecological information to obtain the soil ecological value includes:

[0017] Calculate the soil ecological value according to the soil organic matter content, the soil pH value, the soil density and the land use type to obtain the soil ecological value;

[0018] The soil ecological value is calculated by the following formula:

[0019] So i =W1·OM i +W2·(1-|pH i -pH opt |)+W3·H i +W4·(1-ρ i )

[0020] Wherein, So i is the soil ecological value of the i-th sub-region, OM i is the soil organic matter content of the i-th sub-region, pH i is the soil pH value of the i-th sub-region, pH opt is the optimal soil pH value, H i is the soil water content of the i-th sub-region, ρ i is the soil density of the i-th sub-region, and W1, W2, W3 and W4 are preset weight coefficients.

[0021] Preferably, calculating according to the ecological information to obtain a land ecological value, including:

[0022] Calculating a soil ecological value according to the soil organic matter content, the soil pH value, the soil density, and the land use type to obtain a soil ecological value;

[0023] The soil ecological value is calculated by the following formula:

[0024] So i = W1·OM i + W2·(1 - |pH i - pH opt |)+ W3·H i + W4·(1 - ρ i )

[0025] Wherein, So i is the soil ecological value of the i-th sub-region, OM i is the soil organic matter content of the i-th sub-region, pH i is the soil pH value of the i-th sub-region, pH opt is the optimal soil pH value, H i is the soil water content of the i-th sub-region, ρ i is the soil density of the i-th sub-region, and W1, W2, W3, and W4 are preset weight coefficients.

[0026] Preferably, calculating according to the ecological information to obtain a vegetation ecological value; including:

[0027] Calculating a vegetation ecological value according to the vegetation coverage, the vegetation biomass, the vegetation type, and the vegetation density to obtain a vegetation ecological value;

[0028] The vegetation ecological value is calculated by the following formula:

[0029]

[0030] Wherein, Ve i is the vegetation ecological value of the i-th sub-region, C i is the vegetation coverage of the i-th sub-region, B i is the vegetation biomass of the i-th sub-region, B ref is the preset maximum vegetation biomass, T i is the vegetation type of the i-th sub-region, D i is the vegetation density of the i-th sub-region, D ref is the preset maximum vegetation density value, and W9, W 11 , W 11 and W 12 are preset weight coefficients.

[0031] Preferably, the sub-region ecological value is obtained by weighted operation according to the soil ecological value, the land ecological value and the vegetation ecological value, and the overall ecological value of the mining area is obtained by calculating the mean value of all the sub-region ecological values, including:

[0032] According to the soil ecological value, the land ecological value, and the vegetation ecological value, the sub-region ecological value is obtained to get the sub-region ecological value;

[0033] The sub-region ecological value is obtained by calculation through the following formula:

[0034] E i = D1·So i + D2·La i + D3·Ve i

[0035] wherein, E i is the ecological value of the i-th sub-region, So i is the soil ecological value of the i-th sub-region, La i is the land ecological value of the i-th sub-region, Ve i is the vegetation ecological value of the i-th sub-region, and D1, D2 and D3 are the weight coefficients corresponding to the soil ecological value, the land ecological value and the vegetation ecological value respectively; according to the sub-region ecological value, the overall ecological value is calculated to obtain the overall ecological value of the mining area;

[0036] The overall ecological value of the mining area is obtained by calculation through the following formula:

[0037]

[0038] wherein, E is the overall ecological value of the mining area, E i is the ecological value of the i-th sub-region, and n is the total number of sub-regions in the mining area.

[0039] Preferably, the binary tree calculation is performed according to the spatial information to obtain the spatial value of the mining area, including:

[0040] According to the land slope, the first binary tree classification is performed to obtain the first-layer node value;

[0041] According to the altitude, the second binary tree classification is performed to obtain the second-layer node value;

[0042] According to the land aspect, the third binary tree classification is performed to obtain the third-layer node value;

[0043] According to the land undulation degree, the fourth binary tree classification is performed to obtain the fourth-layer node value;

[0044] Calculate the mining area space value of the sub-region according to the first-layer node value, the second-layer node value, the third-layer node value, and the fourth-layer node value, and obtain the mining area space value of the sub-region;

[0045] The mining area space value of the sub-region is calculated by the following formula:

[0046] Sp i = K1·N 1i + K2·N 2i + K3·N 3i + K4·N 4i

[0047] Wherein, Sp i is the mining area space value of the i-th sub-region, N 1i is the first-layer node value of the i-th sub-region, N 2i is the second-layer node value of the i-th sub-region, N 3i is the third-layer node value of the i-th sub-region, N 4i is the fourth-layer node value of the i-th sub-region, and K1, K2, K3, and K4 are the corresponding node weight coefficients.

[0048] Preferably, the method of summarizing the mining area space values of all sub-regions and performing mean value calculation to obtain the overall mining area space value includes:

[0049] The mining area space value is calculated by the following formula:

[0050]

[0051] Wherein, Sp is the overall mining area space value, Sp i is the mining area space value of the i-th sub-region, and n is the total number of mining area sub-regions.

[0052] Preferably, the method of calculating the mining area reconstruction value according to the overall mining area ecological value and the overall mining area space value includes:

[0053] The mining area reconstruction value is calculated by the following formula:

[0054]

[0055] Wherein, R is the mining area reconstruction value, F is the overall mining area ecological value, Sp is the mining area space value, and α, β, and γ are preset proportionality coefficients.

[0056] In a second aspect, the present invention provides a low-cost ecological reconstruction system for a mining area based on big data, including:

[0057] A data acquisition module for acquiring the ecological information and spatial information of each sub-region in the mining area;

[0058] An ecological value calculation module, which is used to calculate respectively according to the ecological information to obtain a soil ecological value, a land ecological value and a vegetation ecological value;

[0059] A mining area overall ecological value module, which is used to obtain a sub-region ecological value by weighting the soil ecological value, the land ecological value and the vegetation ecological value, and calculate the mean value according to all the sub-region ecological values to obtain the mining area overall ecological value;

[0060] A sub-region spatial value module, which is used to construct a binary tree structure for classification according to the spatial information to determine the node values of each layer, and calculate the mining area spatial value of the sub-region according to the node values of each layer and the corresponding node weight coefficients;

[0061] A mining area overall spatial value module, which summarizes the mining area spatial values of all sub-regions and calculates the mean value to obtain the mining area overall spatial value;

[0062] A mining area reconstruction value module, which is used to calculate according to the mining area overall ecological value and the mining area overall spatial value to obtain the mining area reconstruction value;

[0063] A priority output module, which is used to sort according to the magnitude of the mining area reconstruction value to obtain the regional reconstruction priority and output the regional reconstruction priority.

[0064] In a third aspect, the present invention further provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the big data-based low-cost ecological reconstruction method for mining areas described in any one of the above.

[0065] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the big data-based low-cost ecological reconstruction method for mining areas described in any one of the above.

[0066] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a low-cost ecological reconstruction method for mining areas based on big data. The method includes: obtaining the ecological information and spatial information of each sub-region in the mining area, respectively calculating according to the ecological information to obtain the soil ecological value, land ecological value, and vegetation ecological value, weighting according to the soil ecological value, the land ecological value, and the vegetation ecological value to obtain the sub-region ecological value, and performing mean calculation according to all the sub-region ecological values to obtain the overall ecological value of the mining area. According to the spatial information, a binary tree structure is constructed for classification to determine the node values of each layer, and the mining area spatial value of the sub-region is calculated according to the node values of each layer and the corresponding node weight coefficients. The mining area spatial values of all sub-regions are summarized and mean calculated to obtain the overall spatial value of the mining area. According to the overall ecological value and the overall spatial value of the mining area, calculations are performed to obtain the mining area reconstruction value, the regional reconstruction priorities are obtained by sorting according to the magnitudes of the mining area reconstruction values, and the regional reconstruction priorities are output.

[0067] In the present invention, the method includes: obtaining the ecological information and spatial information of each sub-region in the mining area, and performing step-by-step calculations on the ecological information to respectively obtain the soil ecological value, land ecological value, and vegetation ecological value. Then, the three types of ecological values are combined to calculate the overall ecological value of the mining area. At the same time, the spatial information is classified layer by layer through the binary tree algorithm to obtain the mining area spatial value. Finally, based on the overall ecological value and the mining area spatial value of the mining area, reconstruction calculations are performed, the mining area reconstruction value is output, the regional reconstruction priorities are obtained by sorting according to the magnitudes of the mining area reconstruction values, and the regional reconstruction priorities are output. The method can realize the quantitative calculation of ecological data and maximize the ecological restoration effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 is a schematic flowchart of a low-cost ecological reconstruction method for mining areas based on big data provided by the first embodiment of the present invention;

[0069] Figure 2 is a schematic diagram of a low-cost ecological reconstruction system for mining areas based on big data provided by the second embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0070] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0071] Refer to Figure 1, the first embodiment of the present invention provides a low-cost ecological reconstruction method for mining areas based on big data, including the following steps:

[0072] S11, obtain the ecological information and spatial information of each sub-region in the mining area;

[0073] S12, respectively calculate according to the ecological information to obtain the soil ecological value, land ecological value and vegetation ecological value;

[0074] S13, weight the soil ecological value, the land ecological value and the vegetation ecological value to obtain the sub-region ecological value, and calculate the average value according to all the sub-region ecological values to obtain the overall ecological value of the mining area;

[0075] S14, construct a binary tree structure for classification according to the spatial information to determine the node values of each layer, and calculate the mining area spatial value of the sub-region according to the node values of each layer and the corresponding node weight coefficients;

[0076] S15, summarize the mining area spatial values of all sub-regions and calculate the average value to obtain the overall spatial value of the mining area;

[0077] S16, calculate according to the overall ecological value of the mining area and the overall spatial value of the mining area to obtain the mining area reconstruction value;

[0078] S17, sort according to the size of the mining area reconstruction value to obtain the regional reconstruction priority, and output the regional reconstruction priority.

[0079] In step S11, obtain the ecological information and spatial information of each sub-region in the mining area, and the ecological information includes: soil organic matter content, soil pH value, soil water content, soil density, land use type, land use intensity, precipitation, soil thickness, vegetation coverage, vegetation biomass, vegetation type and vegetation density;

[0080] The spatial information includes: land slope, altitude, land aspect and land relief.

[0081] It should be noted that in step S11, obtaining the ecological information and spatial information of each sub-region in the mining area is the basis for realizing the ecological reconstruction of the mining area. The acquisition of ecological information includes soil organic matter content, soil pH value, soil water content, and soil density, and these parameters are mainly obtained through on-site sampling and laboratory analysis. In the mining area, according to the principle of grid sampling, evenly distributed sampling points are set in each sub-region, and soil samples with a depth of 0 to 20 cm are collected using standard soil sampling equipment. After the soil samples are air-dried, ground, and sieved, the soil organic matter content is determined in the laboratory using the potassium dichromate oxidation method or the high-temperature ignition method, and is expressed as a percentage. The soil pH value is determined by the solution method with a soil-water ratio of 1:5. A certain amount of dry soil sample is added to distilled water, stirred evenly, and after standing, the acidity and alkalinity are measured using a precision pH meter, and the value range is between 4 and 9. The soil water content is determined by the drying method. After the fresh soil sample is dried to a constant weight at 105 °C, the mass ratio of water evaporation is calculated to reflect the water content state of the soil. The soil density is calculated by weighing the mass of a soil sample with a known volume and calculating the mass density per unit volume, and the data result is expressed in grams per cubic centimeter.

[0082] The acquisition of land use type and land use intensity relies on remote sensing image data and geographic information system (GIS) technology. In the remote sensing image, combined with the surface cover characteristics of the mining area, the land use type is classified and identified, and the mining area is divided into categories such as bare land, waste dump, abandoned land, road, and construction land. The land use intensity is quantitatively evaluated through land cover change data and the degree of human activity interference, and is analyzed by combining on-site investigations and historical data to generate land use intensity grades. The precipitation data is provided by meteorological stations in the region, and is statistically analyzed based on long-term precipitation monitoring data according to the time period (annual average precipitation), and the results are recorded in millimeters. The soil thickness is obtained by the method of drilling measurement. Representative boreholes are selected in each sub-region for profile analysis, and the actual thickness of the soil layer is measured, and the data is directly recorded in centimeters or meters.

[0083] The vegetation coverage is calculated through remote sensing technology and the normalized difference vegetation index (NDVI). The satellite image data is used to analyze the reflectance of the vegetation. The value range of NDVI is between 0 and 1, and the higher the value, the better the vegetation coverage. The vegetation biomass is obtained by field sampling and measuring the dry weight of vegetation per unit area, and is estimated by combining biomass conversion factors to reflect the growth quality of the vegetation. The vegetation type is classified and identified by combining high-resolution remote sensing image recognition and on-site investigation, and the vegetation is divided into types such as trees, shrubs, and herbaceous vegetation. The vegetation density is calculated by counting the number of vegetation per unit area in the selected quadrats, and the vegetation density value per unit area is obtained, which reflects the distribution and growth density of the vegetation.

[0084] The acquisition of spatial information includes land slope, altitude, land aspect, and land relief, which are mainly extracted and calculated through digital elevation model (DEM) data and spatial analysis techniques. The land slope is obtained by raster calculation of elevation data to get the inclination angle of the slope surface, expressed in degrees. The altitude is directly obtained through remote sensing data or topographic survey data, representing the absolute elevation of the ground surface. The land aspect is extracted by analyzing topographic data to obtain the orientation of the slope surface, divided into different categories such as eastward, southward, westward, and northward. The land relief is calculated by the height difference between the highest and lowest points in the area, reflecting the degree of terrain height change, and the value is recorded in meters. These spatial parameters are quantitatively extracted through digital elevation data and GIS spatial analysis tools, which can accurately reflect the topographic conditions of the mining area.

[0085] The above data are combined through means such as remote sensing technology, geographic information system, on-site survey, and laboratory analysis, ensuring that the ecological information and spatial information of each sub-region are true, accurate, and quantifiable, providing scientific data support for subsequent ecological value calculation and reconstruction planning.

[0086] In step S12, according to the ecological information, calculations are respectively performed to obtain the soil ecological value, land ecological value, and vegetation ecological value;

[0087] According to the soil organic matter content, the soil pH value, the soil density, and the land use type, soil ecological value calculation is carried out to obtain the soil ecological value;

[0088] The soil ecological value is calculated through the following formula:

[0089] So i =W1·OM i +W2·(1 - |pH i -pH opt |)+W3·H i +W4·(1 - ρ i )

[0090] Where, So i is the soil ecological value of the i-th sub-region, OM i is the soil organic matter content of the i-th sub-region, pH i is the soil pH value of the i-th sub-region, pH opt is the optimal soil pH value, H i is the soil water content of the i-th sub-region, ρ i is the soil density of the i-th sub-region, and W1, W2, W3, and W4 are preset weight coefficients.

[0091] It should be noted that in step S12, the soil ecological value is calculated based on the ecological information. The required ecological information includes soil organic matter content, soil pH value, soil density, and soil water content. These parameters can comprehensively reflect the ecological characteristics of soil quality. During the calculation process, the part of the soil pH value adopts the form of (1 - |pH i - pH opt |). The main reason is that the deviation of the pH value reflects the difference between the soil acidity and alkalinity and the optimal state. The optimal pH value is neutral or close to neutral, such as 7.0. By calculating the absolute difference between the pH value and the optimal value and converting the result into a normalized form between 0 and 1, the quality of the soil acid-base state can be effectively quantified. The lower the score of the soil deviating from the optimal pH value, and the higher the score of the soil close to the optimal pH value, thus scientifically reflecting the ecological suitability of the soil.

[0092] In this step, in order to scientifically and reasonably determine the weight coefficients W1, W2, W3, and W4, values are assigned according to the importance and actual contribution of each parameter to the soil ecological function. The soil organic matter content W1 is assigned 0.4 because organic matter is an important indicator of soil fertility, directly affects plant growth and microbial activities, and is a key factor in measuring the soil ecological function, so it has the highest weight. The soil pH value W2 is assigned 0.3. The suitability of the acidity and alkalinity determines the survival conditions of plant roots and soil organisms. Deviating from the optimal pH value will significantly reduce the soil ecological quality, so its weight is relatively high. The soil water content W3 is assigned 0.2. The water content reflects the soil water supply capacity and affects the root growth of plants and the sustainability of ecosystem restoration. The soil density W4 is assigned 0.1. The soil density mainly reflects the permeability of the soil structure. Excessive density will hinder the penetration of water and air. Although important, its influence degree is relatively low, so it has the lowest weight. Through the above weight distribution, the contributions of each parameter to the soil ecological value are scientifically balanced, truly and accurately reflecting the soil quality and ecological function of each sub-region in the mining area, providing a quantitative basis and decision-making support for subsequent ecological restoration.

[0093] In step S12, according to the ecological information, calculations are carried out separately to obtain the soil ecological value, land ecological value, and vegetation ecological value, including:

[0094] According to the land use type, the land use intensity, the precipitation, and the soil thickness, the land ecological value is calculated to obtain the land ecological value;

[0095] The land ecological value is calculated through the following formula:

[0096] La i = W5·LU i + W6·LS i + W7·P i + W8·Ti

[0097] Among them, La i is the land ecological value of the i-th sub-region, LU i is the land use type of the i-th sub-region, LS i is the land use intensity of the i-th sub-region, P i is the precipitation of the i-th sub-region, T i is the soil thickness of the i-th sub-region, and W5, W6, W7, and W8 are preset weight coefficients.

[0098] It should be noted that the calculation of the land ecological value depends on four parameters: land use type, land use intensity, precipitation, and soil thickness. These four parameters play different roles in the ecosystem. Therefore, the distribution of the weight coefficients W5, W6, W7, and W8 is determined based on the importance and actual contribution of each parameter to the land ecological function.

[0099] The land use type reflects the current land use status and its degree of damage to the ecosystem, and is the core factor affecting the potential for land ecological restoration. Different types of land have different functional contributions to the ecosystem. For example, the restoration potential of bare land is higher than that of construction land, and that of abandoned land is between the two. Therefore, the weight of the land use type is relatively high, assigned a value of 0.4, to fully reflect its importance.

[0100] The land use intensity measures the degree of land development and utilization. The higher the development intensity, the greater the interference to the ecosystem and the greater the difficulty of restoration. Therefore, as an important indicator reflecting the impact of human activities, the land use intensity has a certain weight in the calculation, but is relatively lower than the land use type, assigned a value of 0.2.

[0101] As an important factor affecting the regional water supply capacity, precipitation is a natural support condition for land ecological functions. Sufficient precipitation is conducive to vegetation growth and soil restoration, and enhances the regional ecological restoration ability. Therefore, the importance of precipitation is second only to the land use type, assigned a value of 0.25 to reflect its key role in ecological restoration.

[0102] The soil thickness reflects the soil resource status and soil capacity of the land. The thicker the soil layer, the greater the potential for regional ecological restoration, the easier it is for plant roots to take root, and the stronger the soil stability. However, compared with other parameters, the influence of soil thickness is relatively limited, especially in some extremely disturbed areas, where soil restoration needs to cooperate with other factors. Therefore, a lower weight is assigned, determined to be 0.15.

[0103] In step S12, according to the ecological information, calculations are respectively performed to obtain the soil ecological value, land ecological value, and vegetation ecological value, including:

[0104] Calculate the vegetation ecological value based on the vegetation coverage, the vegetation biomass, the vegetation type, and the vegetation density to obtain the vegetation ecological value;

[0105] The vegetation ecological value is calculated through the following formula:

[0106]

[0107] where, Ve i is the vegetation ecological value of the i-th sub-region, C i is the vegetation coverage of the i-th sub-region, B i is the vegetation biomass of the i-th sub-region, B ref is the preset maximum vegetation biomass, T i is the vegetation type of the i-th sub-region, D i is the vegetation density of the i-th sub-region, D ref is the preset maximum vegetation density value, W9, W 11 、W 11 and W 12 are preset weight coefficients.

[0108] It should be noted that calculating the vegetation ecological value according to the ecological information, the required ecological information includes vegetation coverage, vegetation biomass, vegetation type, and vegetation density. These four parameters can comprehensively reflect the growth status, ecological functions, and restoration potential of vegetation. Through weighted calculation of these parameters, the vegetation ecological value of each sub-region can be scientifically quantified, providing data support for subsequent ecological reconstruction.

[0109] Vegetation coverage reflects the proportion of the ground surface covered by vegetation and is a key indicator for measuring the degree of regional vegetation restoration and soil and water conservation ability. The higher the vegetation coverage, the stronger the ecological functions of the vegetation. B9 is assigned a value of 0.3, reflecting its importance in calculating the vegetation ecological value. Vegetation biomass, as a key parameter for measuring the total amount of vegetation biological resources, is an important indicator of the growth quality of vegetation and reflects the carbon storage capacity of vegetation and the productivity of the ecosystem. When calculating, the vegetation biomass is standardized through where B i is the vegetation biomass of the i-th sub-region, B ref is the preset maximum vegetation biomass. Through standardization, the differences in vegetation resource amounts in different sub-regions can be effectively eliminated, ensuring the comparability of data and normalizing the results between 0 and 1. W 11 is assigned a value of 0.25, reflecting its important role in ecological functions.

[0110] Vegetation type, as an important parameter for measuring the structural and ecological function characteristics of vegetation, directly affects the calculation of the vegetation ecological value. In practical applications, the weight coefficient W of the vegetation type 11 is assigned a value of 0.2. This weight setting is based on the relative contribution degree of the vegetation type in the ecosystem. The vegetation type mainly reflects the structural characteristics of the vegetation, but its influence degree is lower than that of the vegetation coverage and vegetation biomass, because in ecological restoration, the coverage and biomass more directly reflect the quality and effect of vegetation restoration. At the same time, the influence of the vegetation type is slightly lower than that of the vegetation density and is mainly used as a supplementary index to reflect the diversity and functional characteristics of the vegetation community. Setting the weight coefficient to 0.2 not only reasonably reflects the ecological function contribution of the vegetation type but also avoids weakening the weights of other core parameters in the calculation, ensuring the scientificity and accuracy of the overall calculation results.

[0111] The vegetation density is calculated by normalization, where D i is the vegetation density of the i-th sub-region, and D ref is the preset maximum vegetation density value. The vegetation density reflects the distribution and resource amount of vegetation per unit area. The greater the density, the better the vegetation coverage effect and the higher the degree of ecological restoration. Through normalization calculation, the data range between different regions can be unified, the influence of regional differences can be eliminated, and the result can be converted into a value between 0 and 1, making the calculation results consistent. W 12 is assigned a value of 0.25, reflecting its importance to the vegetation ecological function. Especially in the initial stage of vegetation restoration, the density has a direct impact on the coverage and ecological function.

[0112] In step S13, the sub-region ecological value is obtained by weighting the soil ecological value, the land ecological value, and the vegetation ecological value, and the overall ecological value of the mining area is obtained by calculating the mean value of all the sub-region ecological values, including:

[0113] According to the soil ecological value, the land ecological value, and the vegetation ecological value, calculate the sub-region ecological value to obtain the sub-region ecological value;

[0114] The sub-region ecological value is calculated by the following formula:

[0115] E i = D1·So i + D2·La i + D3·Ve i

[0116] where E i is the i-th sub-region ecological value, So i is the i-th sub-region soil ecological value, La i is the i-th sub-region land ecological value, Vei is the vegetation ecological value of the i-th sub-region, and D1, D2, and D3 are the weight coefficients corresponding to the soil ecological value, land ecological value, and vegetation ecological value respectively;

[0117] According to the sub-region ecological value, calculate the total ecological value to obtain the overall ecological value of the mining area;

[0118] The overall ecological value of the mining area is calculated through the following formula:

[0119]

[0120] where E is the overall ecological value of the mining area, and E i is the ecological value of the i-th sub-region, and n is the total number of sub-regions in the mining area.

[0121] It should be noted that according to the soil ecological value, land ecological value, and vegetation ecological value, the overall ecological value of the mining area is calculated. First, the sub-region ecological value is calculated, and then the overall ecological value of the mining area is obtained by summarization. The sub-region ecological value is obtained by weighted summation of the soil ecological value, land ecological value, and vegetation ecological value. These three ecological values respectively represent the soil quality, land function, and vegetation restoration status, and are important indicators for evaluating the ecological level of the sub-region. By calculating the ecological value of each sub-region, its ecological status can be quantified, the ecological differences within the region can be reflected, and data support can be provided for subsequent ecological reconstruction.

[0122] In the setting of the weight coefficients, D1, D2, and D3 respectively represent the weights of the soil ecological value, land ecological value, and vegetation ecological value in the sub-region ecological calculation. The weights are determined according to the actual contribution degree of each ecological factor to the overall ecosystem. The soil ecological value reflects the characteristics of soil organic matter, pH value, water content, and density, etc., and is the basis for supporting vegetation growth and ecological restoration. Therefore, D1 is set to 0.4, reflecting the core position of the soil in the ecosystem. The land ecological value reflects the current land use situation, development intensity, and conditions such as natural water and soil thickness, etc., and is an important basis for evaluating land function and sustainable use. D2 is set to 0.3. The vegetation ecological value represents the vegetation coverage, biomass, type, and density in the region. Vegetation is the direct manifestation of ecological restoration. D3 is set to 0.3, which is equally important as the land ecological value, reflecting the key role of vegetation restoration in ecological reconstruction.

[0123] Next, the ecological values of all sub-regions are aggregated, and the overall ecological value of the mining area is obtained by taking the average. The overall ecological value of the mining area can comprehensively reflect the ecological restoration status of the entire mining area and evaluate the overall effect of ecological reconstruction. By reasonably setting the weight coefficients, it is ensured that the three major ecological elements of soil, land, and vegetation each play their roles in the calculation, highlighting the fundamental role of the soil while taking into account the actual contributions of land functions and vegetation restoration. Ultimately, the scientific quantification and evaluation of the ecological status of the mining area are achieved, providing an accurate basis for optimizing subsequent ecological reconstruction plans.

[0124] In step S14, according to the spatial information, a binary tree structure is constructed for classification to determine the node values of each layer, and the mining area spatial value of the sub-region is calculated based on the node values of each layer and the corresponding node weight coefficients, including:

[0125] Perform the first binary tree classification according to the land slope to obtain the node value of the first layer;

[0126] Perform the second binary tree classification according to the altitude to obtain the node value of the second layer;

[0127] Perform the third binary tree classification according to the land aspect to obtain the node value of the third layer;

[0128] Perform the fourth binary tree classification according to the land undulation to obtain the node value of the fourth layer;

[0129] Perform the calculation of the mining area spatial value according to the node value of the first layer, the node value of the second layer, the node value of the third layer, and the node value of the fourth layer to obtain the mining area spatial value of the sub-region;

[0130] The mining area spatial value of the sub-region is calculated through the following formula:

[0131] Sp i =K1·N 1i +K2·N 2i +K3·N 3i +K4·N 4i

[0132] Among them, Sp i is the mining area spatial value of the i-th sub-region, N 1i is the node value of the first layer of the i-th sub-region, N 2i is the node value of the second layer of the i-th sub-region, N 3i is the node value of the third layer of the i-th sub-region, N 4i is the node value of the fourth layer of the i-th sub-region, and K1, K2, K3, and K4 are the corresponding node weight coefficients.

[0133] It should be noted that in this step, the mining area spatial value of the sub-region is calculated through the method of binary tree hierarchical classification. Specifically, it includes four spatial parameters: land slope, altitude, land aspect, and land relief. These parameters are classified layer by layer, and different node values are determined. Combining the node values of each layer and their corresponding node weight coefficients, the mining area spatial value of the sub-region is calculated.

[0134] The corresponding node weight coefficient refers to the weight value assigned to each layer of spatial parameters in the binary tree classification calculation. These weight values are allocated according to the importance and influence degree of each parameter on the mining area spatial characteristics. Multiplying the weight coefficient by the node value ensures that the contributions of different spatial parameters can be scientifically quantified and reflected in the final overall mining area spatial value.

[0135] Specifically, the first-layer node value N 1i is a value obtained based on the land slope classification. The land slope is divided into two categories: high slope and low slope, and different weight coefficients are assigned to them. In the high slope area, due to the larger slope, the risk of soil erosion is higher, and the impact on spatial complexity is more significant. Therefore, the corresponding weight coefficient K1 is higher, set to 0.3; the weight coefficient of the low slope area is lower, set to 0.1. The specific value of the land slope remains unchanged, but its contribution will be adjusted according to the weight coefficient corresponding to the classification result, reflecting the importance of the slope.

[0136] The second-layer node value N 2i is a value obtained based on the altitude classification. The altitude is divided into two categories: high altitude and low altitude. In the high altitude area, the terrain is relatively high, and the construction difficulty and the difficulty of ecological restoration are relatively large. Therefore, the corresponding weight coefficient K2 is higher, set to 0.25; the conditions in the low altitude area are relatively flat, and the weight coefficient is set to 0.15. Here, "corresponding" means that after the classification of the specific altitude value, its weight coefficient is automatically adapted according to the classification result (high altitude or low altitude), ensuring that the impact of this node on the overall spatial value reasonably reflects the importance of altitude.

[0137] The third-layer node value N 3i is a value obtained based on the land aspect classification. The land aspect is divided into two categories: sunny slope and shady slope. In the sunny slope area, due to strong sunlight and high temperature, the ecological restoration conditions are relatively superior, and the weight coefficient K3 is higher, set to 0.2; in the shady slope area, the sunlight is weaker, and the impact on spatial conditions is smaller, and the weight coefficient is set to 0.1. The corresponding weight coefficient indicates that the node values under different aspects are weighted to make their influence degree conform to the actual terrain conditions.

[0138] The fourth-layer node value N 4iIt is a value obtained by classifying the land undulation degree. The land undulation degree is divided into two categories: high undulation and low undulation. The terrain complexity in the high undulation area is greater, the engineering construction difficulty is higher, and the restriction on ecological reconstruction is also stronger. Therefore, the weight coefficient K4 is higher and is set to 0.2; the weight coefficient in the low undulation area is set to 0.1. Through the corresponding allocation of the weight coefficients, the influence degree of the undulation degree node value is effectively quantified in the calculation.

[0139] Finally, the mining area spatial value of the i-th sub-region is obtained by summing the product of the node values of each layer and their corresponding weight coefficients. Specifically, each node value will automatically match with the weight coefficient corresponding to its classification during the calculation to ensure that the actual influence of each spatial parameter is accurately reflected in the overall calculation. This "corresponding" relationship enables the node values at different levels to be reasonably weighted according to their actual contributions to the spatial complexity, thereby effectively improving the scientificity and accuracy of the calculation results.

[0140] Through this method, the mining area spatial value of the sub-region can fully reflect the influence weights of each spatial parameter while retaining the characteristics of the original spatial numerical values.

[0141] In step S15, the mining area spatial values of all sub-regions are summarized and averaged to obtain the overall mining area spatial value, including:

[0142] The overall mining area spatial value is calculated by the following formula:

[0143]

[0144] Among them, Sp is the overall mining area spatial value, Sp i is the mining area spatial value of the i-th sub-region, and n is the total number of mining area sub-regions.

[0145] It should be noted that in step S15, the mining area spatial values of all sub-regions are summarized and averaged to obtain the overall mining area spatial value. The purpose of this calculation method is to comprehensively reflect the spatial characteristics and terrain conditions of the entire mining area by quantitatively evaluating the spatial information of each sub-region, ensuring that the results are accurate and operable.

[0146] In the specific implementation process, the mining area is divided into several sub-regions, and the mining area spatial value Sp of each sub-region iCalculated by the method based on binary tree classification in the previous steps. The overall spatial value Sp of the mining area is a value obtained by comprehensively considering spatial information such as land slope, altitude, land aspect, and land relief, representing the complexity of the spatial conditions and the difficulty of ecological restoration in each sub-region. In this step, the mining area spatial values of each sub-region are summarized to obtain the total of the sub-region spatial values. By calculating the ratio of the sub-region total to the number of sub-regions, the overall spatial value Sp of the mining area is obtained, that is, the average value of the spatial values of all sub-regions. Specifically, the calculation method of the overall spatial value of the mining area is to add up the mining area spatial values of each sub-region and then divide by the total number n of sub-regions, so as to quantitatively evaluate the overall spatial situation of the mining area.

[0147] In actual operation, the spatial value Sp of each sub-region i is calculated from spatial information parameters, including the node values of each layer such as slope, altitude, aspect, and relief. After these values are weighted with different weights within each sub-region, they can already reflect the spatial complexity and ecological reconstruction conditions of the sub-region. Therefore, by summarizing and averaging the spatial values of these sub-regions, the overall spatial conditions of the entire mining area can be evaluated as a whole without losing the details of spatial features, and the overall spatial value Sp of the mining area can be obtained. This method ensures the consistency of the influence of the spatial values of each sub-region on the final result, and at the same time eliminates the deviation caused by the too high or too low spatial value of an individual sub-region.

[0148] Finally, the overall spatial value Sp of the mining area can comprehensively reflect the overall topographic conditions and spatial complexity of the mining area, providing a quantitative basis for subsequent ecological reconstruction. If the overall spatial value of the mining area is relatively high, it indicates that the topographic complexity of the mining area is relatively large, the conditions such as slope, altitude, and relief are relatively harsh, and the difficulty of ecological reconstruction is relatively high; on the contrary, if the overall spatial value is relatively low, it indicates that the topographic conditions of the mining area are relatively gentle, and the feasibility and convenience of ecological restoration are better.

[0149] In step S16, according to the overall ecological value of the mining area and the overall spatial value of the mining area, calculations are performed to obtain the mining area reconstruction value, including:

[0150] The mining area reconstruction value is calculated through the following formula:

[0151]

[0152] where R is the overall reconstruction value of the mining area, E is the overall ecological value of the mining area, Sp is the spatial value of the mining area, and α, β, and γ are preset proportionality coefficients.

[0153] It is worth noting that in this step, the mining area reconstruction value is finally obtained based on the overall ecological value and the overall spatial value of the mining area. The overall ecological value of the mining area reflects the overall quality of the soil, land and vegetation ecology, while the overall spatial value of the mining area integrates the terrain slope, altitude, slope direction and undulation, reflecting the impact of spatial conditions on ecological reconstruction. In the calculation, the relationship between the overall ecological value and the overall spatial value of the mining area is integrated through the segmented balance method to ensure that the calculation result takes into account both the advantages and disadvantages of the ecological conditions and the constraints and coordination of spatial factors. Among them, the spatial value is calculated through The structure is processed mainly to introduce the difference between ecological value and spatial value. When the overall ecological value E and spatial value Sp of the mining area are close, the value of the difference term |E-Sp| is small, so that the denominator tends to 1. At this time, the weight of the spatial value has a greater impact, indicating that the ecological conditions and spatial conditions are more coordinated, and the overall potential of ecological reconstruction is high. When the difference between the two is large, the value of the difference term |E-Sp||E-Sp| increases, and the denominator increases accordingly, thereby punishing the spatial value to a certain extent, reflecting the limitation of the mismatch between ecological conditions and spatial conditions on the reconstruction effect. This processing method can dynamically balance the influence of ecological value and spatial value, making the calculation results of the mining area reconstruction value more scientific and reasonable.

[0154] The proportional coefficients α, β, and γ respectively represent the influence of the overall ecological value of the mining area, the overall spatial value of the mining area, and the difference between the two on the reconstruction value of the mining area. α is assigned a value of 0.5, because the overall ecological value of the mining area is the core indicator reflecting the ecological quality of the mining area and plays a decisive role in the reconstruction results; β is assigned a value of 0.4, and the overall spatial value of the mining area reflects the constraints of spatial conditions and provides auxiliary support for the reconstruction process, but the degree of influence is slightly lower than the ecological value; γ is assigned a value of 0.1, which is used to adjust the influence of the difference between the ecological value and the spatial value on the calculation results, ensuring that when the two are not coordinated, the contribution of the spatial value is appropriately reduced to reflect the actual difficulty of ecological reconstruction.

[0155] The calculated mining area reconstruction value R is a comprehensive evaluation result, and its value can be used to guide the priority division of mining area ecological reconstruction. The larger the value, the better the coordination between the ecological conditions and spatial conditions in the area, the higher the potential for ecological restoration, and the more suitable it is for ecological reconstruction to be prioritized; areas with smaller values indicate that the ecological conditions and spatial conditions do not match, and reconstruction is more difficult, requiring resource tilt or special treatment in planning. Through this method, the mining area reconstruction value can scientifically quantify the comprehensive effects of ecological and spatial factors, and provide an objective and accurate decision-making basis for mining area ecological reconstruction.

[0156] In step S17, the mining area reconstruction values are sorted to obtain regional reconstruction priorities, and the regional reconstruction priorities are output.

[0157] It should be noted that in step S17, the mine area reconstruction value refers to the overall reconstruction value of each independent mine area, rather than the sub-areas within the mine area. The specific operation is to sort the reconstruction values of multiple mine areas, and output the reconstruction priority based on the sorting result. The size of the mine area reconstruction value directly reflects the coordination degree between the overall ecological conditions and spatial conditions of the mine area, and quantitatively calculates the ecological reconstruction potential and reconstruction difficulty of the mine area through scientific calculation. The basis for sorting is the size of the mine area reconstruction value. The larger the reconstruction value, the more coordinated the ecological conditions and spatial conditions of the mine area, the higher the ecological restoration potential, and the relatively lower the reconstruction difficulty, making it suitable for prioritized ecological reconstruction; for mine areas with smaller reconstruction values, it indicates that there is a certain mismatch between the ecological conditions and spatial conditions, and the investment cost for reconstruction is relatively large, making it suitable for subsequent progressive governance.

[0158] In the specific implementation process, first, the reconstruction values of multiple mine areas are numerically sorted in descending order. This process can use computer programs for data processing. After inputting the reconstruction value of each mine area into the system, the numerical sorting is automatically completed, generating a list of mine area reconstruction priorities. When outputting the results, the sorted priority list can be visually displayed, for example, in the form of a table, to intuitively present the reconstruction priorities of different mine areas. For example, mine areas with a high priority ranking can be marked as "first-level priority reconstruction areas", indicating that these areas have a high ecological restoration potential and a high return on resource investment; mine areas with a low ranking are marked as "secondary reconstruction areas".

[0159] The calculation of the mine area reconstruction value is based on the coordination between the ecological value and the spatial value, comprehensively considering the ecological quality of soil, land, and vegetation, as well as the influence of spatial conditions (such as slope, elevation, aspect, and relief). Through scientific weight allocation and balance processing, the mine area reconstruction value can accurately reflect the overall ecological conditions and spatial conditions of each mine area, providing objective data support for sorting. In addition, the sorting result can also provide a clear implementation path for subsequent ecological restoration planning. For example, for mine areas with a high priority, ecological projects such as soil improvement and vegetation restoration can be carried out intensively. For mine areas with a low priority, preliminary surveys, monitoring, and planning reserves can be carried out to gradually promote the ecological reconstruction work.

[0160] Through this method, the sorting of the mine area reconstruction value can guide the development of ecological reconstruction work, ensure the accuracy and effectiveness of resource investment, maximize the ecological restoration benefits, and promote the high-quality sustainable restoration of the mine area ecosystem.

[0161] Referring to Figure 2 , the second embodiment of the present invention provides a low-cost ecological reconstruction system for mine areas based on big data, including:

[0162] A data acquisition module for acquiring the ecological information and spatial information of each sub-area in the mine area;

[0163] An ecological value calculation module, which is used to calculate respectively according to the ecological information to obtain a soil ecological value, a land ecological value and a vegetation ecological value;

[0164] A mining area overall ecological value module, which is used to weight the soil ecological value, the land ecological value and the vegetation ecological value to obtain a sub-region ecological value, and perform a mean value calculation according to all the sub-region ecological values to obtain a mining area overall ecological value;

[0165] A sub-region spatial value module, which is used to construct a binary tree structure for classification according to the spatial information to determine the node values of each layer, and calculate the mining area spatial value of the sub-region according to the node values of each layer and the corresponding node weight coefficients;

[0166] A mining area overall spatial value module, which summarizes the mining area spatial values of all sub-regions and performs a mean value calculation to obtain a mining area overall spatial value;

[0167] A mining area reconstruction value module, which is used to calculate according to the mining area overall ecological value and the mining area overall spatial value to obtain a mining area reconstruction value;

[0168] A priority output module, which is used to sort according to the magnitudes of the mining area reconstruction values to obtain a regional reconstruction priority, and output the regional reconstruction priority.

[0169] It should be noted that a low-cost ecological reconstruction system for a mining area based on big data provided by an embodiment of the present invention is used to execute all the process steps of a low-cost ecological reconstruction method for a mining area based on big data in the above embodiment. The working principles and beneficial effects of the two correspond one by one, and thus will not be elaborated here.

[0170] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a low-cost ecological reconstruction program for a mining area based on big data. When the processor executes the computer program, the steps in each of the above embodiments of the low-cost ecological reconstruction method for a mining area based on big data are implemented, such as Figure 1 the step S11 shown. Or, when the processor executes the computer program, the functions of each module / unit in each of the above device embodiments are implemented, such as the mining area overall ecological value module.

[0171] Exemplarily, the computer program can be divided into one or more modules / units. The one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the electronic device.

[0172] The electronic device may be a computing device such as a desktop computer, notebook, palm computer, and smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above components are only examples of the electronic device and do not constitute a limitation on the electronic device. It may include more or fewer components than the above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, a bus, etc.

[0173] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the electronic device and connects various parts of the entire electronic device using various interfaces and lines.

[0174] The memory can be used to store the computer program and / or module. The processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and by calling the data stored in the memory. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one magnetic disk storage device, flash device, or other volatile solid-state storage devices.

[0175] Among them, if the modules / units integrated in the electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0176] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0177] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. It is particularly pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A low-cost ecological reconstruction method for mining areas based on big data, characterized in that, Executed by a computer, including: Obtaining ecological information and spatial information of each sub-region in the mining area; Calculating respectively according to the ecological information to obtain soil ecological value, land ecological value and vegetation ecological value; Weighting according to the soil ecological value, the land ecological value and the vegetation ecological value to obtain the sub-region ecological value, and calculating the average value according to all the sub-region ecological values to obtain the overall ecological value of the mining area; According to the spatial information, constructing a binary tree structure for classification to determine the node values of each layer, and calculating the mining area spatial value of the sub-region according to the node values of each layer and the corresponding node weight coefficients; Summarizing the mining area spatial values of all sub-regions and calculating the average value to obtain the overall spatial value of the mining area; Calculating according to the overall ecological value of the mining area and the overall spatial value of the mining area to obtain the mining area reconstruction value; Sorting according to the size of the mining area reconstruction value to obtain the regional reconstruction priority, and outputting the regional reconstruction priority.

2. The low-cost ecological reconstruction method for mining areas based on big data according to claim 1, characterized in that The ecological information includes: soil organic matter content, soil pH value, soil water content, soil density, land use type, land use intensity, precipitation, soil thickness, vegetation coverage, vegetation biomass, vegetation type and vegetation density; The spatial information includes: land slope, elevation, land aspect and land relief.

3. The method for low-cost ecological reconstruction of mining areas based on big data according to claim 2, wherein The calculating according to the ecological information to obtain the soil ecological value includes: Calculating the soil ecological value according to the soil organic matter content, the soil pH value, the soil density and the land use type to obtain the soil ecological value; The soil ecological value is calculated by the following formula: So i = W1·OM i + W2·(1 - |pH i - pH opt |) + W3·H i + W4·(1 - ρ i ) where, So i is the soil ecological value of the i-th sub-region, OM i is the soil organic matter content of the i-th sub-region, pH i is the soil pH value of the i-th sub-region, pH opt is the optimal soil pH value, H i is the soil water content of the i-th sub-region, ρ i is the soil density of the i-th sub-region, and W1, W2, W3, and W4 are preset weight coefficients.

4. The method for low-cost ecological reconstruction of mining areas based on big data according to claim 2, wherein, The calculating according to the ecological information to obtain the land ecological value includes: Calculating the land ecological value according to the land use type, the land use intensity, the precipitation and the soil thickness to obtain the land ecological value; The land ecological value is calculated by the following formula: La i = W5·LU i + W6·LS i + W7·P i + W8·T i Among them, La i is the land ecological value of the i-th sub-region, LU i is the land use type of the i-th sub-region, LS i is the land use intensity of the i-th sub-region, P i is the precipitation of the i-th sub-region, T i is the soil thickness of the i-th sub-region, and W5, W6, W7, and W8 are preset weight coefficients.

5. The method for low-cost ecological reconstruction of mining areas based on big data according to claim 2, characterized in that, The calculating according to the ecological information to obtain the vegetation ecological value includes: Calculating the vegetation ecological value according to the vegetation coverage, the vegetation biomass, the vegetation type and the vegetation density to obtain the vegetation ecological value; The vegetation ecological value is calculated by the following formula: Among them, Ve i is the vegetation ecological value of the i-th sub-region, C i is the vegetation coverage of the i-th sub-region, B i is the vegetation biomass of the i-th sub-region, B ref is the preset maximum vegetation biomass, T i is the vegetation type of the i-th sub-region, D i is the vegetation density of the i-th sub-region, D ref is the preset maximum vegetation density value, W9, W 11 、W 11 and W 12 are preset weight coefficients.

6. The method for low-cost ecological reconstruction of mining areas based on big data according to claim 1, characterized in that The weighting operation according to the soil ecological value, the land ecological value and the vegetation ecological value to obtain the sub-region ecological value, and calculating the average value according to all the sub-region ecological values to obtain the overall ecological value of the mining area includes: The sub-region ecological value is calculated by the following formula: E i = D1·So i + D2·La i + D3·Ve i Among them, E i is the ecological value of the i-th sub-region, So i is the soil ecological value of the i-th sub-region, La i is the land ecological value of the i-th sub-region, Ve i is the vegetation ecological value of the i-th sub-region, and D1, D2, and D3 are the weight coefficients corresponding to the soil ecological value, land ecological value, and vegetation ecological value respectively; The overall ecological value of the mining area is calculated by the following formula: Among them, E is the overall ecological value of the mining area, and E i is the ecological value of the i-th sub-region, and n is the total number of sub-regions in the mining area.

7. The method for low-cost ecological reconstruction of mining areas based on big data according to claim 2, characterized in that, The constructing a binary tree structure for classification according to the spatial information to determine the node values of each layer, and calculating the mining area spatial value of the sub-region according to the node values of each layer and the corresponding node weight coefficients includes: Conducting the first binary tree classification according to the land slope to obtain the node value of the first layer; Conducting the second binary tree classification according to the elevation to obtain the node value of the second layer; Conducting the third binary tree classification according to the land aspect to obtain the node value of the third layer; Conducting the fourth binary tree classification according to the land relief to obtain the node value of the fourth layer; Calculate the mining area spatial value of the sub-region based on the first-layer node value, the second-layer node value, the third-layer node value, and the fourth-layer node value, to obtain the mining area spatial value of the sub-region; The mining area spatial value of the sub-region is calculated through the following formula: Sp i = K1·N 1i + K2·N 2i + K3·N 3i + K4·N 4i Among them, Sp i is the mining area space value of the i-th sub-region, N 1i is the first-layer node value of the i-th sub-region, N 2i is the second-layer node value of the i-th sub-region, N 3i is the third-layer node value of the i-th sub-region, N 4i is the fourth-layer node value of the i-th sub-region, and K1, K2, K3, and K4 are the corresponding node weight coefficients.

8. The method for low-cost ecological reconstruction of mining areas based on big data according to claim 1, wherein, Summarize the mining area spatial values of all sub-regions and perform mean calculation to obtain the overall mining area spatial value, including: The overall mining area spatial value is calculated through the following formula: Among them, Sp is the overall spatial value of the mining area, and Spi i is the spatial value of the mining area of the i-th sub-region, and n is the total number of sub-regions in the mining area.

9. The low-cost ecological reconstruction method for mining areas based on big data according to claim 1, characterized in that Calculate based on the overall mining area ecological value and the overall mining area spatial value to obtain the mining area reconstruction value, including: The mining area reconstruction value is calculated through the following formula: Where R is the mining area reconstruction value, E is the overall mining area ecological value, Sp is the overall mining area spatial value, and α, β, and γ are preset proportionality coefficients.

10. A low-cost ecological reconstruction system for mining areas based on big data, characterized in that, Including: A data acquisition module for acquiring the ecological information and spatial information of each sub-region in the mining area; An ecological value calculation module for calculating the soil ecological value, land ecological value, and vegetation ecological value respectively according to the ecological information; An overall mining area ecological value module for obtaining the sub-region ecological value by weighting the soil ecological value, the land ecological value, and the vegetation ecological value, and calculating the overall mining area ecological value by performing mean calculation on all the sub-region ecological values; A sub-region spatial value module for constructing a binary tree structure for classification according to the spatial information to determine the node values of each layer, and calculating the mining area spatial value of the sub-region according to the node values of each layer and the corresponding node weight coefficients; An overall mining area spatial value module that summarizes the mining area spatial values of all sub-regions and performs mean calculation to obtain the overall mining area spatial value; A mining area reconstruction value module for calculating the mining area reconstruction value according to the overall mining area ecological value and the overall mining area spatial value; A priority output module for sorting according to the magnitude of the mining area reconstruction value to obtain the regional reconstruction priority and outputting the regional reconstruction priority.

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