Mining area ecological environment assessment method, device, equipment and medium

Abstract

The invention relates to the technical field of data processing, in particular to a mining area ecological environment assessment method and device, equipment and a medium. The method comprises the steps that geological distribution and influence factor values are accurately obtained, the habitat degradation index is calculated to determine the threat factor, then the sensitivity of each land utilization type to the threat factor and the influence degree of the influence factor to the habitat are analyzed, and comprehensive evaluation of the ecological environment of the mining area is achieved. By loading and training the initial InVEST model, the customized target model is obtained, the actual situation of the mining area can be reflected more accurately, and the evaluation precision is improved. And finally, inputting the geological distribution and the influence factor value into the target model to obtain an evaluation result, and obtaining the ecological data of the mining area without field measurement and sampling analysis, so that the efficiency of evaluating the habitat of the mining area is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, equipment and medium for evaluating the ecological environment of a mining area. Background Art

[0002] Mining area ecological and environmental assessment is a key research area in the field of mine development and ecological protection. With the continuous development of mineral resources, the sustainability of mining area ecosystems is gaining increasing attention. To achieve ecological restoration and environmental protection in mining areas, scientific assessments of mining area ecosystems are necessary. Currently, mining area ecological and environmental assessments have become a core component of mine ecological management. Their results not only guide mine ecological restoration projects but also provide data support for policymaking, thereby promoting green development in the mining industry. The development of mining area ecological and environmental assessment technology is of great significance to the protection of mining area ecosystems.

[0003] Existing technologies primarily rely on traditional on-site assessment methods to obtain mining area ecological data through field measurements and sampling analysis. However, these traditional assessment methods are time-consuming and labor-intensive, making them difficult to meet the needs of rapid assessments in large-scale mining areas. Summary of the Invention

[0004] In order to improve the efficiency of mining area habitat assessment, the present application provides a mining area ecological environment assessment method, device, equipment and medium.

[0005] In the first aspect, the present application provides a mining area ecological environment assessment method, which adopts the following technical solutions: A mining area ecological environment assessment method, comprising: Obtaining the geological distribution and influencing factor values of the mining area to be evaluated in the current period, wherein the geological distribution is the distribution corresponding to each land use type; Calculating the habitat degradation index corresponding to each land use type, and determining the habitat threat factor based on the habitat degradation index; Determine the sensitivity of each land use type to each habitat threat factor, and determine the degree of impact of each influencing factor on the ecological environment; Loading an initial InVEST model, and training the initial InVEST model based on each sensitivity and the influence of each factor to obtain a target InVEST model; The geological distribution and the influencing factor values are input into the target InVEST model, and the output of the target InVEST model is obtained to obtain an evaluation result of the mining area to be evaluated.

[0006] By employing this technical solution, a comprehensive assessment of the mining area's ecological environment was achieved by accurately acquiring geological distribution and influencing factor values, calculating a habitat degradation index to identify threat factors, and then analyzing the sensitivity of each land use type to threat factors and the extent to which these factors impacted the habitat. By loading and training the initial InVEST model, a customized target model was generated that more accurately reflects the actual conditions of the mining area and improves assessment accuracy. Finally, the geological distribution and influencing factor values were input into the target model to generate assessment results, eliminating the need for field measurements and sampling analysis to obtain mining area ecological data, thereby improving the efficiency of mining area habitat assessments.

[0007] In a possible implementation, the calculation of the habitat degradation index corresponding to each land use type includes: Obtaining a target grid cell corresponding to the land use type, and obtaining an interference factor value and an evaluation index value corresponding to each target grid cell, wherein the interference factor value includes a mining area and a mining depth, and the evaluation index value includes a corresponding value of vegetation coverage, species richness, soil quality, and carbon sequestration capacity; Determine the evaluation weight corresponding to each evaluation index value, and calculate the suitability corresponding to each target grid cell based on the evaluation index value and the corresponding evaluation weight; Acquire historical type data corresponding to the land use type, wherein the historical type data includes historical interference factor values and historical impact ranges corresponding to historical periods; Determining a type influence coefficient and a type distance attenuation coefficient corresponding to the land use type based on the historical type data and the interference factor value; Based on the suitability, the type influence coefficient and the type distance attenuation coefficient, the habitat degradation index corresponding to the land use type is calculated.

[0008] In a possible implementation, determining the type influence coefficient and the type distance attenuation coefficient corresponding to the land use type based on the historical type data and the interference factor value includes: Obtaining an initial regression model, and training the initial regression model based on the historical type data to obtain a regional regression model; The interference factor value is input into the regional regression model, and the output of the regional regression model is obtained to obtain the type influence coefficient and the type distance attenuation coefficient corresponding to the land use type.

[0009] In a possible implementation, calculating the habitat degradation index corresponding to the land use type based on the suitability, the type influence coefficient, and the type distance attenuation coefficient includes: Obtaining a target distance corresponding to each target grid cell, where the target distance is a straight-line distance between the target grid cell and the center of the mining activity; Substitute the suitability of each target grid cell, the type influence coefficient, and the type distance attenuation coefficient into Formula 1 to calculate the habitat degradation index corresponding to each target grid cell, where Formula 1 is: , where k is the adjustment coefficient; Based on the habitat degradation index of each target grid cell, the habitat degradation index corresponding to the land use type is calculated.

[0010] In a possible implementation, the process of determining the sensitivity of each land use type to each habitat threat factor and calculating the sensitivity of each land use type to each habitat threat factor includes: Obtaining a grid distance between a target grid cell of the land use type and a threat grid cell of the habitat threat factor, and determining an impact distance corresponding to each threat grid cell based on a type distance attenuation coefficient of each threat grid cell; Based on the impact distance, a grid distance that is not greater than the corresponding impact distance is screened out from the grid distances as a marked distance, and a target grid cell corresponding to the marked distance is determined as a marked grid cell, and a threat grid cell corresponding to the marked distance is determined as a marked threat grid cell; Establish the spatial attenuation coefficient corresponding to each marked threat grid cell, the spatial attenuation coefficient = , where λ is the decay rate and d is the marker distance; Obtaining the threat weight corresponding to the habitat threat factor; Substitute the habitat degradation index, spatial attenuation coefficient, and threat weight of each marked threat grid unit into Formula 2, and substitute the total area corresponding to the land use type into Formula 2 to calculate the sensitivity of the land use type to the habitat threat factor. Formula 2 is: , where n is the number of marked threat grid cells.

[0011] In one possible implementation, the influencing factors include regional GDP, total population at the end of the year, population density, night light index, annual average temperature, annual average precipitation, NDVI, and mineral distribution. The process of determining the degree of influence of each influencing factor on the ecological environment includes: Standardizing the impact factor value to obtain a standard impact factor value; Determine the spatial attenuation function corresponding to each mineral resource and determine the basic weight corresponding to the influencing factors; Obtaining the spatial effect coefficient of the mining area to be evaluated; Determine a time decay factor corresponding to the impact factor, where the time decay factor is a dynamic factor or a static factor; Substitute the standard impact factor value, spatial attenuation function, basic weight, spatial effect coefficient and time attenuation factor into Formula 3 to calculate the factor influence degree of the impact factor corresponding to the ecological environment. Formula 3 is factor influence degree = standard impact factor value * (basic weight + spatial attenuation function * spatial effect coefficient) * time attenuation factor.

[0012] In a possible implementation, the initial InVEST model is trained based on each sensitivity and the influence of each factor to obtain a target InVEST model, including: Acquire historical mining area data, including historical geological distribution, historical impact factor values, historical sensitivity, historical factor impact degree, and historical assessment results corresponding to each historical period; Based on the geological distribution and the influencing factor values, basic parameters of the initial InVEST model are set to obtain a first InVEST model; Based on each sensitivity and each factor influence degree, setting the sensitivity parameters and factor influence degree parameters of the first InVEST model to obtain a second InVEST model; The second InVEST model is iteratively trained based on the historical mining area data to obtain a target InVEST model.

[0013] In a second aspect, the present application provides a mining area ecological environment assessment device, which adopts the following technical solution: A mining area ecological environment assessment device, comprising: An acquisition module is used to obtain the geological distribution and influencing factor values of the mining area to be evaluated in the current period, wherein the geological distribution is the distribution corresponding to each land use type; A calculation module is used to calculate the habitat degradation index corresponding to each land use type, and determine the habitat threat factor based on the habitat degradation index; Determination module, used to determine the sensitivity of each land use type to each habitat threat factor, and determine the degree of influence of each influencing factor on the ecological environment; A training module is used to load an initial InVEST model and train the initial InVEST model based on each sensitivity and the influence of each factor to obtain a target InVEST model; An input module is used to input the geological distribution and the influencing factor values into the target InVEST model, and obtain the output of the target InVEST model to obtain an evaluation result of the mining area to be evaluated.

[0014] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device, comprising: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the method described in any one of the first aspects above.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium, comprising: storing a computer program that can be loaded by a processor and execute any one of the methods described in the first aspect above.

[0016] In summary, this application has the following beneficial technical effects: By accurately acquiring geological distribution and influencing factor values, calculating the habitat degradation index to identify threat factors, and then analyzing the sensitivity of various land use types to threat factors and the extent of the impact of these factors on habitats, a comprehensive assessment of the mining area's ecological environment is achieved. By loading and training the initial InVEST model, a customized target model is generated that more accurately reflects the actual conditions of the mining area and improves assessment accuracy. Finally, the geological distribution and influencing factor values are input into the target model to generate assessment results, eliminating the need for field measurements and sampling analysis to obtain mining area ecological data, thereby improving the efficiency of mining area habitat assessments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a mining area ecological environment assessment method provided in an embodiment of the present application; Figure 2 This is a block diagram of a mining area ecological environment assessment device provided in an embodiment of the present application; Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The following is combined with Figure 1 -Attached Figure 3 This application is described in further detail.

[0019] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0020] In order to facilitate understanding of the technical solutions proposed in this application, several elements that will be introduced in the description of this application are first introduced here. It should be understood that the following introduction is only for the convenience of understanding these elements, so as to understand the content of the embodiments of this application, and does not necessarily cover all possible situations.

[0021] The term "habitat" was first coined and defined in 1917. It originally described an area where organisms can survive, specifically the space where individual or grouped organisms undergo life succession. Habitats are attached to land, so specific land types are referred to as habitats. Habitat quality refers to the ability of an ecosystem to ensure the continued reproduction of species. Habitat quality assessment investigates the impact of changes in ecological structure on the survival of organisms within a habitat. Currently, methods for assessing habitat quality primarily rely on field survey indicators and parameter-based models. Traditional methods are more suitable for studying small areas and provide more accurate and reliable results. However, with the rapid development of 3S technology, scholars both domestically and internationally are conducting assessments based on land use data and further adjusting parameters in conjunction with functional models. Commonly used models include the SolVEST model, the MaxEnt model, the HSI model, and the InVEST model. Compared to previous assessment methods, the InVEST model offers the advantages of reduced data requirements and highly visualized results. Existing technologies have assessed and predicted habitat quality at the Three Gorges Basin scale, evaluated urban habitat degradation based on land use data, evaluated wetland evolution and its impact on habitat quality, and studied habitat quality at the urban fringe. Previous research has focused on specific study areas, such as provinces, cities, counties, river basins, and national parks. These studies have primarily examined the spatiotemporal variation in habitat quality, analysis of habitat degradation, and the response of land use changes to habitat quality. However, further research is needed to analyze habitat quality changes and the factors influencing them in key mining areas.

[0022] The present application embodiment provides a method for evaluating the ecological environment of a mining area, such as Figure 1As shown, the method provided in the embodiment of the present application is performed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present application. The method includes steps S101 to S105, wherein: Step S101: Obtain the geological distribution and influencing factor values of the mining area to be evaluated in the current period.

[0023] The geological distribution refers to the distribution of each land use type. Specifically, the geological distribution refers to the spatial distribution of different land use types (such as cultivated land, forest land, construction land, etc.) within the mining area.

[0024] Influencing factors are various factors that affect the ecological environment of the mining area, which may include natural factors (such as annual average temperature, annual average precipitation, and mineral distribution) and human factors (such as regional GDP, total population at the end of the year, population density, night light index, etc.).

[0025] Specifically, the electronic device connects to a satellite remote sensing data platform data source to obtain satellite images of the mining area to be assessed. Using image recognition and classification software (such as ENVI and Erdas), the satellite images are processed to identify different land use types (such as cultivated land, forest land, construction land, and mining land), thereby obtaining the corresponding distribution of each land use type. Influencing factor values, such as regional GDP, year-end total population, population density, night light index, annual average temperature, annual average precipitation, NDVI (Normalized Difference Vegetation Index), and mineral distribution data, are obtained from statistical department databases and environmental monitoring station databases. The current period is a specific time period set for assessment, such as a year or a quarter.

[0026] Step S102: Calculate the habitat degradation index corresponding to each land use type, and determine the habitat threat factor based on the habitat degradation index.

[0027] The Habitat Degradation Index (HDI) indicates the degree to which a land use type is threatened by threatening factors. Higher values indicate a higher degree of threat to the land use type and a greater likelihood of causing habitat degradation. The degree of habitat degradation for a particular land use type is determined by habitat threat factors that threaten the ecological environment in mining areas, leading to habitat degradation.

[0028] Specifically, ArcGIS can be used in combination with relevant ecological model plug-ins to calculate the habitat degradation index corresponding to each land use type based on the obtained land use type distribution and influencing factor values, using an algorithm that takes interference factors and evaluation indicators into account.

[0029] The calculated habitat degradation index is then analyzed, and a preset threshold value (which may be adjusted based on actual circumstances and is not limited in this embodiment of the present application) is obtained from the mining area database corresponding to the mining area to be assessed. Land use types exceeding the threshold value are then identified as habitat threat factors. For example, if the habitat degradation index for mining land is high, then the mining land is identified as a habitat threat factor.

[0030] In this embodiment, the habitat degradation index corresponding to each land use type is calculated. The calculation process of the habitat degradation index of each land use type includes: Obtain the target grid cell corresponding to the land use type, and obtain the interference factor value and evaluation index value corresponding to each target grid cell. The interference factor value includes the mining area and mining depth, and the evaluation index value includes the corresponding values of vegetation coverage, species richness, soil quality, and carbon sequestration capacity. Determine the evaluation weight corresponding to each evaluation index value, and calculate the suitability corresponding to each target grid cell based on the evaluation index value and the corresponding evaluation weight; Obtain historical type data corresponding to the land use type, the historical type data including historical interference factor values and historical impact ranges corresponding to historical periods; Based on historical type data and interference factor values, determine the type impact coefficient and type distance attenuation coefficient corresponding to the land use type; Based on suitability, type impact coefficient and type distance attenuation coefficient, the habitat degradation index corresponding to the land use type is calculated.

[0031] Among them, suitability reflects the suitability of the target grid unit for biological survival and maintenance of ecosystem functions, and the higher the value, the more suitable it is.

[0032] Specifically, satellite images of the mining area to be assessed can be obtained and processed using image recognition and classification software (such as ENVI and Erdas) to identify different land use types (such as cultivated land, forest land, construction land, and mining land). Target grid cells corresponding to the designated land use type are then selected. For example, assuming the designated land use type is mining land, all grid cells belonging to mining land are selected as target grid cells. Furthermore, for each target grid cell, the corresponding interference factor value can be retrieved from the mining area database, such as by querying the mining area and mining depth data within that grid cell. Similarly, data recorded by vegetation coverage monitoring equipment is read from the mining area database to obtain vegetation coverage values, species richness values, soil quality-related values, and carbon sequestration capacity values. At regular intervals, personnel store the collected mining area, mining depth, species richness values, soil quality-related values, and carbon sequestration capacity values corresponding to each mining location on an electronic device for data synchronization and storage. Simultaneously, the electronic device is connected to the vegetation coverage monitoring equipment to directly read the monitored locations and data.

[0033] Furthermore, by invoking a pre-defined weighting model (such as one using the analytic hierarchy process or expert scoring method), the corresponding evaluation weight for each evaluation indicator is determined based on the land use type. For example, species richness is given a higher weight for land use types important for ecological protection. The evaluation indicator value for each target grid cell is multiplied by the corresponding evaluation weight, and the products of each indicator are added together to obtain the suitability value for each target grid cell. For example, if the vegetation cover weight is 0.3, the value is 0.6; the species richness weight is 0.4, the value is 0.5; the soil quality weight is 0.2, the value is 0.7; and the carbon sequestration capacity weight is 0.1, the value is 0.8. Then, the suitability for this grid cell is 0.3 × 0.6 + 0.4 × 0.5 + 0.2 × 0.7 + 0.1 × 0.8.

[0034] Furthermore, by accessing the mining area database and filtering out historical data based on land use type and a specified historical period (e.g., the past 10 years), we can extract historical interference factor values (e.g., mining area and mining depth data for different years) and historical impact ranges (e.g., the extent of the impact of the target grid cell on the surrounding environment in the past, which may be reflected in geographic boundary data or spatial distribution of impact levels).

[0035] Furthermore, based on historical type data and interference factor values, the type impact coefficient and type distance attenuation coefficient corresponding to the land use type can be determined, and based on the suitability, type impact coefficient and type distance attenuation coefficient, the habitat degradation index corresponding to the land use type can be calculated.

[0036] Specifically, in this embodiment, based on the historical type data and the interference factor value, the type influence coefficient and the type distance attenuation coefficient corresponding to the land use type are determined, including: Obtain an initial regression model, and train the initial regression model based on historical type data to obtain a regional regression model; The interference factor value is input into the regional regression model, and the output of the regional regression model is obtained to obtain the type influence coefficient and type distance attenuation coefficient corresponding to the land use type.

[0037] Specifically, a pre-set initial regression model (such as a linear regression model, a logistic regression model, or other common regression model types) is retrieved from the mining area database. The previously acquired historical data (including historical interference factor values corresponding to historical periods and historical impact ranges) is then organized and preprocessed. This includes checking data integrity, addressing missing values, and normalizing the data to ensure it meets the requirements for model training.

[0038] Furthermore, using data processing and analysis software (such as tools related to the Scikit-learn library in Python and the R language environment), the preprocessed historical data is fed into an initial regression model. Training parameters (preset learning rate and number of iterations) are then set to train the initial regression model. During training, the model parameters are continuously adjusted to better fit the historical data, ultimately resulting in a regional regression model.

[0039] Furthermore, the current interference factor values (including mining area and mining depth) are obtained and the necessary format conversion and preprocessing are performed on these data to make them acceptable to the regional regression model. Then, using data analysis software or a programming interface, the preprocessed interference factor values are input into the trained regional regression model and run. The regional regression model performs calculations and analysis based on the input interference factor values, and outputs the corresponding results using its internal algorithms and optimized parameters. Electronic equipment then obtains the output of the regional regression model and extracts the type influence coefficient and type distance attenuation coefficient corresponding to the land use type from the output of the regional regression model.

[0040] In this embodiment, the habitat degradation index corresponding to the land use type is calculated based on the suitability, type influence coefficient, and type distance decay coefficient, including: Obtain the target distance corresponding to each target grid cell, where the target distance is the straight-line distance between the target grid cell and the center of the mining activity; Substitute the suitability, type influence coefficient, and type distance attenuation coefficient of each target grid cell into Formula 1 to calculate the habitat degradation index corresponding to each target grid cell. Formula 1 is: , where k is the adjustment coefficient; Based on the habitat degradation index of each target grid cell, the habitat degradation index corresponding to the land use type is calculated.

[0041] Specifically, the coordinates of the mining activity center and the coordinate values of each target grid cell are obtained from the mining area database. Spatial analysis tools or algorithms (such as a two-dimensional distance calculation method based on the Pythagorean theorem, or a distance calculation formula applicable to three-dimensional space) are used to calculate the straight-line distance between each target grid cell and the mining activity center, thereby obtaining the target distance corresponding to each target grid cell. Furthermore, a distribution image corresponding to each target grid cell is obtained from the mining area database. Based on the edge lines of each target grid cell, the cell area corresponding to each target grid cell is calculated (this can be done using a GIS field calculator (such as ArcGIS "CalculateGeometryAttributes" or QGIS "FieldCalculator"), calling the built-in area function, and entering coordinate system parameters, ensuring that the calculation units are standard area units (such as square meters or square kilometers). Formula: Area = Geometric Area (automatically calculated, based on a projected coordinate system)). The mining area corresponding to each target grid cell is calculated based on the ratio of the distribution image to the actual image.

[0042] Furthermore, based on the obtained suitability value of each target grid cell, and based on the obtained type influence coefficient and type distance attenuation coefficient corresponding to the land use type, the suitability, type influence coefficient, mining area, type distance and target distance of each target grid cell are substituted into Formula 1. , calculated using numerical calculation software, to obtain the habitat degradation index corresponding to each target grid cell. The value of k can be determined based on experience, research objectives, or through model debugging.

[0043] Furthermore, after obtaining the habitat degradation index corresponding to each target grid cell, a simple average method can be used to add the habitat degradation indices of all target grid cells and divide the sum by the total number of grid cells. Alternatively, a weighted average method can be used to assign different weights to each grid cell according to its area, and these habitat degradation indices can be aggregated to obtain the habitat degradation index corresponding to the land use type. This index comprehensively reflects the overall degree of habitat degradation for that land use type.

[0044] Step S103: Determine the sensitivity of each land use type to each habitat threat factor, and determine the degree of influence of each influencing factor on the ecological environment.

[0045] Sensitivity indicates the sensitivity of a land use type to habitat threats, reflecting the ease with which habitat quality declines when threatened. Factor impact measures the magnitude and importance of each factor's impact on the mining area's ecological environment.

[0046] Specifically, to determine the sensitivity of each land-use type to each habitat threat, historical data can be analyzed to establish a sensitivity matrix between land-use types and threat factors. For example, wetlands may be more sensitive to threats like water pollution, while grasslands may be less sensitive. To determine the degree to which each influencing factor affects the ecological environment, methods such as correlation analysis, principal component analysis, regression analysis, and the analytic hierarchy process can be used to calculate the weight or correlation coefficient of each influencing factor, thereby representing its impact on the ecological environment.

[0047] In this embodiment, the sensitivity of each land use type to each habitat threat factor is determined. The calculation process of the sensitivity of each land use type to each habitat threat factor includes: Obtain the grid distance between the target grid cell of the land use type and the threat grid cell of the habitat threat factor, and determine the impact distance corresponding to each threat grid cell based on the type distance attenuation coefficient of each threat grid cell; Based on the influence distance, a grid distance that is not greater than the corresponding influence distance is selected from the grid distances as a marked distance, and the target grid cell corresponding to the marked distance is determined as a marked grid cell, and the threat grid cell corresponding to the marked distance is determined as a marked threat grid cell; Establish the spatial attenuation coefficient corresponding to each marked threat grid cell, spatial attenuation coefficient = , where λ is the decay rate and d is the marker distance; Obtain the threat weight corresponding to the habitat threat factor; Substitute the habitat degradation index, spatial attenuation coefficient, and threat weight of each marked threat grid cell into Formula 2, and substitute the total area corresponding to the land use type into Formula 2 to calculate the sensitivity of the land use type to the habitat threat factor. Formula 2 is: , where n is the number of marked threat grid cells.

[0048] Specifically, the coordinates of the target grid cell (the grid cell of the land use type) and the coordinate values of the threat grid cell (the grid cell of the habitat threat factor) are obtained from the mining area database. Spatial analysis tools or algorithms (such as a two-dimensional distance calculation method based on the Pythagorean theorem or a distance calculation formula applicable to three-dimensional space) are used to calculate the straight-line distance between the target grid cell and the threat grid cell, thereby obtaining the grid distance. Furthermore, the type distance attenuation coefficient corresponding to the habitat threat factor is used as the type distance attenuation coefficient for each threat grid cell. Combined with the maximum impact distance = ln(0.01) / β, where β is the coefficient in the exponential decay model, the impact distance of each grid cell is calculated.

[0049] Furthermore, the calculated influence distance of each threat grid unit is compared with the previously obtained grid distance data. For each grid distance, it is determined whether it is not greater than the influence distance of the corresponding threat grid unit. If it is not greater than the influence distance of the corresponding threat grid unit, the grid distance is marked as the marked distance. At the same time, the target grid unit corresponding to the marked distance is marked as the marked grid unit, and the corresponding threat grid unit is marked as the marked threat grid unit. Furthermore, the value of the pre-set attenuation rate λ is obtained from the mining area database (this value can be determined through experience or experiment based on the characteristics of the study area, the nature of the threat factor, and other factors). For each marked threat grid cell, the corresponding marking distance d is obtained. Using mathematical calculation tools and according to the target formula: spatial attenuation coefficient = Calculate the spatial attenuation coefficient corresponding to each marked threat grid cell.

[0050] Obtain threat weight data corresponding to habitat threat factors from the mining area database. These weight data can be determined by expert scoring, hierarchical analysis method, etc., reflecting the relative importance of the habitat threat factor among all threat factors. Further, calculate the sum of the unit areas of the grid cells corresponding to the land use type to obtain the total area corresponding to the land use type, and substitute the habitat degradation index, total area corresponding to the land use type, spatial attenuation coefficient and threat weight of each marked threat grid cell into Formula 2 , the sensitivity of the land use type to the habitat threat factor is calculated. It is worth noting that the calculation method of the unit area of the grid cell corresponding to the land use type is the same as the method of calculating the "unit area corresponding to each target grid cell" in step S102 above, and this embodiment will not be repeated here.

[0051] In this embodiment, the influencing factors include regional GDP, total population at the end of the year, population density, night light index, annual average temperature, annual average precipitation, NDVI, and mineral distribution. The degree of influence of each influencing factor on the ecological environment is determined. The process of determining the degree of influence of each influencing factor on the ecological environment includes: Normalize the impact factor value to obtain the standard impact factor value; Determine the spatial attenuation function corresponding to each mineral resource and determine the basic weights corresponding to the influencing factors; Obtain the spatial effect coefficient of the mining area to be evaluated; Determine the time decay factor corresponding to the impact factor, where the time decay factor is a dynamic factor or a static factor; Substitute the standard impact factor value, spatial attenuation function, basic weight, spatial effect coefficient and time attenuation factor into Formula 3 to calculate the factor influence degree of the ecological environment corresponding to the impact factor. Formula 3 is factor influence degree = standard impact factor value * (basic weight + spatial attenuation function * spatial effect coefficient) * time attenuation factor.

[0052] After obtaining the specific values of all influencing factors (regional GDP, total population at the end of the year, population density, night light index, annual average temperature, annual average precipitation, NDVI and mineral distribution), the minimum-maximum standardization method can be used to standardize the influencing factor values. Specifically, for each influencing factor value, the minimum and maximum values of the influencing factor values are obtained. For each specific value of each influencing factor, the minimum and maximum values of the influencing factor values are obtained according to the formula Calculate, where x is the original impact factor value, and are the minimum and maximum values of the impact factor, respectively. is the normalized value.

[0053] Furthermore, if the influencing factor is mineral distribution, the farthest distance and the closest distance between the center point of the mining area and the edge point of the mining area are obtained, and based on the formula spatial attenuation coefficient = 1-closest distance / farthest distance, the spatial attenuation coefficient corresponding to each mining area is calculated, and the mean of the spatial attenuation coefficient is calculated to obtain the effect coefficient of the mining area to be evaluated; if the influencing factor is an influencing factor other than mineral distribution, the spatial attenuation coefficient of the mining area to be evaluated is determined to be 1. Furthermore, if the influencing factor is a static factor (such as average annual precipitation, average annual temperature, mineral distribution), the time attenuation rate of the influencing factor is determined to be 0; if the influencing factor is a dynamic factor (gross domestic product, total population at the end of the year, population density, night light index, NDVI), the time attenuation rate is determined to be 0 by the formula time attenuation rate = The time decay rate is calculated, where T is the time window, which can be 5 years. is the impact factor value corresponding to the current period, is the impact factor value in the time window before the current period.

[0054] Furthermore, the basic weight and spatial effect coefficient corresponding to the influencing factor are obtained from the mining area database, and the standard influencing factor value, spatial attenuation function, basic weight, spatial effect coefficient and time attenuation factor are substituted into Formula 3: Factor influence degree = standard influencing factor value * (basic weight + spatial attenuation function * spatial effect coefficient) * time attenuation factor, and the factor influence degree of the ecological environment corresponding to the influencing factor is calculated. Among them, the spatial effect coefficient can be obtained by comprehensive analysis and quantification based on multiple factors such as the topography, ecosystem structure, and land use pattern of the mining area. The basic weight can be obtained by scoring by experts in related fields, and this embodiment of the application is not limited to this.

[0055] Furthermore, each influencing factor is subjected to the above steps to calculate the degree of influence of each influencing factor on the ecological environment.

[0056] Step S104: Load the initial InVEST model, and train the initial InVEST model based on each sensitivity and the influence degree of each factor to obtain a target InVEST model.

[0057] Specifically, the InVEST model software is retrieved and loaded from the mining area database, and initial model parameters are imported. Data on the sensitivity of each land-use type to each habitat threat factor, as well as the degree of impact of each influencing factor on the ecological environment, are organized and input according to the InVEST model's input requirements. An iterative training approach (e.g., the process of adjusting parameters multiple times, running the model, and evaluating the results, as described above) is used to continuously optimize the model parameters until the model output meets certain accuracy requirements or the predetermined number of training cycles is reached, resulting in the target InVEST model.

[0058] In this embodiment, based on each sensitivity and the influence of each factor, the initial InVEST model is trained to obtain the target InVEST model, including: Obtain historical mining area data, including historical geological distribution, historical impact factor values, historical sensitivity, historical factor impact degree, and historical assessment results corresponding to each historical period; Based on the geological distribution and the influencing factor values, the basic parameters of the initial InVEST model are set to obtain the first InVEST model; Based on each sensitivity and each factor influence degree, the sensitivity parameters and factor influence degree parameters of the first InVEST model are set to obtain a second InVEST model; The second InVEST model is iteratively trained based on historical mining area data to obtain the target InVEST model.

[0059] Relevant data corresponding to each historical period is obtained, filtered, and extracted from the mining area database. This includes historical geological distribution (the distribution of various land-use types during historical periods), historical impact factor values (such as regional GDP and year-end total population values during historical periods), historical sensitivity (the sensitivity of each land-use type to each habitat threat during historical periods), historical factor impact (the impact of each impact factor on the ecological environment during historical periods), and historical assessment results (previous mining area ecological and environmental assessment results, such as past habitat quality indices). The initial InVEST model software is then loaded, and the acquired geological distribution (distribution of various land-use types) and impact factor values (such as regional GDP and year-end total population) data for the current mining area to be assessed are organized and converted according to the InVEST model's input requirements.

[0060] Based on the geological distribution and influencing factor values, set the basic parameters in the InVEST model parameter setting interface. For example, set the ecosystem type parameters based on the distribution of land use types, and adjust the parameters related to human activities based on the influencing factor values. After completing the parameter settings, the first InVEST model with basic parameter settings is obtained.

[0061] Furthermore, the calculated sensitivity data of each land use type to each habitat threat factor, as well as the factor influence data of each influencing factor on the ecological environment, are obtained. For the sensitivity parameters, corresponding parameter values can be set according to the sensitivity of each land use type to each habitat threat factor to reflect the differences in the responses of different land use types to threats. For example, the parameter values corresponding to land use types with high sensitivity are set larger, making them more sensitive to threats in the model calculation. For the factor influence degree parameters, the relevant parameter values can be adjusted according to the factor influence degree of each influencing factor to reflect the differences in the intensity of the effects of different influencing factors on the ecological environment. For example, the parameter weights corresponding to influencing factors with high influence degrees are set higher. After completing the setting of the sensitivity parameters and the factor influence degree parameters, run the InVEST model again to obtain the second InVEST model after this step of parameter setting.

[0062] The acquired historical mining area data (including historical geological distribution, historical influencing factor values, historical sensitivity, historical factor impact degree, and historical assessment results) are organized and prepared according to the input requirements of the InVEST model, and the historical mining area data are input into the second InVEST model, which is then run to obtain the output results.

[0063] The model's output is compared with historical evaluation results, and the difference (e.g., root mean square error, mean absolute error, etc.) is calculated. Based on the magnitude and direction of the difference, the relevant parameters of the second InVEST model (which may include baseline parameters, sensitivity parameters, and factor influence parameters) are adjusted. Parameter adjustment can be done manually or using automated optimization algorithms (e.g., genetic algorithms, simulated annealing algorithms, etc.). Repeat the above process of inputting data, running the model, comparing results, and adjusting parameters for multiple iterations until the difference between the model's output and historical evaluation results reaches the predetermined accuracy requirement (e.g., the error is less than a certain threshold) or the predetermined number of iterations is reached. After iterative training, the final model is the target InVEST model.

[0064] Step S105: Input the geological distribution and the influencing factor values into the target InVEST model, and obtain the output of the target InVEST model to obtain the evaluation result of the mining area to be evaluated.

[0065] Among them, the evaluation results are various indicators and conclusions about the ecological environment of the mining area to be evaluated, which are output by the target InVEST model based on the input geological distribution and influencing factor value data after calculation and analysis. They are used to evaluate the quality of the mining area's ecological environment, ecosystem service functions and other aspects.

[0066] Specifically, the acquired and organized geological distribution (land use type distribution) and influencing factor values for the mining area to be assessed are input into the target InVEST model according to its input format requirements. The target InVEST model is then run, and the model performs calculations and analysis based on the input data and pre-trained parameters.

[0067] Furthermore, the output results of the model, such as the predicted habitat quality index, ecosystem service value, and biodiversity indicators, are obtained and sorted out to obtain the ecological and environmental assessment results of the mining area to be assessed.

[0068] The embodiment of the present application provides a method for evaluating the ecological environment of a mining area. By accurately obtaining the geological distribution and influencing factor values, calculating the habitat degradation index to determine the threat factors, and then analyzing the sensitivity of each land use type to the threat factors and the degree of influence of the influencing factors on the habitat, a comprehensive evaluation of the ecological environment of the mining area is achieved. By loading and training the initial InVEST model, a customized target model is obtained, which can more accurately reflect the actual situation of the mining area and improve the accuracy of the evaluation. Finally, the geological distribution and influencing factor values are input into the target model to obtain the evaluation results. There is no need to obtain mining area ecological data through field measurement and sampling analysis, thereby improving the efficiency of mining area habitat evaluation.

[0069] The above embodiment introduces a mining area ecological environment assessment method from the perspective of method flow, and the following embodiment introduces a mining area ecological environment assessment device from the perspective of virtual modules or virtual units. Please refer to the following embodiments for details.

[0070] See also Figure 2 The mining area ecological environment assessment device 20 may specifically include: an acquisition module 201, a calculation module 202, a determination module 203, a training module 204, and an input module 205, wherein: A mining area ecological environment assessment device 20, comprising: An acquisition module 201 is used to obtain the geological distribution and influencing factor values of the mining area to be evaluated in the current period, where the geological distribution is the distribution corresponding to each land use type; The calculation module 202 is used to calculate the habitat degradation index corresponding to each land use type, and determine the habitat threat factor based on the habitat degradation index; Determination module 203, for determining the sensitivity of each land use type to each habitat threat factor, and determining the degree of influence of each influencing factor on the ecological environment; The training module 204 is used to load the initial InVEST model and train the initial InVEST model based on each sensitivity and the influence of each factor to obtain a target InVEST model; The input module 205 is used to input the geological distribution and the influencing factor values into the target InVEST model, and obtain the output of the target InVEST model to obtain the evaluation result of the mining area to be evaluated.

[0071] In one possible implementation of the embodiment of the present application, the calculation module 202 calculates the habitat degradation index corresponding to each land use type. In the calculation process of the habitat degradation index of each land use type, the calculation module 202 is specifically configured to: Obtain the target grid cell corresponding to the land use type, and obtain the interference factor value and evaluation index value corresponding to each target grid cell. The interference factor value includes the mining area and mining depth, and the evaluation index value includes the corresponding values of vegetation coverage, species richness, soil quality, and carbon sequestration capacity. Determine the evaluation weight corresponding to each evaluation index value, and calculate the suitability corresponding to each target grid cell based on the evaluation index value and the corresponding evaluation weight; Obtain historical type data corresponding to the land use type, the historical type data including historical interference factor values and historical impact ranges corresponding to historical periods; Based on historical type data and interference factor values, determine the type impact coefficient and type distance attenuation coefficient corresponding to the land use type; Based on suitability, type impact coefficient and type distance attenuation coefficient, the habitat degradation index corresponding to the land use type is calculated.

[0072] In one possible implementation of the embodiment of the present application, the calculation module 202 is specifically configured to: Obtain an initial regression model, and train the initial regression model based on historical type data to obtain a regional regression model; The interference factor value is input into the regional regression model, and the output of the regional regression model is obtained to obtain the type influence coefficient and type distance attenuation coefficient corresponding to the land use type.

[0073] In one possible implementation of the embodiment of the present application, the calculation module 202 is specifically configured to calculate the habitat degradation index corresponding to the land use type based on the suitability, the type influence coefficient, and the type distance decay coefficient: Obtain the target distance corresponding to each target grid cell, where the target distance is the straight-line distance between the target grid cell and the center of the mining activity; Substitute the suitability, type influence coefficient, and type distance attenuation coefficient of each target grid cell into Formula 1 to calculate the habitat degradation index corresponding to each target grid cell. Formula 1 is: , where k is the adjustment coefficient; Based on the habitat degradation index of each target grid cell, the habitat degradation index corresponding to the land use type is calculated.

[0074] In one possible implementation of the embodiment of the present application, the determination module 203, when determining the sensitivity of each land use type to each habitat threat factor and calculating the sensitivity of each land use type to each habitat threat factor, is specifically configured to: Obtain the grid distance between the target grid cell of the land use type and the threat grid cell of the habitat threat factor, and determine the impact distance corresponding to each threat grid cell based on the type distance attenuation coefficient of each threat grid cell; Based on the influence distance, a grid distance that is not greater than the corresponding influence distance is selected from the grid distances as a marked distance, and the target grid cell corresponding to the marked distance is determined as a marked grid cell, and the threat grid cell corresponding to the marked distance is determined as a marked threat grid cell; Establish the spatial attenuation coefficient corresponding to each marked threat grid cell, spatial attenuation coefficient = , where λ is the decay rate and d is the marker distance; Obtain the threat weight corresponding to the habitat threat factor; Substitute the habitat degradation index, spatial attenuation coefficient, and threat weight of each marked threat grid cell into Formula 2, and substitute the total area corresponding to the land use type into Formula 2 to calculate the sensitivity of the land use type to the habitat threat factor. Formula 2 is: , where n is the number of marked threat grid cells.

[0075] In a possible implementation of the embodiment of the present application, the influencing factors include regional GDP, total population at the end of the year, population density, night light index, annual average temperature, annual average precipitation, NDVI, and mineral distribution. The determination module 203 determines the degree of influence of each influencing factor on the ecological environment. The determination process of the degree of influence of each influencing factor on the ecological environment is specifically used to: Normalize the impact factor value to obtain the standard impact factor value; Determine the spatial attenuation function corresponding to each mineral resource and determine the basic weights corresponding to the influencing factors; Obtain the spatial effect coefficient of the mining area to be evaluated; Determine the time decay factor corresponding to the impact factor, where the time decay factor is a dynamic factor or a static factor; Substitute the standard impact factor value, spatial attenuation function, basic weight, spatial effect coefficient and time attenuation factor into Formula 3 to calculate the factor influence degree of the ecological environment corresponding to the impact factor. Formula 3 is factor influence degree = standard impact factor value * (basic weight + spatial attenuation function * spatial effect coefficient) * time attenuation factor.

[0076] In one possible implementation of the embodiment of the present application, the training module 204 trains the initial InVEST model based on each sensitivity and the influence of each factor to obtain a target InVEST model, specifically for: Obtain historical mining area data, including historical geological distribution, historical impact factor values, historical sensitivity, historical factor impact degree, and historical assessment results corresponding to each historical period; Based on the geological distribution and the influencing factor values, the basic parameters of the initial InVEST model are set to obtain the first InVEST model; Based on each sensitivity and each factor influence degree, the sensitivity parameters and factor influence degree parameters of the first InVEST model are set to obtain a second InVEST model; The second InVEST model is iteratively trained based on historical mining area data to obtain the target InVEST model.

[0077] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0078] The embodiment of the present application also introduces an electronic device from the perspective of a physical device, such as Figure 3 As shown, Figure 3 The electronic device 300 shown includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may further include a transceiver 304. It should be noted that in actual applications, the number of transceivers 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0079] Processor 301 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0080] Bus 302 may include a path for transmitting information between the above components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. Bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0081] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0082] The memory 303 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the above method embodiment.

[0083] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc., and can also be servers, etc. Figure 3 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0084] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiment.

[0085] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0086] The above are only some of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A mining area ecological environment assessment method, characterized in that: include: Obtaining the geological distribution and influencing factor values of the mining area to be evaluated in the current period, wherein the geological distribution is the distribution corresponding to each land use type; Calculating the habitat degradation index corresponding to each land use type, and determining the habitat threat factor based on the habitat degradation index; Determine the sensitivity of each land use type to each habitat threat factor, and determine the degree of impact of each influencing factor on the ecological environment; Loading an initial InVEST model, and training the initial InVEST model based on each sensitivity and the influence of each factor to obtain a target InVEST model; The geological distribution and the influencing factor values are input into the target InVEST model, and the output of the target InVEST model is obtained to obtain an evaluation result of the mining area to be evaluated.

2. The mining area ecological environment assessment method according to claim 1, characterized in that: The calculation process of the habitat degradation index corresponding to each land use type includes: Obtaining a target grid cell corresponding to the land use type, and obtaining an interference factor value and an evaluation index value corresponding to each target grid cell, wherein the interference factor value includes a mining area and a mining depth, and the evaluation index value includes a corresponding value of vegetation coverage, species richness, soil quality, and carbon sequestration capacity; Determine the evaluation weight corresponding to each evaluation index value, and calculate the suitability corresponding to each target grid cell based on the evaluation index value and the corresponding evaluation weight; Acquire historical type data corresponding to the land use type, wherein the historical type data includes historical interference factor values and historical impact ranges corresponding to historical periods; Determining a type influence coefficient and a type distance attenuation coefficient corresponding to the land use type based on the historical type data and the interference factor value; Based on the suitability, the type influence coefficient and the type distance attenuation coefficient, the habitat degradation index corresponding to the land use type is calculated.

3. The mining area ecological environment assessment method according to claim 2, characterized in that: The determining, based on the historical type data and the interference factor value, the type influence coefficient and the type distance attenuation coefficient corresponding to the land use type includes: Obtaining an initial regression model, and training the initial regression model based on the historical type data to obtain a regional regression model; The interference factor value is input into the regional regression model, and the output of the regional regression model is obtained to obtain the type influence coefficient and the type distance attenuation coefficient corresponding to the land use type.

4. The mining area ecological environment assessment method according to claim 2 or 3, characterized in that: The calculating of the habitat degradation index corresponding to the land use type based on the suitability, the type influence coefficient, and the type distance attenuation coefficient includes: Obtaining a target distance corresponding to each target grid cell, where the target distance is a straight-line distance between the target grid cell and the center of the mining activity; Substitute the suitability of each target grid cell, the type influence coefficient, and the type distance attenuation coefficient into Formula 1 to calculate the habitat degradation index corresponding to each target grid cell, where Formula 1 is: , where k is the adjustment coefficient; Based on the habitat degradation index of each target grid cell, the habitat degradation index corresponding to the land use type is calculated.

5. The mining area ecological environment assessment method according to claim 2, characterized in that: The process of determining the sensitivity of each land use type to each habitat threat factor includes: Obtaining a grid distance between a target grid cell of the land use type and a threat grid cell of the habitat threat factor, and determining an impact distance corresponding to each threat grid cell based on a type distance attenuation coefficient of each threat grid cell; Based on the impact distance, a grid distance that is not greater than the corresponding impact distance is screened out from the grid distances as a marked distance, and a target grid cell corresponding to the marked distance is determined as a marked grid cell, and a threat grid cell corresponding to the marked distance is determined as a marked threat grid cell; Establish the spatial attenuation coefficient corresponding to each marked threat grid cell, the spatial attenuation coefficient = , where λ is the decay rate and d is the marker distance; Obtaining the threat weight corresponding to the habitat threat factor; Substitute the habitat degradation index, spatial attenuation coefficient, and threat weight of each marked threat grid unit into Formula 2, and substitute the total area corresponding to the land use type into Formula 2 to calculate the sensitivity of the land use type to the habitat threat factor. Formula 2 is: , where n is the number of marked threat grid cells.

6. The mining area ecological environment assessment method according to claim 2, characterized in that: The influencing factors include regional GDP, total population at the end of the year, population density, night light index, annual average temperature, annual average precipitation, NDVI and mineral distribution. The process of determining the degree of influence of each influencing factor on the ecological environment includes: Standardizing the impact factor value to obtain a standard impact factor value; Determine the spatial attenuation function corresponding to each mineral resource and determine the basic weight corresponding to the influencing factors; Obtaining the spatial effect coefficient of the mining area to be evaluated; Determine a time decay factor corresponding to the impact factor, where the time decay factor is a dynamic factor or a static factor; Substitute the standard impact factor value, spatial attenuation function, basic weight, spatial effect coefficient and time attenuation factor into Formula 3 to calculate the factor influence degree of the impact factor corresponding to the ecological environment. Formula 3 is factor influence degree = standard impact factor value * (basic weight + spatial attenuation function * spatial effect coefficient) * time attenuation factor.

7. The mining area ecological environment assessment method according to claim 1, characterized in that: The initial InVEST model is trained based on each sensitivity and the influence of each factor to obtain a target InVEST model, including: Acquire historical mining area data, including historical geological distribution, historical impact factor values, historical sensitivity, historical factor impact degree, and historical assessment results corresponding to each historical period; Based on the geological distribution and the influencing factor values, basic parameters of the initial InVEST model are set to obtain a first InVEST model; Based on each sensitivity and each factor influence degree, setting the sensitivity parameters and factor influence degree parameters of the first InVEST model to obtain a second InVEST model; The second InVEST model is iteratively trained based on the historical mining area data to obtain a target InVEST model.

8. A mining area ecological environment assessment device, characterized in that: include: An acquisition module is used to obtain the geological distribution and influencing factor values of the mining area to be evaluated in the current period, wherein the geological distribution is the distribution corresponding to each land use type; A calculation module is used to calculate the habitat degradation index corresponding to each land use type, and determine the habitat threat factor based on the habitat degradation index; Determination module, used to determine the sensitivity of each land use type to each habitat threat factor, and determine the degree of influence of each influencing factor on the ecological environment; A training module is used to load an initial InVEST model and train the initial InVEST model based on each sensitivity and the influence of each factor to obtain a target InVEST model; An input module is used to input the geological distribution and the influencing factor values into the target InVEST model, and obtain the output of the target InVEST model to obtain an evaluation result of the mining area to be evaluated.

9. An electronic device, characterized in that: The electronic device includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the mining area ecological environment assessment method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed in a computer, the computer is caused to execute the mining area ecological environment assessment method according to any one of claims 1 to 7.

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