Ecological environment evaluation method and device for coal mine area and mining area environment evaluation system

Abstract

The invention provides an ecological environment evaluation method and device for a coal mine area and a mining area environment evaluation system. The method comprises the steps that a plurality of environmental indexes of a coal mine area are obtained, and the environmental indexes comprise one or more of soil quality, water quality index, vegetation coverage rate, air quality index, noise level, waste water discharge amount, waste gas discharge amount, biodiversity index, heavy metal content and greening coverage area; the relevancy between the environment indexes is obtained, and the relevancy is the changed relevancy degree between any two environment indexes; a first weight coefficient is determined according to the relevancy, and the first weight coefficient and the relevancy are in positive correlation; and performing weighted average at least according to the plurality of environment indexes and the first weight coefficients corresponding to the environment indexes to obtain an evaluation score. According to the scheme, the problem of low accuracy of the evaluation result of the coal mine environment in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the technical field of ecological environment evaluation in coal mining areas, and in particular, to an ecological environment evaluation method, device, computer program product and mining area environment evaluation system for coal mining areas. Background Art

[0002] With the continuous development of social economy, coal has been widely used as an important energy resource. However, the environmental problems caused by coal mining and processing have attracted great attention. Therefore, it is of great practical significance to evaluate the ecological environment of coal mining areas.

[0003] In the current scheme, multiple indicators of coal mines can be collected and quantitative evaluation can be performed directly through multiple indicators, but the accuracy of the evaluation results obtained in this way is low. Summary of the invention

[0004] The main purpose of the present application is to provide an ecological environment evaluation method, device, computer program product and mining area environment evaluation system for a coal mine area, so as to at least solve the problem of low accuracy of the evaluation results of the coal mine environment in the prior art.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for evaluating the ecological environment of a coal mining area is provided, comprising: obtaining multiple environmental indicators of a coal mining area, wherein the environmental indicators include one or more of soil quality, water quality indicators, vegetation coverage, air quality index, noise level, wastewater discharge, waste gas discharge, biodiversity index, heavy metal content, and green coverage area; obtaining the correlation between the environmental indicators, wherein the correlation is the degree of association between any two of the environmental indicators; determining a first weight coefficient based on the correlation, wherein the first weight coefficient and the correlation are positively correlated; and obtaining an evaluation score by performing weighted averaging at least based on multiple environmental indicators and the first weight coefficient corresponding to the environmental indicators.

[0006] Optionally, obtaining the correlation between the environmental indicators includes: calculating the correlation according to a first formula, wherein the first formula is:

[0007]

[0008] ρ(X,Y) represents the correlation, X represents the first environmental index, Y represents the second environmental index, E represents the mathematical expectation, μ X represents the covariance of the first environmental indicator, μ Y represents the covariance of the second environmental indicator, σ X represents the standard deviation of the first environmental indicator, σ Yrepresents the standard deviation of the second environmental indicator, represents the average value of the first environmental indicator, represents the average value of the second environmental indicator, and n represents the number of the environmental indicators.

[0009] Optionally, obtaining the correlation between the environmental indicators includes: calculating the correlation according to a second formula, wherein the second formula is:

[0010]

[0011] c(X,Y) represents the correlation, |X| represents the absolute value of the first environmental indicator, and |Y| represents the absolute value of the second environmental indicator.

[0012] Optionally, obtaining the correlation between the environmental indicators includes: constructing a correlation generation model, wherein the correlation generation model is trained using multiple sets of training data, each set of training data includes historical environmental indicators obtained within a historical time period and historical correlations corresponding to the historical environmental indicators; inputting the environmental indicators into the correlation generation model to obtain the correlations corresponding to the environmental indicators.

[0013] Optionally, after obtaining an evaluation score by performing weighted averaging at least based on multiple environmental indicators and the first weight coefficients corresponding to the environmental indicators, the method further includes: using a decision tree algorithm to determine the importance score of each environmental indicator to the evaluation score; updating the first weight coefficient based on the importance score to obtain an updated first weight coefficient; and performing weighted averaging based on multiple environmental indicators and the updated first weight coefficients corresponding to the environmental indicators to obtain a target evaluation score.

[0014] Optionally, after obtaining an evaluation score by weighted averaging at least a plurality of the environmental indicators and the first weight coefficients corresponding to the environmental indicators, the method further includes: obtaining a change amplitude and a change rate of the environmental indicators, wherein the change amplitude is the amount of change of the environmental indicator from a first time point to a second time point, and the change rate is the speed at which the environmental indicator changes within a unit time; obtaining a second weight coefficient and a third weight coefficient, wherein the second weight coefficient is positively correlated with the change amplitude, and the third weight coefficient is positively correlated with the change rate; calculating the weighted average of the change amplitude of the environmental indicator and the second weight coefficient corresponding to the change amplitude to obtain a change amplitude score; calculating the change rate of the environmental indicator and the weighted average of the third weight coefficient corresponding to the change rate to obtain a change rate score; calculating the average of the evaluation score, the change amplitude score and the change rate score to obtain a comprehensive score.

[0015] Optionally, after obtaining an evaluation score by weighted averaging at least a plurality of the environmental indicators and the first weight coefficients corresponding to the environmental indicators, the method further includes: constructing a strategy generation model, wherein the strategy generation model is trained using multiple sets of training data, each set of the multiple sets of training data includes historical environmental indicators, historical relevance, and historical evaluation scores obtained within a historical time period, and historical governance plans corresponding to the historical environmental indicators, the historical relevance, and the historical evaluation scores, wherein the historical governance plan is a historical strategy for improving the ecological environment of a coal mining area; inputting the environmental indicators, the relevance, and the evaluation score into the strategy generation model to obtain a governance plan for the environmental indicators, the relevance, and the evaluation score.

[0016] According to another aspect of the present application, an ecological environment evaluation device for a coal mine area is provided, comprising: a first acquisition unit, used to acquire multiple environmental indicators of the coal mine area, wherein the environmental indicators include one or more of soil quality, water quality indicators, vegetation coverage, air quality index, noise level, wastewater discharge, waste gas discharge, biodiversity index, heavy metal content, and green coverage area; a second acquisition unit, used to acquire the correlation between the environmental indicators, wherein the correlation is the degree of association between changes in any two of the environmental indicators; a first determination unit, used to determine a first weight coefficient based on the correlation, wherein the first weight coefficient and the correlation are positively correlated; a first evaluation unit, used to perform weighted averaging based on at least a plurality of the environmental indicators and the first weight coefficient corresponding to the environmental indicators to obtain an evaluation score.

[0017] According to another aspect of the present application, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the steps of any one of the methods for evaluating the ecological environment of a coal mine area are implemented.

[0018] According to another aspect of the present application, a mining area environmental assessment system is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of the ecological environment assessment methods for the coal mining area.

[0019] By applying the technical solution of the present application, the trend of the change impact between two environmental indicators can be compared. The two environmental indicators may be positively correlated or negatively correlated. Then, weights are assigned to each indicator based on the correlation between the environmental indicators, which can reflect the relative importance of each indicator in the overall ecological environment evaluation. For example, if the water quality indicator shows a high correlation with multiple other indicators, then during the evaluation, the water quality indicator may be given a higher weight to reflect its core position in the ecological environment of the coal mining area. Since the weight allocation method of this solution can reflect the correlation between the indicators, compared with the traditional method of treating each environmental indicator independently, the solution of this application can more comprehensively evaluate the environmental status, avoid the evaluation bias caused by ignoring the interaction between indicators, and thus improve the accuracy of the environmental evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0021] Figure 1 A hardware structure block diagram of a mobile terminal for executing an ecological environment evaluation method for a coal mine area provided in an embodiment of the present application is shown;

[0022] Figure 2 A schematic diagram of a process for evaluating an ecological environment in a coal mine area according to an embodiment of the present application is shown;

[0023] Figure 3 A structural block diagram of an ecological environment evaluation device for a coal mine area provided according to an embodiment of the present application is shown.

[0024] The above drawings include the following reference numerals:

[0025] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0026] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0027] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] As introduced in the background technology, the accuracy of the evaluation results of the coal mine environment in the prior art is low. To solve the above problems, the embodiments of the present application provide an ecological environment evaluation method, device, computer program product and mining area environment evaluation system for a coal mine area.

[0030] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0031] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal for an ecological environment evaluation method for a coal mine area according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is for illustration only and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0032] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the display method of device information in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0033] In this embodiment, a method for evaluating the ecological environment of a coal mine area that runs on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0034] Figure 2 FIG. 1 is a flow chart of an ecological environment evaluation method for a coal mine area according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0035] Step S201, obtaining multiple environmental indicators of the coal mine area, wherein the environmental indicators include one or more of soil quality, water quality index, vegetation coverage, air quality index, noise level, wastewater discharge, waste gas discharge, biodiversity index, heavy metal content, and green coverage area;

[0036] Specifically, when conducting an ecological and environmental assessment of a coal mining area, it is first necessary to collect a series of indicator data reflecting the state of the environment. The environmental indicators mentioned here cover multiple aspects, including but not limited to soil quality, water quality, vegetation status, air quality, noise pollution, wastewater and waste gas emissions, biodiversity, and green coverage. These indicators can comprehensively reflect the ecological and environmental status of the coal mining area, and data acquisition may be achieved through various methods such as on-site sampling, monitoring equipment recording, and remote sensing data analysis.

[0037] Soil is the foundation of the ecosystem in coal mining areas, and its quality directly affects plant growth, microbial activity, and the migration and transformation of pollutants. Soil quality assessment usually includes soil structure (such as porosity, aggregates), soil organic matter content, mineral composition, water and air content, pH value, salt content, and the concentration of pollutants (such as heavy metals).

[0038] Water quality indicators reflect the physical, chemical and biological characteristics of water bodies, including turbidity, dissolved oxygen, chemical oxygen demand (COD), biochemical oxygen demand (BOD), pH value, total dissolved solids (TDS), heavy metal concentration, microbial pollution level, etc. The quality of water directly affects the health of aquatic ecosystems and human health.

[0039] Vegetation coverage refers to the proportion of land covered by vegetation in a certain area, which reflects the productivity and biodiversity of the ecosystem. A high vegetation coverage usually means that the ecosystem is in a good health state and can provide good carbon sinks, water conservation and biological habitats.

[0040] The Air Quality Index is an indicator that converts the concentration of multiple air pollutants (such as sulfur dioxide, nitrogen oxides, particulate matter, ozone, etc.) into a single value to describe the degree of air pollution. The higher the AQI value, the worse the air quality.

[0041] Noise levels assess the intensity of sound in an environment and can be generated by machinery, traffic or human activity. Long-term exposure to high levels of noise can have negative effects on human health and wildlife behavior.

[0042] Wastewater discharge refers to the total amount of wastewater discharged into the environment during coal mining, washing, processing and other activities in coal mining areas. Pollutants in wastewater may include suspended solids, chemicals and organic matter, causing pollution to water bodies and soil.

[0043] Waste gas emissions measure the total amount of gases emitted into the atmosphere from coal mining areas, including but not limited to dust, sulfides, nitrogen oxides and greenhouse gases. Waste gas emissions not only affect air quality, but may also have an impact on the global climate.

[0044] The biodiversity index reflects the richness and evenness of species in an ecosystem, and usually includes indicators such as species number, species richness, species evenness, etc. High biodiversity means that the ecosystem is healthier, more stable, and has stronger resistance and resilience.

[0045] Heavy metals (such as lead, mercury, cadmium, etc.) accumulate in the environment, causing long-term, cumulative impacts on ecosystems and human health. High levels of heavy metals are often associated with pollution.

[0046] Green coverage area refers to the total area of ​​green land, forests and other green vegetation within the coal mining area. It is another way to express vegetation coverage, reflecting the degree of ecological restoration and the effect of environmental beautification.

[0047] Step S202, obtaining the correlation between the above environmental indicators, wherein the above correlation is the degree of association between changes in any two of the above environmental indicators;

[0048] Specifically, interpreting and analyzing correlation coefficient results involves determining positive or negative associations between indicators.

[0049] Specifically, after collecting environmental indicator data, it is necessary to calculate the correlation between any two environmental indicators through mathematical statistical methods (such as Pearson correlation coefficient, Spearman rank correlation coefficient, etc.). The correlation measures the strength of the association when two indicators change, and the value range is usually between -1 and +1, where +1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation.

[0050] Step S203, determining a first weight coefficient according to the correlation, wherein the first weight coefficient and the correlation are positively correlated;

[0051] Specifically, weight allocation is performed based on the degree of relevance of the indicators.

[0052] Specifically, after clarifying the correlation between indicators, the weight coefficient of each indicator needs to be determined according to the correlation. The weight coefficient reflects the importance of an indicator in the overall evaluation. The determination method can be to directly use the correlation as the weight or to convert the correlation through a certain function. The first weight coefficient is positively correlated with the correlation, which means that the more correlated the indicator, the greater the weight it occupies in the evaluation system, which ensures that those indicators with significant impact on the ecological environment are more fully reflected in the final evaluation score.

[0053] Step S204, performing weighted averaging at least based on the plurality of environmental indicators and the first weight coefficients corresponding to the environmental indicators to obtain an evaluation score.

[0054] Specifically, the correlation analysis method of the present application is used to conduct ecological environment assessment.

[0055] Specifically, by multiplying each environmental indicator by its corresponding first weight coefficient and then taking a weighted average of all the results, a comprehensive evaluation score of the ecological environment in the coal mining area can be obtained. In the process of weighted averaging, the role of the weight coefficient is to adjust the contribution of different indicators to the total score to ensure that the final score can accurately reflect the overall status of the ecological environment in the coal mining area. This scoring method is more scientific than a simple average because it takes into account the correlation and importance differences between indicators.

[0056] In addition, each environmental indicator may be evaluated to obtain an evaluation score for each environmental indicator, and then a weighted average may be performed according to a first weight coefficient corresponding to each evaluation score to obtain an evaluation score.

[0057] For each environmental indicator, the evaluation method can be AHP, PCA, EIA, LCA, grey correlation, etc.

[0058] Analytic Hierarchy Process (AHP): AHP is an evaluation method that decomposes complex problems into hierarchical structures and determines the weights of various factors through comparative judgment. In ecological environmental evaluation, it can help determine the relative importance of different ecological factors (such as water, air, and soil) and conduct comprehensive evaluation.

[0059] Principal Component Analysis (PCA): PCA is a data dimensionality reduction technique that can transform multiple related indicators into a few principal components that contain most of the original information. In ecological and environmental evaluation, PCA can be used to simplify the indicator system, reduce the complexity of data processing, and retain the main information.

[0060] Environmental Impact Assessment (EIA): EIA is a systematic assessment method used to evaluate the possible impact of an activity (such as mining, construction projects) on the environment, including direct and indirect, long-term and short-term impacts. EIA usually includes project description, impact prediction, alternative analysis and mitigation measures.

[0061] Life Cycle Assessment (LCA): LCA is a method to evaluate the environmental impact of a product or service throughout its life cycle, from raw material acquisition, production, use to waste disposal. In the ecological and environmental evaluation of coal mining and processing, LCA can comprehensively analyze the impact of the coal industry on the environment.

[0062] Grey correlation analysis is a method used to deal with incomplete information and evaluate the correlation between indicators. In ecological environment evaluation, it can be used to analyze the correlation between evaluation indicators and standard values, thereby determining key influencing factors.

[0063] Through this embodiment, the trend of the change impact between two environmental indicators can be compared. The two environmental indicators may be positively correlated or negatively correlated. Then, weights are assigned to each indicator based on the correlation between the environmental indicators, which can reflect the relative importance of each indicator in the overall ecological environment evaluation. For example, if the water quality indicator shows a high correlation with multiple other indicators, then during the evaluation, the water quality indicator may be given a higher weight to reflect its core position in the ecological environment of the coal mining area. Since the weight allocation method of this scheme can reflect the correlation between the indicators, compared with the traditional method of treating each environmental indicator independently, the scheme of this application can more comprehensively evaluate the environmental status, avoid the evaluation bias caused by ignoring the interaction between indicators, and thus improve the accuracy of the environmental evaluation results.

[0064] Specifically, in traditional ecological environment evaluation methods, a large number of indicators are often required to be evaluated, and the correlation analysis between indicators is an important basis for the accuracy of the evaluation results. The existing ecological environment evaluation of coal mining areas is mainly based on multi-indicator quantitative evaluation, and the impact of the correlation between indicators on the evaluation results has not been considered. The scheme of this application is intended to provide a correlation analysis method between various indicators.

[0065] Specifically, the scheme of the present application determines the correlation between indicators through mathematical calculations and empirical analysis, further improving the accuracy and reliability of the ecological environment evaluation in coal mining areas.

[0066] Specifically, the basic idea of ​​determining weights based on the correlation between environmental indicators is that those environmental indicators that are closely related to the evaluation target or highly correlated with other important indicators will be given higher weights. This is because changes in these indicators have a greater impact on the overall evaluation results and have a more direct or comprehensive reflection of the status of the ecological environment. There are many ways to determine weights. The following uses the Pearson correlation coefficient as an example to explain in detail:

[0067] First, the correlation coefficient method (such as the Pearson correlation coefficient) is used to calculate the correlation between all environmental indicators. The value range of the correlation coefficient is usually between -1 and +1. The closer the value is to +1, the stronger the positive correlation is, the closer the value is to -1, the stronger the negative correlation is, and the closer the value is to 0, the weaker the correlation is. According to the calculated correlation coefficient, the indicators are ranked in importance. Generally speaking, a group of indicators that are highly positively or negatively correlated with the evaluation target (such as the overall health of the ecological environment in the coal mining area) and have a strong correlation with other indicators will be considered important indicators. The weight distribution function can convert the correlation coefficient into a weight. A simple weight distribution function can be to use the absolute value of the correlation coefficient as the direct basis for the weight, or to use nonlinear functions such as square functions and logarithmic functions to convert the correlation into a weight, which can amplify or reduce the impact of the correlation. Since the directly calculated correlation weights may not sum to 1, these weights need to be normalized to ensure that the sum of all indicator weights is 1. Common normalization methods include maximum normalization and sum normalization. Finally, the normalized weights are used to calculate the evaluation score. Indicators with higher weights account for a larger proportion of the overall evaluation, and vice versa.

[0068] Assume that there are three environmental indicators: A (soil quality), B (water quality index), and C (vegetation coverage), and the goal is to evaluate the ecological and environmental health of the coal mining area.

[0069] After calculation, the following correlation coefficients were obtained: the Pearson correlation coefficient between A and B was 0.8, the correlation coefficient between A and C was -0.7, and the correlation coefficient between B and C was -0.6.

[0070] The importance of A can be based on its average correlation with the other two indicators, which is 0.8 here.

[0071] The importance of B can be based on its average correlation with A and C, which is (0.8-0.6) / 2=0.1.

[0072] The importance of C is similar, here it is taken as -0.65 (taking the absolute value of the negative correlation).

[0073] The square of the absolute value of the correlation coefficient is chosen as the preliminary weight basis to amplify the correlation impact between indicators.

[0074] The initial weight of A = (0.8)^2 = 0.64.

[0075] The initial weight of B = (0.1)^2 = 0.01.

[0076] The initial weight of C = (-0.65)^2 = 0.4225.

[0077] The initial weights add up to 0.64+0.01+0.4225=1.0725. To ensure that the sum of the weights is 1, the initial weights need to be normalized:

[0078] The normalized weight of A = 0.64 / 1.0725≈0.5967.

[0079] The normalized weight of B = 0.01 / 1.0725≈0.0093.

[0080] The normalized weight of C = 0.4225 / 1.0725≈0.3940.

[0081] The final normalized weights are 0.5967 for A, 0.0093 for B, and 0.3940 for C. This means that when calculating the comprehensive evaluation score of the ecological environment in coal mining areas, soil quality (A) will have the largest weight and have the greatest impact on the evaluation results, followed by vegetation coverage (C), and finally water quality index (B).

[0082] Specifically, before calculating the relevance of the present application, a simple preliminary judgment can also be performed, and the specific judgment formula is:

[0083]

[0084] X and Y represent different environmental indicators. n represents the nth first environmental indicator, Y n Represents the nth second environmental indicator. A simple preliminary screening can determine the approximate distribution range of the correlation between indicators. If a deep analysis is performed, the following operations are required. The above formula is only exemplary, and any variation of the formula falls within the scope of protection of this application.

[0085] In the specific implementation process, obtaining the correlation between the above environmental indicators can be achieved through the following steps: calculating the above correlation according to a first formula, wherein the above first formula is:

[0086]

[0087] ρ(X,Y) represents the above correlation, X represents the first environmental index, Y represents the second environmental index, E represents the mathematical expectation, μ X represents the covariance of the first environmental index, μ Y represents the covariance of the second environmental index, σ X represents the standard deviation of the first environmental index, σ Y represents the standard deviation of the second environmental index mentioned above, represents the average value of the first environmental index mentioned above, represents the average value of the second environmental indicator, and n represents the number of the environmental indicators. The above formula is only exemplary, and any variation of the formula falls within the protection scope of this application.

[0088] In this solution, the correlation between environmental indicators can be more accurately calculated using the first formula of the present application.

[0089] Specifically, the Pearson Correlation Coefficient can be used to calculate the correlation between environmental indicators. The Pearson Correlation Coefficient is a commonly used indicator in statistics to measure the degree of linear correlation between two variables.

[0090] Covariance measures the consistency of the changing trends of two variables. If the covariance is positive, it means that when the values ​​of the two variables increase or decrease at the same time, they tend to change in the same way; if the covariance is negative, it means that when one variable increases, the other variable tends to decrease.

[0091] The standard deviation is a measure of the dispersion of data and reflects the fluctuation of the data relative to the mean.

[0092] Suppose two environmental indicators are being evaluated in a coal mining area: soil quality (X) and water quality (Y). A set of data points are collected, each containing the measured values ​​of (X) and (Y). By applying the above formula, the correlation between (X) and (Y) can be calculated. If the result is a positive value (such as 0.7), it means that the soil quality is positively correlated with the water quality indicator, that is, the water quality is also good in places with good soil quality; if the result is a negative value (such as -0.4), it indicates that the two are negatively correlated, that is, the water quality may be poor when the soil quality is good. The size of the value measures the strength of this correlation.

[0093] In some embodiments, obtaining the correlation between the above environmental indicators can be specifically achieved by the following steps: calculating the above correlation according to a second formula, wherein the above second formula is:

[0094]

[0095] c(X,Y) represents the above correlation, |X| represents the absolute value of the above first environmental index, and |Y| represents the absolute value of the above second environmental index. The above formula is only exemplary, and any variation of the formula falls within the protection scope of this application.

[0096] In this solution, the correlation between environmental indicators can be more accurately calculated using the second formula of the present application.

[0097] Specifically, when ρ≤1, |ρ|=1, X and Y are correlated, and when |ρ=0, X and Y are uncorrelated. Specifically, when c≤1, |c|=1, X and Y are correlated, and when |c|=0, X and Y are uncorrelated.

[0098] Specifically, the correlation of the solution of the present application can be calculated using the Pearson correlation coefficient.

[0099] In the specific implementation process, the correlation between the above-mentioned environmental indicators can be obtained through the following steps: constructing a correlation generation model, wherein the above-mentioned correlation generation model is trained using multiple sets of training data, and each set of training data in the above-mentioned multiple sets of training data includes historical environmental indicators obtained within a historical time period and historical correlations corresponding to the above-mentioned historical environmental indicators; inputting the above-mentioned environmental indicators into the above-mentioned correlation generation model to obtain the above-mentioned correlations corresponding to the above-mentioned environmental indicators.

[0100] In this solution, the correlation generation model can learn and capture the complex relationships between indicators in historical data, including linear and nonlinear relationships, as well as the dynamic changes of time series data. Based on limited historical data, the correlation of indicators in future or current data sets can be predicted, so that more accurate correlation can be obtained when dealing with nonlinear relationships, time series data, and a large number of indicators.

[0101] Specifically, for each set of historical data, the historical correlations between each environmental indicator are calculated using statistical methods (such as Pearson correlation coefficient, Spearman rank correlation coefficient, etc.) These historical correlations will be used as labels or target variables for model training.

[0102] Specifically, the collected historical environmental indicators and correlation data are preprocessed, including data cleaning (removing outliers and missing values), data normalization (making indicators of different dimensions comparable on the same scale), feature selection (selecting indicators that have an important impact on the model prediction performance), etc.

[0103] Specifically, according to the nature of the problem and the characteristics of the data, choose an appropriate machine learning model, such as linear regression, support vector machine (SVM), LSTM, random forest, neural network, etc. Linear models are suitable for processing linear relationships, while complex models such as neural networks can capture more complex nonlinear relationships.

[0104] Specifically, the selected model is trained using the training dataset using historical environmental indicators as input features and historical relevance as output targets. The training process involves adjusting the model parameters to minimize the error between the predicted relevance and the actual historical relevance.

[0105] Specifically, the prediction performance and generalization ability of the model are checked through cross-validation, test set evaluation, etc. Based on the evaluation results, it may be necessary to adjust the model parameters or feature selection to improve the prediction accuracy.

[0106] Specifically, new environmental indicator data is input into the trained model, and the model outputs the predicted indicator correlation. In this way, the correlation between different environmental indicators can be estimated even without historical correlation data.

[0107] Specifically, in the scheme of the present application, relevant data indicators of the coal mining area are collected; an indicator correlation analysis model is constructed; the collected data indicators are input to calculate the correlation coefficient; the correlation coefficient results are interpreted and analyzed; the correlation relationship between different indicators is determined based on the correlation coefficient results; and a weight allocation method is used to reflect the importance of each indicator in the ecological environment evaluation.

[0108] In some embodiments, after performing weighted averaging at least based on multiple environmental indicators and the first weight coefficients corresponding to the environmental indicators to obtain an evaluation score, the method further includes the following steps: using a decision tree algorithm to determine the importance score of each environmental indicator to the evaluation score; updating the first weight coefficient based on the importance score to obtain an updated first weight coefficient; performing weighted averaging based on multiple environmental indicators and the updated first weight coefficients corresponding to the environmental indicators to obtain a target evaluation score.

[0109] In this scheme, the importance of environmental indicators is determined and the weights are updated through the decision tree algorithm, which can significantly improve the accuracy, efficiency and adaptability of ecological environmental evaluation, while also enhancing the interpretability and application value of the evaluation results.

[0110] Specifically, the decision tree algorithm is used to evaluate and update the weight of environmental indicators in the comprehensive evaluation score, so as to obtain a more accurate target evaluation score. Decision tree is a common machine learning algorithm, commonly used for classification and regression tasks. It builds a tree-structured model by learning the rules in the data. Each internal node represents a test on an attribute, each branch represents a test result, and each leaf node represents a category or output value.

[0111] Specifically, historical ecological environment indicator data are collected, an initial weight (i.e., the first weight coefficient) is assigned to each indicator, and a preliminary comprehensive evaluation score is calculated. These data should include multiple environmental indicators (such as soil quality, water quality, air quality, vegetation coverage, etc.) and corresponding preliminary evaluation scores.

[0112] Specifically, using these historical data as input, the decision tree algorithm will learn how to predict the preliminary evaluation score based on the environmental indicators. During the training process, the decision tree will divide the data according to the different indicator values ​​and find the indicators and indicator values ​​that can best predict the score.

[0113] Specifically, after the decision tree model is trained, the importance of each environmental indicator for the evaluation score prediction can be evaluated. Decision tree algorithms usually provide a mechanism to calculate the importance of features (i.e., environmental indicators), which is usually based on the frequency of use of the feature in the model, information gain, or contribution to reducing impurity.

[0114] Specifically, the weights of environmental indicators can be adjusted based on the feature importance calculated by the decision tree. Indicators with high importance scores will be given higher weight coefficients because they contribute more to the prediction of the evaluation score.

[0115] Specifically, the updated first weight coefficient is used to perform weighted averaging on multiple environmental indicators to obtain a more accurate target evaluation score. The weighted average can better reflect the influence of each indicator on the final evaluation score.

[0116] Assume that there is a set of ecological environment data in coal mining areas, including the following four environmental indicators: soil quality (SQ), water quality index (WQ), air quality index (AQ), and vegetation coverage (VC). First, a preliminary evaluation score is calculated based on the preliminary weights.

[0117] The initial weight distribution is as follows: SQ=0.3, WQ=0.25, AQ=0.3, VC=0.15.

[0118] A model was trained using a decision tree algorithm, which inputs historical environmental indicator data and outputs preliminary evaluation scores. During the training process, the decision tree algorithm learned that soil quality (SQ) and air quality index (AQ) are key indicators for predicting scores, while vegetation coverage (VC) and water quality index (WQ) contribute relatively little.

[0119] The feature importance calculated by the decision tree is as follows: SQ = 40%, WQ = 20%, AQ = 35%, VC = 5%.

[0120] Based on the feature importance calculated by the decision tree, the preliminary weights are updated to obtain the updated first weight coefficients: SQ=0.4, WQ=0.15, AQ=0.35, VC=0.1.

[0121] Then, the updated weight coefficients are used to perform a weighted average on the current environmental indicators to calculate the new target evaluation score. For example, assume that the current environmental indicator values ​​are as follows: SQ = 80, WQ = 70, AQ = 60, VC = 90.

[0122] The new target evaluation score is calculated as follows: target evaluation score = 72.5.

[0123] By determining the importance of environmental indicators through the decision tree algorithm and updating the weights accordingly, it can be ensured that the final evaluation score more comprehensively reflects the indicators that have a significant impact on the ecological environment, thereby improving the accuracy and reliability of the evaluation.

[0124] In the specific implementation process, after obtaining the evaluation score by weighted averaging at least a plurality of the above-mentioned environmental indicators and the above-mentioned first weight coefficients corresponding to the above-mentioned environmental indicators, the above-mentioned method also includes the following steps: obtaining the change amplitude and change rate of the above-mentioned environmental indicators, wherein the above-mentioned change amplitude is the change amount of the above-mentioned environmental indicators from the first time point to the second time point, and the above-mentioned change rate is the speed at which the above-mentioned environmental indicators change within a unit time; obtaining the second weight coefficient and the third weight coefficient, wherein the above-mentioned second weight coefficient and the above-mentioned change amplitude are positively correlated, and the above-mentioned third weight coefficient and the above-mentioned change rate are positively correlated; calculating the above-mentioned change amplitude of the above-mentioned environmental indicators and the weighted average value of the above-mentioned second weight coefficient corresponding to the above-mentioned change amplitude to obtain the change amplitude score; calculating the above-mentioned change rate of the above-mentioned environmental indicators and the weighted average value of the above-mentioned third weight coefficient corresponding to the above-mentioned change rate to obtain the change rate score; calculating the average value of the above-mentioned evaluation score, the above-mentioned change amplitude score and the above-mentioned change rate score to obtain the comprehensive score.

[0125] In this scheme, the change range and rate of environmental indicators are quantified and the weights are adjusted based on these changes to obtain the final evaluation score, i.e. the comprehensive score. This approach introduces a dynamic assessment perspective by considering the changes in environmental indicators over time, providing a more comprehensive assessment of environmental conditions.

[0126] Specifically, the magnitude of change refers to the actual amount of change in an environmental indicator from one point in time to another. For example, the soil quality index changes from 50 to 60, and the magnitude of change is 10. The rate of change refers to the speed at which an environmental indicator changes per unit of time. If the soil quality index changes from 50 to 60 in one year, then the rate of change is 10 units per year.

[0127] Specifically, the second weight coefficient is positively correlated with the magnitude of change of environmental indicators. This means that indicators with large magnitude of change will be given higher weights because they have a greater impact on changes in environmental conditions. The third weight coefficient is positively correlated with the rate of change of environmental indicators. Similarly, indicators with high rates of change will receive higher weights because they may indicate a trend of rapid deterioration of environmental conditions.

[0128] Specifically, the change range score and change rate score of each environmental indicator are first calculated, that is, the change range and change rate are weighted averaged with the corresponding weight coefficient. Then, the evaluation score (i.e., the static indicator score), the change range score and the change rate score are added together, and the average is taken to obtain the final comprehensive score. This comprehensive score not only reflects the immediate status of the environmental indicators, but also takes into account their changing trends over time.

[0129] In some embodiments, after obtaining an evaluation score by weighted averaging at least a plurality of the above-mentioned environmental indicators and the above-mentioned first weight coefficients corresponding to the above-mentioned environmental indicators, the above-mentioned method further includes the following steps: constructing a strategy generation model, wherein the above-mentioned strategy generation model is trained using multiple sets of training data, and each set of training data in the above-mentioned multiple sets of training data includes historical environmental indicators, historical relevance, and historical evaluation scores obtained within a historical time period, and historical governance plans corresponding to the above-mentioned historical environmental indicators, the above-mentioned historical relevance, and the above-mentioned historical evaluation scores, wherein the historical governance plan is a historical strategy for improving the ecological environment of coal mining areas; inputting the above-mentioned environmental indicators, the above-mentioned relevance, and the above-mentioned evaluation scores into the above-mentioned strategy generation model to obtain a governance plan for the above-mentioned environmental indicators, the above-mentioned relevance, and the above-mentioned evaluation scores.

[0130] In this solution, the strategy generation model can provide personalized, data-based decision support for ecological and environmental governance in coal mining areas, helping managers take more scientific and effective measures to improve environmental conditions and promote sustainable development.

[0131] Specifically, the construction of the strategy generation model is based on multiple sets of training data, which include historical environmental indicator values, historical correlations between these indicators, historical evaluation scores, and corresponding historical governance plans. Through machine learning algorithms (such as decision trees, random forests, neural networks, etc.), the model learns the mapping relationship from environmental data to governance plans, that is, understands which governance plans have proven to be effective under which environmental conditions in the past, so as to predict the best governance strategy under similar environmental conditions in the future.

[0132] Specifically, multiple sets of training data are the basis for model construction. These data sets are usually collected by professional environmental monitoring agencies, including detailed environmental indicator data within a historical time span (such as several years), historical correlation data between corresponding indicators, historical evaluation scores calculated based on these data, and historical governance plans implemented for these evaluation scores and their effectiveness evaluation.

[0133] Specifically, the model receives current environmental indicators, relevance, and evaluation scores as input, and then uses the learned patterns and strategies to output a governance plan for the current environmental situation.

[0134] Specifically, by increasing the diversity and quantity of data, the generalization ability of the model can be improved, so that it can generate effective governance recommendations when facing different coal mining areas or different environmental conditions. The model can be designed to receive real-time environmental monitoring data, which means that governance solutions can be dynamically generated based on the latest environmental changes, improving response speed and governance effectiveness. Using multiple different machine learning models, such as ensemble learning methods, can improve the accuracy and stability of predictions and reduce the error of a single model. Combining the model with the knowledge of domain experts, the output of the model is further optimized and verified through expert rules or consultation mechanisms to ensure that the generated governance solutions are both scientific and practical. The effects of the governance solutions generated by the model after implementation are fed back to the model as new training data, and the model is continuously iterated and optimized so that it can better adapt to environmental changes and actual governance effects.

[0135] For example, collect environmental indicator data for the area over the past five years, including soil quality, water quality, air quality, vegetation coverage, etc. At the same time, record the relevance and evaluation scores of these indicators at different time points, as well as the historical governance plans implemented (such as greening projects, construction of wastewater treatment facilities, air quality monitoring plans, etc.) and the evaluation data of the results of these plans. Use the collected multiple sets of training data to train a supervised learning model, such as random forest. The training goal of the model is to learn the relationship between environmental indicators, relevance, evaluation scores and governance plans to ensure that the model can predict effective strategies. After the model training is completed, we can input the current environmental indicators, relevance and evaluation score data in real time. For example, the current soil quality evaluation score is low, but the vegetation coverage score is high, and there is a positive correlation between the two. The model predicts and generates governance plans based on the input data. For example, the model may recommend adding greening projects to further improve vegetation coverage, because this will indirectly improve soil quality; at the same time, given the deteriorating trend of air quality scores, the model may recommend strengthening air pollution control measures. Apply the governance plan generated by the model to the coal mining area to carry out environmental governance activities. After implementation, collect environmental data regularly to evaluate the governance effect, including the degree of improvement and changes in the relevance of environmental indicators. Feedback the implementation results to the model as new training data to continuously optimize the model so that it can generate more accurate and effective governance strategies. For example, if it is found that increasing greening projects and combining soil remediation measures can more effectively improve soil quality and vegetation coverage, then this new strategy combination should be included in the model's training data to guide future decisions.

[0136] In addition, the risk level can also be determined based on the evaluation score. First, a risk level system needs to be defined, which is usually divided into multiple levels such as low risk, medium risk, and high risk. The division of each level can be determined based on the threshold of environmental indicators, or based on expert evaluation and historical data statistics. For example, an evaluation score of 90 points or more may be defined as low risk, 70-89 points as medium risk, and below 70 points as high risk.

[0137] As time goes by and environmental conditions change, the risk level thresholds may need to be adjusted periodically. This can be achieved by collecting new environmental data, reassessing the distribution of indicators, and evaluating the effectiveness of historical governance solutions.

[0138] Based on the evaluation scores and risk levels, a risk warning system can be established. When the evaluation scores fall into the high-risk range, the system automatically triggers a warning and sends an alert to relevant departments, prompting them to take immediate measures.

[0139] Different risk levels require different governance strategies. Low risk levels may only require continuous monitoring, medium risk levels may require preliminary environmental governance activities, and high risk levels may require emergency intervention and a comprehensive environmental restoration plan. The decision support system can automatically recommend the best governance plan based on the risk level.

[0140] Risk levels are not only used for internal decision-making, but also for communication with the public, the media and other stakeholders. A clear risk level classification helps improve the public's understanding of environmental conditions and strengthen support for environmental protection measures.

[0141] In summary, in the scheme of the present application, by collecting various relevant data indicators of the coal mining area, constructing an indicator correlation analysis model and calculating the correlation coefficient, the correlation between different indicators is determined. In addition, the present invention also uses a weight distribution method to reflect the importance of each indicator in the ecological environment evaluation. This method can improve the accuracy and reliability of the ecological environment evaluation of the coal mining area, and provide technical support for environmental protection in the process of coal mining and processing.

[0142] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the method for evaluating the ecological environment in a coal mine area of ​​the present application will be described in detail below in conjunction with specific embodiments.

[0143] Embodiment 1:

[0144] Taking a coal mining area as an example, we collected ten relevant data indicators: soil quality, water quality index, vegetation coverage, air quality index, noise level, wastewater discharge, waste gas emission, biodiversity index, heavy metal content, and green coverage area. Based on the collected data, we constructed a linear regression model, calculated the correlation coefficient between each indicator, and further interpreted and analyzed the correlation coefficient results.

[0145] According to the calculation results, the following conclusions are drawn:

[0146] The correlation coefficient between soil quality and water quality index is 0.75;

[0147] The correlation coefficient between soil quality and vegetation coverage is -0.6;

[0148] The correlation coefficient between soil quality and air quality index is -0.52;

[0149] The correlation coefficient between soil quality and noise level is -0.45;

[0150] The correlation coefficient between water quality index and vegetation coverage is -0.59;

[0151] The correlation coefficient between water quality index and air quality index is -0.68;

[0152] The correlation coefficient between water quality index and noise level is -0.35;

[0153] The correlation coefficient between vegetation coverage and air quality index is 0.58;

[0154] The correlation coefficient between vegetation coverage and noise level is 0.4;

[0155] The correlation coefficient between the air quality index and noise level is -0.48.

[0156] Based on the above results, the following conclusions can be drawn:

[0157] Soil quality and water quality indicators are positively correlated, indicating that better soil quality is usually accompanied by good water quality.

[0158] There was a negative correlation between soil quality and vegetation cover, air quality index, and noise level, indicating that poor soil quality is often accompanied by lower vegetation cover and higher air pollution and noise levels.

[0159] There is a negative correlation between water quality indicators and vegetation coverage and air quality index, that is, areas with better water quality usually have higher vegetation coverage and lower air pollution.

[0160] There is also a correlation between vegetation coverage and air quality index and noise level, meaning that higher vegetation coverage may be associated with better air quality and lower noise levels.

[0161] There is a negative correlation between the air quality index and noise level, indicating that higher air pollution is often accompanied by higher noise levels.

[0162] Embodiment 2:

[0163] In the ecological environment evaluation of another coal mining area, the method of this application was used for evaluation. After correlation coefficient calculation and interpretation analysis, the following conclusions were drawn: the correlation coefficient between water quality index and soil quality was 0.82, the correlation coefficient between soil quality and vegetation coverage was -0.75, and the correlation coefficient between water quality index and vegetation coverage was -0.68. According to the weight distribution, the water quality index was given a higher weight to reflect its importance to the ecological environment evaluation of the coal mining area.

[0164] The embodiment of the present application also provides an ecological environment evaluation device for a coal mine area. It should be noted that the ecological environment evaluation device for a coal mine area in the embodiment of the present application can be used to execute the ecological environment evaluation method for a coal mine area provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been described will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0165] The following is an introduction to the ecological environment evaluation device for the coal mine area provided in the embodiment of the present application.

[0166] Figure 3 1 is a structural block diagram of an ecological environment evaluation device for a coal mine area according to an embodiment of the present application. Figure 3 As shown, the device comprises:

[0167] The first acquisition unit 10 is used to acquire multiple environmental indicators of the coal mine area, wherein the environmental indicators include one or more of soil quality, water quality index, vegetation coverage, air quality index, noise level, wastewater discharge, waste gas discharge, biodiversity index, heavy metal content, and green coverage area;

[0168] A second acquisition unit 20 is used to acquire the correlation between the above environmental indicators, wherein the above correlation is the degree of association between any two of the above environmental indicators;

[0169] A first determining unit 30, configured to determine a first weight coefficient according to the correlation, wherein the first weight coefficient and the correlation are positively correlated;

[0170] The first evaluation unit 40 is used to perform weighted averaging based on at least a plurality of the environmental indicators and the first weight coefficients corresponding to the environmental indicators to obtain an evaluation score.

[0171] Through this embodiment, the trend of the change impact between two environmental indicators can be compared. The two environmental indicators may be positively correlated or negatively correlated. Then, weights are assigned to each indicator based on the correlation between the environmental indicators, which can reflect the relative importance of each indicator in the overall ecological environment evaluation. For example, if the water quality indicator shows a high correlation with multiple other indicators, then during the evaluation, the water quality indicator may be given a higher weight to reflect its core position in the ecological environment of the coal mining area. Since the weight allocation method of this scheme can reflect the correlation between the indicators, compared with the traditional method of treating each environmental indicator independently, the scheme of this application can more comprehensively evaluate the environmental status, avoid the evaluation bias caused by ignoring the interaction between indicators, and thus improve the accuracy of the environmental evaluation results.

[0172] In a specific implementation process, the second acquisition unit includes a first calculation module, and the first calculation module is used to calculate the above correlation according to a first formula, wherein the above first formula is:

[0173]

[0174] ρ(X,Y) represents the above correlation, X represents the first environmental index, Y represents the second environmental index, E represents the mathematical expectation, μ X represents the covariance of the first environmental index, μ Y represents the covariance of the second environmental index, σ X represents the standard deviation of the first environmental index, σ Y represents the standard deviation of the second environmental index mentioned above, represents the average value of the first environmental index mentioned above, represents the average value of the second environmental indicator, and n represents the number of the environmental indicators. The above formula is only exemplary, and any variation of the formula falls within the protection scope of this application.

[0175] In this solution, the correlation between environmental indicators can be more accurately calculated using the first formula of the present application.

[0176] In some embodiments, the second acquisition unit includes a second calculation module, and the second calculation module is used to calculate the above correlation according to a second formula, wherein the above second formula is:

[0177]

[0178] c(X,Y) represents the above correlation, |X| represents the absolute value of the above first environmental index, and |Y| represents the absolute value of the above second environmental index. The above formula is only exemplary, and any variation of the formula falls within the protection scope of this application.

[0179] In this solution, the correlation between environmental indicators can be more accurately calculated using the second formula of the present application.

[0180] During the specific implementation process, the second acquisition unit includes a construction module and a processing module. The construction module is used to construct a correlation generation model, wherein the above-mentioned correlation generation model is obtained by training using multiple groups of training data, and each group of training data in the above-mentioned multiple groups of training data includes historical environmental indicators obtained within a historical time period and historical correlations corresponding to the above-mentioned historical environmental indicators; the processing module is used to input the above-mentioned environmental indicators into the above-mentioned correlation generation model to obtain the above-mentioned correlation corresponding to the above-mentioned environmental indicators.

[0181] In this solution, the correlation generation model can learn and capture the complex relationships between indicators in historical data, including linear and nonlinear relationships, as well as the dynamic changes of time series data. Based on limited historical data, the correlation of indicators in future or current data sets can be predicted, so that more accurate correlation can be obtained when dealing with nonlinear relationships, time series data, and a large number of indicators.

[0182] In some embodiments, the above-mentioned device also includes a second determination unit, an updating unit and a second evaluation unit. The second determination unit is used to use a decision tree algorithm to determine the importance score of each of the above-mentioned environmental indicators to the above-mentioned evaluation score after performing weighted averaging based on at least multiple of the above-mentioned environmental indicators and the above-mentioned first weight coefficients corresponding to the above-mentioned environmental indicators to obtain an evaluation score; the updating unit is used to update the above-mentioned first weight coefficient based on the above-mentioned importance score to obtain an updated first weight coefficient; the second evaluation unit is used to perform weighted averaging based on multiple of the above-mentioned environmental indicators and the above-mentioned updated first weight coefficients corresponding to the above-mentioned environmental indicators to obtain a target evaluation score.

[0183] In this scheme, the importance of environmental indicators is determined and the weights are updated through the decision tree algorithm, which can significantly improve the accuracy, efficiency and adaptability of ecological environmental evaluation, while also enhancing the interpretability and application value of the evaluation results.

[0184] In the specific implementation process, the device also includes a third acquisition unit, a fourth acquisition unit, a first calculation unit, a second calculation unit and a third evaluation unit. The third acquisition unit is used to obtain the change amplitude and change rate of the environmental indicator after obtaining the evaluation score by weighted averaging at least a plurality of the environmental indicators and the first weight coefficient corresponding to the environmental indicator, wherein the change amplitude is the change amount of the environmental indicator from the first time point to the second time point, and the change rate is the speed at which the environmental indicator changes within a unit time; the fourth acquisition unit is used to obtain the second weight coefficient and the third weight coefficient, wherein the second weight coefficient is positively correlated with the change amplitude, and the third weight coefficient is positively correlated with the change rate; the first calculation unit is used to calculate the weighted average of the change amplitude of the environmental indicator and the second weight coefficient corresponding to the change amplitude to obtain the change amplitude score; the second calculation unit is used to calculate the weighted average of the change rate of the environmental indicator and the third weight coefficient corresponding to the change rate to obtain the change rate score; the third evaluation unit is used to calculate the average of the evaluation score, the change amplitude score and the change rate score to obtain a comprehensive score.

[0185] In this scheme, the change range and rate of environmental indicators are quantified and the weights are adjusted based on these changes to obtain the final evaluation score, i.e. the comprehensive score. This approach introduces a dynamic assessment perspective by considering the changes in environmental indicators over time, providing a more comprehensive assessment of environmental conditions.

[0186] In some embodiments, the above-mentioned device also includes a construction unit and a processing unit, the construction unit is used to construct a strategy generation model after obtaining an evaluation score by weighted averaging at least a plurality of the above-mentioned environmental indicators and the above-mentioned first weight coefficients corresponding to the above-mentioned environmental indicators, wherein the above-mentioned strategy generation model is trained using multiple sets of training data, each set of the above-mentioned multiple sets of training data includes historical environmental indicators, historical relevance, historical evaluation scores obtained within a historical time period, and historical governance plans corresponding to the above-mentioned historical environmental indicators, the above-mentioned historical relevance and the above-mentioned historical evaluation scores, wherein the historical governance plan is a historical strategy for improving the ecological environment of the coal mining area; the processing unit is used to input the above-mentioned environmental indicators, the above-mentioned relevance and the above-mentioned evaluation scores into the above-mentioned strategy generation model to obtain a governance plan for the above-mentioned environmental indicators, the above-mentioned relevance and the above-mentioned evaluation scores.

[0187] In this solution, the strategy generation model can provide personalized, data-based decision support for ecological and environmental governance in coal mining areas, helping managers take more scientific and effective measures to improve environmental conditions and promote sustainable development.

[0188] The above-mentioned ecological environment evaluation device for coal mine area includes a processor and a memory, and the above-mentioned first acquisition unit, second acquisition unit, first determination unit and first evaluation unit are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions. The above-mentioned modules are all located in the same processor; or, the above-mentioned modules are located in different processors in the form of any combination.

[0189] The processor includes a kernel, and the kernel retrieves the corresponding program unit from the memory. One or more kernels can be provided, and the problem of low accuracy of the evaluation result of the coal mine environment in the prior art can be solved by adjusting the kernel parameters.

[0190] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0191] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the ecological environment evaluation method for the coal mine area.

[0192] An embodiment of the present invention provides a processor, which is used to run a program, wherein the ecological environment evaluation method of the coal mine area is executed when the program is run.

[0193] The embodiment of the present invention provides a device, the device includes a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, at least the steps of the method for evaluating the ecological environment of the coal mine area are implemented. The device in this article can be a server, a PC, a PAD, a mobile phone, etc.

[0194] A computer program product includes a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the ecological environment evaluation method for the coal mine area in each embodiment of the present application are implemented.

[0195] The present application also provides a mining area environmental assessment system, comprising one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any of the above-mentioned ecological environment assessment methods for coal mining areas.

[0196] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0197] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0198] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0199] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0200] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0201] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0202] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0203] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0204] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0205] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for evaluating the ecological environment of a coal mining area, characterized in that: include: Acquire multiple environmental indicators of the coal mine area, wherein the environmental indicators include one or more of soil quality, water quality index, vegetation coverage, air quality index, noise level, wastewater discharge, waste gas discharge, biodiversity index, heavy metal content, and green coverage area; Obtaining the correlation between the environmental indicators, wherein the correlation is the degree of association between changes in any two of the environmental indicators; Determine a first weight coefficient according to the correlation, wherein the first weight coefficient and the correlation are positively correlated; An evaluation score is obtained by performing a weighted average based on at least a plurality of the environmental indicators and the first weight coefficients corresponding to the environmental indicators.

2. The method according to claim 1, characterized in that Obtaining the correlation between the environmental indicators, including: The correlation is calculated according to a first formula, wherein the first formula is: ρ(X,Y) represents the correlation, X represents the first environmental index, Y represents the second environmental index, E represents the mathematical expectation, μ X represents the covariance of the first environmental indicator, μ Y represents the covariance of the second environmental indicator, σ X represents the standard deviation of the first environmental indicator, σ Y represents the standard deviation of the second environmental indicator, represents the average value of the first environmental indicator, represents the average value of the second environmental indicator, and n represents the number of the environmental indicators.

3. The method according to claim 2, characterized in that Obtaining the correlation between the environmental indicators, including: The correlation is calculated according to a second formula, wherein the second formula is: c(X, Y) represents the correlation, |X| represents the absolute value of the first environmental indicator, and |Y| represents the absolute value of the second environmental indicator.

4. The method according to claim 1, characterized in that: Obtaining the correlation between the environmental indicators, including: Constructing a relevance generation model, wherein the relevance generation model is obtained by training using multiple sets of training data, each set of training data in the multiple sets of training data includes historical environmental indicators obtained within a historical time period and historical relevance corresponding to the historical environmental indicators; The environmental indicator is input into the correlation generation model to obtain the correlation corresponding to the environmental indicator.

5. The method according to claim 1, characterized in that After obtaining an evaluation score by performing weighted averaging at least according to the plurality of environmental indicators and the first weight coefficients corresponding to the environmental indicators, the method further includes: Using a decision tree algorithm, determine the importance score of each of the environmental indicators to the evaluation score; Updating the first weight coefficient according to the importance score to obtain an updated first weight coefficient; A target evaluation score is obtained by performing weighted averaging based on the plurality of environmental indicators and the updated first weight coefficients corresponding to the environmental indicators.

6. The method according to claim 1, characterized in that After obtaining an evaluation score by performing weighted averaging at least according to the plurality of environmental indicators and the first weight coefficients corresponding to the environmental indicators, the method further includes: Obtaining a change range and a change rate of the environmental indicator, wherein the change range is the amount of change of the environmental indicator from a first time point to a second time point, and the change rate is the speed at which the environmental indicator changes in a unit time; Acquire a second weight coefficient and a third weight coefficient, wherein the second weight coefficient is positively correlated with the change amplitude, and the third weight coefficient is positively correlated with the change rate; Calculating the change range of the environmental indicator and a weighted average of the second weight coefficients corresponding to the change range to obtain a change range score; Calculating the change rate of the environmental indicator and a weighted average of the third weight coefficient corresponding to the change rate to obtain a change rate score; The average of the evaluation score, the change range score and the change rate score is calculated to obtain a comprehensive score.

7. The method according to any one of claims 1 to 6, characterized in that: After obtaining an evaluation score by performing weighted averaging at least according to the plurality of environmental indicators and the first weight coefficients corresponding to the environmental indicators, the method further includes: Constructing a strategy generation model, wherein the strategy generation model is trained using multiple sets of training data, each set of training data in the multiple sets of training data includes historical environmental indicators, historical relevance, and historical evaluation scores obtained within a historical time period, and historical governance plans corresponding to the historical environmental indicators, the historical relevance, and the historical evaluation scores, wherein the historical governance plans are historical strategies for improving the ecological environment of coal mining areas; The environmental indicators, the relevance and the evaluation score are input into the strategy generation model to obtain a governance plan based on the environmental indicators, the relevance and the evaluation score.

8. An ecological environment evaluation device for a coal mine area, characterized in that: include: A first acquisition unit is used to acquire multiple environmental indicators of the coal mine area, wherein the environmental indicators include one or more of soil quality, water quality index, vegetation coverage, air quality index, noise level, wastewater discharge, waste gas discharge, biodiversity index, heavy metal content, and green coverage area; A second acquisition unit is used to acquire the correlation between the environmental indicators, wherein the correlation is the degree of association between changes in any two of the environmental indicators; A first determining unit, configured to determine a first weight coefficient according to the correlation, wherein the first weight coefficient and the correlation are positively correlated; The first evaluation unit is used to perform weighted averaging at least according to a plurality of the environmental indicators and the first weight coefficients corresponding to the environmental indicators to obtain an evaluation score.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for evaluating the ecological environment of a coal mine area described in any one of claims 1 to 7 are implemented.

10. A mining area environmental assessment system, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing the ecological environment evaluation method of a coal mining area as described in any one of claims 1 to 7.

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