Ecological assessment method, system and equipment for acid underground water of abandoned mine and storage medium

Abstract

The invention belongs to the field of ecological assessment, and particularly relates to an ecological assessment method, system and equipment for abandoned mine acidic underground water and a storage medium, physical and chemical parameters of the abandoned mine acidic underground water are obtained through sensors and laboratory analysis, and the physical and chemical parameters comprise the pH value, the heavy metal concentration, the dissolved oxygen content, the temperature and the conductivity; denoising, normalizing and interpolating the acquired physical and chemical parameters to eliminate abnormal values and ensure the integrity of the data; based on a machine learning algorithm, historical data are utilized to train an ecological risk assessment model, and the model comprises a support vector machine, a random forest and a neural network; according to a model output result, dividing the ecological risk into three levels of low risk, medium risk and high risk, and generating a corresponding risk report; and according to the risk level, restoration and treatment suggestions including an acidic water treatment scheme, an underground water monitoring plan and ecological restoration measures are automatically generated, and the method has the effects of providing a more comprehensive and more detailed assessment result, thereby providing a scientific basis for an environmental protection decision.

Description

Technical Field

[0001] The present invention relates to the field of ecological assessment, and in particular to an abandoned mine acidic groundwater ecological assessment method, system, equipment and storage medium. Background Art

[0002] With the increase in mining activities worldwide, abandoned mines and their associated acidic groundwater are attracting increasing attention. Abandoned mines often contain large amounts of metal sulfides. When exposed to groundwater, these compounds decompose to produce sulfuric acid, which lowers the groundwater pH and creates an acidic environment. Acidic groundwater not only pollutes the groundwater resources themselves but can also affect surrounding soils and ecosystems through surface runoff and even enter groundwater systems, further impacting drinking water safety and aquatic ecosystems. Therefore, developing effective assessment methods and monitoring systems to timely and accurately evaluate the environmental impacts of acidic groundwater in abandoned mines is crucial for protecting the environment and safeguarding human health.

[0003] Traditional assessment methods are often based on monitoring and analysis of the acidic groundwater environment, but these methods are usually unable to fully reflect the true condition of the ecosystem. For example, simply measuring pH and metal ion concentration can only provide direct physical and chemical information of acidic groundwater, lacking a comprehensive understanding of the ecosystem. In addition, many assessment methods are relatively complex and costly in operation, requiring professionals to conduct long-term data collection and analysis, and are difficult to apply to the quick and easy needs of practical applications. Therefore, there is an urgent need for a method and system that can more quickly and accurately assess the ecological impact of acidic groundwater in abandoned mines, so as to better manage abandoned mine resources and protect the environmental ecology.

[0004] Existing ecological assessment systems typically focus on monitoring a single dimension, such as water quality or biodiversity, without considering the interactions and impacts between them. This one-way monitoring not only limits information but also leads to the neglect of key ecological factors, such as microorganisms and dissolved organic matter, which are directly related to ecosystem health and stability. Therefore, innovative assessment methods and systems should comprehensively consider multiple influencing factors, providing more comprehensive and detailed assessment results, thereby providing a scientific basis for environmental protection decisions.

[0005] In response to the above technical defects, a solution for the ecological assessment of acidic groundwater in abandoned mines, including a method, system, equipment and storage medium, is proposed. Summary of the Invention

[0006] The main purpose of the present invention is to provide a method, system, equipment and storage medium for ecological assessment of acidic groundwater in abandoned mines, aiming to solve the technical problems in the prior art.

[0007] The present invention proposes an ecological assessment method for acidic groundwater in abandoned mines, comprising: Obtaining physical and chemical parameters of the acidic groundwater in abandoned mines through sensors and laboratory analysis, wherein the physical and chemical parameters include pH value, heavy metal concentration, dissolved oxygen content, temperature, and conductivity; De-noising, normalization and interpolation processing are performed on the collected physical and chemical parameters to eliminate outliers and ensure data integrity; Using historical data to train an ecological risk assessment model based on a machine learning algorithm, wherein the ecological risk assessment model includes a support vector machine, a random forest, and a neural network; Based on the model output, the ecological risks are divided into three levels: low risk, medium risk and high risk, and corresponding risk reports are generated; Automatically generate remediation and treatment recommendations based on risk levels, where the treatment recommendations include acidic water treatment plans, groundwater monitoring plans, and ecological restoration measures.

[0008] Furthermore, the step of obtaining the physical and chemical parameters of the acidic groundwater in abandoned mines through sensors and laboratory analysis includes: Select representative sampling points based on the geological structure and groundwater distribution characteristics of the abandoned mine, and the sampling points should cover different areas of the mine; Use portable and fixed sensors to monitor groundwater physical and chemical parameters in real time, and calibrate the sensors before use to ensure the accuracy of the measurement results; Calibration is performed using standard solutions and samples of known concentration, which are then transmitted to a computer and cloud storage via a data logger and wireless transmission module.

[0009] Furthermore, the steps of performing denoising, normalization and interpolation processing on the collected physical and chemical parameters include: Import the collected physical and chemical parameter data into the data processing tool and draw a data distribution graph; Initially identify outliers, check the distribution of processed data to ensure that outliers have been removed, and check the distribution of normalized data to ensure that all parameters are within the same range; Use data processing tools to identify missing values ​​in the data, apply the selected interpolation method to each physicochemical parameter, and compare the data distribution before and after interpolation to ensure that the interpolated data are reasonable and do not introduce bias; Integrate the denoised, normalized, and interpolated data into a data frame and save the processed data in a commonly used format to ensure data traceability and accessibility; Check that the processed data is as expected and ensure that no new outliers and errors have been introduced.

[0010] Furthermore, the step of training the ecological risk assessment model using historical data based on a machine learning algorithm includes: Collecting historical data, wherein the historical data includes physical and chemical parameters and corresponding ecological risk levels; Obtain data from sensor monitoring and laboratory analysis in abandoned mines to ensure data diversity and representativeness, covering different time periods and different regions; Clean and organize data to make it suitable for input to machine learning models, and perform logarithmic and other transformations on skewed data to improve data distribution; Select the most important physical and chemical parameters for ecological risk assessment, calculate the correlation between each parameter and ecological risk, and select the parameters with high correlation; Lasso regression and random forest feature importance analysis were used for feature selection, and ecological and geological knowledge were combined to select machine learning algorithms suitable for ecological risk assessment.

[0011] Furthermore, the steps of classifying ecological risks into three levels of low risk, medium risk, and high risk according to the model output results and generating corresponding risk reports include: Determine whether the model output is a probability value or a direct category label. If the output is a probability value, set thresholds for high risk, medium risk, and low risk. Low risk means the risk probability is low and the impact on the ecosystem is small; medium risk means there is a certain risk and requires attention and monitoring; high risk means the risk probability is high and immediate remediation measures are required; Classify each sample into the corresponding risk level according to the model output results; Collect relevant data, including physical and chemical parameters, geographic location, and historical data, and ensure that the data is accurate, complete, and complies with reporting format requirements; Propose specific restoration and remediation recommendations for different risk levels, use charts to display risk distribution, use GIS maps to display the geographical distribution of risks, summarize assessment results and make comprehensive recommendations; Use Python's report generation library and R's report generation tools to integrate text, charts, and maps into reports, ensuring coherence and logical clarity; Team members and technical experts will review the report content to ensure that the data is accurate and the analysis is reasonable. The report will then be submitted to relevant management departments and experts for review and will be modified and improved based on feedback.

[0012] Furthermore, the step of automatically generating repair and governance suggestions based on risk levels includes: Select appropriate acid water treatment solutions based on risk level; Low risk, using simple chemical neutralization treatment and regular pH testing to ensure that acidic water is effectively neutralized; For medium risk, a combination of chemical neutralization and physical isolation is used to build small-scale sewage treatment facilities to centrally treat acidic water. High risk: Use a combination of advanced chemical neutralization, bioremediation, and physical isolation to build large-scale sewage treatment facilities to deeply treat acidic water; Implement groundwater isolation projects to prevent the spread of acidic water and develop appropriate groundwater monitoring plans based on risk levels; Automatically generate remediation and governance recommendations based on risk levels and predefined rules, using templated report formats; Fill in corresponding governance recommendations based on risk levels, write scripts in Python and other programming languages, and generate governance recommendations based on model output results.

[0013] The present invention also provides an abandoned mine acidic groundwater ecological assessment system, comprising: A data acquisition module is used to obtain the physical and chemical parameters of the acidic groundwater in abandoned mines through sensors and laboratory analysis, wherein the physical and chemical parameters include pH value, heavy metal concentration, dissolved oxygen content, temperature and conductivity; The preprocessing module is used to perform denoising, normalization, and interpolation on the collected physical and chemical parameters to eliminate outliers and ensure data integrity; A model building module for training an ecological risk assessment model using historical data based on a machine learning algorithm, wherein the ecological risk assessment model includes a support vector machine, a random forest, and a neural network; The risk assessment module is used to classify ecological risks into three levels: low risk, medium risk, and high risk based on the model output results, and generate corresponding risk reports; The optimization suggestion module is used to automatically generate remediation and treatment suggestions based on the risk level, wherein the treatment suggestions include acidic water treatment plans, groundwater monitoring plans and ecological restoration measures.

[0014] Furthermore, the data acquisition uses portable and fixed sensors to monitor the physical and chemical parameters of groundwater in real time, and uses standard solutions and samples of known concentrations to calibrate the sensors to ensure the accuracy of the measurement results. The sensor monitoring data is transmitted to a computer and cloud storage through a data collector and a wireless transmission module. Historical data is used to train an ecological risk assessment model. The model input includes physical and chemical parameter data, geological structure data, surrounding environmental data, and historical pollution data. The model output includes ecological risk levels and risk reports. Spatial analysis is performed based on a geographic information system to predict the spread range of acidic groundwater. Combined with geological and environmental data, the potential impact area of ​​the pollution is assessed. Based on the risk level, remediation and treatment recommendations are generated, including acidic water treatment plans, groundwater monitoring plans, and ecological restoration measures. The remediation cost and time are estimated to provide an economically feasible treatment plan.

[0015] According to one aspect of the present invention, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method for ecological assessment of acidic groundwater in abandoned mines when executing the computer program.

[0016] According to one aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for ecological assessment of acidic groundwater in abandoned mines are implemented.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. In a method for ecological assessment of acidic groundwater in abandoned mines according to the present invention, the physical and chemical parameters of acidic groundwater in abandoned mines, including pH value, heavy metal concentration, dissolved oxygen content, temperature and conductivity, are obtained through sensors and laboratory analysis; the collected physical and chemical parameters are denoised, normalized and interpolated to eliminate outliers and ensure data integrity; based on a machine learning algorithm, an ecological risk assessment model is trained using historical data, and the model includes a support vector machine, a random forest and a neural network; based on the model output results, the ecological risk is divided into three levels: low risk, medium risk and high risk, and a corresponding risk report is generated; based on the risk level, remediation and governance recommendations are automatically generated, including acidic water treatment plans, groundwater monitoring plans and ecological restoration measures, which has the effect of providing more comprehensive and detailed assessment results, thereby providing a scientific basis for environmental protection decision-making.

[0018] 2. In an ecological assessment system for acidic groundwater in abandoned mines of the present invention, a data acquisition module is used to obtain the physical and chemical parameters of acidic groundwater in abandoned mines through sensors or laboratory analysis, including pH value, heavy metal concentration, dissolved oxygen content, temperature and conductivity; a data preprocessing module is used to denoise, normalize and interpolate the collected physical and chemical parameters to eliminate outliers and ensure the integrity of the data; a model construction module is used to train an ecological risk assessment model based on a machine learning algorithm, and the model includes but is not limited to a support vector machine, a random forest or a neural network; a risk assessment module is used to divide the ecological risk into three levels of low risk, medium risk and high risk according to the output results of the model, and generate a corresponding risk report; an optimization suggestion module is used to automatically generate repair or governance suggestions according to the risk level, including acidic water treatment plans, groundwater monitoring plans and ecological restoration measures, which has the effect of intelligent supervision of groundwater ecological assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 Schematic diagram of a method flow according to an embodiment of the present invention.

[0020] Figure 2 FIG. 1 is a schematic diagram of a system structure according to an embodiment of the present invention.

[0021] Figure 3 This is a schematic diagram of the internal structure of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] like Figure 1 As shown, the present application provides an ecological assessment method for acidic groundwater in abandoned mines, comprising: S1: Obtain the physical and chemical parameters of acidic groundwater in abandoned mines through sensors and laboratory analysis, including pH, heavy metal concentration, dissolved oxygen content, temperature and conductivity; S2: De-noising, normalization and interpolation of the collected physical and chemical parameters to eliminate outliers and ensure data integrity; S3: Based on machine learning algorithms, using historical data to train ecological risk assessment models, the models include support vector machines, random forests and neural networks; S4: Based on the model output, the ecological risks are divided into three levels: low risk, medium risk and high risk, and corresponding risk reports are generated; S5: Automatically generate remediation and governance recommendations based on risk levels, including acidic water treatment plans, groundwater monitoring plans, and ecological restoration measures.

[0024] In one embodiment, the geographical location and geological structure, using a geographic information system to obtain the location of the mine, the surrounding topography and geological structure data, and the mining history, ore deposit type and hydrogeological conditions of the mine through geological exploration reports, groundwater monitoring data including collecting groundwater samples from the mine and its surrounding areas, measuring pH value, sulfate ion concentration, heavy metal (such as lead, mercury, cadmium) concentration, dissolved oxygen content and other indicators, recording groundwater flow, flow direction and water temperature data, soil and vegetation conditions including collecting soil samples, analyzing their pH, organic matter content and heavy metal content, investigating the types, coverage and health of surrounding vegetation, and evaluating the overall condition of the ecosystem, historical data including collecting mining records before the mine was closed, drainage Record and monitor the surrounding environment data to understand the causes of acidic groundwater and its historical changing trends. Groundwater chemical analysis includes using laboratory analytical methods to determine the pH value, sulfate ion concentration and heavy metal content of groundwater, assessing its acidification degree and pollution level, and analyzing the changing trend of acidic groundwater by comparing historical data to determine its impact on the surrounding environment. Ecological risk assessment model: Use ecological risk assessment models (such as the Canadian Ecological Risk Assessment Model CERAP) to quantitatively assess the ecological risk of acidic groundwater. Based on the assessment results, the risk is divided into three levels: low risk, medium risk and high risk. Geographic Information System (GIS) analysis includes using GIS technology to draw groundwater distribution maps and Flow path map, analyze the potential impact of acidic groundwater on surrounding water bodies, soil and vegetation. The risk level classification includes low risk, which means the concentration of acidic groundwater is low and has little impact on the surrounding ecosystem; medium risk, which means the concentration of acidic groundwater is high and has a certain impact on the surrounding soil and vegetation; high risk, which means the concentration of acidic groundwater is extremely high and poses a serious threat to the surrounding ecosystem and human health. The treatment suggestions include regular monitoring of the acidification of groundwater and soil in low-risk areas, conducting comprehensive monitoring once a year, strengthening vegetation protection in surrounding areas, preventing soil erosion, and implementing small-scale soil improvement measures, such as applying lime to neutralize acidity; and increasing the monitoring frequency in medium-risk areas, conducting groundwater and soil monitoring once a quarter, and implementing acidic water treatment. Treatment projects, such as building small sewage treatment facilities, neutralizing acidic groundwater, carrying out vegetation restoration projects, planting acid-resistant plants, improving the regional ecological environment, implementing soil remediation projects, using chemical remediation and bioremediation technologies to improve soil quality, and implementing emergency treatment measures in high-risk areas. Immediately build large-scale sewage treatment facilities, carry out deep treatment of acidic groundwater, implement groundwater isolation projects to prevent acidic water from spreading to surrounding areas, carry out large-scale vegetation restoration and soil remediation projects, rebuild the ecosystem, implement a real-time groundwater monitoring system, conduct comprehensive monitoring once a week to ensure the effectiveness of treatment measures, and implement treatment measures based on the assessment results and treatment recommendations, formulate a detailed implementation plan, and allocate resources and personnel.Acidic water treatment, groundwater monitoring, and ecological restoration measures were implemented strictly according to plan. Effectiveness evaluation and verification regularly tracked the effectiveness of treatment measures and assessed changes in ecological risks. By comparing monitoring data before and after treatment, we verified the effectiveness of treatment measures. Based on the evaluation results, we adjusted treatment measures to ensure the achievement of treatment goals. Through the implementation of this ecological assessment method, we successfully conducted a comprehensive assessment of the ecological risks of acidic groundwater in abandoned mines and proposed targeted treatment recommendations. These measures will help improve the regional ecological environment and protect the health and safety of surrounding residents. We recommend further strengthening monitoring and treatment in the future to ensure the sustainable development of the ecosystem.

[0025] Specifically, the steps of obtaining the physical and chemical parameters of acidic groundwater in abandoned mines through sensors and laboratory analysis include selecting representative sampling points based on the geological structure and groundwater distribution characteristics of the abandoned mine. The sampling points should cover different areas of the mine, and use portable and fixed sensors to monitor the physical and chemical parameters of groundwater in real time. Before use, the sensors are calibrated to ensure the accuracy of the measurement results. Standard solutions and samples of known concentrations are used for calibration, and the data are transmitted to computers and cloud storage through data collectors and wireless transmission modules.

[0026] Specifically, the steps of denoising, normalizing and interpolating the collected physical and chemical parameters to eliminate outliers and ensure the integrity of the data include importing the collected physical and chemical parameter data into a data processing tool, drawing a data distribution graph, preliminarily identifying outliers, checking the distribution of the processed data to ensure that the outliers have been removed, checking the distribution of the normalized data to ensure that all parameters are in the same range, using the data processing tool to identify missing values ​​in the data, applying the selected interpolation method to each physical and chemical parameter, comparing the data distribution before and after interpolation to ensure that the interpolated data is reasonable and does not introduce bias, integrating the denoised, normalized and interpolated data into a data frame, saving the processed data in a commonly used format to ensure the traceability and accessibility of the data, checking whether the processed data meets expectations, and ensuring that no new outliers and errors are introduced.

[0027] Specifically, the steps of training an ecological risk assessment model using historical data based on a machine learning algorithm include collecting historical data, including physical and chemical parameters and corresponding ecological risk levels; obtaining data from sensor monitoring and laboratory analysis of abandoned mines to ensure the diversity and representativeness of the data, covering different time periods and different regions, cleaning and organizing the data to make it suitable for the input of the machine learning model, performing logarithmic transformation and other transformations on skewed distributed data to improve the data distribution, selecting the physical and chemical parameters that are most important for ecological risk assessment, calculating the correlation between each parameter and the ecological risk, selecting parameters with high correlation, using methods such as Lasso regression and random forest feature importance analysis for feature selection, and combining ecological and geological knowledge to select a machine learning algorithm suitable for ecological risk assessment. In step S2, the weight of each parameter is determined according to the importance of the ecological impact. For example, the pH value may have a higher weight because it directly affects the acidity and alkalinity of the water. Data collection and standardization: collect the measured data of each parameter and standardize each parameter so that it is between 0 and 1. The weighted average calculation is performed, and the standardized parameter values ​​are multiplied by their respective weights. The sum is obtained to obtain a comprehensive evaluation index. Based on the comprehensive evaluation index, the ecological health status of the groundwater is judged. Specifically, the collected data are comprehensively managed and the comprehensive evaluation index is calculated as follows: ; in, Comprehensive evaluation index, i is the current number of components, k is the number of principal components, is the eigenvalue, is the eigenvector; The support vector regression model is used to predict groundwater ecological health. The prediction model is as follows: ; in, For the prediction results of groundwater ecological health, is the kernel function, is the Lagrange multiplier, is the bias term, is the value of the current parameter x, The value of the y parameter currently associated with the x parameter.

[0028] Specifically, the steps of dividing ecological risks into three levels: low risk, medium risk and high risk according to the output results of the model, and generating corresponding risk reports include determining whether the model output is a probability value or a direct category label. If the output is a probability value, setting thresholds for high risk, medium risk and low risk. Low risk means low risk probability and small impact on the ecosystem. Medium risk means certain risk and needs attention and monitoring. High risk means high risk probability and needs immediate control measures. According to the output results of the model, each sample is classified into the corresponding risk level, and relevant data, including physical and chemical parameters, geographical location, and historical data, are collected to ensure that the data is accurate and complete and meets the requirements of the report format. Specific restoration and control suggestions are put forward for different risk levels. Risk distribution is displayed using charts and the geographical distribution of risks is displayed using GIS maps. The assessment results are summarized and comprehensive suggestions are put forward. Python's report generation library and R's report generation tool are used to integrate text, charts and maps into the report to ensure that the content is coherent and the logic is clear. Team members and technical experts review the report content to ensure that the data is accurate and the analysis is reasonable. The report is submitted to relevant management departments and experts for review and modified and improved based on feedback.

[0029] Specifically, the steps of automatically generating remediation and governance suggestions according to the risk level, including acidic water treatment plans, groundwater monitoring plans and ecological restoration measures, include selecting an appropriate acidic water treatment plan according to the risk level; for low risk, using simple chemical neutralization treatment and regularly testing pH values ​​to ensure that the acidic water is effectively neutralized; for medium risk, using a combination of chemical neutralization and physical isolation to build small sewage treatment facilities and centrally treat the acidic water; for high risk, using a combination of advanced chemical neutralization, biological remediation and physical isolation to build large sewage treatment facilities and deeply treat the acidic water; implementing groundwater isolation projects to prevent the spread of acidic water; formulating a corresponding groundwater monitoring plan according to the risk level; automatically generating remediation and governance suggestions according to the risk level and predefined rules; using a templated report format to fill in the corresponding governance suggestions according to the risk level; and writing scripts in Python and other programming languages ​​to generate governance suggestions based on the model output results.

[0030] In one embodiment, the governance recommendations corresponding to the risk levels are defined as follows: low risk characteristics are low risk probability and small impact on the ecosystem. It is recommended to conduct regular monitoring and reduce the monitoring frequency to 1-2 times a year; preventive measures are taken to strengthen environmental protection measures in the area to prevent further development of potential risks; ecological restoration does not require immediate large-scale restoration measures, but small-scale ecological improvements can be carried out; medium risk characteristics are certain risks that require attention and monitoring; it is recommended to strengthen monitoring and increase the monitoring frequency to once every quarter and every six months; risk control takes preliminary governance measures, such as local acidic water treatment, groundwater diversion, etc.; ecological restoration implements small-scale ecological restoration projects, such as vegetation restoration, soil improvement, etc.; high risk characteristics have a high risk probability and have an impact on the ecosystem and There is a great threat to human health. It is recommended that emergency treatment be taken immediately and comprehensive treatment measures be taken, such as large-scale acidic water treatment, groundwater isolation, etc., strengthen real-time monitoring, once a week and once a day, ecological restoration, and implement large-scale ecological restoration projects, such as vegetation reconstruction, soil restoration, water purification, etc. According to the risk level, choose the appropriate acidic water treatment plan. For low risk, use simple chemical neutralization treatment, such as adding lime and sodium carbonate, and regularly test the pH value to ensure that the acidic water is effectively neutralized. For medium risk, use chemical neutralization combined with physical isolation to build small sewage treatment facilities for centralized treatment of acidic water. For high risk, use a combination of advanced chemical neutralization, biological restoration and physical isolation to build large sewage treatment facilities for deep treatment of acidic water. Treatment, implementation of groundwater isolation projects, to prevent the spread of acidic water, according to the risk level, the development of corresponding groundwater monitoring plans, low-risk monitoring frequency of 1-2 times a year, monitoring indicators for pH value, heavy metal concentration, dissolved oxygen, conductivity, etc., the monitoring area selects representative areas for monitoring, medium-risk monitoring frequency is once a quarter and once every six months, the monitoring indicator is to increase the monitoring of acidic water characteristic indicators (such as sulfate ion concentration), the monitoring area by expanding the monitoring scope to cover potential risk areas, high-risk monitoring frequency is once a week and once a day, the monitoring indicator is to comprehensively monitor all physical and chemical parameters, including pH value, heavy metal concentration, sulfate ion concentration, etc., the monitoring area by covering all high-risk areas and installing real-time monitoring equipment in key locations, based on According to the risk level, the corresponding ecological restoration measures are formulated. For low-risk vegetation restoration, the planting of acid-resistant plants is to improve the regional ecological environment. For soil improvement, the application of lime and other neutralizers is to improve soil acidity. For ecological monitoring, the regular monitoring of ecological restoration effects is to be carried out. For medium-risk vegetation restoration, the large-scale planting of acid-resistant plants is to form an ecological barrier. For soil remediation, the chemical remediation and biological remediation technologies are to be used to improve soil quality. For water purification, the construction of small wetlands and artificial ecosystems is to purify polluted water bodies. For high-risk vegetation restoration, the implementation of large-scale vegetation restoration projects is to rebuild the ecosystem. For soil remediation, the use of advanced chemical remediation and biological remediation technologies is to thoroughly improve soil quality. For water purification, the construction of large wetlands and artificial ecosystems is to deeply purify polluted water bodies.Groundwater governance is to implement groundwater extraction and treatment projects to prevent the spread of pollution. The tools and methods are rule engines that automatically generate remediation and governance suggestions based on risk levels and predefined rules. Templated reports use templated report formats to fill in corresponding governance suggestions based on risk levels. Automated scripts are scripts written in Python and other programming languages ​​to generate governance suggestions based on model output results. The steps are to input model output results, input the risk level (low, medium, high) output by the model into the system, match governance suggestions, match corresponding governance suggestions based on predefined rules and databases, generate reports, integrate governance suggestions into reports, form final remediation and governance plans, verify the feasibility of the suggestions, and have them reviewed by ecological restoration experts and experts in related fields. The content of the recommendations is verified to ensure their scientific nature and feasibility. Optimization involves optimizing the governance recommendations based on feedback from actual operations to improve their adaptability and effectiveness. Implementation of governance measures involves developing a detailed implementation plan based on the generated governance recommendations and allocating resources and personnel. Tracking and evaluation involves regularly tracking the effectiveness of governance measures, assessing changes in ecological risks, and adjusting governance measures based on the assessment results to ensure the achievement of governance goals. Through these steps, restoration and governance recommendations, including acidic water treatment plans, groundwater monitoring plans, and ecological restoration measures, can be automatically generated based on the output of the ecological risk assessment model. This not only improves the scientific nature and pertinence of governance recommendations, but also provides an efficient support tool for ecological restoration and governance, contributing to the sustainable development of ecosystems.

[0031] like Figure 2 As shown, the present invention also provides an abandoned mine acidic groundwater ecological assessment system, comprising: A data acquisition module is used to obtain the physical and chemical parameters of the acidic groundwater in abandoned mines through sensors and laboratory analysis, wherein the physical and chemical parameters include pH value, heavy metal concentration, dissolved oxygen content, temperature and conductivity; The preprocessing module is used to perform denoising, normalization, and interpolation on the collected physical and chemical parameters to eliminate outliers and ensure data integrity; A model building module for training an ecological risk assessment model using historical data based on a machine learning algorithm, wherein the ecological risk assessment model includes a support vector machine, a random forest, and a neural network; The risk assessment module is used to classify ecological risks into three levels: low risk, medium risk, and high risk based on the model output results, and generate corresponding risk reports; The optimization suggestion module is used to automatically generate remediation and treatment suggestions based on the risk level, wherein the treatment suggestions include acidic water treatment plans, groundwater monitoring plans and ecological restoration measures.

[0032] Specifically, the data acquisition uses portable and fixed sensors to monitor the physical and chemical parameters of groundwater in real time, and uses standard solutions and samples of known concentrations to calibrate the sensors to ensure the accuracy of the measurement results. The sensor monitoring data is transmitted to a computer and cloud storage through a data collector and a wireless transmission module. Historical data is used to train an ecological risk assessment model. The model input includes physical and chemical parameter data, geological structure data, surrounding environmental data, and historical pollution data. The model output includes ecological risk levels and risk reports. Spatial analysis is performed based on a geographic information system to predict the spread range of acidic groundwater. Combined with geological and environmental data, the potential impact area of ​​the pollution is assessed. Based on the risk level, remediation and treatment recommendations are generated, including acidic water treatment plans, groundwater monitoring plans, and ecological restoration measures. The remediation cost and time are estimated to provide an economically feasible treatment plan.

[0033] In one embodiment, this system aims to assess the risk of acidic groundwater in abandoned mines to the surrounding ecosystem and provide corresponding remediation recommendations. The system architecture is divided into five main modules: data acquisition, data processing, risk assessment, remediation recommendation generation, and user interface. The modules work together to achieve a complete process from data collection to result output.

[0034] The data acquisition module is a sensor network that deploys multiple sensors to monitor groundwater pH, sulfate ion concentration, heavy metal content, dissolved oxygen and other parameters in real time. Laboratory analysis is to collect water samples regularly and conduct detailed chemical analysis to obtain more accurate data. Geographic Information System (GIS) is to integrate geographic information such as mine location, geological structure, surrounding terrain, etc. to provide spatial data support for subsequent analysis. The data processing module is data cleaning to perform pre-processing such as denoising and filling missing values ​​on the collected raw data to ensure data quality. Data storage is to use relational databases and big data storage systems to store processed data for subsequent analysis. Data integration is to integrate data from different sources and establish a unified data warehouse to support Multi-dimensional analysis, the risk assessment module is an ecological risk model that uses the CERAP model and other ecological risk assessment models to calculate the ecological risk index based on groundwater chemical parameters and geographic information. The risk level is divided into three levels: low risk, medium risk and high risk, providing a basis for governance recommendations. Trend analysis is to identify risk change trends and predict future risks through time series analysis. The rule engine automatically generates corresponding governance recommendations based on risk levels and predefined governance rules. Solution optimization is to optimize governance solutions based on factors such as cost and feasibility, and provide multiple options for user reference. Dynamic adjustment is to dynamically adjust governance recommendations based on real-time monitoring data and governance effect feedback to ensure the effectiveness of the solution. Data can be Visualization is to use charts, maps and other forms to intuitively display groundwater monitoring data, risk assessment results and governance recommendations; interactive reports are to generate detailed assessment reports, and users can view and download reports through the interface; operational guidance is to provide implementation guidelines and operational steps for governance measures to help users smoothly implement governance plans; system implementation steps are demand analysis to clarify the functional requirements and performance requirements of the system and determine the interaction methods of each module; system design is to design the overall architecture of the system and determine the technical implementation plan of each module; data collection and integration is to deploy sensor networks and establish data collection and integration processes; data processing and storage is to develop data cleaning and storage modules to ensure the accuracy and availability of data; risk assessment model development is to realize ecological risk Evaluation model, test model accuracy and reliability, governance suggestion generation module development is to design rule engine and optimization algorithm, generate scientific governance suggestions, user interface development is to design and implement user-friendly interface, support data visualization and report generation, system testing and optimization is to conduct comprehensive functional testing and performance testing, optimize the system according to feedback, system deployment and operation is to deploy the system to the actual operating environment, conduct real-time monitoring and evaluation, system advantages and innovations are real-time monitoring is to achieve real-time monitoring of groundwater through sensor network, timely detect risk changes, intelligent assessment is to use advanced ecological risk models and rule engines to provide scientific assessment and suggestions, data visualization is to intuitively display the assessment results to help users quickly understand the risk situation,Dynamic adjustment involves dynamically optimizing treatment plans based on real-time data and treatment results, improving system adaptability. In a certain abandoned mine area, the deployment of this system successfully identified high-risk areas and provided targeted treatment recommendations. After implementing treatment measures, groundwater acidity was significantly reduced, surrounding vegetation recovered well, and ecological risks were effectively controlled. By building an abandoned mine acid groundwater ecological assessment system, ecological risks can be effectively assessed and managed, providing a scientific basis for treatment decision-making. The system's modular design and intelligent functions make it scalable and adaptable, making it suitable for abandoned mine areas of different sizes and types.

[0035] like Figure 3 As shown, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned abandoned mine acidic groundwater ecological assessment method are implemented.

[0036] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above-mentioned method for ecological assessment of acidic groundwater in abandoned mines when executed by a processor.

[0037] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database and other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) and flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and RAM bus dynamic RAM (RDRAM).

[0038] It should be noted that, in this document, the terms "comprises," "includes," and any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, and also includes elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0039] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for ecological assessment of acidic groundwater in abandoned mines, characterized in that: include: Obtaining physical and chemical parameters of the acidic groundwater in abandoned mines through sensors and laboratory analysis, wherein the physical and chemical parameters include pH value, heavy metal concentration, dissolved oxygen content, temperature, and conductivity; De-noising, normalization and interpolation processing are performed on the collected physical and chemical parameters to eliminate outliers and ensure data integrity; Using historical data to train an ecological risk assessment model based on a machine learning algorithm, wherein the ecological risk assessment model includes a support vector machine, a random forest, and a neural network; Based on the model output, the ecological risks are divided into three levels: low risk, medium risk and high risk, and corresponding risk reports are generated; Automatically generate remediation and treatment recommendations based on risk levels, where the treatment recommendations include acidic water treatment plans, groundwater monitoring plans, and ecological restoration measures.

2. The method for ecological assessment of acidic groundwater in abandoned mines according to claim 1, characterized in that: The steps of obtaining the physical and chemical parameters of the acidic groundwater in abandoned mines through sensors and laboratory analysis include: Select representative sampling points based on the geological structure and groundwater distribution characteristics of the abandoned mine, and the sampling points should cover different areas of the mine; Use portable and fixed sensors to monitor groundwater physical and chemical parameters in real time, and calibrate the sensors before use to ensure the accuracy of the measurement results; Calibration is performed using standard solutions and samples of known concentration, which are then transmitted to a computer and cloud storage via a data logger and wireless transmission module.

3. The method for ecological assessment of acidic groundwater in abandoned mines according to claim 1, characterized in that: The steps of performing denoising, normalization and interpolation processing on the collected physical and chemical parameters include: Import the collected physical and chemical parameter data into the data processing tool and draw a data distribution graph; Initially identify outliers, check the distribution of processed data to ensure that outliers have been removed, and check the distribution of normalized data to ensure that all parameters are within the same range; Use data processing tools to identify missing values ​​in the data, apply the selected interpolation method to each physicochemical parameter, and compare the data distribution before and after interpolation to ensure that the interpolated data are reasonable and do not introduce bias; Integrate the denoised, normalized, and interpolated data into a data frame and save the processed data in a commonly used format to ensure data traceability and accessibility; Check that the processed data is as expected and ensure that no new outliers and errors have been introduced.

4. The method for ecological assessment of acidic groundwater in abandoned mines according to claim 1, characterized in that: The step of training the ecological risk assessment model using historical data based on a machine learning algorithm includes: Collecting historical data, wherein the historical data includes physical and chemical parameters and corresponding ecological risk levels; Obtain data from sensor monitoring and laboratory analysis in abandoned mines to ensure data diversity and representativeness, covering different time periods and different regions; Clean and organize data to make it suitable for input to machine learning models, and perform logarithmic and other transformations on skewed data to improve data distribution; Select the most important physical and chemical parameters for ecological risk assessment, calculate the correlation between each parameter and ecological risk, and select the parameters with high correlation; Lasso regression and random forest feature importance analysis were used for feature selection, and ecological and geological knowledge were combined to select machine learning algorithms suitable for ecological risk assessment.

5. The method for ecological assessment of acidic groundwater in abandoned mines according to claim 1, characterized in that: The steps of classifying ecological risks into three levels of low risk, medium risk and high risk according to the model output results and generating corresponding risk reports include: Determine whether the model output is a probability value or a direct category label. If the output is a probability value, set thresholds for high risk, medium risk, and low risk. Low risk has a low probability of risk and a small impact on the ecosystem; medium risk has a moderate probability of risk and requires attention and monitoring; high risk has a high probability of risk and requires immediate remediation measures; Classify each sample into a corresponding risk level based on the model output and collect relevant data, including physical and chemical parameters, geographic location, and historical data, ensuring that the data is accurate, complete, and complies with reporting format requirements; Propose specific restoration and remediation recommendations for different risk levels, use charts to display risk distribution, use GIS maps to display the geographical distribution of risks, summarize assessment results and make comprehensive recommendations; Use Python's report generation library and R's report generation tools to integrate text, charts, and maps into reports, ensuring coherence and logical clarity; Team members and technical experts will review the report content to ensure that the data is accurate and the analysis is reasonable. The report will then be submitted to relevant management departments and experts for review and will be modified and improved based on feedback.

6. The method for ecological assessment of acidic groundwater in abandoned mines according to claim 1, characterized in that: The steps of automatically generating repair and governance suggestions based on risk levels include: Select appropriate acidic water treatment options based on risk levels. These options include low-risk treatment using simple chemical neutralization and regular pH testing to ensure effective neutralization of the acidic water; medium-risk treatment using a combination of chemical neutralization and physical isolation, with the construction of small-scale sewage treatment facilities for centralized treatment of the acidic water; and high-risk treatment using a combination of advanced chemical neutralization, bioremediation, and physical isolation, with the construction of large-scale sewage treatment facilities for advanced treatment of the acidic water. Implement groundwater isolation projects to prevent the spread of acidic water and develop corresponding groundwater monitoring plans based on risk levels; Automatically generate remediation and governance recommendations based on risk levels and predefined rules, using templated report formats; Fill in corresponding governance recommendations based on risk levels, write scripts in Python and other programming languages, and generate governance recommendations based on model output results.

7. An abandoned mine acidic groundwater ecological assessment system, characterized in that: include: A data acquisition module is used to obtain the physical and chemical parameters of the acidic groundwater in abandoned mines through sensors and laboratory analysis, wherein the physical and chemical parameters include pH value, heavy metal concentration, dissolved oxygen content, temperature and conductivity; The preprocessing module is used to perform denoising, normalization, and interpolation on the collected physical and chemical parameters to eliminate outliers and ensure data integrity; A model building module for training an ecological risk assessment model using historical data based on a machine learning algorithm, wherein the ecological risk assessment model includes a support vector machine, a random forest, and a neural network; The risk assessment module is used to classify ecological risks into three levels: low risk, medium risk, and high risk based on the model output results, and generate corresponding risk reports; The optimization suggestion module is used to automatically generate remediation and treatment suggestions based on the risk level, wherein the treatment suggestions include acidic water treatment plans, groundwater monitoring plans and ecological restoration measures.

8. The abandoned mine acidic groundwater ecological assessment system according to claim 7, characterized in that: The data acquisition method uses portable and fixed sensors to monitor the physical and chemical parameters of groundwater in real time. Standard solutions and samples of known concentrations are used to calibrate the sensors to ensure the accuracy of the measurement results. The sensor monitoring data is transmitted to a computer and cloud storage through a data collector and a wireless transmission module. Historical data is used to train an ecological risk assessment model. The model input includes physical and chemical parameter data, geological structure data, surrounding environmental data, and historical pollution data. The model output includes an ecological risk level and a risk report. Spatial analysis is performed based on a geographic information system to predict the spread of acidic groundwater. Combined with geological and environmental data, the potential impact area of ​​the pollution is assessed. Based on the risk level, remediation and treatment recommendations are generated, including an acidic water treatment plan, a groundwater monitoring plan, and ecological restoration measures. The remediation cost and time are estimated to provide an economically feasible treatment plan.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for ecological assessment of acidic groundwater in abandoned mines according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for ecological assessment of acidic groundwater in abandoned mines according to any one of claims 1 to 6 are implemented.

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