Intelligent environment monitoring and treatment system for surface mine

Abstract

The invention relates to the technical field of mine restoration, and discloses an intelligent surface mine environment monitoring and treatment system, which comprises an environment monitoring and multi-modal data acquisition module, a data processing module and a data processing module, the data acquisition module is used for acquiring various types of environmental data such as air quality, noise, water pollution, soil erosion and geological disasters through a sensor network, an unmanned aerial vehicle, satellite remote sensing and other detection devices, and performing local data processing; and the environment optimization and decision-making module based on edge computing and deep learning is configured to deploy computing resources at a data acquisition end close to a mining area by utilizing an edge computing technology and perform real-time processing and complex multivariate analysis on the multi-source environment data through a deep learning algorithm. Through multi-modal data acquisition and an environment optimization and decision module based on edge calculation and deep learning, multi-dimensional environment data from a mining area can be rapidly processed.

Description

Technical Field

[0001] The present invention relates to the technical field of mine restoration, and specifically to an intelligent environmental monitoring and governance system for open-pit mines. Background Art

[0002] Open-pit mining, also known as surface mining, is a process of removing the overburden on the ore body to obtain the required minerals, that is, the process of extracting useful minerals from an open-pit mining site. The operations of open-pit mining mainly include processes such as drilling, blasting, loading, transportation, and waste dumping. According to the continuity of operations, it can be divided into discontinuous, continuous, and semi-continuous types. Compared with underground mining, open-pit mining has the advantages of full resource utilization, low dilution rate, being suitable for large-scale mechanical construction, fast mine construction, large output, high labor productivity, low cost, good labor conditions, and safe production.

[0003] The mine environment involves various pollution sources and complex geographical and ecological factors. Traditional monitoring systems rely on a single data source (such as ground monitoring equipment or remote sensing data), and it is difficult to obtain all-round and multi-angle mine environment information in real time. In addition, there is a lack of unified standards between different data sources, and inconsistent data formats make data integration difficult, thus affecting the scientificity and timeliness of governance decisions.

[0004] With the continuous advancement of mine development, the impact of environmental changes shows obvious non-linear characteristics. Traditional decision-making systems based on experience and rules are unable to effectively handle multi-variable environmental changes. For example, under different mining schemes, the impacts on aspects such as air quality, ecological restoration progress, and water body pollution cannot be efficiently calculated and prospectively evaluated. This static and single decision-making support method makes it difficult for existing governance schemes to cope with complex mine environments.

[0005] Based on the above deficiencies of the existing technology, the present invention proposes an intelligent environmental monitoring and governance system for open-pit mines. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technology, the present invention provides an intelligent environmental monitoring and governance system for open-pit mines, and solves the problem that the mine environment involves various pollution sources and complex geographical and ecological factors, and traditional monitoring systems rely on a single data source (such as ground monitoring equipment or remote sensing data), making it difficult to obtain all-round and multi-angle mine environment information in real time.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent environmental monitoring and governance system for open-pit mines, comprising:

[0008] The environmental monitoring and multi-modal data acquisition module is arranged at key positions in the mining area and is used to collect various types of environmental data such as air quality, noise, water pollution, soil erosion, and geological disasters through sensor networks, drones, satellite remote sensing, and other detection devices, and perform local data processing;

[0009] The environmental optimization and decision-making module based on edge computing and deep learning is configured to use edge computing technology to deploy computing resources at the data acquisition end close to the mining area, and perform real-time processing and complex multi-variable analysis on multi-source environmental data through deep learning algorithms to generate optimal environmental governance strategies. This module can handle non-linear environmental problems and continuously optimize governance decisions according to historical data and real-time data through a self-learning mechanism;

[0010] The governance measures and automation control module is integrated into the system and is used to automatically implement environmental governance measures according to the output results of the environmental optimization and decision-making module based on edge computing and deep learning, including pollution control, ecological restoration, and emergency response. This module can automatically adjust the operating state of equipment through a closed-loop feedback control mechanism and be linked with the virtual twin environment in real time to achieve intelligent control and precise operation;

[0011] The distributed energy network and ecological regulation module, based on intelligent microgrid technology, integrates different energies (such as solar energy, wind energy, geothermal energy, etc.) in the mining area into a distributed energy network, and optimizes energy utilization and ecological balance in real time through intelligent regulation algorithms. This module can dynamically allocate and preferentially distribute clean energy, reduce energy consumption during the governance process, and ensure the best balance between resource development and ecological protection by analyzing ecological impacts;

[0012] The environmental restoration optimization module is used to evaluate the ecological environment of the mining area, generate restoration plans, and monitor the restoration effects. This module includes an ecological assessment unit, a restoration plan generation unit, and a restoration effect monitoring unit, and can adjust restoration strategies according to real-time monitoring data to ensure the persistence and effectiveness of restoration effects;

[0013] The integrated management and multi-source data fusion module is configured to integrate and manage the data of each module in the system, provide a global view and an operation interface. This module realizes the integration and collaboration of various types of data through multi-dimensional data fusion technology, and provides dynamic data analysis and real-time decision support for users through artificial intelligence algorithms;

[0014] The mining area intelligent physical environment simulation and virtual twin module generates a virtual twin environment by constructing a high-precision three-dimensional simulation model of the mining area physical environment and combining it with real-time data, and is used to predict and simulate various environmental change scenarios and their impacts on the mining area environment. This module includes a physical environment modeling unit, a virtual twin generation unit, and a scenario simulation and prediction unit, and provides comprehensive environmental simulation and analysis through the combination of virtual and real.

[0015] Preferably, the physical environment modeling unit in the intelligent physical environment simulation and virtual twin module of the mining area includes high-precision terrain scanning equipment and an environmental feature recognition algorithm, which are used to generate a three-dimensional terrain model and an environmental feature map of the mining area. The environmental feature recognition algorithm is based on a multi-scale convolutional neural network (MSCNN), and its structure is as follows:

[0016]

[0017] Among them, x is the input high-resolution terrain data, f i is the activation function, is the convolution operation with kernel size k i w i and b i are the weight and bias respectively, N is the number of network layers. Through this algorithm, multi-scale feature extraction is performed on the mining area data, and a three-dimensional terrain model and an environmental feature map are generated.

[0018] Preferably, the virtual twin generation unit includes a real-time data fusion engine and a virtual environment rendering engine. The real-time data fusion engine adopts a fusion algorithm that combines Kalman filtering and Bayesian inference to achieve precise fusion of multi-source data. This fusion algorithm performs data fusion through the following formula:

[0019]

[0020] P k =(I - K k H)P k|k-1

[0021] K k =P k|k-1 H T (HP k|k-1 H T +R) -1

[0022]

[0023] P k|k-1 =AP k-1 A T +Q

[0024] p(x k |z 1:k 0) ∝ p(z k |x k ) ∫ p(x k |x k-1 ) p(x k-1 |z 1:k-1 ) dx k-1

[0025] Among them, is the fused estimated state, P k is the covariance matrix of the state estimation, K k is the Kalman gain, p(x k |z 1:k ) is the posterior probability density function calculated in Bayesian inference. The multi-source data is processed by this fusion algorithm, and the result is input into the virtual environment rendering engine to generate the virtual twin environment of the mining area.

[0026] Preferably, the scenario simulation and prediction unit includes a multi-scenario prediction model and a scenario simulation control system. The multi-scenario prediction model specifically includes a historical data analysis unit, a real-time data processing unit, and a scenario evolution calculation unit. By combining historical data, real-time monitoring data, and the virtual twin environment, the environmental changes under different scenarios are predicted. The scenario simulation control system includes a scenario parameter adjustment unit and a simulation result evaluation unit, which are used to set and adjust scenario parameters and generate governance strategy suggestions for different scenarios.

[0027] Preferably, the scenario simulation and prediction unit can generate the changes in the mining area environment at different time scales, including short-term, medium-term, and long-term prediction results. The prediction model specifically includes a short-term dynamic prediction unit, a medium-term trend prediction unit, and a long-term environmental evolution unit, which are respectively used to combine real-time data and historical trends to generate environmental change predictions at multiple time scales and provide data support for the intelligent decision-making support and dynamic regulation module to ensure the foresight and effectiveness of governance strategies at different time scales.

[0028] Preferably, the intelligent decision-making support and dynamic regulation module includes a decision analysis unit, a regulation optimization unit, and a feedback self-learning unit. Based on the simulation results generated by the intelligent physical environment simulation and virtual twin module of the mining area, the module can automatically adjust the environmental governance strategy. The module obtains real-time feedback through the closed-loop feedback control unit, dynamically corrects the governance plan, and continuously optimizes the strategy in combination with the self-learning mechanism, thereby improving the sustainability and adaptability of the governance effect.

[0029] Preferably, the governance measures and automation control module includes a governance implementation unit, an equipment control unit, and a virtual-real linkage unit. The module is in real-time linkage with the virtual twin environment. The governance implementation unit simulates the proposed governance measures in the virtual environment in advance, and the equipment control unit adjusts the operating parameters of the actual equipment. The virtual-real linkage unit ensures that the simulation results in the virtual environment can be dynamically feedback to the actual equipment operation, thereby improving the accuracy and adaptability of the governance measures.

[0030] Preferably, the resource sustainable utilization module includes a resource evaluation unit, a mining path optimization unit, and a resource utilization feedback unit. The module evaluates different mining strategies through the resource simulation model of the virtual twin environment, combines the resource evaluation unit to evaluate the resource utilization efficiency and ore recovery rate, generates the optimal mining path through the mining path optimization unit, and the resource utilization feedback unit provides real-time feedback on the mining effect and corresponding optimization suggestions to ensure the efficient utilization of resources and the minimum damage to the environment.

[0031] Preferably, the integrated management and multi-source data fusion module includes a data integration unit, a data visualization unit, and a data analysis and decision support unit. The data integration unit is used to integrate data from different sources (such as virtual twin environment data, real-time monitoring data, etc.). The data visualization unit provides a user-friendly interface and can display virtual and real data simultaneously. The data analysis and decision support unit provides real-time decision support for each module of the system through multi-dimensional data analysis.

[0032] Preferably, the integrated management and multi-source data fusion module can display the real environment and the virtual twin environment of the mining area on a single platform. Users can perform various operation simulations in the virtual twin environment through the interactive operation unit. The module also includes an optimization feedback unit, which is used to feedback the results simulated and verified in the virtual environment to the actual mining area operation, so as to achieve a closed-loop optimization of the combination of virtual and real, and improve the accuracy of operation and governance efficiency.

[0033] Working principle: The environmental monitoring and multi-modal data acquisition module collects various environmental data of the mining area in real time, such as air quality, noise, water pollution, soil erosion, geological disasters, etc., and processes the data locally. Subsequently, the intelligent decision-making support and dynamic regulation module generates the optimal environmental governance strategy based on the collected and processed environmental data, using data analysis and machine learning algorithms, and adaptively adjusts the governance measures through intelligent prediction and real-time regulation functions. The governance measures and automation control module automatically implements the corresponding environmental governance measures according to the output results of the decision-making support module, including pollution control, ecological restoration, and emergency response, and can automatically control and monitor the governance equipment and facilities in the mining area. The resource sustainable utilization module is used to evaluate the distribution and reserves of resources in the mining area, optimize the mining and allocation of resources, and monitor the utilization efficiency of resources. The environmental restoration optimization module generates a restoration plan through the evaluation of the ecological environment of the mining area, and ensures the effectiveness of ecological restoration by monitoring the restoration effect. The comprehensive management and multi-source data fusion module integrates and fuses the data of each module, and displays multi-dimensional data through a data visualization platform and an interactive interface, and finally realizes the unified management of the system. The intelligent physical environment simulation and virtual twin module of the mining area constructs a high-precision three-dimensional simulation model of the mining area, and generates a virtual twin environment in combination with real-time data, which is used to predict and simulate various environmental change scenarios and their impacts on the mining area, and finally provides a basis for the intelligent decision-making and automation control of the system.

[0034] The present invention provides an intelligent environmental monitoring and governance system for open-pit mines. It has the following beneficial effects:

[0035] 1. Through the multi-modal data acquisition and the environmental optimization and decision-making module based on edge computing and deep learning, the present invention can quickly process multi-dimensional environmental data from the mining area. Especially on the basis of multi-source data fusion, quantum computing can solve complex multi-variable environmental problems that are difficult to handle by traditional computing methods. This gives the system significant advantages in dealing with complex non-linear environmental changes, can generate multiple environmental governance plans simultaneously, and achieve fast and accurate optimization decisions through parallel computing, providing forward-looking strategies for mining area governance.

[0036] 2. The present invention realizes the virtual-real linkage of governance measures and automation control through virtual twin technology, enabling the governance measures to be pre-verified and optimized in the virtual environment to ensure the accuracy and safety of actual operations. At the same time, through the closed-loop feedback mechanism, the system can continuously obtain real-time feedback data during the actual operation process and dynamically adjust the operating state of the governance equipment to ensure that the governance strategy is always highly matched with the current environmental conditions, improving the sustainability and adaptability of the governance effect.

[0037] 3. The distributed energy network and ecological regulation module of the present invention realizes the integration and optimal allocation of multiple energy sources through intelligent microgrid technology, maximizing the preferential utilization of clean energy and reducing the negative environmental impact of traditional energy consumption. Through intelligent regulation algorithms, the system can achieve a high degree of coordination between resource development and ecological protection, dynamically adjust the energy distribution strategy, and evaluate the environmental impact of resource extraction in real time through the ecological regulation module to ensure the long-term balance between energy utilization and ecological protection, further promoting the sustainable development of the mining area. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is the framework flowchart of the present invention;

[0039] Figure 2 is the schematic diagram of the framework of the scenario simulation and prediction unit of the present invention;

[0040] Figure 3 is the schematic diagram of the framework of the prediction model of the present invention;

[0041] Figure 4 is the schematic diagram of the framework of the resource decision-making and dynamic regulation module of the present invention;

[0042] Figure 5 is the schematic diagram of the framework of the resource sustainable utilization module of the present invention;

[0043] Figure 6 is the schematic diagram of the framework of the integrated management and multi-source data fusion module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0045] Embodiment 1:

[0046] Please refer to the attached Figure 1 - attached Figure 6 , the embodiment of the present invention provides an intelligent environmental monitoring and governance system for open-pit mines, including:

[0047] An environmental monitoring and multi-modal data acquisition module, arranged at key positions in the mining area, is used to collect various types of environmental data such as air quality, noise, water pollution, soil erosion, and geological disasters through a sensor network, unmanned aerial vehicles, satellite remote sensing, and other detection devices, and perform local data processing;

[0048] An environment optimization and decision-making module based on edge computing and deep learning, configured to utilize edge computing technology to deploy computing resources at the data collection end near the mining area, perform real-time processing and complex multi-variable analysis on multi-source environmental data through deep learning algorithms, generate optimal environmental governance strategies, the module can handle non-linear environmental problems, and continuously optimize governance decisions according to historical data and real-time data through a self-learning mechanism;

[0049] A governance measures and automation control module, integrated into the system, used to automatically implement environmental governance measures according to the output results of the environment optimization and decision-making module based on edge computing and deep learning, including pollution control, ecological restoration and emergency response, the module can automatically adjust the operating state of the equipment through a closed-loop feedback control mechanism, and be linked with the virtual twin environment in real time to achieve intelligent control and precise operation;

[0050] A distributed energy network and ecological regulation module, based on intelligent microgrid technology, integrates different energies (such as solar energy, wind energy, geothermal energy, etc.) in the mining area into a distributed energy network, and optimizes energy utilization and ecological balance in real time through intelligent regulation algorithms, the module can dynamically allocate and preferentially distribute clean energy, while reducing energy consumption during the governance process, and ensure the best balance between resource development and ecological protection by analyzing ecological impacts;

[0051] An environmental restoration optimization module, used to evaluate the ecological environment of the mining area, generate restoration plans and monitor the restoration effects, the module includes an ecological assessment unit, a restoration plan generation unit and a restoration effect monitoring unit, and can adjust the restoration strategy according to real-time monitoring data to ensure the persistence and effectiveness of the restoration effect;

[0052] A comprehensive management and multi-source data fusion module, configured to integrate and manage the data of each module in the system, provide a global view and an operation interface, the module realizes the integration and collaboration of various data through multi-dimensional data fusion technology, and provides users with dynamic data analysis and real-time decision support through artificial intelligence algorithms;

[0053] A mining area intelligent physical environment simulation and virtual twin module, by constructing a high-precision three-dimensional simulation model of the mining area physical environment and combining it with real-time data, generates a virtual twin environment, used to predict and simulate various environmental change scenarios and their impacts on the mining area environment, the module includes a physical environment modeling unit, a virtual twin generation unit and a scenario simulation and prediction unit, and provides comprehensive environmental simulation and analysis through the combination of virtual and real;

[0054] Please refer to the appendix Figure 1 - Appendix Figure 6, the physical environment modeling unit in the intelligent physical environment simulation and virtual twin module of the mining area includes high-precision terrain scanning equipment and an environmental feature recognition algorithm, which are used to generate a three-dimensional terrain model and an environmental feature map of the mining area. The environmental feature recognition algorithm is based on a multi-scale convolutional neural network (MSCNN), and its structure is as follows:

[0055]

[0056] where x is the input high-resolution terrain data, f i is the activation function, is the convolution operation with kernel size k i w i and b i are the weight and bias respectively, and N is the number of network layers. Through this algorithm, multi-scale feature extraction is performed on the mining area data, and a three-dimensional terrain model and an environmental feature map are generated.

[0057] Specifically, the physical environment modeling unit includes high-precision terrain scanning equipment and an environmental feature recognition algorithm, which are used to generate a three-dimensional terrain model and an environmental feature map of the mining area. The environmental feature recognition algorithm is based on the multi-scale convolutional neural network (MSCNN) structure. By performing multi-scale feature extraction on the input high-resolution terrain data, accurate analysis and processing of the mining area data are realized, and finally a high-precision three-dimensional terrain model and an environmental feature map are generated; by introducing the multi-scale convolutional neural network algorithm, the complex features of the mining area environment can be better captured and analyzed, the accuracy of three-dimensional terrain modeling and the accuracy of environmental feature recognition can be improved, and more reliable basic data support is provided for the intelligent environment monitoring and governance system of the mining area.

[0058] Please refer to Appendix Figure 1 -Appendix Figure 6 , the virtual twin generation unit includes a real-time data fusion engine and a virtual environment rendering engine. The real-time data fusion engine adopts a fusion algorithm that combines Kalman filtering and Bayesian inference to achieve precise fusion of multi-source data. This fusion algorithm performs data fusion through the following formula:

[0059]

[0060] P k =(I-K k H)P k|k-1

[0061] K k =P k|k-1 H T (HP k|k-1 H T +R) -1

[0062]

[0063] P k|k-1 = AP k-1 A T + Q

[0064] p(x k |z 1:k ) ∝ p(z k |x k ) ∫ p(x k |x k-1 ) p(x k-1 |z 1:k-1 ) dx k-1

[0065] Among them, is the fused estimated state, P k is the covariance matrix of state estimation, K k is the Kalman gain, p(x k |z 1:k ) is the posterior probability density function calculated in Bayesian inference. By processing multi-source data through this fusion algorithm and inputting the results into the virtual environment rendering engine, a virtual twin environment of the mining area is generated.

[0066] Specifically, the real-time data fusion engine adopts a fusion algorithm that combines Kalman filtering and Bayesian inference to achieve precise fusion of multi-source data. Through this fusion algorithm, data from different sensors and data sources are processed, and the fused data is input into the virtual environment rendering engine, thereby generating a high-precision virtual twin environment of the mining area; by combining the advantages of Kalman filtering and Bayesian inference, the accuracy and robustness of multi-source data fusion can be significantly improved, ensuring the authenticity and reliability of the virtual twin environment, and providing more accurate support for the dynamic monitoring and prediction of the mining area environment.

[0067] Please refer to Appendix Figure 1 - Appendix Figure 6 , the scenario simulation and prediction unit includes a multi-scenario prediction model and a scenario simulation control system. The multi-scenario prediction model specifically includes a historical data analysis unit, a real-time data processing unit, and a scenario evolution calculation unit. By combining historical data, real-time monitoring data, and the virtual twin environment, environmental changes under different scenarios are predicted. The scenario simulation control system includes a scenario parameter adjustment unit and a simulation result evaluation unit, which are used to set and adjust scenario parameters and generate governance strategy suggestions for different scenarios.

[0068] Specifically, by combining historical data, real-time monitoring data, and virtual twin environments, the environmental changes in different scenarios of the mining area are predicted. The historical data analysis unit is used to mine the long-term trends in historical data. The real-time data processing unit obtains the latest environmental data of the mining area by connecting with devices such as sensor networks and satellite remote sensing. The scenario evolution calculation unit calculates and simulates the future environmental changes based on multi-dimensional scenario data (including climate change, geological activities, mining behaviors, etc.) and generates prediction results for different scenarios. The scenario simulation control system includes a scenario parameter adjustment unit and a simulation result evaluation unit. The scenario parameter adjustment unit is used to adjust scenario parameters according to user settings or algorithm recommendations, such as mining intensity, weather changes, ecological restoration efforts, etc. The simulation result evaluation unit evaluates and analyzes the simulation results for different scenarios, combines the simulation calculations of historical and real-time data, and finally generates environmental governance strategy suggestions for different scenarios. The entire system forms a closed-loop scenario simulation and regulation process by combining historical data, real-time data, and the simulation results of virtual twins, providing comprehensive and reliable data support for the intelligent decision-making support and dynamic regulation module, and ensuring efficient and accurate governance measure adjustment and optimization under complex and dynamic environmental conditions.

[0069] Please refer to the appendix Figure 1 - appendix Figure 6 The scenario simulation and prediction unit can generate the changes in the mining area environment at different time scales, including short-term, medium-term, and long-term prediction results. The prediction model specifically includes a short-term dynamic prediction unit, a medium-term trend prediction unit, and a long-term environmental evolution unit, which are respectively used to generate environmental change predictions at multiple time scales by combining real-time data and historical trends, and provide data support for the intelligent decision-making support and dynamic regulation module to ensure the foresight and effectiveness of governance strategies at different time scales.

[0070] Specifically, by combining real-time data and historical trends, short-term, medium-term, and long-term environmental change predictions are generated respectively. The short-term dynamic prediction unit quickly responds to environmental changes based on real-time data and provides immediate predictions for the next few hours to days. The medium-term trend prediction unit combines historical trends and environmental data to simulate the environmental evolution in the next few months to years. The long-term environmental evolution unit uses the virtual twin environment to predict the ecological evolution on a long time scale and evaluates the impacts of resource extraction, climate change, geological activities, etc. on the long-term environment of the mining area. The multi-time scale prediction results generated by each unit are uniformly processed by the time scale fusion unit and provide multi-level data support for the intelligent decision-making support and dynamic regulation module to ensure that the governance strategy is forward-looking and effective at different time scales. Finally, the optimized governance measures are fed back to each execution module through the decision feedback unit to achieve precise regulation and continuous optimization of the mining area environmental governance.

[0071] Please refer to the appendix Figure 1 - Appendix Figure 6 The intelligent decision-making support and dynamic regulation module includes a decision analysis unit, a regulation optimization unit, and a feedback self-learning unit. Based on the simulation results generated by the intelligent physical environment simulation and virtual twin module of the mining area, the module can automatically adjust the environmental governance strategy. The module obtains real-time feedback through a closed-loop feedback control unit, dynamically corrects the governance plan, and continuously optimizes the strategy through a self-learning mechanism, thereby enhancing the sustainability and adaptability of the governance effect.

[0072] Specifically, the intelligent decision-making support and dynamic regulation module includes a decision analysis unit, a regulation optimization unit, and a feedback self-learning unit. This module automatically adjusts the environmental governance strategy based on the simulation results generated by the intelligent physical environment simulation and virtual twin module of the mining area. The decision analysis unit comprehensively analyzes the environmental data, virtual simulation results, and scenario simulation prediction data of the mining area to generate a preliminary governance plan. The regulation optimization unit further deeply optimizes the preliminary governance plan based on real-time environmental data and simulation feedback to ensure that the strategy can respond to dynamic environmental changes in real time. The module obtains the actual execution feedback of each governance measure through a closed-loop feedback control unit, evaluates the governance effect in combination with the virtual-real linkage mechanism, and corrects the plan in real time. The feedback self-learning unit continuously optimizes and adaptively adjusts the governance strategy by accumulating feedback data during the execution process, enhancing the learning ability and intelligent level of the system. The entire process realizes closed-loop control through an adaptive regulation mechanism, thereby enhancing the sustainability and adaptability of the governance strategy, and ultimately ensuring that the system can effectively respond to complex environmental changes and continuously optimize the environmental governance effect of the mining area.

[0073] Please refer to the appendix Figure 1 - Appendix Figure 6 The governance measure and automation control module includes a governance implementation unit, an equipment control unit, and a virtual-real linkage unit. The module realizes real-time linkage with the virtual twin environment, simulates the proposed governance measures in advance in the virtual environment through the governance implementation unit, and adjusts the operating parameters of the actual equipment through the equipment control unit. The virtual-real linkage unit ensures that the simulation results in the virtual environment can be dynamically fed back to the actual equipment operation, thereby enhancing the accuracy and adaptability of the governance measures.

[0074] Specifically, the module is in real-time linkage with the virtual twin environment, capable of simulating and verifying the feasibility of governance measures in the virtual environment. The governance implementation unit simulates the proposed governance plan through the virtual twin environment, evaluates in advance the effects of governance measures under different scenarios, and optimizes the governance strategy according to the simulation results. The equipment control unit is responsible for applying the optimized governance measures to actual operations. By precisely controlling the operating parameters of the mining area governance equipment, it ensures that each governance step is consistent with the results of virtual simulation, thereby improving governance efficiency. The virtual-real linkage unit ensures that the simulation results in the virtual environment can be dynamically transmitted to the equipment control unit through continuous data synchronization and feedback mechanisms, and timely adjusts the operating behavior of the equipment. The unit can feed back the actual equipment operation data into the virtual environment for secondary simulation verification to ensure the accuracy and adaptability of governance measures in actual operations. Finally, through virtual-real closed-loop management, the governance measures are highly flexible and responsive in the complex and changing mining area environment.

[0075] Please refer to the appendix Figure 1 - appendix Figure 6 The resource sustainable utilization module includes a resource evaluation unit, a mining path optimization unit, and a resource utilization feedback unit. Through the resource simulation model of the virtual twin environment, the module evaluates different mining strategies, combines the resource evaluation unit to evaluate resource utilization efficiency and ore recovery rate, and generates the optimal mining path through the mining path optimization unit. The resource utilization feedback unit provides real-time feedback on the mining effect and corresponding optimization suggestions to ensure the efficient utilization of resources and the minimum damage to the environment.

[0076] Specifically, through the resource simulation model of the virtual twin environment, different mining strategies are comprehensively evaluated to achieve the efficient utilization of resources and the minimum damage to the environment. The resource evaluation unit combines the resource distribution model in the virtual twin environment to comprehensively evaluate the resource reserves, ore grade, mining difficulty, etc. in the mining area, and calculates the resource utilization efficiency and ore recovery rate. Based on the resource evaluation results, the mining path optimization unit generates the optimal mining path, optimizes the path selection during mining by combining the topographic and geological conditions and the performance of mining equipment, and ensures the efficient extraction of resources and the minimum ecological damage. The resource utilization feedback unit is responsible for real-time monitoring of the actual situation during mining. By collecting equipment operation data, resource consumption data, and environmental impact data, it provides real-time feedback on the mining effect and corresponding optimization suggestions. The resource utilization feedback unit is in dynamic linkage with the virtual twin environment to ensure that unexpected situations encountered during mining can be timely fed back to the virtual environment for simulation adjustment, thereby optimizing subsequent mining strategies, achieving the sustainable and efficient utilization of resources and the long-term protection of the environment. The entire module ensures the sustainability of resource utilization and the efficiency of the mining process through a closed-loop optimization mechanism.

[0077] Please refer to the appendixFigure 1 - Attachment Figure 6 , the integrated management and multi-source data fusion module includes a data integration unit, a data visualization unit, and a data analysis and decision support unit. The data integration unit is used to integrate data from different sources (such as virtual twin environment data, real-time monitoring data, etc.). The data visualization unit provides a user-friendly interface that can display virtual and real data simultaneously. The data analysis and decision support unit provides real-time decision support for each module of the system through multi-dimensional data analysis.

[0078] Specifically, through the efficient integration and analysis of multi-source data, comprehensive management support is provided for mine environmental monitoring and governance. The data integration unit is used to integrate data from different sources, including virtual twin environment data, real-time monitoring data, historical environmental data, and other external data sources (such as meteorological and geological data, etc.). Through unified data standardization processing, the interoperability and consistency of multi-source data are ensured. The data visualization unit provides a user-friendly operation interface that can display data of the virtual environment and the real environment simultaneously, and is presented through multi-level visualization charts, dynamic maps, and timelines, enabling users to intuitively understand the current environmental conditions, resource utilization situations, and simulated future change trends in the mine area. The data analysis and decision support unit analyzes the integrated multi-source data through multi-dimensional data analysis, using big data analysis algorithms and intelligent decision-making models to generate real-time governance suggestions and optimization plans, and provides decision support for each module of the system. This unit can predict and evaluate data trends, resource utilization efficiency, environmental impacts, etc., thereby helping users make more scientific decisions based on the analysis results. Each unit is interconnected through data streams and feedback mechanisms to ensure the real-time and accuracy of data analysis. Finally, through a closed-loop data management mechanism, a reliable data foundation and decision-making basis are provided for other modules of the system (such as the intelligent decision support and dynamic regulation module, the governance measures and automation control module), thereby improving the governance efficiency and decision-making accuracy of the entire system.

[0079] Please refer to Attachment Figure 1 - Attachment Figure 6 , the integrated management and multi-source data fusion module can display the real environment and virtual twin environment of the mine area on a single platform. Users can perform various operation simulations in the virtual twin environment through the interaction operation unit. The module also includes an optimization feedback unit, which is used to feedback the results simulated and verified in the virtual environment to the actual mine operation, thereby realizing a closed-loop optimization of the combination of virtual and real, and improving the accuracy and governance efficiency of the operation.

[0080] Specifically, the data fusion and display unit integrates real-time monitoring data of the mining area, data generated by the virtual twin environment, and historical environment data, and synchronously displays the real environment of the mining area and the virtual simulation results on the platform. Users can intuitively understand the current situation of the mining area and the simulated future evolution trend through this unit. The interactive operation unit provides a flexible operation interface for users. Users can simulate different operation scenarios and governance strategies in the virtual twin environment, such as adjusting the mining strategy, optimizing the equipment operation parameters, and predicting environmental changes, and observe the simulation results of different operation plans in real time. The optimization feedback unit is responsible for feeding back the results simulated and verified in the virtual environment to the operation process of the actual mining area. Through the data synchronization mechanism, the optimization strategies and parameter adjustments in the virtual twin environment are transmitted to the real devices and governance measures to ensure a high degree of consistency between the actual operation and the simulation results, thus realizing the closed-loop management of the combination of virtual and real. Through this module, each unit is interconnected through the data flow and feedback mechanism to ensure the accuracy of governance decisions and the rapid application of optimization effects, thereby improving the intelligent level and operation efficiency of mining area environmental governance, and ultimately achieving the efficient management of the mining area environment and the sustainable utilization of resources.

[0081] Embodiment 2: Quantum Computing-Driven Environmental Optimization and Decision-Making

[0082] This embodiment demonstrates an intelligent environmental monitoring and governance system for open-pit mines based on quantum computing, with a focus on the environmental optimization and decision-making module based on edge computing and deep learning. This module achieves efficient governance under complex environmental conditions through the following multiple steps:

[0083] Multi-modal data collection: Through devices such as sensor networks, drones, and satellite remote sensing, the system real-time obtains multi-dimensional environmental data such as air quality, noise, water pollution, soil erosion, and geological activities in the mining area, and transmits this data to the quantum computing-driven module for processing.

[0084] Data preprocessing: Before the multi-modal data enters quantum computing, it first passes through a preprocessing module to remove redundant data, fill in missing data, and standardize data from different sources to ensure the unity and consistency of the data.

[0085] Quantum computing parallel processing: The system inputs the preprocessed environmental data into the quantum computing module, and uses the parallel computing ability of quantum algorithms to quickly generate predictions of environmental changes under multiple possible scenarios. Quantum computing can simultaneously process the non-linear relationships of multiple variables, especially multi-dimensional complex problems that are difficult to solve by traditional computing, such as the non-linear impact of resource extraction on the ecosystem.

[0086] Multi - scenario Simulation and Evaluation: The system simulates different governance strategies through quantum computing, analyzes the long - term impact of different governance measures on the mining area environment, including changes in scenarios such as pollutant diffusion, ecological restoration progress, and geological activities.

[0087] Strategy Generation and Optimization: Based on the results of quantum computing, the system automatically generates the optimal environmental governance strategy, and further optimizes these strategies through the intelligent decision - making support and dynamic regulation module to ensure that the governance measures are forward - looking and adaptable.

[0088] Result Verification and Application: The decision - making results generated by the quantum - computing - driven module will be simulated and verified through the virtual twin environment to ensure the feasibility and effectiveness of the strategy. Finally, the verified strategy will be used for actual mining area governance to ensure the best balance between resource utilization and ecological protection.

[0089] Compared with traditional decision - making systems, quantum computing can significantly improve the generation speed and accuracy of governance strategies in the complex and changeable mining area environment, especially showing unique advantages in dealing with multi - variable and multi - scenario situations.

[0090] Example 3: Virtual - physical Linkage between Governance Measures and Automation Control

[0091] This example details how the governance measures and the automation control module achieve precise environmental governance through virtual - physical linkage. The specific steps are as follows:

[0092] Virtual Twin Environment Construction: First, the system constructs a high - precision three - dimensional simulation model of the mining area through the physical environment modeling unit and the virtual twin generation unit. The simulation model is combined with the real - time data of the mining area to form a dynamic virtual twin environment.

[0093] Governance Measure Simulation: Governance measures are simulated in the virtual environment. The governance implementation unit is used to simulate the effects of different governance measures, such as pollutant control, ecological restoration, and emergency response, etc., to evaluate the effects of these measures in actual application in advance.

[0094] Strategy Optimization and Selection: After the simulation is completed, the system will evaluate the effects of governance measures. Through quantitative indicators such as pollutant removal rate and ecological restoration time, the optimal governance strategy is selected. The multi - objective optimization algorithm ensures that the selected strategy is effective not only in a single dimension but also comprehensively considering all environmental factors.

[0095] Equipment Control and Parameter Adjustment: Once the strategy is confirmed, the equipment control unit will work in coordination with the virtual - physical linkage unit, responsible for transmitting the optimal strategy in the virtual twin environment to the actual governance equipment. The equipment control unit automatically adjusts the operating parameters of the actual equipment, such as the flow rate of the pump and the operating time of the filtration system, etc., to achieve the most precise control.

[0096] Real-time monitoring and feedback: The virtual-real linkage unit ensures that the feedback information in actual operations can be transmitted back to the virtual twin environment in a timely manner for secondary verification. The system monitors the operating status of actual equipment in real-time in the virtual twin to ensure that the actual execution effect is consistent with the expectation.

[0097] Closed-loop optimization: Through the continuous virtual-real linkage mechanism, the system continuously obtains feedback during the actual governance process and optimizes the equipment parameters in real-time according to environmental changes, so as to ensure that the governance measures always maintain the best state.

[0098] This virtual-real linkage governance mechanism makes the environmental governance process highly accurate and adaptable, significantly reducing the trial-and-error cost and improving the governance efficiency.

[0099] Embodiment 4: Intelligent Application of Distributed Energy Network and Ecological Regulation

[0100] This embodiment demonstrates the application of the distributed energy network and ecological regulation module in the environmental governance of open-pit mines. Its working process includes the following steps:

[0101] Energy collection and integration: Clean energy sources in the mining area (such as solar energy, wind energy, and geothermal energy) are integrated into a distributed energy network through intelligent microgrid technology. The energy network monitors the output of each energy source in real-time and makes dynamic adjustments according to weather changes, energy consumption demands, etc.

[0102] Energy demand prediction: The system predicts the energy demands of the mining area under different scenarios through big data analysis and prediction algorithms, combined with historical electricity consumption data, current energy demands, and future environmental changes, providing data support for energy distribution.

[0103] Intelligent energy distribution: The system uses intelligent adjustment algorithms to distribute energy in real-time according to energy demands and priorities. The system preferentially allocates clean energy, such as solar energy and wind energy, reduces the consumption of traditional energy, and ensures the optimal utilization of energy through the distributed energy scheduling module.

[0104] Ecological impact analysis: The distributed energy network analyzes the environmental impacts of different energy distribution schemes through linkage with the ecological regulation module. The intelligent adjustment algorithm adjusts the energy distribution strategy according to ecological feedback to ensure the balance between ecological protection and energy utilization.

[0105] Energy consumption optimization and feedback: The system dynamically adjusts the energy utilization strategy by monitoring the energy consumption situation in real-time and combining the data feedback of the environmental restoration module, reducing resource waste and environmental damage.

[0106] Closed-loop control and optimization: The system continuously optimizes the operation of the distributed energy network through the feedback mechanism to ensure the maximization of energy utilization efficiency, while reducing carbon emissions and the long-term impact on the mining area ecosystem.

[0107] By intelligently adjusting the distributed energy network, the mining area can achieve more efficient energy management while minimizing energy consumption and ecosystem damage during environmental governance.

[0108] Example 5: Intelligent Optimization of Mining Area Environmental Restoration

[0109] This example describes how the environmental restoration optimization module achieves ecological environmental restoration of the mining area through multi-level closed-loop optimization. The specific steps are as follows:

[0110] Ecological Assessment and Data Collection: The system first comprehensively assesses the ecological environment of the mining area through the ecological assessment unit, and combines with the multi-modal data collection module to obtain ecological data such as water bodies, soil, and vegetation in the mining area.

[0111] Restoration Plan Generation: The system uses a multi-objective optimization algorithm to generate multiple restoration plans based on the ecological assessment results. The restoration plan generation unit will consider multiple factors such as the efficiency of restoration, long-term effects, and costs, and select the optimal plan that can produce quick results and has the ability to maintain in the long term.

[0112] Restoration Measure Simulation and Verification: The restoration measures are simulated in a virtual twin environment, including water pollution control, soil restoration, vegetation restoration, etc. By simulating the implementation effects of the restoration plan, the system evaluates the feasibility of the restoration measures to ensure the accuracy of the restoration effects.

[0113] Real-time Restoration Implementation: The restoration measures are implemented in the actual mining area through the equipment control unit, and the system automatically executes the restoration tasks to ensure the completion of pollution control and ecological restoration in the shortest time. The real-time monitoring system will track the restoration progress and generate feedback data.

[0114] Restoration Effect Monitoring and Feedback: The restoration effect monitoring unit monitors the progress of ecological restoration in real time and detects the actual effects of the restoration measures. By continuously tracking indicators such as water quality, soil, and vegetation, the system can identify problems during the restoration process and make corresponding adjustments.

[0115] Closed-loop Optimization and Long-term Tracking: The system continuously adjusts the restoration measures through a closed-loop optimization mechanism to ensure the durability and sustainability of the restoration effects. The system also feeds back the restoration data to the ecological assessment unit for long-term environmental tracking and assessment.

[0116] This multi-level closed-loop optimization mechanism ensures the continuous effectiveness of environmental restoration and avoids the dynamic environmental changes that are easily overlooked in traditional static restoration methods.

[0117] Example 6: Intelligent Decision-making Support for Integrated Management and Multi-source Data Fusion

[0118] This embodiment demonstrates how the integrated management and multi-source data fusion module provides intelligent decision-making support for the environmental monitoring and governance of open-pit mines. The specific steps are as follows:

[0119] Multi-source data collection and integration: The system integrates and processes virtual twin environment data, real-time monitoring data, historical environmental data, and external data (such as meteorological and geological data) through a data integration unit to ensure the wide range and real-time nature of data sources.

[0120] Data standardization and cleaning: The data integration unit standardizes the multi-source data collected, eliminates format differences between different data sources, and removes noise data and fills in missing values through cleaning algorithms to ensure the accuracy and integrity of the data.

[0121] Data visualization and display: The processed data is displayed on a unified platform through a data visualization unit. Users can simultaneously view the change trends of the real environment and the virtual twin environment in the mining area. The visualization unit provides multi-dimensional data display, including spatial distribution maps, time series graphs, and scenario simulation results, etc.

[0122] Intelligent decision-making analysis: The data analysis and decision-making support unit deeply analyzes the multi-source data through big data analysis and artificial intelligence algorithms. The system generates different governance suggestions and optimization plans based on the analysis results and predicts the governance effects under different scenarios.

[0123] User interaction and decision feedback: Users can simulate operations on the governance measures in the virtual environment through an interaction operation unit. After the simulation results are verified by an optimization feedback unit, they will be fed back to the actual operation to ensure the consistency of the virtual and real environments.

[0124] Closed-loop optimization and decision update: The system continuously updates the decision-making support suggestions through a data feedback mechanism in real time to ensure the continuous improvement of the real-time nature and forward-looking nature of decisions in the complex and changeable mining area environment.

[0125] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent environmental monitoring and governance system for open-pit mines, characterized in that, Including: An environmental monitoring and multi-modal data acquisition module, arranged at key positions in the mining area, for collecting various types of environmental data such as air quality, noise, water pollution, soil erosion, and geological disasters through sensor networks, drones, satellite remote sensing, and other detection devices, and performing local data processing; An environmental optimization and decision-making module based on edge computing and deep learning, configured to deploy computing resources at the data acquisition end near the mining area using edge computing technology, perform real-time processing and complex multi-variable analysis on multi-source environmental data through deep learning algorithms, generate optimal environmental governance strategies, this module can handle non-linear environmental problems, and continuously optimize governance decisions according to historical data and real-time data through a self-learning mechanism; A governance measures and automation control module, integrated into the system, for automatically implementing environmental governance measures according to the output results of the environmental optimization and decision-making module based on edge computing and deep learning, including pollution control, ecological restoration, and emergency response, this module can automatically adjust the operating state of equipment through a closed-loop feedback control mechanism, and is linked with the virtual twin environment in real time to achieve intelligent control and precise operation; A distributed energy network and ecological regulation module, based on intelligent microgrid technology, integrates different energies (such as solar energy, wind energy, geothermal energy, etc.) in the mining area into a distributed energy network, and optimizes energy utilization and ecological balance in real time through intelligent regulation algorithms, this module can dynamically allocate and preferentially distribute clean energy, while reducing energy consumption during the governance process, and ensuring the best balance between resource development and ecological protection by analyzing ecological impacts; An environmental restoration optimization module, for evaluating the ecological environment of the mining area, generating restoration plans, and monitoring the restoration effects, this module includes an ecological evaluation unit, a restoration plan generation unit, and a restoration effect monitoring unit, and can adjust the restoration strategy according to real-time monitoring data to ensure the persistence and effectiveness of the restoration effect; A comprehensive management and multi-source data fusion module, configured to integrate and manage the data of each module in the system, provide a global view and operation interface, this module realizes the integration and collaboration of various types of data through multi-dimensional data fusion technology, and provides users with dynamic data analysis and real-time decision-making support through artificial intelligence algorithms; A mining area intelligent physical environment simulation and virtual twin module, by constructing a high-precision three-dimensional simulation model of the mining area physical environment and combining it with real-time data, generates a virtual twin environment, for predicting and simulating various environmental change scenarios and their impacts on the mining area environment, this module includes a physical environment modeling unit, a virtual twin generation unit, and a scenario simulation and prediction unit, providing comprehensive environmental simulation and analysis through the combination of virtual and real; 2. The intelligent environmental monitoring and governance system for open-pit mines according to claim 1, wherein The physical environment modeling unit in the mining area intelligent physical environment simulation and virtual twin module includes high-precision terrain scanning equipment and an environmental feature recognition algorithm, for generating a three-dimensional terrain model and an environmental feature map of the mining area, the environmental feature recognition algorithm is based on a multi-scale convolutional neural network (MSCNN), and its structure is: Among them, x is the input high-resolution terrain data, f i is the activation function, is the convolution operation with kernel size k i w i and b i are the weight and bias respectively. N is the number of network layers. Through this algorithm, multi-scale feature extraction is performed on the mining area data, and a three-dimensional terrain model and an environmental feature map are generated.

3. The intelligent environmental monitoring and governance system for open-pit mines according to claim 1, characterized in that, The virtual twin generation unit includes a real-time data fusion engine and a virtual environment rendering engine. The real-time data fusion engine uses a fusion algorithm that combines Kalman filtering and Bayesian inference to achieve precise fusion of multi-source data. This fusion algorithm performs data fusion through the following formula: P k = (I - K k H)P k|k-1 K k = P k|k-1 H T (HP k|k-1 H T + R) -1 P k|k-1 = AP k-1 A T + Q p(x k |z 1:k ) ∝ p(z k |x k ) ∫ p(x k |x k-1 ) p(x k-1 |z 1:k-1 ) dx k-1 Among them, is the fused estimated state, P k is the covariance matrix of the state estimation, K k is the Kalman gain, p(x k |z 1:k ) is the posterior probability density function calculated in Bayesian inference. The multi-source data is processed by this fusion algorithm, and the result is input into the virtual environment rendering engine to generate the virtual twin environment of the mining area.

4. The intelligent environmental monitoring and governance system for open-pit mines according to claim 1, characterized in that, The scenario simulation and prediction unit includes a multi-scenario prediction model and a scenario simulation control system. The multi-scenario prediction model specifically includes a historical data analysis unit, a real-time data processing unit, and a scenario evolution calculation unit. By combining historical data, real-time monitoring data, and the virtual twin environment, it predicts environmental changes under different scenarios. The scenario simulation control system includes a scenario parameter adjustment unit and a simulation result evaluation unit, which are used to set and adjust scenario parameters and generate governance strategy suggestions for different scenarios.

5. The intelligent environmental monitoring and governance system for open-pit mines according to claim 1, wherein, The scenario simulation and prediction unit can generate the changes in the mining area environment at different time scales, including short-term, medium-term, and long-term prediction results. The prediction model specifically includes a short-term dynamic prediction unit, a medium-term trend prediction unit, and a long-term environmental evolution unit, which are respectively used to generate environmental change predictions at multiple time scales by combining real-time data and historical trends, and provide data support for the intelligent decision-making support and dynamic regulation module to ensure the forward-looking and effectiveness of governance strategies at different time scales.

6. The intelligent environmental monitoring and governance system for open-pit mines according to claim 1, wherein, The intelligent decision-making support and dynamic regulation module includes a decision analysis unit, a regulation optimization unit, and a feedback self-learning unit. Based on the simulation results generated by the mining area intelligent physical environment simulation and virtual twin module, this module can automatically adjust the environmental governance strategy. This module obtains real-time feedback through the closed-loop feedback control unit dynamically corrects the governance plan, and continuously optimizes the strategy by combining the self-learning mechanism, thereby enhancing the sustainability and adaptability of the governance effect.

7. The intelligent environmental monitoring and governance system for open-pit mines according to claim 1, characterized in that, The governance measures and automation control module includes a governance implementation unit, an equipment control unit, and a virtual-real linkage unit. This module realizes real-time linkage with the virtual twin environment. The governance implementation unit pre-simulates the proposed governance measures in the virtual environment, and the equipment control unit adjusts the operating parameters of actual equipment. The virtual-real linkage unit ensures that the simulation results in the virtual environment can be dynamically fed back to the actual equipment operation, thereby enhancing the accuracy and adaptability of governance measures.

8. The intelligent environmental monitoring and governance system for open-pit mines according to claim 1, wherein, The resource sustainable utilization module includes a resource evaluation unit, a mining path optimization unit, and a resource utilization feedback unit. This module evaluates different mining strategies through the resource simulation model of the virtual twin environment, combines the resource evaluation unit to evaluate the resource utilization efficiency and ore recovery rate, generates the optimal mining path through the mining path optimization unit, and the resource utilization feedback unit provides real-time feedback on the mining effect and corresponding optimization suggestions to ensure the efficient utilization of resources and the minimum damage to the environment.

9. The intelligent environmental monitoring and governance system for open-pit mines according to claim 1, characterized in that The comprehensive management and multi-source data fusion module includes a data integration unit, a data visualization unit, and a data analysis and decision support unit. The data integration unit is used to integrate data from different sources (such as virtual twin environment data, real-time monitoring data, etc.). The data visualization unit provides a user-friendly interface and can display virtual and real data simultaneously. The data analysis and decision support unit provides real-time decision support for each module of the system through multi-dimensional data analysis.

10. The intelligent environmental monitoring and governance system for open-pit mines according to claim 1, characterized in that The comprehensive management and multi-source data fusion module can display the real environment and virtual twin environment of the mining area on a single platform. Users can perform various operation simulations in the virtual twin environment through the interaction operation unit. The module also includes an optimization feedback unit, which is used to feedback the results simulated and verified in the virtual environment to the actual mining area operation, so as to achieve a closed-loop optimization of the combination of virtual and real, and improve the accuracy of operation and governance efficiency.

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