A smart mine management and control method based on digital twins

Abstract

The present invention relates to the field of mine management technology, and discloses a smart mine management and control method based on digital twins, comprising: step 1, collecting various types of data in the mine environment through Internet of Things technology, sensors and drone equipment, the data including the operating status of mining equipment, the quality of ore, environmental change information and geological data, and performing noise removal, outlier processing and data integrity verification after data collection; step 2, after completing data collection and processing, using the data to construct a multi-scale digital twin model. By combining multi-scale modeling with nonlinear dynamics, the macro-micro dynamic coupling problem existing in the mine environment is solved, the digital twin model is updated in real time, the mine system state is estimated, and it is ensured that the model can respond in real time when the environment changes, thereby obtaining the effect of improving the accuracy of the digital twin model and enhancing the real-time performance.

Description

Technical Field

[0001] The present invention relates to the technical field of mine management, and specifically to a smart mine management and control method based on digital twins. Background Art

[0002] With technological advancements, mine management faces challenges, including low production efficiency, resource waste, equipment failure, and safety hazards. Traditional mine management and control methods rely on manual decision-making and static models, lacking real-time perception and prediction of the mining environment. This leaves room for improvement in both efficiency and safety. This is especially true when faced with volatile environments and emergencies, as traditional methods are unable to respond quickly and effectively, increasing the risk of resource waste, equipment loss, and safety accidents.

[0003] In recent years, digital twin technology, as an emerging management tool, has been widely adopted in the industrial sector. Digital twin technology collects data from physical systems in real time and builds corresponding virtual models, enabling comprehensive monitoring and dynamic management of the systems. However, in the mining sector, the complexity and variability of mining environments still limit existing digital twin applications. Specifically, existing digital twin models cannot simultaneously handle dynamic changes at both macro and micro scales; the real-time performance and accuracy of model updates are poor, making them ineffective in supporting real-time decision-making for complex and unexpected events; and in resource scheduling and production process optimization, traditional control algorithms often fail to fully account for environmental changes and system constraints, resulting in suboptimal optimization results.

[0004] Therefore, achieving efficient and accurate digital twin model updates in a mining environment, and improving production efficiency and decision-making response speed through optimized control, has become a major challenge facing current mine management and control technology. To overcome the aforementioned technical bottlenecks, this paper proposes a smart mine management and control method based on digital twins to address these issues. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a smart mine management and control method based on digital twins to solve the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a smart mine management and control method based on digital twins, comprising:

[0007] Step 1: Use IoT technology, sensors, and drones to collect various data from the mining environment. The data includes the operating status of mining equipment, ore quality, environmental change information, and geological data. After data collection, noise removal, outlier processing, and data integrity verification are performed.

[0008] Step 2: After data collection and processing, a multi-scale digital twin model is constructed using the data. The multi-scale digital twin model combines the macro and micro processes in the mine system. The macro process describes the overall mining progress and equipment status of the mine, while the micro process describes the local influencing factors of ore layer changes and equipment wear.

[0009] Step 3: Based on the constructed multi-scale digital twin model, nonlinear dynamic equations are used to model the dynamic changes of the mining system. The nonlinear dynamic equations describe the nonlinear relationships between various links in the mining system and consider the complex interactions between equipment operating status and mining resources. The inputs of the nonlinear dynamic equations include the current state of the mining system, equipment operating control variables, and interference factors in the environment.

[0010] Step 4: By combining Bayesian filtering and Kalman filtering, the state of the mine system is estimated in real time. The Bayesian filter provides a priori estimates of the state, and the Kalman filter corrects the estimate of the system state based on real-time data collected by sensors.

[0011] Step 5: Based on the real-time estimate obtained in step 4, the optimal control algorithm is used to calculate the update strategy for each time step and optimize the control input. The optimal control strategy provides the best decision basis by weighing the model error and the consumption of control input energy;

[0012] Step 6: Update the state of the digital twin model in real time according to the optimal control strategy in step 5, and adjust the parameters of the digital twin model by applying the optimal control input at each time step;

[0013] Step 7. After the digital twin model is updated, conduct a comprehensive assessment of mine production based on the updated model. Based on the status information provided by the model, adjust the mine operation plan, optimize equipment utilization and resource scheduling. At the same time, use the model to monitor the mine environment in real time to promptly identify potential safety hazards and environmental issues, and formulate and implement corresponding preventive measures.

[0014] Preferably, in step 2, the construction of the multi-scale digital twin model adopts the asymptotic expansion method, wherein the macroscopic process is represented by the following equation:

[0015]

[0016] Among them, u(x, t) is the state variable of the mine, L0 is the macro-scale dynamic process, L1 is the micro-scale process, ∈ represents the coupling effect of macro- and micro-processes, represents the rate at which the state variables (x, t) of the mining system change with time, Represents a high-order small quantity.

[0017] Preferably, in step 2, the nonlinear dynamic equation adopts the following form to describe the complex interaction between mining equipment and resources:

[0018]

[0019] in, is the state of the mining system, f(x(t), u(t)) is the nonlinear dynamic function of the mining system, u(t) is the control input, and η(t) is the external disturbance.

[0020] Preferably, in step 4, a combination of Bayesian filtering and Kalman filtering is used to estimate the mine status in real time, wherein the recursive formula of Kalman filtering is:

[0021]

[0022] in, is the state estimate at the current moment, is the state estimate of the previous moment, K k is the Kalman gain, z k is the observation value at the current moment, and H is the observation matrix.

[0023] Preferably, in step 5, the optimal control algorithm adopts a dynamic programming optimization update strategy, and the objective function is:

[0024] Among them, J is the objective function, x(t) is the actual state of the mine, is the state of the digital twin model, Q is the weighting matrix, R is the weight of the control input, u(t) is the control input, and T is the optimization time window.

[0025] Preferably, the control input u(t) is calculated by an optimal control algorithm, which is a dynamic programming or reinforcement learning algorithm, and is used to calculate the optimal update amount at each time step to minimize the error and cost in the objective function J.

[0026] Preferably, in step 6, the real-time update step is based on the optimal control input of each time step, and the state is updated using the following formula:

[0027]

[0028] Where u(t) is the control input, represents the control input u(t) that minimizes the subsequent objective function, x(t) is the actual state of the mine, is the state of the digital twin model, Represents the actual state x(t) and the digital twin model state The error between represents the cost of the control input u(t), Q is the weighting matrix, and R is the weight of the control input.

[0029] Preferably, the updating step adjusts the operation plan of mine production, including equipment scheduling, ore transportation and resource allocation, based on real-time sensor data and the updated digital twin model status.

[0030] Preferably, the safety risk assessment of mining operations is performed based on the updated digital twin model, and the assessment is performed according to the following formula:

[0031]

[0032] Among them, S(t) is the security risk assessment value,

[0033] is the risk prediction function, which represents the combination of the model and real-time data to predict potential safety hazards in mining operations.

[0034] x(t) is the actual state of the mine, is the state of the digital twin model, η(t) is the external disturbance;

[0035] The safety risk assessment result S(t) is used to automatically adjust the mining operation process, optimize the operation plan, and implement corresponding preventive measures based on the assessment result.

[0036] Preferably, the optimization strategy for mine production scheduling is implemented according to the following formula:

[0037] min{f(prod efficiency), g(envirimpact), h(risk assess)},

[0038] Among them, f (prod efficiency) represents the production efficiency optimization function, g (envir impact) is the environmental impact minimization function, and h (risk assess) is the safety risk control function.

[0039] The present invention provides a smart mine management and control method based on digital twins. It has the following beneficial effects:

[0040] 1. The present invention solves the problem of macro-micro dynamic coupling in the mining environment by combining multi-scale modeling with nonlinear dynamics, updates the digital twin model in real time, estimates the state of the mining system, and ensures that the model can respond in real time when the environment changes, thereby improving the accuracy and real-time performance of the digital twin model.

[0041] 2. The present invention uses the optimal control algorithm to comprehensively optimize the system error and the energy consumption of the control input in the objective function, calculate the optimal control strategy, realize the intelligent scheduling of mining operations and efficient allocation of resources, thereby improving mine production efficiency, reducing resource waste, and lowering operating costs.

[0042] 3. The present invention provides decision support through real-time updates of digital twin models and intelligent decision-making algorithms, enabling mine managers to quickly respond to various changes in mine operations, realize intelligent mine management and control, improve management efficiency, accelerate decision-making speed, and promote the intelligent development of mine management. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Flowchart of the present invention. DETAILED DESCRIPTION

[0044] To help those skilled in the art understand the present invention, the following will provide a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only partial embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0045] The present invention is described in detail below with reference to the accompanying drawings:

[0046] Example:

[0047] Please see the attached Figure 1 , an embodiment of the present invention provides a smart mine management and control method based on digital twins, including:

[0048] Step 1: Use IoT technology, sensors, and drones to collect various data from the mining environment. The data includes the operating status of mining equipment, ore quality, environmental change information, and geological data. After data collection, noise removal, outlier processing, and data integrity verification are performed.

[0049] Step 2: After data collection and processing, a multi-scale digital twin model is constructed using the data. The multi-scale digital twin model combines the macro and micro processes in the mine system. The macro process describes the overall mining progress and equipment status of the mine, while the micro process describes the local influencing factors of ore layer changes and equipment wear.

[0050] Step 3: Based on the constructed multi-scale digital twin model, nonlinear dynamic equations are used to model the dynamic changes of the mining system. Nonlinear dynamic equations describe the nonlinear relationships between various links in the mining system and consider the complex interactions between equipment operating status and mining resources. The inputs to the nonlinear dynamic equations include the current state of the mining system, equipment operating control variables, and interference factors in the environment.

[0051] Step 4: By combining Bayesian filtering and Kalman filtering, the state of the mining system is estimated in real time. The Bayesian filter provides a priori estimation of the state, and the Kalman filter corrects the estimate of the system state based on the real-time data collected by the sensors.

[0052] Step 5: Based on the real-time estimate obtained in step 4, the optimal control algorithm is used to calculate the update strategy for each time step and optimize the control input. The optimal control strategy provides the best decision basis by weighing the model error and the consumption of control input energy;

[0053] Step 6: Update the state of the digital twin model in real time according to the optimal control strategy in step 5, and adjust the parameters of the digital twin model by applying the optimal control input at each time step;

[0054] Step 7. After the digital twin model is updated, conduct a comprehensive assessment of mine production based on the updated model. Based on the status information provided by the model, adjust the mine operation plan, optimize equipment utilization and resource scheduling. At the same time, use the model to monitor the mine environment in real time to promptly identify potential safety hazards and environmental issues, and formulate and implement corresponding preventive measures.

[0055] Benefits of Step 1: Ensures the authenticity and accuracy of collected data, avoiding model errors and decision-making errors caused by incomplete and inaccurate data. Improves data quality, laying a solid foundation for subsequent model construction and analysis. Ensures automation and efficiency of the data processing process, reducing manual intervention and errors.

[0056] Benefits of Step 2: By combining macro- and micro-processes, the model comprehensively reflects the multi-dimensional dynamic changes in the mine. This enables the model to accurately describe the complex mining environment and respond to changes at different levels and scales within the mine, improving the model's adaptability and predictive capabilities.

[0057] The benefits of step 3: It captures the complex, nonlinear interactions between equipment operating status and resources within a mining environment, accurately reflecting the complexity of the mining environment. It provides a precise model, improving the ability to predict system dynamics and avoiding the simplifications and inadequacies of traditional linear models.

[0058] Benefits of Step 4: Providing accurate state estimates effectively addresses system uncertainty and external disturbances, ensuring model stability in dynamic environments. Combining Kalman and Bayesian filtering provides precise state predictions, reducing estimation biases caused by real-time data changes.

[0059] Benefits of Step 5: It provides a mathematical foundation for optimizing mining operations. It can automatically adjust equipment operation and ore extraction processes based on the mine's real-time status to maximize resource utilization. By optimizing control strategies, it reduces unnecessary energy consumption, lowers production costs, and improves operational efficiency.

[0060] The benefit of step 6: The digital twin model is always synchronized with the mine's actual environment. The model can reflect the mine's production status in real time, ensuring the timeliness of decision-making. By updating at each time step, the model can make timely adjustments to the mine's operation progress, equipment status, and resource allocation factors.

[0061] Benefits of Step 7: Comprehensive, real-time production monitoring and decision support improve the accuracy and efficiency of mine management. By promptly identifying potential safety hazards and environmental issues, early warnings can be provided and appropriate preventive measures can be taken, mitigating safety risks and ensuring the sustainability of mine production.

[0062] In step 2, the multi-scale digital twin model is constructed using the asymptotic expansion method, where the macroscopic process is represented by the following equation:

[0063]

[0064] Among them, u(x, t) is the state variable of the mine, L0 is the macro-scale dynamic process, L1 is the micro-scale process, ∈ represents the coupling effect of macro- and micro-processes, represents the rate at which the state variables (x, t) of the mining system change with time, Represents a high-order small quantity.

[0065] The asymptotic expansion method effectively couples the macro- and micro-processes of a mining system, comprehensively describing its dynamics at different scales. While the macro-process reflects the overall operational status of the mine, the micro-process meticulously captures the impact of local factors such as equipment wear and ore layer changes, ensuring the multi-dimensional accuracy of the digital twin model.

[0066] Through multi-scale modeling, the digital twin model can focus on local details in the mining system when processing a wide range of mining data, avoiding the situation where micro-level influences are ignored in traditional models, and making the model highly accurate and adaptable when responding to environmental changes and equipment failures.

[0067] It allows for flexible adjustment of the coupling degree between macro and micro processes in different application scenarios. If a certain factor in the mining environment becomes important, the influence of the micro process can be enhanced, and vice versa, it can be weakened, providing flexibility and optimization space.

[0068] In step 2, the nonlinear dynamic equations take the following form to describe the complex interaction between mining equipment and resources:

[0069]

[0070] in, is the state of the mining system, f(x(t), u(t)) is the nonlinear dynamic function of the mining system, u(t) is the control input, and η(t) is the external disturbance.

[0071] The equations accurately capture the complex nonlinear relationships between various links in the mining system. Through nonlinear dynamic models, the nonlinear behavior generated by the interaction of multiple factors in the mining system can be reflected, ensuring the accuracy of the model in describing the system dynamics.

[0072] Nonlinear dynamic models can handle the impact of external disturbances on mining production processes. By introducing the disturbance term η(t) into the model, the impact of disturbances on mine production status can be promptly reflected, thereby enhancing the adaptability and robustness of the digital twin model and ensuring the system maintains stable operation in a changing environment.

[0073] By accurately describing the interaction between mining equipment and resources, nonlinear dynamic equations enable optimal control of the system under varying conditions. In practical applications, adjusting the control input u(t) optimizes equipment scheduling and resource allocation, improving mining efficiency and resource utilization. Furthermore, the system can adjust control strategies based on real-time feedback to avoid resource waste.

[0074] In step 4, the combination of Bayesian filtering and Kalman filtering is used to estimate the mine status in real time. The recursive formula of Kalman filtering is:

[0075]

[0076] in, is the state estimate at the current moment, is the state estimate of the previous moment, K k is the Kalman gain, z k is the observation value at the current moment, and H is the observation matrix.

[0077] By combining Bayesian and Kalman filtering, we can leverage the prior information of Bayesian filtering and the real-time data correction capabilities of Kalman filtering to significantly improve the accuracy of mine system state estimation. Bayesian filtering provides an initial estimate of the system state, while Kalman filtering corrects the estimate based on real-time sensor data, ensuring accurate estimation of mine state, especially in the presence of noise and uncertainty in the mining environment.

[0078] In mining operations, external disturbances can affect the estimated state of the system. Kalman filtering can dynamically adjust the state estimate based on new observations, while Bayesian filtering can provide comprehensive prior information to help the system cope with external disturbances.

[0079] By estimating the status of the mine system in real time, managers can make decisions based on accurate status information. k A real-time feedback mechanism is provided to maximize the impact of the latest observation data on the estimation by adjusting the weight of the state estimation, thereby providing a basis for decision-making when controlling mining operations and improving the flexibility and adaptability of the mining production process.

[0080] In step 5, the optimal control algorithm adopts dynamic programming optimization update strategy, and the objective function is:

[0081]

[0082] Among them, J is the objective function, x(t) is the actual state of the mine, is the state of the digital twin model, Q is the weighting matrix, R is the weight of the control input, u(t) is the control input, and T is the optimization time window.

[0083] The control input u(t) is calculated by an optimal control algorithm, which is a dynamic programming or reinforcement learning algorithm that is used to calculate the optimal update amount at each time step to minimize the error and cost in the objective function J.

[0084] By optimizing the objective function using a dynamic programming algorithm, we can accurately calculate the optimal control input u(t) at each time step, ensuring that the mining system's operational decisions are optimal at each moment. This optimization strategy enables efficient mining operations, reduces resource waste, and improves production efficiency.

[0085] The objective function is to calculate the actual state error The weighted values ​​of error and control input energy are weighted to ensure a reasonable balance between system error and control input cost. Through the weight matrices Q and R, the system can flexibly adjust the relative importance of error and control input according to different requirements, realizing a control strategy that meets actual needs.

[0086] Through the optimal control algorithm, resource scheduling and equipment operation in the mine production process can be globally optimized rather than locally optimized, which means that the system can calculate the optimal update amount at each time step, making the mine's production process coordinated and efficient, and reducing losses caused by delayed and inaccurate decisions.

[0087] Dynamic programming algorithms are particularly well-suited for solving complex nonlinear problems. In mining systems, factors such as equipment wear and ore layer variations can lead to complex and nonlinear system dynamics. Dynamic programming algorithms accurately handle complex nonlinear interactions, ensuring that the optimization process accounts for potential influencing factors, improving decision accuracy and overall system performance.

[0088] In step 6, the real-time update step is based on the optimal control input at each time step and uses the following formula to update the state:

[0089]

[0090] Where u(t) is the control input, represents the control input u(t) that minimizes the subsequent objective function, x(t) is the actual state of the mine, is the state of the digital twin model, Represents the actual state x(t) and the digital twin model state The error between represents the cost of the control input u(t), Q is the weighting matrix, and R is the weight of the control input.

[0091] The update step adjusts the mine production operation plan, including equipment scheduling, ore transportation, and resource allocation, based on real-time sensor data and the updated digital twin model status.

[0092] The safety risk assessment of mining operations is conducted based on the updated digital twin model, using the following formula:

[0093]

[0094] Among them, S(t) is the security risk assessment value,

[0095] is the risk prediction function, which represents the combination of the model and real-time data to predict potential safety hazards in mining operations.

[0096] x(t) is the actual state of the mine, is the state of the digital twin model, η(t) is the external disturbance;

[0097] The safety risk assessment results S(t) are used to automatically adjust the mining operation process, optimize the operation plan, and implement corresponding preventive measures based on the assessment results.

[0098] By updating the state based on the optimal control input at each time step, operational decisions for the mining system are ensured to be real-time and optimal. By optimizing the error and cost of the control input, the digital twin model can be adjusted at each time step to reflect the actual state of the mine operation in real time, reducing decision lags and improving the accuracy and responsiveness of mining operations.

[0099] The update process automatically adjusts mine production plans based on real-time data and the updated state of the digital twin model. Equipment scheduling, ore transportation, and resource allocation are optimized based on the real-time estimated mine status, achieving efficient utilization of mine resources and balanced production processes, avoiding resource waste and inefficiencies caused by poor planning.

[0100] This technology monitors the status of mining operations in real time and uses a digital twin model to simulate mine dynamics, predicting potential safety hazards. By combining safety risk assessment formulas with real-time data and model status, risks can be identified promptly and preventive measures can be taken to avoid accidents.

[0101] Based on the results of safety risk assessments, the system automatically adjusts mining operations and optimizes operational plans to ensure both production efficiency and safety. By automatically taking preventative measures based on risk assessments, the system can provide early warnings and automatically adjust operational plans before unexpected risks occur, avoiding human error and decision-making delays, and improving the automation and intelligence of mining operations.

[0102] The optimization strategy of mine production scheduling is implemented according to the following formula:

[0103] min{f(prod efficiency), g(envir impact), h(risk assess)},

[0104] Among them, f (prod efficiency) represents the production efficiency optimization function, g (envir impact) is the environmental impact minimization function, and h (risk assess) is the safety risk control function.

[0105] This invention achieves intelligent and efficient mine production scheduling by introducing a comprehensive optimization strategy that optimizes production efficiency, minimizes environmental impact, and controls safety risks. This optimization strategy improves mine production efficiency and resource utilization, while prioritizing environmental protection and safety risk management. Through real-time monitoring and a data-driven decision-making system, mine operations can be dynamically adjusted in complex environments, ensuring both production efficiency and safety, and driving mine operations towards intelligent, green, and sustainable development.

[0106] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A smart mine management and control method based on digital twins, characterized in that: include: Step 1: Use IoT technology, sensors, and drones to collect various data from the mining environment. The data includes the operating status of mining equipment, ore quality, environmental change information, and geological data. After data collection, noise removal, outlier processing, and data integrity verification are performed. Step 2: After data collection and processing, a multi-scale digital twin model is constructed using the data. The multi-scale digital twin model combines the macro and micro processes in the mine system. The macro process describes the overall mining progress and equipment status of the mine, while the micro process describes the local influencing factors of ore layer changes and equipment wear. Step 3: Based on the constructed multi-scale digital twin model, nonlinear dynamic equations are used to model the dynamic changes of the mining system. The nonlinear dynamic equations describe the nonlinear relationships between various links in the mining system and consider the complex interactions between equipment operating status and mining resources. The inputs of the nonlinear dynamic equations include the current state of the mining system, equipment operating control variables, and interference factors in the environment. Step 4: By combining Bayesian filtering and Kalman filtering, the state of the mine system is estimated in real time. The Bayesian filter provides a priori estimates of the state, and the Kalman filter corrects the estimate of the system state based on real-time data collected by sensors. Step 5: Based on the real-time estimate obtained in step 4, the optimal control algorithm is used to calculate the update strategy for each time step and optimize the control input. The optimal control strategy provides the best decision basis by weighing the model error and the consumption of control input energy; Step 6: Update the state of the digital twin model in real time according to the optimal control strategy in step 5, and adjust the parameters of the digital twin model by applying the optimal control input at each time step; Step 7. After the digital twin model is updated, conduct a comprehensive assessment of mine production based on the updated model. Based on the status information provided by the model, adjust the mine operation plan, optimize equipment utilization and resource scheduling. At the same time, use the model to monitor the mine environment in real time to promptly identify potential safety hazards and environmental issues, and formulate and implement corresponding preventive measures.

2. The method for intelligent mine management and control based on digital twin according to claim 1, characterized in that: In step 2, the multi-scale digital twin model is constructed using the asymptotic expansion method, where the macroscopic process is represented by the following equation: Among them, u(x, t) is the state variable of the mine, L0 is the macro-scale dynamic process, L1 is the micro-scale process, ∈ represents the coupling effect of macro- and micro-processes, represents the rate at which the state variables (x, t) of the mining system change with time, Represents a high-order small quantity.

3. The method for intelligent mine management and control based on digital twin according to claim 1, characterized in that: In step 2, the nonlinear dynamic equation takes the following form to describe the complex interaction between mining equipment and resources: in, is the state of the mining system, f(x(t), u(t)) is the nonlinear dynamic function of the mining system, u(t) is the control input, and η(t) is the external disturbance.

4. The method for intelligent mine management and control based on digital twin according to claim 1, characterized in that: In step 4, the combination of Bayesian filtering and Kalman filtering is used to estimate the mine status in real time, wherein the recursive formula of Kalman filtering is: in, is the state estimate at the current moment, is the state estimate of the previous moment, K k is the Kalman gain, z k is the observation value at the current moment, and H is the observation matrix.

5. The method for intelligent mine management and control based on digital twin according to claim 1, characterized in that: In step 5, the optimal control algorithm adopts a dynamic programming optimization update strategy, and the objective function is: Among them, J is the objective function, x(t) is the actual state of the mine, is the state of the digital twin model, Q is the weighting matrix, R is the weight of the control input, u(t) is the control input, and T is the optimization time window.

6. The method for intelligent mine management and control based on digital twin according to claim 5, characterized in that: The control input u(t) is calculated by an optimal control algorithm, which is a dynamic programming or reinforcement learning algorithm, and is used to calculate the optimal update amount at each time step to minimize the error and cost in the objective function J.

7. The method for intelligent mine management and control based on digital twin according to claim 1, characterized in that: In step 6, the real-time update step is based on the optimal control input of each time step, and the state is updated using the following formula: Where u(t) is the control input, represents the control input u(t) that minimizes the subsequent objective function, x(t) is the actual state of the mine, is the state of the digital twin model, Represents the actual state x(t) and the digital twin model state The error between represents the cost of the control input u(t), Q is the weighting matrix, and R is the weight of the control input.

8. The method for intelligent mine management and control based on digital twin according to claim 7, characterized in that: The updating step adjusts the operation plan of mine production, including equipment scheduling, ore transportation and resource allocation, based on real-time sensor data and the updated digital twin model status.

9. The method for intelligent mine management and control based on digital twin according to claim 1, characterized in that: The safety risk assessment of mining operations is performed based on the updated digital twin model, and the assessment is performed according to the following formula: Among them, S(t) is the security risk assessment value, is the risk prediction function, which represents the combination of the model and real-time data to predict potential safety hazards in mining operations. x(t) is the actual state of the mine, is the state of the digital twin model, η(t) is the external disturbance; The safety risk assessment result S(t) is used to automatically adjust the mining operation process, optimize the operation plan, and implement corresponding preventive measures based on the assessment result.

10. The method for intelligent mine management and control based on digital twin according to claim 1, characterized in that: The optimization strategy for mine production scheduling is implemented according to the following formula: min{f(prod efficiency), g(envir impact), h(risk assess)}, Among them, f (prod efficiency) represents the production efficiency optimization function, g (envir impact) is the environmental impact minimization function, and h (risk assess) is the safety risk control function.

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