Coal mining water control method based on underground water flow velocity and flow direction measurement

Abstract

The invention relates to the technical field of underground water flow velocity and flow direction measurement, and discloses a coal mining water control method based on underground water flow velocity and flow direction measurement, which comprises the following steps: deploying underground water monitoring equipment to collect underground water level, flow velocity and flow direction data around a mine in real time; establishing an underground water flow model based on a fractional order diffusion equation, and simulating flow velocity and flow direction changes of underground water around the mine; modeling of groundwater flow is carried out on a macroscale, a mesoscale and a microscale by combining with a multi-scale analysis method. According to the method, accurate prediction of the flow velocity and the flow direction of underground water is realized by introducing a fractional order diffusion equation, multi-scale analysis, a self-adaptive grid technology and machine learning optimization. Compared with the prior art, nonlinear and dynamic changes of underground water flowing can be effectively treated, and the calculation precision and efficiency of the model are improved; and the prediction accuracy is improved by automatically optimizing model parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of measuring the velocity and direction of groundwater flow, and specifically to a method for preventing and controlling water in coal mine mining based on measuring the velocity and direction of groundwater flow. Background Art

[0002] In existing methods for predicting groundwater flow and preventing and controlling water in coal mine mining, most rely on traditional hydrogeological models, and most of these models are based on steady-state flow assumptions such as Darcy's law. Traditional methods are applicable to environments where groundwater flow is relatively stable, but in the face of dynamically changing groundwater flow, these models show great limitations. Especially during the process of coal mine mining, the water flow rate and direction are affected by various factors such as mining depth, rock formation structure, and water source changes, making groundwater flow extremely complex and unstable. Therefore, existing technologies cannot meet the actual needs of groundwater flow in the coal mine mining environment when simulating complex water flow behaviors.

[0003] The main problems of the existing technologies include: First, most traditional models are based on linear assumptions and ignore the non-linear characteristics of groundwater flow. Especially in a rapidly changing mining environment, they cannot accurately describe the lag effect and historical dependence of water flow; Second, the fixed grid method cannot effectively handle the sharp changes in groundwater flow. Especially during the mining process, the sudden changes in water flow in local areas cannot be finely simulated by a coarse grid, resulting in insufficient calculation accuracy; Third, parameter adjustment in existing technologies mostly relies on manual experience and lacks an automatic optimization mechanism, and cannot dynamically adjust the model according to real-time data, causing the prediction results to deviate from the actual situation in a complex environment; Finally, although there are already some decision support systems in existing technologies, these systems are not fully integrated with the groundwater flow model, resulting in the lack of real-time and intelligence in the adjustment of water prevention and control measures. Compared with the present invention, the existing technologies have significant deficiencies in dealing with dynamic groundwater flow and real-time water prevention and control decisions. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technologies, the present invention provides a method for preventing and controlling water in coal mine mining based on measuring the velocity and direction of groundwater flow, which solves the problems of inaccurate prediction of groundwater flow, inability of the model to handle dynamic changes, lack of real-time optimization, and intelligent decision support in the existing technologies.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for preventing and controlling water in coal mine mining based on measuring the velocity and direction of groundwater flow, including the following steps: Deploy groundwater monitoring equipment to collect real-time data on the groundwater level, velocity, and direction around the mine; Establish a groundwater flow model based on the fractional diffusion equation to simulate the changes in the velocity and direction of groundwater around the mine; Combined with the multi-scale analysis method, groundwater flow modeling is carried out at the macroscopic scale, mesoscopic scale and microscopic scale respectively, and the groundwater flow is comprehensively simulated through multi-scale coupling; The adaptive grid technology is used to divide the grid of the groundwater flow area to improve the calculation accuracy of the model, and the grid density is dynamically adjusted according to the changes in flow velocity and direction; The real-time collected groundwater monitoring data is fused with the groundwater flow model, and the model parameters are optimized through data fusion technology; Based on the optimized groundwater flow model, the changes in groundwater flow velocity and direction are predicted in real time, and decision-making support is provided for water control measures during coal mine mining.

[0006] Preferably, the groundwater flow model is a groundwater flow model based on the fractional diffusion equation. By introducing the memory effect of flow, the model accurately describes the nonlinear and time-delay characteristics of groundwater flow during mining.

[0007] Preferably, the multi-scale analysis method includes using the finite element method for overall modeling of groundwater flow at the macroscopic scale, using the finite difference method for local simulation of the mine mining area at the mesoscopic scale, and using the pore medium model to simulate the diffusion and infiltration of water flow in microscopic pores at the microscopic scale.

[0008] Preferably, the adaptive grid technology dynamically adjusts the grid density by using high-precision grids in areas with drastic water flow changes and coarser grids in areas with relatively stable water flow changes, so as to improve the accuracy and efficiency of groundwater flow calculation.

[0009] Preferably, the data fusion technology includes combining real-time monitoring data with historical groundwater flow data, and optimizing the parameters of the groundwater flow model through machine learning algorithms to improve the prediction accuracy of the model for groundwater flow changes.

[0010] Preferably, the decision support system automatically generates a water control emergency plan according to the prediction results of the groundwater flow model and provides corresponding water control measure suggestions for coal mine management personnel.

[0011] Preferably, the real-time monitoring data includes data on groundwater level, flow velocity, flow direction and groundwater head, and the data is transmitted to the central processing unit in real time through a wireless transmission system for analysis and optimization.

[0012] Preferably, the optimized groundwater flow model is adaptively adjusted through a deep learning model based on real-time monitoring data and historical data, so that the model can automatically adapt to the water flow changes during coal mine mining.

[0013] Preferably, the groundwater flow model further reduces prediction errors through error analysis and optimization algorithms, and ensures that the model can provide accurate groundwater flow predictions under complex geological conditions.

[0014] Preferably, the water prevention and control measures include lowering the groundwater level and controlling the flow direction of groundwater through precipitation wells and drainage systems to prevent water disasters, and adjusting the mining plan in real time according to the groundwater flow model.

[0015] The present invention provides a method for preventing and controlling water in coal mining based on the measurement of groundwater flow velocity and direction. It has the following beneficial effects: 1. The present invention adopts a technical solution combining fractional-order diffusion equations with multi-scale analysis, and introduces the "memory effect" to describe the nonlinear and time-varying characteristics of groundwater flow, thereby achieving the technical effect of accurately simulating the velocity and direction of groundwater flow. Compared with the existing model that only uses the traditional Darwin's law, it lacks a comprehensive control over the complexity and dynamic changes of groundwater flow. The present invention effectively solves the problem that traditional methods cannot cope with non-steady-state and complex groundwater flow.

[0016] 2. The present invention dynamically adjusts the calculation accuracy through adaptive grid technology, uses high-precision grids in areas where water flow changes dramatically, and uses coarser grids in areas where water flow is relatively stable, thereby achieving the technical effect of optimizing calculation efficiency and accuracy. Different from the fixed grid calculation used in the prior art, the present invention solves the shortcomings of fixed grids that are low in efficiency and cannot adapt to dynamic changes in complex mine environments.

[0017] 3. The present invention realizes the dynamic adjustment of the model to real-time data and historical data through the technical solution of optimizing model parameters through data fusion and machine learning, making the prediction results of groundwater flow more accurate and stable. Compared with the limitations of data processing and model parameter adjustment in the prior art, the present invention automatically optimizes model parameters through intelligent algorithms, solving the problem that the existing solutions cannot handle dynamic changes and uncertainties.

[0018] 4. The present invention introduces a technical solution that combines a decision support system with a groundwater flow model, generates water prevention and control measures in real time based on prediction results, and provides a scientific basis for decision-making. Compared with the prior art that relies on manual experience to formulate water prevention and control measures, the decision-making system of the present invention automatically processes mine water flow change data, solving the problems of low efficiency and slow response of traditional manual decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention 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 the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] Please refer to the attached Figure 1 , the embodiment of the present invention provides a method for preventing and controlling water in coal mining based on the measurement of underground water flow velocity and direction, including the following steps: Step 1: Collection and real-time monitoring of underground water flow velocity and direction data In this embodiment, the real-time collection of data such as underground water flow velocity, direction, and water level is carried out through a comprehensive monitoring system. This system includes multiple monitoring devices, usually including water level gauges, flow meters, pressure sensors, etc. The water level gauge is used to measure the water level of groundwater in real time, the flow meter is used to measure the flow velocity of groundwater, and the pressure sensor provides dynamic pressure data of the water flow. All these data can be used to reflect the flow characteristics and change trends of groundwater.

[0022] As an option, the monitoring devices transmit real-time data to the central processing unit through wireless communication technology. This process can ensure that the data of each monitoring point in the mine is quickly and stably uploaded to the system. These data not only include the current underground water flow velocity, direction, and water level values, but also contain historical data over a certain period of time, which can be effectively used for subsequent model training and optimization.

[0023] Specifically, the deployment location and quantity of the monitoring devices need to be determined according to the scale of the mine, the geological characteristics of the mining area, and the hydrogeological conditions. In the key parts of the mine, especially in the areas prone to water disasters, the sensors should be arranged densely enough to ensure the accuracy of the data. To ensure the accuracy and efficiency of the monitoring data, the accuracy of the monitoring devices should reach a certain standard. For example, the water level gauge should be accurate to the millimeter level, and the flow meter should be able to measure the change in the water flow rate and respond to rapidly changing water flow velocities.

[0024] In a possible implementation manner, the devices used are low-power, high-precision intelligent sensors, which have self-diagnosis and automatic calibration functions, and can ensure the stability and accuracy of the data during long-term use. In addition, the sensor system can adapt to changes in the mine environment, such as measurement deviations caused by temperature and humidity changes, and correct the data through built-in algorithms, thereby improving the reliability of the data.

[0025] After the real-time collected data is transmitted to the central processing unit, it is first preprocessed, including denoising, data normalization, and missing value imputation. Specifically, data normalization can be achieved by normalizing the real-time monitoring data, making different data dimensions comparable, while denoising filters out environmental noise to ensure the accuracy of the collected data.

[0026] In some embodiments, to further improve the real-time performance and integrity of the data, multiple data receiving nodes may be set up in the mine. Each node is wirelessly connected to the central processing unit and transmits data through a local area network or a wide area network. These receiving nodes can be configured at different monitoring points and have a data caching function to ensure that data can be temporarily stored in case of network interruption and automatically uploaded after the network is restored.

[0027] The design of this real-time data collection and transmission system not only ensures the efficient transmission of groundwater flow data but also provides essential first-hand data support for subsequent analysis and modeling work. The real-time performance and accuracy of the data directly affect the effectiveness of the subsequent water flow model. Therefore, in the implementation of the present invention, the reliability and accuracy of the monitoring system are crucial.

[0028] In this embodiment, after the real-time monitoring information after data collection is transmitted to the central processing unit, the input parameters of the model are updated in real time by combining with the previous historical data. This method ensures that important parameters such as groundwater flow velocity, flow direction, and water level can accurately reflect the current groundwater conditions and provides the latest dynamic data for model prediction.

[0029] In summary, the collection and real-time monitoring of groundwater flow velocity and direction data in step 1 are not only the first step of the present invention but also directly affect the accuracy of all subsequent analysis and decision-making. By deploying high-precision monitoring equipment, ensuring stable data transmission, preprocessing and normalizing the data, the accurate understanding of groundwater flow conditions is ultimately achieved, providing real-time and effective technical support for water control methods.

[0030] Step 2: Construction of the groundwater flow model based on the fractional diffusion equation In this embodiment, the establishment of the groundwater flow model is based on the fractional diffusion equation, which has the important advantage of dealing with the hysteresis and nonlinear characteristics of water flow. Compared with the traditional steady-state diffusion equation, the fractional diffusion equation can accurately describe the time-varying characteristics of groundwater flow velocity and direction in the mine.

[0031] In general, traditional groundwater flow models use Darcy's law to describe the propagation of water flow in porous media. However, during coal mining, the behavior of water flow is affected by complex geological strata, mining methods, and water level changes. Traditional models are difficult to accurately simulate this unstable and non-linear flow process. Therefore, the introduction of the fractional diffusion equation compensates for the deficiencies of traditional methods in these complex situations.

[0032] As an option, the fractional diffusion equation can capture the memory effect of the flow by introducing the fractional derivative ( . This parameter reflects the influence of the historical state of water flow on the current flow state, thus more realistically simulating the changes in groundwater flow velocity and direction in the coal mining environment. The general form of the fractional diffusion equation is as follows:

[0033] Where, represents the groundwater head, is the fractional derivative, indicating the historical dependence of water flow, is the diffusion coefficient, describing the diffusion characteristics of groundwater flow, is the Laplace operator, representing the spatial variation of water flow.

[0034] In this equation, represents the degree of memory of water flow, usually choosing a value between 0 and 1. When, the equation degenerates into the traditional classical diffusion equation; while for less than 1 , it can describe more complex water flow behaviors, including hysteresis effects and non-uniform diffusion.

[0035] Specifically, the introduction of the fractional diffusion equation can effectively solve the accuracy problem of groundwater flow prediction during mine exploitation. Groundwater flow is not a simple linear process. The velocity and direction of water flow are affected by the mining depth, rock formation characteristics, and water source changes, resulting in highly non-linear and time-varying flow characteristics. Therefore, traditional steady-state models cannot accurately predict the changes in water flow.

[0036] In a possible implementation, the calculation of the groundwater head can be achieved through numerical solution. Due to the non-integer nature of the fractional derivative, numerical solution methods need to adopt specialized discretization methods. For example, common numerical methods include the finite difference method (FDM), the finite element method (FEM), and the spectral method. These methods can effectively handle the discontinuities and complexities in the fractional equation and provide a stable solution framework for the numerical solution of the groundwater head.

[0037] In some embodiments, to improve the computational efficiency and accuracy of the model, a multi-scale method can be combined to integrate the discrete process of the model with geological characteristics. Through multi-scale modeling, we can apply suitable solution methods at different spatial scales. Coarse grids are used for simulation in large-scale regions, while finer grids are used for high-precision calculations in local areas. This method enables the model to quickly solve problems within a large mining area while ensuring the simulation accuracy of fine regions near the mining area.

[0038] Furthermore, the diffusion coefficient is usually a function related to spatial position and time in practical applications. During coal mining, the flow of groundwater is affected by the mining depth of the mine, the permeability of the pore medium, and the fracture distribution. Therefore, the diffusion coefficient will change with the progress of mine mining. To accurately simulate this change, a dynamic diffusion coefficient model based on geological exploration and hydrogeological monitoring data can be used. This model will combine the actual groundwater flow velocity, flow direction, and geological characteristics of the mining area to update the diffusion coefficient in real time, thereby improving the prediction accuracy of the model.

[0039] In the specific implementation process, the diffusion coefficient can be solved and optimized in the following way: First, conduct surveys on the geological conditions around the mine to determine the main characteristics of groundwater flow, including permeability, fracture density, etc.; then, use measured data and historical data, combined with the groundwater flow model, to repeatedly adjust the value of the diffusion coefficient until the output of the model matches the actual monitoring data. In this way, the model can reflect the changes in groundwater flow during the coal mining process in real time.

[0040] As a supplement, the fractional-order diffusion equation can also be adaptively adjusted according to different mining stages and geological characteristics to adapt to the actual situation of groundwater flow during different coal mine mining processes. Through this flexible model construction method, the present invention can provide more accurate water control strategies and effectively predict the changing trends of groundwater flow velocity and flow direction.

[0041] In summary, the core content of step 2 is the establishment of a groundwater flow model based on the fractional-order diffusion equation. By introducing the fractional-order derivative, the nonlinear diffusion and time-varying characteristics of groundwater can be accurately described. This model can better reflect the groundwater flow characteristics during coal mining, avoiding the limitations of traditional steady-state models that cannot handle complex water flow problems, and providing scientific and accurate technical support for subsequent water flow prediction and water control decision-making.

[0042] Step 3: Multi-scale analysis and model coupling In this embodiment, based on the multi-scale characteristics of groundwater flow, first, a macroscopic model of groundwater flow in a large area around the mine is established. Then, a local simulation of the mine exploitation area is further carried out. Finally, the seepage and diffusion behavior of water flow in pores is refined through a microscopic-scale model. Models at different scales adopt numerical methods suitable for their spatial range and accuracy requirements to ensure that the models at each scale can efficiently and accurately reflect the characteristics of groundwater flow.

[0043] Specifically, the macroscopic-scale model mainly focuses on the overall trend of groundwater flow. By using the finite element method (FEM), water flow is simulated in a large area of the mining area. The finite element method can handle complex boundary conditions and geological structures, has high accuracy for the calculation of overall water flow, and can handle groundwater flow in a large range. In the macroscopic model, the changes in groundwater head, the distribution of flow velocity and flow direction will be systematically calculated to provide initial flow conditions for the subsequent local model.

[0044] As an option, the mesoscopic-scale model focuses on the local water flow behavior in the mine exploitation area. At this stage, the finite difference method (FDM) is used to refine the simulation of groundwater flow in the mine exploitation area. The finite difference method can effectively handle the transient changes in groundwater flow and is particularly suitable for areas with significant dynamic changes. Through this method, during the mine exploitation process, according to the changes in geological structures, the changes in water flow in the exploitation area can be accurately calculated, including flow velocity, flow direction, and possible water flow leakage or water inrush points.

[0045] In some embodiments, the microscopic-scale model mainly models the water flow in pore media and fracture zones. Water flow exhibits different permeability and diffusion characteristics at the microscopic scale, especially in fracture zones and high-permeability regions. In order to accurately simulate this process, the present invention uses a fractional diffusion equation to describe the diffusion behavior of water flow in microscopic pores, especially its hysteresis effect and non-uniform diffusion phenomenon. The fractional derivative provides an effective tool to accurately capture the historical dependence of groundwater flow, especially in complex geological environments.

[0046] Specifically, the diffusion equation at the microscopic scale is as follows:

[0047] Among them, represents the groundwater head, is the fractional derivative, describing the memory effect of water flow, is the diffusion coefficient, is the Laplace operator, describing the spatial variation of water flow. Since groundwater flow is affected by the heterogeneity of rock formations and pore structures, the diffusion coefficient Therefore, the micro-scale model needs to be adjusted according to the specific geological conditions of the mine to ensure that the model can reflect the differences in permeability of water flowing through various porous media.

[0048] In one possible implementation, the micro-model can combine geological exploration data and hydrological monitoring data to dynamically adjust the diffusion coefficient and head calculation for different mine mining areas and water flow paths. At this stage, the simulation accuracy of the model can be further improved by refining the physical parameters of the porous medium.

[0049] Multiscale coupling technology is one of the key technologies in this step. By coupling models of different scales, it can ensure that the calculation results of each scale can interact with each other, thereby obtaining more accurate groundwater flow predictions. Generally speaking, macroscale models provide large-scale water flow trends and flow direction information, while mesoscale and microscale models conduct in-depth analysis of the local characteristics of water flow in a more detailed range. The multiscale coupling method ensures that models at different scales can be efficiently integrated and provides comprehensive support for actual water prevention and control measures.

[0050] As an option, the coupling process can use dynamic grid technology. This technology optimizes the accuracy of model calculations by adjusting the calculation results of different scales at the grid level. During the mining process, due to the large changes in water flow rate and flow direction, the use of dynamic grid technology can adaptively adjust the accuracy of the calculation grid according to the speed and direction of the water flow. In this way, fine grids are used in areas where the water flow changes drastically, while coarse grids are used in areas where the water flow is relatively stable, thereby achieving a balance between calculation accuracy and efficiency.

[0051] In summary, this step can accurately simulate the changes in groundwater flow velocity and direction through multi-scale analysis and model coupling technology, and provide a scientific basis for subsequent water prevention and control measures. Through the coupling of macro, meso and micro scale models, it can ensure that all aspects of groundwater flow in the coal mining process are fully considered, providing comprehensive decision support for mine management and water hazard prevention.

[0052] Step 4: Application of adaptive grid technology In this embodiment, the application of adaptive grid technology includes the following key steps: First, based on the established groundwater flow model, by analyzing the changes in groundwater flow velocity and flow direction, the areas where the water flow changes more drastically are identified. Then, a finer grid is used for calculation in these areas, while a coarser grid is used in areas where the water flow changes more smoothly to optimize the calculation efficiency.

[0053] Specifically, the accuracy of mesh generation depends on the rate of change of groundwater flow and the demand for computing resources. Generally, in the mine exploitation area and at the points of sudden change in water flow (such as fracture zones or water gushing areas), there are significant changes in water flow velocity. At this time, finer meshes are required for precise calculations to ensure that the changes in flow can be accurately captured. For other relatively stable areas, coarser meshes can be used to effectively reduce the calculation time and resource consumption, thus achieving a balance between calculation accuracy and efficiency.

[0054] As an option, the mesh refinement can be achieved by local refinement methods. In these local areas, the mesh size becomes smaller, thereby improving the simulation accuracy. Where the water flow changes violently, especially at the points of sudden water flow change or water gushing during the mining process, the fine mesh can effectively capture these instantaneous changes and promptly feedback them to the model for adaptive adjustment.

[0055] Specifically, the degree of mesh refinement is determined by the rate of change of groundwater flow velocity and direction. If the rate of change of velocity is large, it indicates that the water flow changes violently, and these areas need to be refined. By calculating the gradients of velocity and direction in space, the areas that require higher-precision meshes are determined. The calculation of gradients can be achieved through finite difference methods or finite element methods. During the calculation process, the areas with larger gradients will be automatically refined, while in the areas with relatively stable velocity changes, coarser meshes can be used for calculation.

[0056] In some embodiments, the dynamic adjustment of the adaptive mesh technology can be completed through the following steps: First, by calculating the rate of change of groundwater flow velocity and direction, a dynamic mesh of groundwater flow is generated; then, during the calculation process, the mesh generation is adjusted according to the real-time water flow changes. This dynamic adjustment process can flexibly change the mesh density according to the actual mining situation, improving the adaptability and calculation efficiency of the model.

[0057] In another possible implementation, the adaptive mesh technology can also be combined with mesh optimization algorithms, such as the Laplace smoothing algorithm, to further optimize the mesh distribution. The Laplace smoothing algorithm can smooth the mesh, making the mesh generation more uniform, reducing the irregularity of the mesh distribution, and ensuring the stability of the numerical solution method. These optimization algorithms can ensure the accurate calculation of water flow in complex areas while avoiding unnecessary waste of computing resources.

[0058] In addition, the adaptive grid technology used in this embodiment can be combined with the computing resource allocation and management system. Specifically, the calculation process of the model can be carried out on a high-performance computing platform. Combining with the distributed computing technology, the grid density can be dynamically adjusted according to the real-time feedback of the computing load, so as to effectively allocate computing resources. In the areas where the water flow changes violently, more computing resources are allocated, while in the stable areas, fewer computing resources are allocated, thus realizing a more efficient computing process.

[0059] The application of the adaptive grid technology can greatly improve the calculation accuracy of the groundwater flow model and optimize the calculation efficiency. In the complex coal mine mining environment, the change of water flow is highly dynamic, and it is difficult to meet the accuracy requirements by using the fixed grid calculation method. By dynamically adjusting the grid density, it can ensure the accurate simulation of important areas in the calculation process while reducing unnecessary calculation workload. Finally, the groundwater flow simulation based on the adaptive grid technology can provide more reliable prediction results for coal mine water control and support more scientific water control decisions.

[0060] In summary, in this step, through the adaptive grid technology, the refinement degree of the grid is flexibly adjusted. High-precision grids are used in the areas where the water flow changes greatly, and coarse grids are used in the areas where the water flow is stable for calculation, ensuring the accuracy and calculation efficiency of the groundwater flow simulation. This technology can better adapt to the complexity of groundwater flow, improve the application effect of the model in the actual mine mining process, and provide strong support for water control.

[0061] Step 5: Data fusion and model optimization In this embodiment, the data fusion technology first synthesizes the real-time groundwater flow velocity, flow direction and water level data obtained from different monitoring devices (such as water level gauges, flow meters, pressure sensors, etc.). These monitoring data come from various areas of the mine and are transmitted to the central processing unit through the wireless communication system. During the data fusion process, the accuracy and stability of each data source are first evaluated to ensure the reliability of all data. The data fusion technology combines the data from multiple data sources and processes them through algorithms such as weighted average and Kalman filtering, and finally outputs a comprehensive and accurate groundwater flow data.

[0062] As an option, the weighted average algorithm used in the data fusion process can assign different weights to each data source according to the accuracy and measurement range of different devices. The weighted average method avoids prediction errors caused by the failure of a single device or data anomaly by weighting the data sources. Specifically, the collected water flow data will be automatically weighted according to the location, accuracy of the device and the reliability of historical data, and then all data sources will be weighted to obtain an optimal water flow prediction input.

[0063] In some embodiments, the Kalman filtering algorithm is used to further improve the accuracy of data fusion. The Kalman filter is a recursive algorithm that can effectively extract useful information from a series of incomplete or noisy data. By continuously updating and optimizing the real-time data, the Kalman filter can reduce the uncertainty of the system and improve the stability and accuracy of groundwater flow data.

[0064] Once accurate groundwater flow data is obtained through data fusion technology, the next step is to use machine learning optimization algorithms to adjust and optimize the parameters of the groundwater flow model. Generally, machine learning algorithms such as neural networks, support vector machines (SVMs), or deep learning algorithms are applied in the model optimization process.

[0065] Specifically, the machine learning optimization process first uses historical data to preliminarily train the model. Through training, the model can learn the laws and trends of groundwater flow. Subsequently, the model is adjusted and optimized according to real-time monitoring data. As an option, the neural network model can be trained by backpropagation through a large number of input-output data to adjust the weights and biases in the model. In this way, the model can automatically adjust according to the actual situation to adapt to the changes in groundwater flow.

[0066] In a possible implementation, the support vector machine (SVM) is used for model optimization. The support vector machine is a powerful supervised learning algorithm that can effectively handle nonlinear problems. In the optimization process, the SVM constructs an optimal hyperplane to maximize the margin of data points, thereby achieving accurate prediction of groundwater flow. In this step, the kernel function of the support vector machine can be used to capture the nonlinear relationship between groundwater flow velocity and direction, improving the prediction ability of the model.

[0067] In addition, in the implementation process, deep learning technology can be combined to further enhance the model's learning ability for complex groundwater flow patterns. Specifically, by using deep neural networks (DNNs) or convolutional neural networks (CNNs), the model can automatically extract features from a large amount of historical data and update according to real-time data. This deep learning method is particularly suitable for processing complex and high-dimensional datasets. During coal mine exploitation, groundwater flow is often affected by multiple factors, and the deep learning method can effectively integrate this information to optimize the prediction effect of the model.

[0068] The optimized model can achieve adaptive adjustment and fine prediction by continuously adjusting parameters through machine learning. As the characteristics of groundwater flow change continuously during coal mine exploitation, the optimized model can reflect these changes in real time and provide the most accurate basis for subsequent water control decisions.

[0069] In another possible implementation, machine learning optimization can also be combined with a genetic algorithm (GA) or a particle swarm optimization algorithm (PSO). As swarm intelligence optimization algorithms, the genetic algorithm and the particle swarm optimization algorithm can globally optimize model parameters by simulating the natural selection and evolution processes. Through an iterative process, these algorithms gradually find the optimal solution of the groundwater flow model, thereby improving the accuracy of model prediction.

[0070] In summary, the data fusion in this embodiment is combined with the machine learning optimization algorithm, and by continuously optimizing the model parameters, the accuracy and adaptability of the groundwater flow model are improved. During the mine exploitation process, the changes in the flow velocity and direction of groundwater are highly dynamic, and it is difficult for traditional methods to quickly respond to these changes. Through adaptive optimization, the model can adjust in real time and accurately predict the groundwater flow, providing a scientific basis for water control measures and realizing dynamic and real-time water disaster warning during the exploitation process.

[0071] Step 6: Application of the decision support system In this embodiment, the core function of the decision support system is to generate specific water control strategy suggestions based on the groundwater flow prediction data obtained in the previous steps. Specifically, the decision support system will analyze the changing trends of the groundwater flow velocity and direction according to the real-time monitoring data and the optimized groundwater flow model, and automatically generate corresponding emergency plans and water control measures.

[0072] Specifically, the working process of the decision support system is as follows: Real-time data input: Through the data collection and real-time monitoring in the previous steps, data such as the groundwater flow velocity and direction will be input into the decision support system in real time. These data include information such as the current groundwater level, flow velocity, and direction, ensuring that the system can provide accurate predictions and emergency responses at all stages of mine exploitation.

[0073] Groundwater flow prediction and analysis: Based on the aforementioned fractional diffusion equation, multi-scale model, and optimized groundwater flow model, the decision support system will update the prediction results of the groundwater flow velocity and direction in real time. Specifically, the system will simulate the groundwater flow around the mine and in the exploitation area, calculate the changing trends of the groundwater flow, and analyze the possible water disaster risks according to the prediction results.

[0074] As an option, the system will also combine historical data and real-time data, and through intelligent algorithms, analyze the trends and changes of the groundwater flow. Through the machine learning optimization algorithm, the system can predict the possible changes in future water flow and generate relevant water control measures based on this prediction.

[0075] Generation of water control measures: After the changing trend of groundwater flow is predicted, the decision support system will automatically generate an emergency plan for water control according to preset rules or algorithms. For example, if the system predicts that the groundwater flow velocity in a certain area changes drastically in a short period of time, the system may automatically suggest increasing the number of dewatering wells or adjusting the mining progress of the mine to reduce the impact of water flow on the mine. Specifically, the water control measures include but are not limited to: Increasing or adjusting the position and depth of dewatering wells to lower the groundwater level; Adding waterproof and leak-proof facilities in areas with water inrush risks; Adjusting the mining depth and sequence of the mining area to reduce the impact of water flow; Adjusting the working state of the mine drainage system to ensure the timely discharge of water flow.

[0076] Real-time decision support: Through the decision support system, mine managers can timely adjust the mining strategy and emergency response of the mine according to the water control measures given by the system. The system not only provides water control suggestions, but also can, according to the actual situation, monitor the implementation effect of the measures in real time and automatically update the water control strategy. For example, in the initial stage, if a slight abnormality in water flow is predicted, the system may suggest preventive measures; while when the water flow changes violently, the system will trigger more urgent emergency measures.

[0077] In a possible implementation, the decision support system can also be linked with the automated control system on the mine site to automatically execute some water control measures. For example, when the system predicts that the water flow velocity in a certain area suddenly increases, the automated control system can quickly respond to the water flow change and prevent water disasters from occurring by adjusting the operation of dewatering wells and enabling standby drainage facilities.

[0078] As an option, deep learning algorithms can be applied to the decision-making process of the decision support system, especially under complex geological conditions where the changes in groundwater flow show non-linear and highly dynamic characteristics. By using deep learning models, the system can self-learn and optimize, and automatically adjust water control decisions according to the changes in historical data and real-time data, improving the accuracy and timeliness of decisions.

[0079] The introduction of the decision support system enables coal mine managers to quickly make corresponding decisions based on the real-time prediction of groundwater flow. The automated decision-making function of the system reduces the errors caused by manual intervention, improves the efficiency and accuracy of water disaster emergency response. At the same time, based on the optimized groundwater flow model, the system can accurately predict water flow changes, early warn potential water disaster risks, and provide a scientific basis for water control.

[0080] In summary, through the application of the decision support system in this step, the groundwater flow prediction is closely integrated with the water control measures in the coal mining process, forming an intelligent water disaster early warning and decision support system. Through the input of real-time data, the analysis of prediction results, and the generation of water control measures, the decision support system can provide accurate decision-making suggestions for coal mine management, thereby effectively reducing the water disaster risk and ensuring the safety and production efficiency of the mine.

[0081] Step 7: Error Analysis and Model Validation In the previous steps, we have completed the establishment, optimization, and application of the groundwater flow model, ensuring that the model can accurately predict the groundwater flow velocity and direction and provide effective support for water control measures. However, the accuracy and stability of the model are crucial for the reliability of the final prediction. Therefore, in the seventh step of the present invention, error analysis and model validation are carried out to ensure that by comparing with the actual monitoring data, the errors in the model are continuously corrected to improve the actual application effect of the model.

[0082] Generally, the establishment and optimization of the groundwater flow model are based on historical data and real-time data. However, due to the dynamic changes of the water flow and the continuously changing geological conditions during the coal mine mining process, there may be certain errors in the prediction results of the model. Therefore, error analysis is a key step to ensure the accuracy of the model. By comparing the model prediction results with the actual monitoring data, potential problems in the model can be systematically identified and appropriate measures can be taken for optimization.

[0083] In this embodiment, the error analysis and model validation include two aspects: on the one hand, by comparing the differences between the model prediction results and the actual groundwater flow data, the error value is calculated; on the other hand, the model parameters are adjusted through an optimization algorithm to ensure that the error is within an acceptable range. Specifically, the implementation process of the steps is as follows: Error Analysis: The first step of error analysis is to obtain the differences between the model prediction results and the actual monitoring data. To perform effective error calculation, we need to compare the model prediction results with the actual monitored groundwater flow velocity, direction, and water level data. In this process, the actual data can be obtained through groundwater monitoring equipment (such as water level gauges, flow meters, pressure sensors, etc.), ensuring the accuracy and reliability of the data.

[0084] In some embodiments, the calculation of the error can be quantified by the mean square error (MSE) or the root mean square error (RMSE). Specifically, the error of the model can be calculated by the following formula:

[0085] where is the predicted groundwater head of the model, is the actually observed groundwater head, is the number of data points. In this way, the prediction error of the model can be quantified.

[0086] Through error analysis, we can identify regions or parameters where the model has large prediction biases, enabling more precise adjustment and optimization. Error analysis not only checks the accuracy of the model but also provides a basis for subsequent optimization.

[0087] Model validation and optimization: After error analysis, we enter the model validation stage. In this stage, by repeatedly testing and optimizing the model parameters, we ensure that the model can work stably under different groundwater flow conditions. To verify the accuracy of the model, we further compare the model's prediction results with on-site monitoring data and use different validation methods for cross-validation.

[0088] As an option, cross-validation methods can be used during the validation process. Cross-validation is a common statistical method that divides the data into multiple subsets, repeatedly trains and validates the model, thus ensuring the model's stable performance on different datasets. For example, by dividing the historical data into a training set and a test set, the model is trained and tested multiple times to ensure that the model does not overfit a specific dataset.

[0089] Specifically, model validation includes the following steps: Compare the historical monitoring data with the simulation results to ensure that the prediction accuracy of the model meets the requirements; According to the results of error analysis, adjust the parameters in the model and repeatedly validate until the error reaches a reasonable range; Use data from different sources for cross-validation to ensure that the model has broad adaptability.

[0090] Model optimization: Through error analysis and the validation process, we can identify the error sources in the model and adjust them using optimization algorithms. Common optimization methods include the Particle Swarm Optimization algorithm (PSO), Genetic Algorithm (GA), etc.

[0091] In the Particle Swarm Optimization algorithm, by simulating the movement of particles in the search space, the optimal solution is searched for. The particles adjust the model parameters by continuously updating their own experience and the group experience, making the model's prediction results tend to the true value. Particle Swarm Optimization can effectively handle multi-parameter optimization problems and can find near-optimal solutions in a relatively short time.

[0092] In addition, the Genetic Algorithm is an optimization algorithm based on natural selection. Through processes such as selection, crossover, and mutation, the Genetic Algorithm can continuously optimize the model parameters. With this algorithm, we can adjust the diffusion coefficient and fractional-order parameter in the fractional-order diffusion equation Key parameters such as

[0093] In some embodiments, when using an optimization algorithm, the update of the model parameters is based on the results of error analysis and adjusted in combination with the actual data of mine exploitation. In this way, the optimized model can more accurately predict the groundwater flow situation and provide a more reliable basis for water control.

[0094] In summary, the error analysis and model verification in step 7 optimize the groundwater flow model in various ways to ensure the prediction accuracy and applicability of the model. By comparing with the actual monitoring data, combining error calculation and optimization algorithm, the model is continuously corrected, and finally a reliable water control tool applicable to mine exploitation is formed. Through this process, the present invention can ensure the stable operation of the model in a complex and dynamic mine environment and provide accurate water flow prediction and decision-making support.

[0095] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A water control method for coal mining based on the measurement of groundwater flow velocity and direction, characterized in that The following steps are involved: Deploy groundwater monitoring equipment to collect real-time data on groundwater levels, flow rates and directions around the mine; A groundwater flow model was established based on the fractional diffusion equation to simulate the changes in the velocity and direction of groundwater around the mine. Combined with multi-scale analysis methods, groundwater flow is modeled at macro, meso and micro scales, and groundwater flow is simulated comprehensively through multi-scale coupling; Adaptive grid technology is used to grid the groundwater flow area to improve the model calculation accuracy, and the grid density is dynamically adjusted according to the changes in flow velocity and direction; Fusing the real-time collected groundwater monitoring data with the groundwater flow model, and optimizing the model parameters through data fusion technology; Based on the optimized groundwater flow model, the changes in groundwater flow velocity and direction are predicted in real time, and decision support is provided for water prevention and control measures during coal mining.

2. The water control method for coal mining based on the measurement of groundwater flow velocity and direction according to claim 1, characterized in that, The groundwater flow model is a groundwater flow model based on a fractional-order diffusion equation. The model accurately describes the nonlinear and time-lag characteristics of groundwater flow during the mining process by introducing the memory effect of the flow.

3. The water control method for coal mining based on the determination of underground water flow velocity and direction according to claim 1, characterized in that, The multi-scale analysis method includes using the finite element method to perform overall modeling of groundwater flow at the macro scale, using the finite difference method to perform local simulation of the mine mining area at the meso scale, and using the porous medium model to simulate the diffusion and penetration of water in microscopic pores at the micro scale.

4. The water control method for coal mining based on the determination of the underground water flow velocity and direction according to claim 1, characterized in that, The adaptive grid technology uses high-precision grids in areas where water flow changes dramatically, and uses coarser grids in areas where water flow changes more smoothly, and dynamically adjusts the grid density to improve the accuracy and efficiency of groundwater flow calculations.

5. The water control method for coal mining based on the measurement of underground water flow velocity and direction according to claim 1, characterized in that, The data fusion technology includes combining real-time monitoring data with historical groundwater flow data, and optimizing the parameters of the groundwater flow model through a machine learning algorithm to improve the model's prediction accuracy for groundwater flow changes.

6. The water control method for coal mining based on the measurement of groundwater flow velocity and direction according to claim 1, characterized in that, The decision support system automatically generates a water prevention and control emergency plan based on the prediction results of the groundwater flow model and provides corresponding water prevention and control measures to coal mine managers.

7. The water control method for coal mining based on the measurement of underground water flow velocity and direction according to claim 1, characterized in that, The real-time monitoring data includes groundwater level, flow velocity, flow direction and groundwater data, and the data is transmitted to the central processing unit in real time through a wireless transmission system for analysis and optimization.

8. The water control method for coal mining based on the measurement of underground water flow velocity and direction according to claim 1, characterized in that, The optimized groundwater flow model is based on real-time monitoring data and historical data, and is adaptively adjusted through a deep learning model, so that the model can automatically adapt to water flow changes during coal mining.

9. The water control method for coal mining based on the measurement of the underground water flow velocity and direction according to claim 1, characterized in that, The groundwater flow model further reduces prediction errors through error analysis and optimization algorithms, and ensures that the model can provide accurate groundwater flow predictions under complex geological conditions.

10. The method for preventing and controlling water in coal mining based on the measurement of underground water flow velocity and direction according to claim 1, characterized in that, The water prevention and control measures include lowering the groundwater level and controlling the groundwater flow direction through precipitation wells and drainage systems to prevent water disasters and adjust the mining plan in real time according to the groundwater flow model.

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