Coal mine directional grouting control method and system based on geological characteristics

Abstract

The invention discloses a coal mine directional grouting control method and system based on geological characteristics, and relates to the technical field of coal mine grouting. The coal mine directional grouting control method comprises the steps that target coal mine geologic features are obtained; evaluating the target coal mine to obtain a coal mine integrity index; constructing a target coal mine pre-grouting model based on the target coal mine geologic features and the coal mine integrity indexes; performing directional grouting design to obtain a coal mine directional grouting model; performing two-stage analysis to obtain an optimized coal mine directional grouting control strategy which is used for adjusting initial coal mine directional grouting control parameters in the coal mine directional grouting model; the coal mine directional grouting control system comprises a pre-grouting analysis module and a directional grouting regulation and control module. The constructed target coal mine pre-grouting model is highly matched with the actual coal mine condition, and the pre-grouting model can fully consider the overall condition of the coal mine through the integral index; and through the directional grouting design, the grouting efficiency is improved, the grouting time is shortened, and the grouting cost is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of coal mine grouting, and in particular to a coal mine directional grouting control method and system based on geological characteristics. Background Art

[0002] Current coal mine grouting technology generally has problems such as poor geological adaptability and extensive grouting path planning; traditional methods rely on empirical hole layout design, which is difficult to accurately match complex geological features, resulting in uneven distribution of grouting materials and residual local weak areas, which can easily cause safety hazards such as water inrush and gas leakage; at the same time, the traditional static grouting model fails to take into account the dynamic changes of geological conditions during the construction process, which often leads to excessive injection of slurry into dense rock formations or failure to effectively cover high-risk areas, resulting in waste of resources or project failure.

[0003] Therefore, it is urgently necessary to integrate geological feature analysis and holistic evaluation methods, construct a dynamic pre-grouting model based on geological changes, and optimize the grouting path in combination with bionic structures to achieve precise targeted delivery of slurry; through a two-stage feedback control mechanism, dynamically adapt to changes in geological conditions, thereby significantly improving grouting efficiency and reliability, ensuring coal mine safety and efficient use of resources. Summary of the invention

[0004] The present invention aims to provide a coal mine directional grouting control method and system based on geological characteristics, so as to improve the efficiency and reliability of coal mine grouting.

[0005] A coal mine directional grouting control method based on geological characteristics comprises the following steps:

[0006] Obtain the geological characteristics of the target coal mine; evaluate the target coal mine based on the geological characteristics of the target coal mine to obtain the overall index of the coal mine; and construct a pre-grouting model of the target coal mine based on the geological characteristics of the target coal mine and the overall index of the coal mine;

[0007] Combined with the grouting bionic structure, the directional grouting design is carried out on the target coal mine pre-grouting model to obtain the coal mine directional grouting model; the coal mine directional grouting model contains the initial coal mine directional grouting control parameters;

[0008] Based on the coal mine directional grouting model and the actual grouting reaction parameters, a two-stage analysis is performed to obtain the optimized coal mine directional grouting control strategy; the two-stage analysis includes real-time grouting feedback analysis and predictive grouting feedback analysis;

[0009] The optimized coal mine directional grouting control strategy is used to adjust the initial coal mine directional grouting control parameters in the coal mine directional grouting model.

[0010] As a preferred technical solution of the present invention, the target coal mine is evaluated based on the geological characteristics of the target coal mine to obtain the coal mine integrity index; meanwhile, the specific steps for constructing the pre-grouting model of the target coal mine based on the geological characteristics of the target coal mine and the coal mine integrity index include:

[0011] The geological characteristics of the target coal mine include geological structure stability characteristics, geological safety coupling characteristics, geological environment impact degree characteristics, and geological three-dimensional characteristics;

[0012] Based on the geological structure stability characteristics, structural stability assessment is carried out to obtain the grouting stability factor;

[0013] Based on the geological safety coupling characteristics, grouting safety assessment is carried out to obtain the grouting safety factor;

[0014] Based on the geological environment impact degree characteristics, environmental impact assessment is carried out to obtain the grouting impact degree factor;

[0015] The grouting stability factor, grouting safety factor, and grouting impact degree factor are subjected to feature fusion through the secondary weight formula to obtain the coal mine integrity index;

[0016] Based on the coal mine integrity index, three-dimensional detail optimization is carried out on the geological three-dimensional characteristics to obtain the pre-grouting model of the target coal mine.

[0017] As a preferred technical solution of the present invention, the pre-grouting model of the target coal mine is subjected to directional grouting design in combination with the grouting bionic structure to obtain the coal mine directional grouting model; the specific steps for including the initial coal mine directional grouting control parameters in the coal mine directional grouting model include:

[0018] The grouting bionic structure includes N grouting simulation roots G n , n = 1, 2,..., N; based on the grouting simulation root G n Set the root growth vector S n , the environmental response parameter H n and the root random error parameter C n ; among them, the initial value of the root growth vector S n is S n0 ; set the total time for the growth of the grouting simulation roots in the grouting bionic structure to be T, t = 1, 2,..., T; t represents the growth time step of the grouting simulation root G n ;

[0019] Based on the grouting bionic structure and the pre-grouting model of the target coal mine, root growth is simulated, and several grouting bionic structures are set to simulate root growth synchronously;

[0020] Within the total time, according to the grouting simulation root G nThe grouting evolution of several grouting bionic structures is carried out with the growth time step of . In each grouting bionic structure, the random error parameter C of the grouting root system n Stochastic fluctuations based on the grouting time-space evolution equation;

[0021] When the growth time step reaches the total time, the grouting directional environment evolution result P is obtained. n ; Based on the evolution results of grouting directional environment P n Select the best growth state of the grouting bionic structure and build a coal mine directional grouting model;

[0022] The initial coal mine directional grouting control parameters are set according to the coal mine directional grouting model.

[0023] As a preferred technical solution of the present invention, the specific steps of performing real-time grouting feedback analysis include:

[0024] The actual grouting reaction parameters include the real-time solidification index of the slurry, the diffusion acoustic wave signal of the slurry and the pressure signal of the grouting orifice;

[0025] The actual grouting reaction parameters are heterogeneously reconstructed to obtain the current diffusion data of the slurry;

[0026] Based on the current diffusion data of slurry and the pre-trained grouting effect evaluation model, the current results of coal mine grouting are obtained.

[0027] As a preferred technical solution of the present invention, the specific steps of performing predictive grouting feedback analysis include:

[0028] The analysis is based on the current diffusion data of slurry combined with the coal mine slurry diffusion model, which includes a reinforcement learning prediction layer and a dynamic plastic strategy layer.

[0029] The reinforcement learning prediction layer is used to make predictions based on the current diffusion data of the slurry to obtain the prediction results of coal mine grouting;

[0030] The dynamic plastic strategy layer is used to perform reverse parameter adjustment according to the coal mine grouting prediction results and the current results of coal mine grouting to obtain the optimized coal mine directional grouting control strategy.

[0031] As a preferred technical solution of the present invention, the coal mine slurry diffusion model is constructed based on the random forest model.

[0032] A coal mine directional grouting control system based on geological characteristics, comprising:

[0033] The pre-grouting analysis module includes a model building unit and a directional design unit; the model building unit is used to obtain the geological characteristics of the target coal mine; the target coal mine is evaluated based on the geological characteristics of the target coal mine to obtain the coal mine integrity index; and the target coal mine pre-grouting model is constructed based on the geological characteristics of the target coal mine and the coal mine integrity index; the directional design unit is used to perform directional grouting design on the target coal mine pre-grouting model in combination with the grouting bionic structure to obtain the coal mine directional grouting model; the coal mine directional grouting model contains the initial coal mine directional grouting control parameters;

[0034] The directional grouting control module includes a double-layer control unit, which is used to perform a two-stage analysis based on the coal mine directional grouting model and the actual grouting reaction parameters to obtain an optimized coal mine directional grouting control strategy; the two-stage analysis includes real-time grouting feedback analysis and predictive grouting feedback analysis; the optimized coal mine directional grouting control strategy is used to adjust the initial coal mine directional grouting control parameters in the coal mine directional grouting model.

[0035] The present invention has the following advantages:

[0036] 1. The present invention constructs a target coal mine pre-grouting model based on the geological characteristics of the target coal mine and the integrity index of the coal mine, which can ensure that the model is highly matched with the actual coal mine situation. The integrity index comprehensively reflects the geological stability of the coal mine, the difficulty of mining and other factors, and incorporates it into the model construction process, so that the pre-grouting model can fully consider the overall situation of the coal mine; by combining the grouting bionic structure to carry out directional grouting design for the target coal mine pre-grouting model, the introduction of the grouting bionic structure can simulate the structural advantages of organisms, avoiding the problems of uneven slurry distribution and waste of slurry in areas that do not need reinforcement that may occur in traditional grouting methods, greatly improving the grouting efficiency and reducing the grouting time and cost.

[0037] 2. The present invention can timely monitor various parameter changes in the grouting process, such as slurry pressure, flow rate, diffusion range, etc., through real-time grouting feedback analysis in the two-stage analysis; once it is found that the actual grouting reaction parameters deviate from the expected ones, the grouting control parameters can be immediately adjusted according to the feedback information. This real-time feedback mechanism makes the grouting process more flexible and controllable, and can effectively respond to various emergencies. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a structural schematic diagram of a coal mine directional grouting control system based on geological characteristics adopted in Example 2 of the present invention. DETAILED DESCRIPTION

[0039] In order to enable persons skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0040] Embodiment 1, a coal mine directional grouting control method based on geological characteristics, comprising the following steps:

[0041] Obtain geological characteristics of target coal mines;

[0042] The target coal mine is evaluated based on its geological characteristics to obtain the coal mine integrity index; at the same time, a target coal mine pre-grouting model is constructed based on the target coal mine geological characteristics and coal mine integrity index, including:

[0043] The geological characteristics of the target coal mine include geological structural stability characteristics, geological safety coupling characteristics, geological environmental impact characteristics and geological three-dimensional characteristics;

[0044] Based on the geological structure stability characteristics, the structural stability assessment is carried out to obtain the grouting stability factor;

[0045] Collect geological exploration data of coal mines, including coal seam thickness, rock strength, structural characteristics (such as faults, folds, etc.), degree of fissure development, groundwater distribution, etc.; Based on the geological exploration data, use stability analysis methods, such as geomechanical models or numerical simulation methods, to evaluate the rock stability of coal mines and obtain the stability characteristics of geological structures; use the stability evaluation results to calculate the grouting stability factor, consider the influence of structural stability on the grouting effect for quantitative analysis, and obtain the grouting stability factor;

[0046] Based on the geological safety coupling characteristics, the grouting safety assessment is carried out to obtain the grouting safety factor;

[0047] Analyze the geological safety coupling characteristics of coal mines, that is, the interaction between mining activities and geological conditions, focusing on the possible impact of mining on coal seams, mines and surrounding environments; evaluate the geological safety of coal mines based on the impact of mining activities, and use safety factor analysis or hazard assessment models to obtain geological safety coupling characteristics; calculate the grouting safety factor based on the geological safety coupling characteristics. Generally speaking, lower geological safety risks will lead to higher grouting safety factors;

[0048] Conduct environmental impact assessment based on geological environmental impact characteristics and obtain grouting impact factors;

[0049] Assess the impact of coal mining activities on the surrounding environment, including groundwater changes, ground subsidence, environmental pollution and other factors; based on the results of coal mine environmental impact assessment, use the multi-factor evaluation method to obtain the geological environment impact characteristics; use the environmental impact analysis model to evaluate the environmental impact of grouting activities through data such as environmental pollutant concentrations and groundwater flow, and obtain the grouting impact factor.

[0050] The grouting stability factor, grouting safety factor and grouting influence factor are integrated by the secondary weight formula to obtain the coal mine integrity index, which includes:

[0051] The secondary weight formula is: ; i=1, 2, 3, used to number the grouting stability factor, grouting safety factor and grouting influence factor;

[0052] αi is the first-level weight index, β is the second-level weight adjustment index; Y 1 represents the grouting stability factor, Y 2 represents the grouting safety factor, Y 3 represents the grouting influence factor, Q represents the coal mine integrity index;

[0053] The method of setting the first-level weight index is to give a preliminary weight value based on the subjective evaluation of the importance of these three factors by professional and technical personnel. The scaling method can be used to evaluate the importance of each factor in the overall model. At the same time, the relative importance of each factor can be calculated through data analysis methods based on sufficient historical data, so as to derive a comprehensive first-level weight index.

[0054] The method of setting the secondary weight adjustment index is to determine it according to the mining environment, geological conditions, mine safety needs and environmental protection requirements of the coal mine. If the geological structure stability requirements of the coal mine are high, the value of the secondary weight adjustment index can be appropriately increased so that the weighted sum of the three occupies an important position in the final overall index.

[0055] Based on the integrity index of coal mine, the three-dimensional details of geological features are optimized to obtain the target coal mine pre-grouting model.

[0056] The geological three-dimensional features are a three-dimensional model composed of the geological structure, coal seam and ore body distribution, groundwater, mining area pressure and other characteristics of the target coal mine; according to the value of the coal mine integrity index, the geological stability, safety and environmental impact of the coal mine are evaluated, and then the geological features in the model are adjusted; for example, in the coal mine area, areas with poor integrity may need to strengthen grouting and obtain a larger weight when deploying grouting holes; while areas with good integrity may optimize resource mining and deploy fewer grouting holes to balance the demand for grouting; according to the Q value, the grouting amount, grouting method and other mine stability parameters of specific areas in the three-dimensional model are adjusted. These adjustments help to enhance the safety of coal mine grouting, and at the same time, the areas that need grouting the most are selected for three-dimensional detail optimization; for example, areas with poor stability may require more grouting treatment, and the grouting depth and coverage area are adjusted; by locally optimizing the geological three-dimensional model, the stability of fault and fissure development areas can be improved, and the grouting effect can be improved; after completing the above optimization, the final target coal mine pre-grouting model is generated to ensure that the model can effectively guide the grouting operation in the actual mining process, while meeting the requirements of geological safety, environmental protection and economic benefits.

[0057] By setting the overall indicators of coal mines, grouting resources can be allocated more reasonably, avoiding wasting grouting resources in unnecessary areas, strengthening grouting work in high-risk areas, and improving the safety of coal mining; it can more accurately reflect the actual geological conditions of coal mines, optimize the actual effect of the three-dimensional model, and improve the effectiveness of the pre-grouting plan.

[0058] The directional grouting design is carried out on the target coal mine pre-grouting model in combination with the grouting bionic structure to obtain the coal mine directional grouting model; the coal mine directional grouting model includes the initial coal mine directional grouting control parameters.

[0059] Combined with the grouting bionic structure, the directional grouting design is carried out on the target coal mine pre-grouting model to obtain the coal mine directional grouting model; the coal mine directional grouting model contains the specific steps of the initial coal mine directional grouting control parameters, including:

[0060] The grouting bionic structure contains N grouting simulated root systems G n , n=1, 2, ..., N; based on grouting simulation root system G n Set the root growth vector S n , environmental response parameter H n and root random error parameter C n ; Among them, the root growth vector S n The initial value is S n0 ; Set the total time of grouting simulation root growth in the grouting bionic structure as T, t=1, 2, ..., T; t represents the grouting simulation root G nThe total time and growth time step are the core parameters for controlling the dynamic growth process of the simulated root system. The essence of this is to transform the continuous grouting process into a computable iterative step through a mathematical discretization method, thereby realizing the refined prediction and regulation of the slurry diffusion path. The total time represents the expected period from the start of grouting to the complete filling of the target area, and is an abstract mapping of the total duration of the grouting project from the beginning to the end in the bionic model. The growth time step is a discretized time unit in the simulation of the grouting bionic structure. Each time step corresponds to a small time period in the actual grouting process. In each time step, the expansion direction, branch generation and random disturbance of the grouting simulated root system are calculated, and real-time interaction with geological conditions is achieved.

[0061] In the grouting bionic structure, each grouting simulates the root system G n Represents an independent grouting path branch, that is, the setting of grouting holes, simulating the growth logic of plant roots, and its behavior is controlled by the following parameters:

[0062] S n0 It is usually set along the direction of maximum principal stress or the dominant orientation of the fracture, such as determined by geological radar data; the root growth vector S n The actual role of is to guide the initial extension direction of the grouting hole, avoid blind hole arrangement, and improve the coverage efficiency of the slurry to the key area. The environmental response parameter H n It reflects the dynamic correlation between grouting effect and geological conditions. Its actual function is to adjust the branch growth priority according to the real-time grouting effect and give priority to strengthening the high-risk areas. The root system random error parameter C n Controllable random disturbances are introduced through the time-space evolution equation to simulate the adaptive exploration ability of the root system in dealing with complex environments. The actual effect is to simulate the resilience when encountering complex geological environments.

[0063] Based on the grouting bionic structure and the target coal mine pre-grouting model to simulate root growth, several grouting bionic structures are set to simulate root growth synchronously;

[0064] The total time for grouting simulation of root system G n The grouting evolution of several grouting bionic structures is carried out with the growth time step of . In each grouting bionic structure, the random error parameter C of the grouting root system n Stochastic fluctuations based on the grouting time-space evolution equation;

[0065] The equation for the time-space evolution of grouting is: ;

[0066] Among them, C 0represents the basic error parameter, tanh() is the hyperbolic tangent function, which is used to smooth the limit, reduce random disturbances, ensure the stable extension of the main path in the high-pressure area, and enhance randomness in the low-pressure area to explore potential channels; ▽R(t) represents the root system G simulated by grouting when the current growth time step is t n Pressure; R 0 is the initial simulation pressure; D(t) represents the grouting simulation root G at the current growth time step t n The depth of e ─σD(t) represents the exponential decay, which is used to suppress the random fluctuation of deep branches; levy(t) represents the random noise that obeys the levy distribution, which indicates the uncertainty of root growth and is used to simulate the accidental large displacement of roots, such as breaking through local rock barriers in coal mines; γ, ε and σ are adjustment parameters, which are set by professional technicians;

[0067] When the growth time step reaches the total time, the grouting directional environment evolution result P is obtained. n ; Based on the evolution results of grouting directional environment P n Select the best growth state of the grouting bionic structure and build a coal mine directional grouting model;

[0068] The initial coal mine directional grouting control parameters are set according to the coal mine directional grouting model.

[0069] The process of directional grouting design for the target coal mine pre-grouting model based on the grouting bionic structure involves the dynamic optimization of multiple parameters to simulate root growth. As the growth time step progresses, the grouting simulated root system continues to expand in space and evolves in time and space. Finally, by evaluating the results of different grouting paths, several directional grouting environmental evolution results are obtained. Each directional grouting environmental evolution result corresponds to a specific grouting effect. At this time, the grouting path can be described as an adaptable and flexible network that can automatically adjust the pressure, depth and path during the grouting process. By evaluating different grouting path results, the grouting bionic structure with the best growth potential is selected. The best structure usually refers to the ability to efficiently cover the key areas of the mining area while ensuring the grouting effect, especially those areas with high risks, complex geological environments or concentrated cracks, to achieve the effect of directional grouting in coal mines. Based on the selected best The grouting bionic structure with the best growth potential is used to construct a directional grouting model for coal mines. The model can optimize the grouting path and grouting amount according to geological conditions, mining progress and safety requirements, thereby improving the safety and production efficiency of coal mines; the bionic structure simulates the growth of the root system to avoid blind hole layout, ensure that the grouting holes can cover the key areas of the coal mine, and improve the grouting effect; simulate the adaptive exploration ability of the root system, so that the grouting path can cope with complex geological environments and flexibly adjust the grouting direction and depth; through real-time adjustment of the grouting strategy, the grouting density in high-risk areas is strengthened to ensure the geological stability and mining safety of the coal mine; directional grouting can reduce unnecessary resource waste and improve the resource utilization efficiency of coal mines while ensuring safety; the model can perform real-time simulation according to actual conditions, predict the grouting effect, and make adaptive adjustments in a dynamic environment to provide decision support for the long-term stable mining of coal mines.

[0070] Based on the coal mine directional grouting model and the actual grouting reaction parameters, a two-stage analysis is performed to obtain the optimized coal mine directional grouting control strategy; the two-stage analysis includes real-time grouting feedback analysis and predictive grouting feedback analysis;

[0071] The specific steps for real-time grouting feedback analysis include:

[0072] The actual grouting reaction parameters include the real-time solidification index of the slurry, the diffusion acoustic wave signal of the slurry and the pressure signal of the grouting orifice;

[0073] The actual grouting reaction parameters are obtained based on sensors. The solidification index sensor is embedded in the slurry pipeline or borehole to monitor the slurry viscosity, temperature and other chemical solidification parameters in real time. High-frequency acoustic wave receivers are arranged along the borehole to capture the vibration signals generated by the slurry flow. At the same time, pressure sensors are installed at the grouting pump outlet and the borehole mouth to record the grouting pressure fluctuation data.

[0074] The actual grouting reaction parameters are heterogeneously reconstructed to obtain the current diffusion data of the slurry;

[0075] The specific steps for heterogeneous reconstruction include:

[0076] The real-time solidification index of the slurry reflects the solidification speed and degree of the slurry, and can show the solidification state of the slurry in the geological environment; the slurry diffusion acoustic wave signal reflects the diffusion behavior of the slurry during the injection process and the shape of the injection channel; the grouting orifice pressure signal records the pressure change at the orifice during the grouting process, which is used to judge the flow state and flow resistance of the slurry;

[0077] Convert the formats of data from different sources to ensure that they can be unified into the same data structure or standard for subsequent analysis; convert the acoustic wave signal, pressure signal and solidification index into unified time series data, combine the data from different sources through time alignment and space matching, integrate the multi-dimensional data features, reconstruct the diffusion state of the slurry, reflect the expansion path and effect of the slurry at different positions and depths, and obtain the current diffusion data of the slurry; this process monitors data changes in real time, and verifies and adjusts the reconstruction results based on field feedback and known models to ensure the accuracy and real-time performance of the model, realize accurate data output of grouting diffusion, and provide real-time support for subsequent grouting effect evaluation;

[0078] Based on the current diffusion data of slurry and the pre-trained grouting effect evaluation model, the current results of coal mine grouting are obtained;

[0079] The training process of the pre-trained grouting effect evaluation model includes:

[0080] The training process of the pre-trained grouting effect evaluation model mainly includes the steps of data preparation, model selection, training and optimization, verification and adjustment. First, a large amount of data from the historical grouting process needs to be collected, including grouting hole position, grouting pressure, grouting time, slurry diffusion effect, mine geological characteristics, etc. These data will be used as training sets to help the model learn the relationship between grouting effect and geological conditions. A grouting effect label is provided for each historical sample, such as slurry coverage, diffusion depth, crack filling degree, etc. These labels will be used as supervision signals to guide the model to learn how to evaluate the grouting effect. The collected data is standardized, denoised, and missing values ​​are processed to ensure the quality of the data. The data can be normalized and adjusted to a unified scale to avoid the differences between different data sources affecting the model effect. Features that have an important impact on the evaluation of grouting effects, such as geological conditions, are selected from the historical grouting data. , grouting pressure, flow rate, time step, etc.; based on the original data, generate some new features, such as calculating the speed of slurry diffusion, the spatial distribution characteristics of grouting holes, the changing trend of geological conditions, etc. These derived features help to improve the accuracy of the model; select the most important features through correlation analysis, principal component analysis and other methods to reduce redundant information and improve the efficiency of model training; according to the nature of the task, select a suitable supervised learning algorithm for model training, and use the training set to train the model. During the training process, the model learns the relationship between the features and the grouting effect by minimizing the loss function. The training process needs to continuously adjust the model parameters to optimize the model performance; evaluate the generalization ability of the model through cross-validation to avoid overfitting; apply the trained grouting effect evaluation model, and the model can predict and evaluate the current grouting effect based on the real-time grouting reaction parameters.

[0081] The specific steps for predictive grouting feedback analysis include:

[0082] The analysis is based on the current diffusion data of slurry combined with the coal mine slurry diffusion model, which includes a reinforcement learning prediction layer and a dynamic plastic strategy layer; the coal mine slurry diffusion model is constructed based on the random forest model;

[0083] The reinforcement learning prediction layer is used to make predictions based on the current diffusion data of the slurry to obtain the prediction results of coal mine grouting;

[0084] The dynamic plastic strategy layer is used to perform reverse parameter adjustment based on the coal mine grouting prediction results and the current coal mine grouting results to obtain the optimized coal mine directional grouting control strategy;

[0085] A multi-branch feature extractor is introduced into the reinforcement learning prediction layer to split the current diffusion data of the slurry. The topological relationship of coal mine directional grouting is modeled using a graph attention network. The temporal change trend of the pressure field is analyzed through a temporal convolutional network and predictive analysis is performed. A model-independent meta-learning framework is introduced into the dynamic plastic strategy layer to enable the strategy layer to quickly adapt to new tasks. At the same time, a dual-objective optimization function is designed to minimize current errors and future potential risks. Based on the basic parameters generated by the random forest model, the parameters are optimized and adjusted to output the optimal optimized coal mine directional grouting control strategy.

[0086] The coal mine slurry diffusion model is built based on the random forest model. The model enhances its prediction ability and adaptability by introducing a reinforcement learning prediction layer and a dynamic plastic strategy layer. In the reinforcement learning prediction layer, the model uses a multi-branch feature extractor to split the current slurry diffusion data, models the topological relationship of coal mine directional grouting through a graph attention network, and uses a temporal convolutional network to analyze the temporal change trend of the pressure field, thereby achieving accurate prediction of the slurry diffusion process. The dynamic plastic strategy layer reversely adjusts parameters according to the prediction results and the current grouting results to optimize the grouting control strategy. In addition, the model also introduces a model-independent meta-learning framework, which enables the strategy layer to quickly adapt to new tasks, and designs a dual-objective optimization function to simultaneously minimize current errors and future potential risks.

[0087] When designing a dual-objective optimization function in the coal mine directional grouting control strategy, we can refer to the common design ideas of multi-objective optimization problems. Specifically, the dual-objective optimization function needs to consider two objectives at the same time: minimizing the current error and minimizing the future potential risk; the current error can be measured by the difference between the actual measured value and the predicted value during the grouting process, and the future potential risk can be evaluated by the long-term impact on the grouting process. Using the weighted sum method, a dual-objective optimization function is defined.

[0088] When training the coal mine slurry diffusion model, the basic parameters are first generated based on the random forest model; the random forest constructs multiple decision trees that introduce randomness for classification or regression prediction to improve the accuracy and generalization ability of the model; during the training process, the Bayesian optimization method is used to tune the hyperparameters of the random forest model. The Bayesian optimization constructs the prior distribution of the objective function and continuously updates the posterior distribution based on the existing observation data, thereby efficiently searching for the optimal hyperparameter combination; in the reinforcement learning prediction layer, the model monitors the data in the slurry diffusion process in real time, and combines the graph attention network and the temporal convolutional network for predictive analysis; the dynamic plastic strategy layer performs reverse parameter adjustment based on the prediction results and the current grouting results to optimize the grouting control strategy; finally, based on the basic parameters generated by the random forest model, combined with the optimization results of the reinforcement learning prediction layer and the dynamic plastic strategy layer, the parameters are further adjusted to output the optimal optimized coal mine directional grouting control strategy.

[0089] By introducing a multi-branch feature extractor to split the current diffusion data of the slurry, we can more carefully analyze the impact of different features on the diffusion of the slurry. Combined with the graph attention network to model the topological relationship of directional grouting in coal mines, we can capture the propagation path and interaction of the slurry in complex geological structures, thereby improving the accuracy and reliability of the prediction; using a time convolutional network to analyze the temporal variation trend of the pressure field, we can make real-time predictions on the dynamic changes in the slurry diffusion process. This time series analysis method can effectively capture the short-term fluctuations and long-term trends in the slurry diffusion process, and provide more accurate decision-making support in the time dimension for grouting operations; introducing a model-independent meta-learning framework in the dynamic plastic strategy layer enables the strategy layer to quickly adapt to new tasks, which means that even under geological conditions, When changes occur or grouting targets are adjusted, the model can also quickly adjust its strategy without the need to train from scratch, which greatly improves the adaptability and flexibility of the model; a dual-objective optimization function is designed to minimize current errors and future potential risks at the same time. This optimization strategy not only focuses on the accuracy of the current grouting effect, but also takes into account possible risks in the future, making the grouting control strategy more robust and reducing potential losses caused by changes in geological conditions or operational errors; on the basis of the basic parameters generated by the random forest model, combined with the optimization results of the reinforcement learning prediction layer and the dynamic plastic strategy layer, the parameters are further adjusted to output the optimal optimized coal mine directional grouting control strategy. This hierarchical optimization method can make full use of the advantages of each layer and ultimately obtain a grouting control strategy with the best comprehensive performance.

[0090] In this embodiment, for example, a target coal mine is located in a complex geological area with multiple rock layers, faults and fissures. In order to ensure the safety and stability of coal mining, it is necessary to carry out directional grouting reinforcement on the coal mine. The geological characteristics of the coal mine are complex, and it is difficult for traditional grouting methods to achieve the ideal reinforcement effect. Therefore, it is decided to adopt a coal mine directional grouting control method based on geological characteristics. Through geological exploration and drilling sampling, the rock stability of the coal mine is analyzed, including the compressive strength and elastic modulus of the rock. The interaction between the geological structure and the grouting process during coal mining is evaluated, and the fault is analyzed. , the impact of cracks on grouting safety; evaluate the potential impact of the grouting process on the surrounding ecological environment, including groundwater pollution risks, surface subsidence, etc.; use 3D geological modeling software to construct a 3D geological model of the coal mine, showing in detail the distribution of rock layers, fault locations and crack orientations; according to the integrity indicators of the coal mine, optimize the 3D geological model in detail to obtain the target coal mine pre-grouting model; design a bionic structure containing multiple grouting simulation root systems for model optimization, and determine the directional grouting model of the coal mine; during the grouting process, collect the real-time solidification index of the slurry, the slurry diffusion acoustic wave signal and the grouting orifice pressure signal; heterogeneously reconstruct the actual grouting reaction parameters collected to obtain the current diffusion data of the slurry, and use the pre-trained grouting effect evaluation model, combined with the current diffusion data of the slurry, to analyze and obtain the current results of coal mine grouting; in the reinforcement learning prediction layer, use a multi-branch feature extractor to split the current diffusion data of the slurry, model the topological relationship through the graph attention network, and analyze the temporal change trend of the pressure field through the time convolution network to predict the future diffusion of the slurry; in the dynamic plastic strategy layer, reverse parameter adjustment is performed according to the prediction results and the current grouting results to optimize the grouting control strategy; based on According to the optimized grouting control strategy, the initial grouting control parameters in the coal mine directional grouting model, such as grouting pressure, grouting time, grouting hole spacing, etc., are adjusted; grouting operations are performed according to the optimized grouting control strategy to ensure the efficiency and safety of the grouting process; the grouting process of this coal mine is precisely controlled, the slurry diffuses evenly, and the coverage area is wide, which effectively strengthens the geological structure of the coal mine; through the real-time feedback and predictive analysis mechanism during the grouting process, the grouting parameters can be adjusted in time, avoiding slurry waste and geological disaster risks, improving the mining safety of coal mines, and at the same time improving mining efficiency.

[0091] Example 2, a coal mine directional grouting control system based on geological characteristics, see Figure 1 As shown, including:

[0092] The pre-grouting analysis module includes a model building unit and a directional design unit; the model building unit is used to obtain the geological characteristics of the target coal mine; the target coal mine is evaluated based on the geological characteristics of the target coal mine to obtain the coal mine integrity index; and the target coal mine pre-grouting model is constructed based on the geological characteristics of the target coal mine and the coal mine integrity index; the directional design unit is used to perform directional grouting design on the target coal mine pre-grouting model in combination with the grouting bionic structure to obtain the coal mine directional grouting model; the coal mine directional grouting model contains the initial coal mine directional grouting control parameters;

[0093] The directional grouting control module includes a double-layer control unit, which is used to perform a two-stage analysis based on the coal mine directional grouting model and the actual grouting reaction parameters to obtain an optimized coal mine directional grouting control strategy; the two-stage analysis includes real-time grouting feedback analysis and predictive grouting feedback analysis; the optimized coal mine directional grouting control strategy is used to adjust the initial coal mine directional grouting control parameters in the coal mine directional grouting model.

[0094] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all such improvements and changes should fall within the scope of protection of the appended claims of the present invention. Parts not described in detail in this specification belong to the prior art known to those skilled in the art.

Claims

1. A coal mine directional grouting control method based on geological characteristics, characterized in that: The following steps are involved: Obtain geological characteristics of target coal mines; The target coal mine is evaluated based on its geological characteristics to obtain the coal mine integrity index; meanwhile, a target coal mine pre-grouting model is constructed based on the target coal mine geological characteristics and coal mine integrity index; Combined with the grouting bionic structure, the directional grouting design is carried out on the target coal mine pre-grouting model to obtain the coal mine directional grouting model; The coal mine directional grouting model contains initial coal mine directional grouting control parameters; Based on the coal mine directional grouting model and the actual grouting reaction parameters, a two-stage analysis is performed to obtain the optimized coal mine directional grouting control strategy; the two-stage analysis includes real-time grouting feedback analysis and predictive grouting feedback analysis; The optimized coal mine directional grouting control strategy is used to adjust the initial coal mine directional grouting control parameters in the coal mine directional grouting model.

2. The method for controlling directional grouting in coal mines based on geological characteristics according to claim 1, characterized in that: The target coal mine is evaluated based on the geological characteristics of the target coal mine to obtain the coal mine integrity index; at the same time, the specific steps of constructing the target coal mine pre-grouting model based on the geological characteristics of the target coal mine and the coal mine integrity index include: The geological characteristics of the target coal mine include geological structural stability characteristics, geological safety coupling characteristics, geological environmental impact characteristics and geological three-dimensional characteristics; Based on the geological structure stability characteristics, the structural stability assessment is carried out to obtain the grouting stability factor; Based on the geological safety coupling characteristics, the grouting safety assessment is carried out to obtain the grouting safety factor; Conduct environmental impact assessment based on geological environmental impact characteristics and obtain grouting impact factors; The grouting stability factor, grouting safety factor and grouting influence factor are integrated by the secondary weight formula to obtain the coal mine integrity index. Based on the integrity index of coal mine, the three-dimensional details of geological features are optimized to obtain the target coal mine pre-grouting model.

3. The method for controlling directional grouting in coal mines based on geological characteristics according to claim 2, characterized in that: Combined with the grouting bionic structure, the directional grouting design is carried out on the target coal mine pre-grouting model to obtain the coal mine directional grouting model; The coal mine directional grouting model contains specific steps for initial coal mine directional grouting control parameters, including: The grouting bionic structure contains N grouting simulated root systems G n , n=1, 2, ..., N; based on grouting simulation root system G n Set the root growth vector S n , environmental response parameter H n and root random error parameter C n ; Among them, the root growth vector S n The initial value is S n0 ; Set the total time of grouting simulation root growth in the grouting bionic structure as T, t=1, 2, ..., T; t represents the grouting simulation root G n The growth time step of Based on the grouting bionic structure and the target coal mine pre-grouting model to simulate root growth, several grouting bionic structures are set to simulate root growth synchronously; The total time for grouting simulation of root system G n The grouting evolution of several grouting bionic structures is carried out with the growth time step of . In each grouting bionic structure, the random error parameter C of the grouting root system n Stochastic fluctuations based on the grouting time-space evolution equation; When the growth time step reaches the total time, the grouting directional environment evolution result P is obtained. n ; Based on the evolution results of grouting directional environment P n Select the best growth state of the grouting bionic structure and build a coal mine directional grouting model; The initial coal mine directional grouting control parameters are set according to the coal mine directional grouting model.

4. The method for controlling directional grouting in coal mines based on geological characteristics according to claim 3, characterized in that: The specific steps for real-time grouting feedback analysis include: The actual grouting reaction parameters include the real-time solidification index of the slurry, the diffusion acoustic wave signal of the slurry and the pressure signal of the grouting orifice; The actual grouting reaction parameters are heterogeneously reconstructed to obtain the current diffusion data of the slurry; Based on the current diffusion data of slurry and the pre-trained grouting effect evaluation model, the current results of coal mine grouting are obtained.

5. The method for controlling directional grouting in coal mines based on geological characteristics according to claim 4, characterized in that: The specific steps for predictive grouting feedback analysis include: The analysis is based on the current diffusion data of slurry combined with the coal mine slurry diffusion model, which includes a reinforcement learning prediction layer and a dynamic plastic strategy layer. The reinforcement learning prediction layer is used to make predictions based on the current diffusion data of the slurry to obtain the prediction results of coal mine grouting; The dynamic plastic strategy layer is used to perform reverse parameter adjustment according to the coal mine grouting prediction results and the current results of coal mine grouting to obtain the optimized coal mine directional grouting control strategy.

6. The method for controlling coal mine directional grouting based on geological characteristics according to claim 5, characterized in that: The coal mine slurry diffusion model is constructed based on the random forest model.

7. A coal mine directional grouting control system based on geological characteristics, characterized in that: The system is used to implement the coal mine directional grouting control method based on geological characteristics as described in any one of claims 1 to 6 above, comprising: The pre-grouting analysis module includes a model building unit and a directional design unit; the model building unit is used to obtain the geological characteristics of the target coal mine; the target coal mine is evaluated based on the geological characteristics of the target coal mine to obtain the coal mine integrity index; and the target coal mine pre-grouting model is constructed based on the geological characteristics of the target coal mine and the coal mine integrity index; the directional design unit is used to perform directional grouting design on the target coal mine pre-grouting model in combination with the grouting bionic structure to obtain the coal mine directional grouting model; the coal mine directional grouting model contains the initial coal mine directional grouting control parameters; The directional grouting control module includes a double-layer control unit, which is used to perform a two-stage analysis based on the coal mine directional grouting model and the actual grouting reaction parameters to obtain an optimized coal mine directional grouting control strategy; the two-stage analysis includes real-time grouting feedback analysis and predictive grouting feedback analysis; the optimized coal mine directional grouting control strategy is used to adjust the initial coal mine directional grouting control parameters in the coal mine directional grouting model.

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