A coal mine underground operation measurement mode and drilling parameter adjustment method, device, medium and product

Abstract

This application discloses a method, device, medium, and product for adjusting a measurement mode and drilling parameters for underground coal mine operations, relating to the field of underground coal mine operations. The method comprises: collecting information data of coal and rock masses during drilling; the information data includes physical and mechanical parameters and stress states; preprocessing the information data; and, based on the preprocessed information data, using a trained reinforcement learning model to determine the optimal measurement mode and optimal drilling parameters during the drilling process, thereby adjusting the measurement mode and drilling parameters. This application can dynamically adjust operating parameters based on real-time data, preventing potential risks and providing strong technical support for underground coal mine operations.

Description

Technical Field

[0001] The present application relates to the field of underground coal mine operations, and in particular to a method, device, medium and product for measuring a coal mine underground operation mode and adjusting drilling parameters. Background Art

[0002] The underground operating environment of coal mines is complex, and the physical and mechanical parameters and stress state of the coal and rock mass have a direct impact on mine safety. Traditional sensor modules typically only measure certain parameters in a single dimension and lack the integration of multiple sensors, resulting in insufficient comprehensiveness and accuracy of the data. Secondly, data acquisition and processing units are often unable to process and analyze large amounts of measurement data in real time, resulting in inefficient data processing and an inability to provide timely feedback. Furthermore, conventional technologies lack dynamic adaptive adjustment and intelligent prediction capabilities, making it impossible to adjust measurement modes and drilling parameters according to real-time environmental changes, resulting in insufficient system adaptability and safety. Summary of the Invention

[0003] The purpose of this application is to provide a mine operation measurement mode and drilling parameter adjustment method, device, medium and product, which can improve the safety of underground coal mine operations.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a mine operation measurement mode and a drilling parameter adjustment method, including:

[0006] Collecting information data of coal and rock masses during drilling; the information data includes physical and mechanical parameters and stress state;

[0007] Preprocessing the information data;

[0008] Based on the preprocessed information data, the trained reinforcement learning model is used to determine the optimal measurement mode and optimal drilling parameters during the drilling process, and the adjustment of the measurement mode and drilling parameters is completed.

[0009] In a second aspect, the present application provides a mine operation measurement mode and drilling parameter adjustment device, comprising:

[0010] The sensor module is used to collect information data of the coal and rock mass during drilling; the information data includes physical and mechanical parameters and stress state;

[0011] a data preprocessing unit, connected to the sensor module, for preprocessing the information data;

[0012] The adjustment module is connected to the data processing unit and is used to determine the optimal measurement mode and optimal drilling parameters in the drilling process based on the preprocessed information data using the trained reinforcement learning model to complete the adjustment of the measurement mode and drilling parameters.

[0013] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned underground mine operation measurement mode and drilling parameter adjustment method.

[0014] In a fourth aspect, the present application provides a computer program product, including a computer program, which implements the above-mentioned underground mine operation measurement mode and drilling parameter adjustment method when executed by a processor.

[0015] According to the specific embodiments provided in this application, this application has the following technical effects:

[0016] This application provides a method, device, medium, and product for adjusting measurement modes and drilling parameters for underground mine operations. Through reinforcement learning and big data analysis, these methods enable intelligent adjustment of measurement modes and drilling parameters. In complex underground environments, these methods can dynamically adjust operating parameters based on real-time data, preventing potential risks and providing strong technical support for underground coal mine operations. This significantly improves the efficiency and safety of coal mine production and reduces the incidence of accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A flow chart of a mine operation measurement mode and a drilling parameter adjustment method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0020] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0021] In an exemplary embodiment, Figure 1 As shown, a method for measuring a mine operation mode and adjusting drilling parameters is provided. The method is executed by a computer device, specifically a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is described by taking the application of the method to the server as an example, and includes the following steps S1 to S3.

[0022] S1: Collecting information data of coal and rock masses during drilling; the information data includes physical and mechanical parameters and stress state.

[0023] Physical parameters include pressure, tensile strength, vibration, sound wave velocity, strain change, etc. of coal rock mass.

[0024] S2: Preprocessing the information data.

[0025] Specifically, the checksum is used to verify the integrity of the information data. If the received data is found to be incomplete, the sensor is requested to resend the data. After receiving the information data, a timestamp needs to be recorded. After completing the timestamp recording, a digital filter is used to filter the information data to remove high-frequency noise and environmental interference.

[0026] S3: Based on the preprocessed information data, the trained reinforcement learning model is used to determine the optimal measurement mode and optimal drilling parameters during the drilling process, and the adjustment of the measurement mode and drilling parameters is completed.

[0027] Specifically, based on the preprocessed information data, a trained reinforcement learning model is used to determine the optimal drilling action; and the measurement mode and drilling parameters corresponding to the optimal drilling action are determined as the optimal measurement mode and optimal drilling parameters.

[0028] The reinforcement learning model consists of a policy network and a value network. The training process of the reinforcement learning model includes:

[0029] (1) Obtain information data of the coal rock mass at time t.

[0030] In the coal and rock mass measurement environment, the state S at time t is collected. t , which contains relevant characteristic data such as the physical and mechanical parameters and stress state of the coal rock mass.

[0031] (2) Based on the information data at time t, the action probability distribution output by the strategy network is adopted, and the ε-greedy strategy is used to determine the optimal drilling action at time t.

[0032] According to the action probability distribution output by the policy network, the ε-greedy strategy is used to select actions, randomly select actions (exploration) with probability ∈, and select the optimal action A output by the policy network with probability 1-∈ t .

[0033] (3) Adjust the measurement mode and drilling parameters according to the optimal drilling action at time t, and obtain the reward value corresponding to the optimal drilling action at time t and the information data at time t+1 from the coal rock environment.

[0034] In state S t Execute the selected action A t , adjust the measurement mode and drilling parameters to adapt to the current coal rock environment, and obtain feedback information from the environment after execution, including the reward value R corresponding to the optimal drilling action at time t t and the state S at time t+1 t+1 , where the reward value R t Calculated based on measurement accuracy, drilling efficiency and safety indicators, used to evaluate action A t The advantages and disadvantages of the model can provide a reference for subsequent model updates.

[0035] (4) Adjust the parameters of the value network according to the information data at time t, the optimal drilling action at time t, the reward value corresponding to the optimal drilling action at time t, and the information data at time t+1, and copy the adjusted value network parameters to the strategy network to complete the training of the strategy network and value network.

[0036] The experience gained (S t , A t , R t , S t+1 ) is stored in the experience replay pool D. The replay pool is a fixed-size memory used to record the operation data of the model in different states. Each time storage is performed, the earliest record is replaced when the replay pool is full.

[0037] Randomly sample small batches of data (Si, Ai, Ri, Si+1) from the replay pool to update the parameters of the value network; calculate the target value Where γ is the discount factor, Q(S i+1 ,a′;θ - ) is the maximum expected value of the next state output by the value network; by minimizing the mean square error loss function Adjust the parameters θ of the value network.

[0038] Calculate the advantage function A(S t ,A t )=Q(S t ,A t )-V(S t ), where V(St ) represents the state value; calculate the policy gradient And based on this gradient, the parameters of the policy network to update.

[0039] Every fixed number of steps, the parameters θ of the current value network are copied to the parameters θ of the policy network. - In order to maintain the stability of the value network during training and ensure the gradual optimization effect of model parameters.

[0040] In another exemplary embodiment of the present application, in order to prevent potential risks, a trained long short-term memory network is used to predict potential danger areas based on preprocessed information data and issue an early warning.

[0041] Specifically, the method includes: predicting the stress state at the next moment based on the preprocessed information data using a trained long short-term memory network; determining the stress state change rate based on the stress state at the next moment; and determining the potential danger area according to the stress state change rate.

[0042] A large amount of historical data of coal and rock masses is collected, including physical and mechanical parameters and stress state. The acquired data are cleaned to remove outliers and noise. Finally, all the data are normalized to the same dimensional range.

[0043] The processed dataset is divided into training set, validation set and test set. The training set is used to optimize the parameters of the long short-term memory network, the validation set is used to monitor the performance of the long short-term memory network on non-training data to avoid overfitting, and the test set is used to finally evaluate the performance of the long short-term memory network.

[0044] Set the shape of the input layer to (time_steps,n features ), where time_steps is the number of time steps used for prediction, n features is the number of features. Then, a single-layer LSTM is set with 64 neurons to ensure that the model has sufficient expressive power when processing the complex time dependence of coal rock data. Next, the output layer is set to 1 neuron and the activation function is linear activation, which is used to predict a single physical quantity of the coal rock. Subsequently, the weights of the LSTM layer and the output layer are assigned and the bias term of the forget gate in the LSTM layer is initialized to 1. Next, the mean square error is selected as the loss function to reduce the prediction error. The Adam optimizer with a learning rate of 0.001 is used for parameter optimization, and the mean square error is used as the expression of the loss function; the mean square error represents the average square difference between the predicted value and the true value, which is used to measure the prediction accuracy of the model.

[0045] The training set is input into the model, and forward propagation is performed to calculate the predicted value. The weights and biases are adjusted to gradually reduce the loss function value. The validation set error is monitored in real time during training to avoid overfitting. If the validation error increases and the training error continues to decrease, a Dropout layer with a ratio of 0.2 is added.

[0046] After each round of training, the validation set is fed into the model. The validation error is calculated to evaluate the model's performance on unseen data. Hyperparameters such as the learning rate and training period are adjusted based on the validation error. Once the model's training and validation results meet expectations, a final evaluation is performed using the test set to verify the model's generalization and stability. After testing is complete, the trained LSTM model's weights and parameter structure are saved for use in real-world applications.

[0047] The application process is as follows:

[0048] The physical and mechanical parameters and stress state of the coal rock mass are collected in real time through the sensor module, and the pre-processed real-time data is input into the trained LSTM model. The LSTM model predicts the stress state of the coal rock mass through the learned time dependency relationship and outputs the predicted value. Then, according to the formula Calculate the rate of change ΔH t Through rate of change analysis, the changing trend of coal and rock mass conditions can be identified to determine whether there is a risk of abnormal increase or decrease, and potential danger areas can be determined. Early warning information can be generated and sent to operators.

[0049] Through the deep learning capabilities of the LSTM layer, it is possible to adaptively learn complex patterns and relationships in time series data, improving the accuracy and stability of predictions. This design is particularly suitable for processing time series data such as the physical and mechanical parameters and stress state of coal rock masses. It can accurately predict the stress state and potential danger areas of coal rock masses, combine historical data with real-time data, evaluate the changing trends and potential risks of the current stress state, generate early warning information and operational recommendations, and improve the safety and efficiency of coal mine production. In this way, this application not only demonstrates advanced technical solutions, but also highlights its great potential and wide applicability in practical applications, providing important technical support and guarantees for the intelligent and safe development of the coal mining industry.

[0050] Based on the same inventive concept, embodiments of the present application also provide a device for implementing the aforementioned underground mine operation measurement mode and drilling parameter adjustment method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the underground mine operation measurement mode and drilling parameter adjustment device provided below can be found in the above-described limitations of the underground mine operation measurement mode and drilling parameter adjustment method, and will not be further elaborated here.

[0051] In an exemplary embodiment, a device for measuring a mine operation mode and adjusting drilling parameters is provided, comprising:

[0052] The sensor module is used to collect information data of the coal and rock mass during the drilling process; the information data includes physical and mechanical parameters and stress state.

[0053] In a specific embodiment, a sensor group is used to measure physical parameters such as pressure, tensile strength, vibration, acoustic wave velocity, strain change, and stress state of the coal rock mass.

[0054] The sensor group includes pressure sensors, stretch sensors, vibration sensors, sound velocity sensors and fiber Bragg grating sensors, strain gauges, magnetic resonance imaging sensors and triaxial stress sensors.

[0055] A data preprocessing unit is connected to the sensor module and is used to preprocess the information data.

[0056] The data preprocessing unit has multi-channel receiving capability and can simultaneously receive data from different sensors with each channel corresponding to one sensor type, thereby improving the efficiency and accuracy of data reception.

[0057] The adjustment module is connected to the data processing unit and is used to determine the optimal measurement mode and optimal drilling parameters in the drilling process based on the preprocessed information data using the trained reinforcement learning model to complete the adjustment of the measurement mode and drilling parameters.

[0058] The underground mine operation measurement mode and drilling parameter adjustment device provided by this application also include:

[0059] The communication module receives data from the data pre-processing unit and sends it to the ground monitoring center, enabling real-time data transmission and remote monitoring, ensuring the real-time and interactive nature of the system. The communication module transmits the received data to the ground station via WiFi or 5G signals.

[0060] The underground mine operation measurement mode and drilling parameter adjustment device provided by this application also include:

[0061] The power module supports the device to work continuously for a long time underground, enhances the stability and durability of the system, and ensures that the equipment can continue to operate in harsh environments.

[0062] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0063] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0064] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0065] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0066] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0067] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0068] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A mine operation measurement mode and drilling parameter adjustment method, characterized in that: include: Collecting information data of coal and rock masses during drilling; the information data includes physical and mechanical parameters and stress state; Physical and mechanical parameters and stress states include pressure, tensile strength, vibration, acoustic wave velocity, and strain changes of coal and rock masses; Preprocessing the information data; Based on the pre-processed information data, the trained reinforcement learning model is used to determine the optimal measurement mode and optimal drilling parameters during the drilling process, and the measurement mode and drilling parameters are adjusted; Determining an optimal measurement mode and optimal drilling parameters in a drilling process based on the preprocessed information data using a trained reinforcement learning model specifically includes determining an optimal drilling action based on the preprocessed information data using a trained reinforcement learning model; determining the measurement mode and drilling parameters corresponding to the optimal drilling action as the optimal measurement mode and optimal drilling parameters; Based on the pre-processed information data, the trained long short-term memory network is used to predict potential danger areas and issue early warnings; Based on the pre-processed information data, the trained long short-term memory network is used to predict the potential danger area. Specifically, the method includes: based on the pre-processed information data, the trained long short-term memory network is used to predict the stress state at the next moment; Determine the stress state change rate based on the stress state at the next moment; the calculation formula for the change rate is ΔH t is the stress state change rate, H t+1 is the stress state at time t+1, H t is the stress state at time t; the potential danger area is determined according to the stress state change rate.

2. The underground mine operation measurement mode and drilling parameter adjustment method according to claim 1, characterized in that: Preprocessing the information data specifically includes: Verifying the integrity of the information data using a check code and recording the information data with a timestamp; After completing the timestamp recording, a digital filter is used to filter the information data for noise and unify the data format.

3. The underground mine operation measurement mode and drilling parameter adjustment method according to claim 1, characterized in that: The reinforcement learning model includes a policy network and a value network; The training process of the reinforcement learning model includes: Obtain information data of coal rock mass at time t; Based on the information data at time t, the action probability distribution output by the strategy network is adopted, and the ε-greedy strategy is used to determine the optimal drilling action at time t; Adjust the measurement mode and drilling parameters according to the optimal drilling action at time t, and obtain the reward value corresponding to the optimal drilling action at time t and the information data at time t+1 from the coal and rock environment; The parameters of the value network are adjusted according to the information data at time t, the optimal drilling action at time t, the reward value corresponding to the optimal drilling action at time t, and the information data at time t+1, and the adjusted value network parameters are copied to the policy network to complete the training of the policy network and value network.

4. A device for measuring the working mode and adjusting the drilling parameters in a mine, characterized in that: include: The sensor module is used to collect information data of the coal and rock mass during drilling; the information data includes physical and mechanical parameters and stress state; the physical and mechanical parameters and stress state include pressure, tensile strength, vibration, acoustic wave velocity, and strain change of the coal and rock mass; a data preprocessing unit, connected to the sensor module, for preprocessing the information data; an adjustment module connected to the data preprocessing unit, for determining the optimal measurement mode and optimal drilling parameters during the drilling process based on the preprocessed information data using a trained reinforcement learning model, and completing the adjustment of the measurement mode and drilling parameters; Based on the pre-processed information data, the trained long short-term memory network is used to predict the potential danger area, specifically including: based on the pre-processed information data, the trained long short-term memory network is used to predict the stress state at the next moment; Determine the stress state change rate based on the stress state at the next moment; the calculation formula for the change rate is ΔH t is the stress state change rate, H t+1 is the stress state at time t+1, H t is the stress state at time t; the potential danger area is determined according to the stress state change rate.

5. The underground mine operation measurement mode and drilling parameter adjustment device according to claim 4, characterized in that: Also includes: The prediction module is connected to the data preprocessing unit and is used to predict potential danger areas based on the preprocessed information data using the trained long short-term memory network and issue an early warning.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the underground mine operation measurement mode and drilling parameter adjustment method according to any one of claims 1 to 3 are implemented.

7. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the underground mine operation measurement mode and drilling parameter adjustment method according to any one of claims 1 to 3 are implemented.

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