Intelligent mine pressure sensing and safety early warning method for fully mechanized caving face of coal mine

Abstract

The invention discloses an intelligent mine pressure sensing and safety early warning method for a coal mine fully-mechanized caving face, and relates to the technical field of mine safety monitoring and intelligent early warning, which comprises the following steps of: arranging mine pressure monitoring equipment on the coal mine fully-mechanized caving face, acquiring related data of mine pressure by adopting various sensing technologies, and carrying out standardization processing on each data source; and uniform data formats are ensured. Through multi-sensor combined monitoring and data processing, the accuracy and reliability of mine pressure monitoring are improved, and the influence of errors of a single sensor is avoided. And in combination with a deep learning model, the prediction capability of mine pressure abnormity is enhanced, risks can be identified in advance, early warning can be given out, and the probability of accidents is reduced. The integration of the safety early warning and the response mechanism ensures that multistage early warning can be quickly triggered when the mine pressure changes suddenly, provides a corresponding emergency scheme, timely deals with the mine safety risk, improves the safety management level of the mine, and guarantees the life safety of miners and the safety of mine facilities.

Description

Technical Field

[0001] The present invention relates to the technical field of mine safety monitoring and intelligent early warning, and in particular to a method for intelligent sensing of mine pressure and safety early warning in a fully mechanized caving working face in a coal mine. Background Art

[0002] Intelligent sensing and safety warning of mine pressure in fully-mechanized coal mining faces refers to the real-time monitoring of mine pressure changes in fully-mechanized coal mining (comprehensive coal mining and top caving) working faces in coal mines through the use of advanced sensor technology and data acquisition and analysis systems. Mine pressure is a key safety indicator in the coal mining process, reflecting the stress state of the strata and its potential threat to miners and equipment. The intelligent sensing system can collect mine pressure data in a timely manner, and use data analysis models to predict possible dangerous situations, such as sudden increases in mine pressure and rock formation fractures. Once the system identifies a potential safety risk, it automatically issues a warning signal, reminding miners and managers to take preventive measures, thereby effectively avoiding the occurrence of safety accidents. This technology not only improves mine safety but also promotes the intelligent management of mining operations.

[0003] The existing technology has the following shortcomings: In the existing technology of intelligent perception of mine pressure and safety warning in coal mine fully-mechanized caving working faces, the accuracy of data fusion and analysis models may lead to serious consequences. Mine pressure monitoring systems usually rely on multiple data sources and sensors to collect data, but if there are deviations between different data sources, or the data fusion algorithm is not accurate enough, it may cause abnormal signals of mine pressure changes to be misjudged. Especially when the mine pressure suddenly increases or other abnormal conditions occur, if the system fails to identify and issue warnings in a timely and accurate manner, miners may not be able to take the necessary safety measures in time, thereby increasing the risk of catastrophic accidents such as mine collapse or explosion. Such problems are particularly prominent in underground mines where data processing is complex and the environment is changing rapidly, and may lead to serious safety hazards.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for intelligent perception of mine pressure and safety warning of coal mine fully mechanized caving working faces. Through multi-sensor joint monitoring and data processing, the accuracy and reliability of mine pressure monitoring are improved, and the influence of single sensor errors is avoided. Combined with the deep learning model, the prediction ability of mine pressure anomalies is enhanced, and risks can be identified and warnings issued in advance, reducing the probability of accidents. The integration of safety warning and response mechanism ensures that multi-level warnings can be quickly triggered when the mine pressure suddenly changes, and provides corresponding emergency plans to respond to mine safety risks in a timely manner, improve the safety management level of the mine, and ensure the safety of miners and mine facilities, so as to solve the problems in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for intelligently sensing mine pressure and providing safety warnings in a fully mechanized caving working face of a coal mine, comprising the following steps:

[0007] Deploy mine pressure monitoring equipment in fully mechanized caving working faces in coal mines, use a variety of sensing technologies to obtain mine pressure-related data, and standardize each data source to ensure a unified data format;

[0008] De-noise, remove outliers, and align the time series of the collected mine pressure data to build a complete and reliable mine pressure data set, improving data quality and consistency;

[0009] Based on the adaptive weighted fusion algorithm, the data from different sensors and monitoring points are calculated and processed, and the weight of each data source is dynamically adjusted to improve the accuracy and stability of the mine pressure monitoring data;

[0010] A machine learning model is combined with historical mine pressure data to build a deep neural network analysis model to predict mine pressure change trends and calculate the safety risk level of each area of ​​the working face;

[0011] Based on the prediction results, multiple mine pressure thresholds are set. Combined with the working face support status and geological conditions, the safety warning level is determined and the alarm mechanism is triggered when the warning conditions are met.

[0012] After the early warning is triggered, a safety disposal plan is automatically generated and pushed to miners and managers. At the same time, the analysis model is continuously optimized based on feedback data to improve the accuracy of the early warning and response efficiency.

[0013] Preferably, in the data collection step, the mine pressure monitoring equipment includes a pressure sensor, a displacement sensor, and a temperature sensor, and the various sensors are used to synchronously monitor the changes in the mine pressure, displacement, and temperature of the working face to comprehensively capture the impact of the mine pressure, stratum changes, and temperature on the mine pressure;

[0014] The pressure sensor is used for real-time measurement of the working face pressure, the displacement sensor is used for monitoring the working face displacement caused by stratum change, and the temperature sensor is used for monitoring the influence of the coal mine environment temperature on the mine pressure; the multi-dimensional data acquisition scheme can more accurately reflect the real pressure environment of the mine, avoid the error caused by single sensor data, and ensure the comprehensiveness and accuracy of subsequent analysis data.

[0015] Preferably, the data preprocessing step further comprises data cleaning and interpolation processing of the collected data to eliminate invalid data and fill in blank data points caused by sensor failure or data loss.

[0016] Data cleaning eliminates abnormal mine pressure data by setting threshold and range, and supplements missing mine pressure data by using interpolation method, to ensure the integrity and continuity of the data set.

[0017] Preferably, the multi-source data fusion step adopts a weighted average method, and by dynamically adjusting the weights of different data sources, the data source with high weight proportion has greater influence on the final analysis result.

[0018] The data fusion method comprises the following steps: first, according to the performance difference of each sensor under different working face and mining conditions, set the initial weight value; second, real-time monitor the stability and accuracy of each data source, and adjust the weight according to the signal quality; finally, use the weighted average algorithm to integrate the information of all data sources, and generate accurate mine pressure data.

[0019] Preferably, the multi-source data fusion specific steps are as follows:

[0020] For each data source, the weight is adjusted according to the real-time data quality and stability, and the weight calculation expression is as follows:

[0021]

[0022] , wherein W i is the weight of the i th data source, Q i is the quality coefficient of the i th data source, S i is the stability coefficient of the i th data source, and n is the total number of data sources.

[0023] All sensor data are weighted and averaged to generate mine pressure data, and the generation formula is as follows:

[0024]

[0025] , wherein P is the fused mine pressure data result, X i is the mine pressure data collected by the i th data source.

[0026] Real-time update data quality and stability, dynamically adjust the weight W i , ensure that the data fusion results are constantly optimized with environmental changes and fluctuations in data quality.

[0027] Preferably, the intelligent analysis and prediction step trains and predicts the mine pressure data by establishing a deep learning neural network model, which includes a multi-layer perceptron and a long short-term memory network, to accurately predict the trend and abnormality of mine pressure changes in complex geological environments.

[0028] The deep learning model models the historical mine pressure data through multiple neurons, extracts potential regularity, and uses the LSTM network to learn time series data, further improving the accuracy of mine pressure prediction.

[0029] Preferably, the specific steps of intelligent analysis and prediction of mine pressure changes are as follows:

[0030] According to the historical mine pressure data, an autoregressive moving average model is constructed to calculate the predicted value of mine pressure, and the calculation expression is as follows:

[0031]

[0032] , where, is the predicted mine pressure value at time t, φ1, φ2, …, φ k is the autoregressive parameter of the ARMA model, P t-1 , P t-2 , …, P t-k is the mine pressure data at historical time from t-1 to t-k, where k is the number of historical data time periods, θ1, θ2, …, θ k is the moving average parameter of the ARMA model, ∈ t-1 , ∈ t-2 , …, ∈ t-k is the prediction residual;

[0033] The final mine pressure prediction result is obtained by further optimizing the prediction results of the historical data and the LSTM neural network, and the calculation expression is as follows:

[0034]

[0035] , where, is the final predicted mine pressure value, f LSTM (P t-1 , P t-2 , …, P t-k is the mine pressure prediction value generated by the LSTM model, λ is the weighting coefficient of the LSTM and ARMA prediction results, is the mine pressure prediction value generated by the ARMA model;

[0036] By combining the output results of ARMA and LSTM networks with the synthetic model, accurate mine pressure prediction values ​​are obtained.

[0037] Preferably, the safety warning determination step adopts a hierarchical warning system based on multi-level thresholds, divides the mine pressure value into multiple safety levels, and dynamically calculates the risk level and issues a corresponding warning signal in combination with the working face support situation, ore layer thickness, and historical data factors;

[0038] The threshold setting sets the normal range, warning range and danger range of the mine pressure value according to the historical distribution of the mine pressure monitoring data and the actual engineering conditions of the mine; the safety warning system compares the current mine pressure value with the set threshold to determine whether the mine is in a safe state. If the mine pressure value exceeds the safety threshold, an alarm will be issued in time to prompt the management personnel to take emergency protective measures.

[0039] Preferably, the safety warning determination step further includes comprehensively determining the safety risk of the mine pressure by setting a dynamic safety factor in combination with the coal mining progress and support status of the working face;

[0040] The dynamic safety factor is adjusted according to the actual coal mining progress and changes in support conditions. By setting the safety factor threshold, when the result of combining the actual mine pressure value with the safety factor exceeds the preset safety range, a safety warning is automatically issued to remind miners to take protective measures in time to prevent catastrophic events in the mine.

[0041] Preferably, the early warning response and optimization step further includes adjusting parameters in the mine pressure prediction model based on feedback data from miners and managers to continuously optimize system performance;

[0042] Feedback data includes but is not limited to feedback from miners after an accident, changes in the mine environment, and equipment conditions.

[0043] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0044] The present invention significantly improves the accuracy and reliability of mine pressure monitoring in mines through the combined use of multiple sensors and real-time monitoring of the data acquisition system. Different types of sensors (such as pressure, displacement and temperature sensors) can fully cover the working surface and the surrounding environment, and realize the synchronous monitoring of mine pressure, displacement and temperature changes. This multi-dimensional data acquisition method enables the mine pressure changes in the mine to be comprehensively and accurately recorded and analyzed, avoiding the monitoring errors caused by failure of a single sensor or changes in the local environment. At the same time, the data preprocessing stage performs denoising and interpolation processing on the collected data, effectively solving the data missing or anomaly caused by sensor failure or environmental influences, ensuring the integrity and consistency of the data, and thus providing more reliable basic data for subsequent intelligent analysis.

[0045] The present invention greatly improves the ability to predict mine safety risks through the introduction of intelligent analysis and deep learning models. The deep learning neural network (such as the LSTM model) used can deeply analyze historical mine pressure data, capture the potential laws in mine pressure changes, and can make effective predictions based on real-time data. Through the long-term dependency analysis of mine pressure data, the system can identify abnormal conditions such as sudden increases and mutations in mine pressure, and issue early warnings before problems arise. This predictive capability enables mines to issue early warnings before dangerous changes in mine pressure occur, greatly reducing the risk of catastrophic accidents in mines. Through continuous learning and optimization of the intelligent analysis system, the prediction model can continuously adapt to the new mine environment, improve the accuracy of mine pressure prediction, and provide strong technical support for mine safety management.

[0046] The present invention integrates safety warning and response mechanisms to ensure that mines can respond quickly to sudden changes in mine pressure, thereby avoiding catastrophic accidents. When the system predicts abnormal mine pressure through intelligent analysis, it will automatically trigger a multi-level warning and provide corresponding safety response measures according to different warning levels. Miners and managers can receive the warning signal in the first time and take corresponding actions based on the safety disposal plan provided by the system. This mechanism not only speeds up the warning response, but also further optimizes the emergency response strategy through real-time feedback interaction with the mine production status. Through this automated and intelligent safety management system, the safety level of the mine has been significantly improved, and the life safety of miners and the safety of production facilities have been better guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0048] Figure 1 This is a method flow chart of the intelligent perception and safety early warning method of mine pressure in a fully mechanized caving working face of a coal mine according to the present invention. DETAILED DESCRIPTION

[0049] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0050] The present invention provides Figure 1The method for intelligently sensing and providing safety warning for mine pressure in a fully mechanized caving working face of a coal mine shown in the figure includes the following steps:

[0051] Deploy mine pressure monitoring equipment in fully mechanized caving working faces in coal mines, use a variety of sensing technologies to obtain mine pressure-related data, and standardize each data source to ensure a unified data format;

[0052] During the data collection process, the mine pressure monitoring equipment includes pressure sensors, displacement sensors, and temperature sensors. These sensors synchronously monitor changes in the mine pressure, displacement, and temperature of the working face to fully capture the impact of mine pressure, stratum changes, and temperature on mine pressure.

[0053] Among them, pressure sensors are used to measure the working face mine pressure in real time, displacement sensors are used to monitor the working face displacement caused by stratum changes, and temperature sensors are used to monitor the impact of the coal mine ambient temperature on the mine pressure; the multi-dimensional data acquisition solution can more accurately reflect the actual pressure environment of the mine, avoid the errors caused by single sensor data, and ensure the comprehensiveness and accuracy of subsequent analysis data.

[0054] De-noise, remove outliers, and align the time series of the collected mine pressure data to build a complete and reliable mine pressure data set, improving data quality and consistency;

[0055] The data preprocessing step further includes data cleaning and interpolation of the collected data to eliminate invalid data and fill in blank data points caused by sensor failure or data loss;

[0056] Data cleaning eliminates abnormal mine pressure data by setting thresholds and ranges, and uses interpolation methods to supplement missing mine pressure data to ensure the integrity and consistency of the data set; the interpolation method can select appropriate algorithms such as linear interpolation and spline interpolation based on historical data trends; this step helps to improve the accuracy of subsequent analysis and avoid the adverse effects of missing data on system analysis.

[0057] Based on the adaptive weighted fusion algorithm, the data from different sensors and monitoring points are calculated and processed, and the weight of each data source is dynamically adjusted to improve the accuracy and stability of the mine pressure monitoring data;

[0058] The multi-source data fusion step uses a weighted average method, which dynamically adjusts the weights of different data sources so that data sources with higher weights have a greater impact on the final analysis results.

[0059] The data fusion method includes the following steps: first, setting initial weight values ​​based on the performance differences of each sensor under different working faces and mining conditions; second, monitoring the stability and accuracy of each data source in real time, and adjusting the weights according to the signal quality; finally, using a weighted average algorithm to integrate the information from all data sources to generate accurate mine pressure data.

[0060] The specific steps of multi-source data fusion are as follows:

[0061] For each data source, the weight is adjusted based on the real-time data quality and stability. The weight calculation expression is as follows:

[0062]

[0063] , where W i is the weight of the i-th data source, Q i is the quality coefficient of the i-th data source, S i is the stability coefficient of the i-th data source, and n is the total number of data sources;

[0064] All sensor data are weighted averaged to generate mine pressure data. The generation formula is as follows:

[0065]

[0066] , where P is the fused mine pressure data result, and X is the final mine pressure value after weighted fusion of all data sources. i is the mine pressure data collected by the i-th data source;

[0067] Update data quality and stability in real time and dynamically adjust weight W i , ensuring that data fusion results are continuously optimized as the environment changes and data quality fluctuates.

[0068] Through this method, the impact of individual sensor failure or data anomaly on mine pressure prediction can be eliminated, and the accuracy of data fusion can be improved.

[0069] A machine learning model is combined with historical mine pressure data to build a deep neural network analysis model to predict mine pressure change trends and calculate the safety risk level of each area of ​​the working face;

[0070] The intelligent analysis and prediction step trains and predicts the mine pressure data by establishing a deep learning neural network model. The neural network model includes a multi-layer perceptron and a long short-term memory (LSTM) network to accurately predict the changing trends and anomalies of mine pressure in complex geological environments.

[0071] The deep learning model uses multiple layers of neurons to model historical mine pressure data, extracts potential regularities, and uses the LSTM network to learn time series data to further improve the accuracy of mine pressure prediction, especially when facing rapidly changing working face environments, and can better handle long-term dependency issues; the model can continuously optimize prediction results based on real-time data during the mining process, improving the mine's safety warning capabilities.

[0072] The specific steps for intelligent analysis and prediction of mine pressure change trends are as follows:

[0073] Based on the historical mine pressure data, an autoregressive moving average (ARMA) model is constructed to calculate the predicted value of the mine pressure. The calculation expression is as follows:

[0074]

[0075] , where is the predicted mine pressure value at time t, φ1, φ2, ..., φ k is the autoregressive (AR) parameter of the ARMA model, P t-1 , P t-2 ,……,P t-k is the mine pressure data at the historical moment, from t-1 to tk, where k is the number of time periods of historical data, θ1, θ2, ..., θ k is the moving average (MA) parameter of the ARMA model, ∈ t-1 ,∈ t-2 、……、∈ t-k is the prediction residual;

[0076] Combining historical data and the prediction results of the LSTM (long short-term memory) neural network, further optimization is performed to obtain the final mine pressure prediction result. The calculation expression is as follows:

[0077]

[0078] , where is the final predicted mine pressure value, which is the result of combining the ARMA model with the LSTM model. LSTM (P t-1 、P t-2 ,……,P t-k ) is the mine pressure prediction value generated by the LSTM model (long short-term memory neural network), λ is the weighting coefficient of the LSTM and ARMA prediction results, is the predicted value of mine pressure generated by the ARMA model;

[0079] By combining the output results of ARMA and LSTM networks with the synthetic model, accurate mine pressure prediction values ​​are obtained.

[0080] By continuously training and optimizing these models, high-precision prediction of mine pressure change trends can be achieved.

[0081] Based on the prediction results, multiple mine pressure thresholds are set. Combined with the working face support status and geological conditions, the safety warning level is determined and the alarm mechanism is triggered when the warning conditions are met.

[0082] The safety warning determination step adopts a hierarchical warning system based on multi-level thresholds, which divides the mine pressure value into multiple safety levels. It also combines the working face support situation, ore layer thickness, and historical data factors to dynamically calculate the risk level and issue corresponding warning signals;

[0083] The threshold setting sets the normal range, warning range and danger range of the mine pressure value according to the historical distribution of the mine pressure monitoring data and the actual engineering conditions of the mine; the safety warning system compares the current mine pressure value with the set threshold to determine whether the mine is in a safe state. If the mine pressure value exceeds the safety threshold, an alarm will be issued in time to prompt the management personnel to take emergency protective measures.

[0084] The safety early warning determination step further includes comprehensively determining the safety risk of mine pressure by setting a dynamic safety factor in combination with the coal mining progress and support status of the working face;

[0085] The dynamic safety factor is adjusted according to the actual coal mining progress and changes in support conditions. By setting the safety factor threshold, when the result of combining the actual mine pressure value with the safety factor exceeds the preset safety range, a safety warning is automatically issued to remind miners to take protective measures in time to prevent catastrophic events in the mine.

[0086] After an early warning is triggered, a safety disposal plan is automatically generated and pushed to miners and managers. At the same time, the analysis model is continuously optimized based on feedback data to improve early warning accuracy and response efficiency.

[0087] The alarm response and optimization steps further include adjusting the parameters in the mine pressure prediction model based on feedback data from miners and managers to continuously optimize system performance;

[0088] Feedback data includes but is not limited to feedback from miners after an accident, changes in the mine environment, and equipment conditions; this data will be used to retrain the system to help improve the accuracy of mine pressure predictions; through continuous feedback and adjustment, the optimized system will be able to dynamically adjust the early warning mechanism according to the new mine environment, thereby improving the overall safety management level.

[0089] Specific Embodiment 1: This embodiment describes in detail the construction and application of a data acquisition and processing system for intelligent pressure sensing and safety early warning methods in fully-mechanized coal mine caving working faces. Accurate monitoring of mine pressure is crucial to ensuring the safety of mine operations. Therefore, a variety of sensors are deployed in fully-mechanized coal mine caving working faces for comprehensive monitoring, including pressure sensors, displacement sensors, temperature sensors, and other diverse equipment. These sensors are strategically placed in key locations within the mine based on the specific needs of the working face and underground environment.

[0090] Specifically, pressure sensors are installed in key locations such as the working face roof, floor, and sidewalls to monitor changes in mine pressure in real time. Displacement sensors are installed within support structures and roadways to monitor ground movement caused by mining. Temperature sensors monitor temperature changes in the mine environment, particularly in deep mines where temperatures are high. This increase in temperature can affect mine pressure, making temperature monitoring crucial for safe prediction of mine pressure.

[0091] These sensors transmit monitoring data in real time to the mine's data processing platform via wireless or wired networks, ensuring rapid and accurate acquisition of mine pressure data in various mine environments. During data collection, all sensor data undergoes a preliminary data cleansing process. The goal of data cleansing is to remove invalid, abnormal, and duplicate data, ensuring high-quality collected data. For example, a set threshold is used to automatically exclude mine pressure data outside the normal range, preventing data errors caused by sensor failure or environmental changes.

[0092] To further improve data reliability and accuracy, missing data is interpolated after data collection. This process uses various interpolation algorithms, such as linear and spline interpolation, to fill in missing points caused by sensor failures or data transmission issues based on historical data and adjacent mine pressure values, thereby ensuring the integrity and continuity of the entire dataset. This data preprocessing provides a high-quality data foundation for subsequent multi-source data fusion and intelligent analysis.

[0093] The data acquisition system also supports real-time monitoring and dynamic adjustment. Even in a dynamic mine environment, mine pressure data can constantly change due to changes in equipment operating conditions or external factors. Real-time adjustment and monitoring of the data acquisition system ensures that data quality remains within acceptable limits, allowing deviations to be corrected promptly to ensure the most accurate data used in subsequent analysis. This system not only captures mine pressure changes in real time but also infers trends over a specific time period based on collected data, providing a crucial basis for mine safety management.

[0094] Through this data collection and processing system, mine managers can grasp the dynamic changes of mine pressure on the working face in the first time, and promptly discover changes in mine pressure that may cause safety hazards, thereby effectively reducing the probability of accidents and providing technical support for safe production in coal mines.

[0095] Specific embodiment 2: This embodiment details the working principles of the multi-source data fusion and intelligent analysis system in the intelligent perception and safety warning method for coal mine working faces. Multi-source data fusion is a key technology for addressing the diversity and variability of mine face pressure monitoring data. Mine environments are complex and changeable, and data collected by sensors in different locations and types often have different signal quality, accuracy, and stability. Therefore, how to efficiently fuse data from different sensors and accurately analyze their mine pressure change trends is the basis for ensuring safety warnings.

[0096] In this embodiment, the weighted average method is first used to fuse the mine pressure data from different sensors and monitoring points. The core idea of ​​the weighted average method is to assign different weights according to the signal quality and stability of each sensor. Specifically, during the data acquisition process, the quality parameters of each data source, such as the signal-to-noise ratio, the calibration status of the sensor, the measurement stability, etc., are calculated in real time to comprehensively evaluate the data reliability of the sensor. Based on these quality assessment results, the weight of each sensor is dynamically adjusted to ensure that sensors with higher signal quality have a greater impact on the final data fusion results. For example, in some cases, some sensors may experience unstable signals due to harsh working environments or aging equipment. At this time, the system will automatically reduce the weight of the sensor to avoid excessive impact on the mine pressure monitoring results.

[0097] After data fusion is complete, the resulting mine pressure data is fed into an intelligent analysis system. This system uses deep learning-based prediction models, such as the Long Short-Term Memory (LSTM) network, to analyze and predict mine pressure data. LSTM networks have powerful time series data processing capabilities, enabling them to capture the long-term dependencies and complex patterns of mine pressure changes. They are particularly well-suited for predicting medium- to long-term trends and cyclical patterns in mine pressure data.

[0098] Using deep learning models, the intelligent analysis system can identify potential risks within mine workings and predict trends in mine pressure. For example, based on historical data, the system can identify abnormal phenomena such as rapid and sudden increases in mine pressure, providing early warnings to miners and managers. Combining the model's predictions, the system generates a mine safety risk assessment report for mine managers, helping them make informed safety decisions.

[0099] Furthermore, the intelligent analysis system's predictions are continuously self-optimizing. The system dynamically updates model parameters based on the mine's actual production conditions, changes in the working surface environment, and actual data feedback. Through continuous learning and adjustment, the system accurately perceives the mine environment and continuously improves the accuracy of mine pressure predictions.

[0100] Through efficient data fusion and intelligent analysis, this implementation method can evaluate the changing trend of mine pressure in real time during mine operation, predict potential safety risks, and provide an accurate basis for subsequent early warning decisions, thereby effectively improving the mine's safe production capabilities. Specific implementation 3:

[0102] In the intelligent perception and safety warning method for mine pressure in fully mechanized coal mining working faces, the safety warning and response mechanism is an important part of ensuring mine safety production. Its core goal is to be able to promptly identify potential safety risks such as sudden changes and abnormal fluctuations in mine pressure, and to take appropriate warning and emergency response measures based on the prediction results to reduce the occurrence of accidents. This implementation method combines a deep learning prediction model, a mine pressure risk assessment system, and an automated safety response mechanism to achieve efficient and intelligent safety management in the coal mine production process, ensuring that the mine can respond promptly to emergencies and effectively deal with potential safety threats.

[0103] The first step in this implementation is to accurately predict the changing trends of mine pressure using a deep learning model. Deep learning models, particularly long short-term memory (LSTM) networks, have excellent time series data processing capabilities and can deeply analyze the long-term dependencies and complex patterns in historical mine pressure data. In this system, historical mine pressure data serves as the basis for model training. Through continuous learning, the model can identify the changing patterns of mine pressure and predict the fluctuation trend of mine pressure over a period of time.

[0104] This deep learning-based prediction method not only handles short-term fluctuations in mine pressure but also identifies potential risks, such as sudden increases in mine pressure in localized areas and complex issues like rock shifting. By continuously optimizing the training model, the system adapts to the dynamic changes in the mine working face and improves prediction accuracy. For example, the deep learning model can predict the continued increase in mine pressure and assess its rate and magnitude, providing timely warnings of the possibility of abnormal mine pressure.

[0105] Based on the predicted trends in mine pressure using deep learning models, the system sets multiple mine pressure safety thresholds to assess the safety risk level of the working face in real time. Mine pressure fluctuations are influenced by a variety of factors, including mining progress, support structure stability, and changing geological conditions. Therefore, mine pressure safety thresholds are not static but are adjusted based on real-time data and the dynamic environment.

[0106] To accurately assess the risk level of mine pressure, the system conducts a comprehensive analysis based on multiple factors, including historical data, real-time monitoring information, and mine geological conditions. For example, after a certain stage of mining, mine pressure may naturally increase, and the stability of the support structure determines the tolerance for this increase. The system combines these factors and dynamically calculates a mine pressure risk assessment value through methods such as weighted averaging and trend analysis. Based on this value, the mine pressure is classified into three levels: normal, warning, and dangerous.

[0107] Once the mine pressure exceeds the preset normal range and enters the warning zone, the system automatically triggers a safety alarm based on the risk level. This warning signal is not only displayed to miners and managers through a visual interface, but also transmitted in real time via wireless communication systems to various safety monitoring points within the mine, ensuring that all relevant personnel are informed and can take action in a timely manner.

[0108] When the mine pressure reaches the warning threshold and triggers an alarm, the system automatically activates a safety response mechanism. This mechanism monitors the mine environment in real time and automatically generates a corresponding safety response plan based on the actual conditions of the mine working face and support structure. Abnormal mine pressure is more than just a simple numerical change; it can indicate potential danger to the mine structure and even be a precursor to a mine collapse or other catastrophic accident. Therefore, the system's response measures need to be highly flexible and targeted.

[0109] The system's emergency response plan includes but is not limited to the following situations:

[0110] Adjust the support structure: When the mine pressure in a local area of ​​the mine increases, the system will analyze the stability of the support structure and recommend measures such as strengthening the support structure and adding support points based on the actual situation to reduce the impact of the mine pressure on the working face.

[0111] Suspend operations: If mine pressure continues to rise and reaches the warning level, the system will recommend suspending related operations, especially in high-risk areas, to ensure miner safety. At the same time, the system will automatically stop the operation of related machinery to prevent accidents caused by equipment failure or sudden increases in mine pressure.

[0112] Evacuate personnel: When mine pressure reaches dangerous levels, the system triggers a higher-level safety alarm and initiates evacuation procedures. By monitoring miners' geographic locations in real time, the system can accurately identify the affected area and guide miners to evacuate quickly through safe passages.

[0113] These emergency response measures, combined with real-time mine data, form a fully automated, intelligent safety early warning and response system. The system's high degree of automation enables immediate response to abnormal mine pressure, minimizing human interference and improving response speed and accuracy.

[0114] To further enhance mine safety management, this implementation incorporates a feedback mechanism for mine managers. After receiving early warning information, miners and managers can provide feedback on the system's safety response and record the implementation of various emergency measures during actual operation. Based on this feedback data, the system continuously optimizes early warning rules and response strategies, ensuring that over the long term, the system can make more accurate safety decisions based on the actual conditions of the mine.

[0115] Furthermore, the system can analyze big data to uncover patterns in historical accidents. This, combined with current mine conditions and production conditions, can further improve mine pressure prediction models and early warning rules, enhancing prediction accuracy and response sensitivity. For example, the system can analyze historical data from different mines and mining methods to generate different mine safety models, providing more precise safety management solutions for future mine operations.

[0116] The core advantage of this implementation lies in the system's full automation and intelligence. By using a deep learning model to predict and analyze mine pressure, combined with dynamic adjustments to real-time data, the system can assess mine pressure risk levels in real time during mine operations and automatically trigger safety warnings and response measures when necessary. Miners and managers only need to take appropriate measures based on the system's warning signals and emergency response recommendations, eliminating the need for manual judgment. This significantly improves the efficiency and accuracy of mine safety management.

[0117] As the mining environment changes, the system's intelligent algorithms continuously adapt to new working faces, support structures, and geological conditions. Through self-learning and self-optimization, the system ensures effective safety warnings even when the mine environment changes. This highly intelligent system not only significantly improves mine safety but also provides more reliable support for coal mine production, ensuring the safety of miners and the stable operation of equipment.

[0118] This implementation enables real-time monitoring and accurate prediction of mine pressure changes in fully mechanized coal mining working faces, effectively preventing and responding to safety incidents such as sudden increases in mine pressure and mine collapses. The system, with its high degree of automation and intelligence, dynamically adjusts safety thresholds and emergency measures based on the mine's actual conditions, maximizing miner safety. This safety early warning and response mechanism not only significantly reduces the risk of human intervention and decision-making errors, but also improves the mine's overall safety management, resulting in significant economic and social benefits.

[0119] The present invention significantly improves the accuracy and reliability of mine pressure monitoring in mines through the combined use of multiple sensors and real-time monitoring of the data acquisition system. Different types of sensors (such as pressure, displacement and temperature sensors) can fully cover the working surface and the surrounding environment, and realize the synchronous monitoring of mine pressure, displacement and temperature changes. This multi-dimensional data acquisition method enables the mine pressure changes in the mine to be comprehensively and accurately recorded and analyzed, avoiding the monitoring errors caused by failure of a single sensor or changes in the local environment. At the same time, the data preprocessing stage performs denoising and interpolation processing on the collected data, effectively solving the data missing or anomaly caused by sensor failure or environmental influences, ensuring the integrity and consistency of the data, and thus providing more reliable basic data for subsequent intelligent analysis.

[0120] The present invention greatly improves the ability to predict mine safety risks through the introduction of intelligent analysis and deep learning models. The deep learning neural network (such as the LSTM model) used can deeply analyze historical mine pressure data, capture the potential laws in mine pressure changes, and can make effective predictions based on real-time data. Through the long-term dependency analysis of mine pressure data, the system can identify abnormal conditions such as sudden increases and mutations in mine pressure, and issue early warnings before problems arise. This predictive capability enables mines to issue early warnings before dangerous changes in mine pressure occur, greatly reducing the risk of catastrophic accidents in mines. Through continuous learning and optimization of the intelligent analysis system, the prediction model can continuously adapt to the new mine environment, improve the accuracy of mine pressure prediction, and provide strong technical support for mine safety management.

[0121] The present invention integrates safety warning and response mechanisms to ensure that mines can respond quickly to sudden changes in mine pressure, thereby avoiding catastrophic accidents. When the system predicts abnormal mine pressure through intelligent analysis, it will automatically trigger a multi-level warning and provide corresponding safety response measures according to different warning levels. Miners and managers can receive the warning signal in the first time and take corresponding actions based on the safety disposal plan provided by the system. This mechanism not only speeds up the warning response, but also further optimizes the emergency response strategy through real-time feedback interaction with the mine production status. Through this automated and intelligent safety management system, the safety level of the mine has been significantly improved, and the life safety of miners and the safety of production facilities have been better guaranteed.

[0122] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0123] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

[0124] It should be noted that, in this document, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0125] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0126] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0127] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0128] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0129] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit.

[0130] The above merely describes some exemplary embodiments of the present application, but the protection scope of the present application is not limited thereto, any modification or replacement not easily thought of by those skilled in the art within the technical range disclosed by the present application should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0131] The above only describes some exemplary embodiments of the present application by way of illustration, and it is needless to say that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present application. Therefore, the above drawings and description are illustrative in nature and should not be understood as limiting the protection scope of the claims of the present application.

Claims

1. A method for intelligently sensing and providing safety warnings for mine pressure in a fully mechanized caving working face of a coal mine, characterized in that: The following steps are involved: Deploy mine pressure monitoring equipment in fully mechanized caving working faces in coal mines, use a variety of sensing technologies to obtain mine pressure-related data, and standardize each data source to ensure a unified data format; De-noise, remove outliers, and align the time series of the collected mine pressure data to build a complete and reliable mine pressure data set, improving data quality and consistency; Based on the adaptive weighted fusion algorithm, the data from different sensors and monitoring points are calculated and processed, and the weight of each data source is dynamically adjusted to improve the accuracy and stability of the mine pressure monitoring data; A machine learning model is combined with historical mine pressure data to build a deep neural network analysis model to predict mine pressure change trends and calculate the safety risk level of each area of ​​the working face; Based on the prediction results, multiple mine pressure thresholds are set. Combined with the working face support status and geological conditions, the safety warning level is determined and the alarm mechanism is triggered when the warning conditions are met. After the early warning is triggered, a safety disposal plan is automatically generated and pushed to miners and managers. At the same time, the analysis model is continuously optimized based on feedback data to improve the accuracy of the early warning and response efficiency.

2. The method for intelligent sensing and safety early warning of mine pressure in a fully mechanized caving working face of a coal mine according to claim 1 is characterized in that: During the data collection process, the mine pressure monitoring equipment includes pressure sensors, displacement sensors, and temperature sensors. These sensors synchronously monitor changes in the mine pressure, displacement, and temperature of the working face to fully capture the impact of mine pressure, stratum changes, and temperature on mine pressure. Among them, pressure sensors are used to measure the working face mine pressure in real time, displacement sensors are used to monitor the working face displacement caused by stratum changes, and temperature sensors are used to monitor the impact of the coal mine ambient temperature on the mine pressure; the multi-dimensional data acquisition solution can more accurately reflect the actual pressure environment of the mine, avoid the errors caused by single sensor data, and ensure the comprehensiveness and accuracy of subsequent analysis data.

3. The method for intelligent sensing and safety early warning of mine pressure in a fully mechanized caving working face of a coal mine according to claim 1 is characterized in that: The data preprocessing step further includes data cleaning and interpolation of the collected data to eliminate invalid data and fill in blank data points caused by sensor failure or data loss; Data cleaning eliminates abnormal mine pressure data by setting thresholds and ranges, and uses interpolation methods to supplement missing mine pressure data to ensure the integrity and consistency of the data set.

4. The method for intelligent sensing and safety early warning of mine pressure in a fully mechanized caving working face of a coal mine according to claim 1 is characterized in that: The multi-source data fusion step uses a weighted average method, which dynamically adjusts the weights of different data sources so that data sources with higher weights have a greater impact on the final analysis results. The data fusion method includes the following steps: first, setting initial weight values ​​based on the performance differences of each sensor under different working faces and mining conditions; second, monitoring the stability and accuracy of each data source in real time, and adjusting the weights according to the signal quality; finally, using a weighted average algorithm to integrate the information from all data sources to generate accurate mine pressure data.

5. The method for intelligently sensing and providing safety warning for mine pressure in a fully mechanized caving working face of a coal mine according to claim 1 is characterized in that: The specific steps of multi-source data fusion are as follows: For each data source, the weight is adjusted based on the real-time data quality and stability. The weight calculation expression is as follows: ， Where W i is the weight of the i-th data source, Q i is the quality coefficient of the i-th data source, S i is the stability coefficient of the i-th data source, and n is the total number of data sources; All sensor data are weighted averaged to generate mine pressure data. The generation formula is as follows: ， Where, P is the fused mine pressure data result, X i is the mine pressure data collected by the i-th data source; Update data quality and stability in real time and dynamically adjust weight W i , ensuring that data fusion results are continuously optimized as the environment changes and data quality fluctuates.

6. The method for intelligently sensing and providing safety warning for mine pressure in a fully mechanized caving working face of a coal mine according to claim 5 is characterized in that: The intelligent analysis and prediction step trains and predicts the mine pressure data by establishing a deep learning neural network model. The neural network model includes a multi-layer perceptron and a long short-term memory network to accurately predict the changing trends and anomalies of mine pressure in complex geological environments. The deep learning model uses multiple layers of neurons to model historical mine pressure data, extracts potential regularities, and uses the LSTM network to learn time series data to further improve the accuracy of mine pressure prediction.

7. The method for intelligently sensing and providing safety warning for mine pressure in a fully mechanized caving working face of a coal mine according to claim 1 is characterized in that: The specific steps for intelligent analysis and prediction of mine pressure change trends are as follows: Based on the historical mine pressure data, an autoregressive moving average model is constructed to calculate the predicted value of the mine pressure. The calculation expression is as follows: ， Where, is the predicted mine pressure value at time t, φ1, φ2, ..., φ k is the autoregressive parameter of the ARMA model, P t-1 , P t-2 ,……,P t-k is the mine pressure data at the historical moment, from t-1 to tk, where k is the number of time periods of historical data, θ1, θ2, ..., θ k is the moving average parameter of the ARMA model, ∈ t-1 ,∈ t-2 、……、∈ t-k is the prediction residual; Combining historical data and the prediction results of the LSTM neural network, further optimization is performed to obtain the final mine pressure prediction result. The calculation expression is as follows: ， Where, is the final predicted rock pressure value, f LSTM (P t-1 、P t-2 ,……,P t-k ) is the predicted value of mine pressure generated by LSTM model, λ is the weighted coefficient of LSTM and ARMA prediction results, is the predicted value of mine pressure generated by the ARMA model; By combining the output results of ARMA and LSTM networks with the synthetic model, accurate mine pressure prediction values ​​are obtained.

8. The method for intelligently sensing and providing safety warning for mine pressure in a fully mechanized caving working face of a coal mine according to claim 1 is characterized in that: The safety warning determination step adopts a hierarchical warning system based on multi-level thresholds, which divides the mine pressure value into multiple safety levels. It also combines the working face support situation, ore layer thickness, and historical data factors to dynamically calculate the risk level and issue corresponding warning signals; Threshold setting: According to the historical distribution of mine pressure monitoring data and the actual mine engineering conditions, the normal range, warning range and danger range of mine pressure values ​​are set; The safety warning system compares the current mine pressure value with the set threshold to determine whether the mine is in a safe state. If the mine pressure value exceeds the safety threshold, an alarm will be issued in time to prompt management personnel to take emergency protective measures.

9. The method for intelligently sensing mine pressure and providing safety warning for a fully mechanized caving working face in a coal mine according to claim 1 is characterized in that: The safety early warning determination step further includes comprehensively determining the safety risk of mine pressure by setting a dynamic safety factor in combination with the coal mining progress and support status of the working face; The dynamic safety factor is adjusted according to the actual coal mining progress and changes in support conditions. By setting the safety factor threshold, when the result of combining the actual mine pressure value with the safety factor exceeds the preset safety range, a safety warning is automatically issued to remind miners to take protective measures in time to prevent catastrophic events in the mine.

10. The method for intelligently sensing and providing safety warning for mine pressure in a fully mechanized caving working face of a coal mine according to claim 1, characterized in that: The early warning response and optimization steps further include adjusting the parameters in the mine pressure prediction model based on feedback data from miners and managers to continuously optimize system performance; Feedback data includes but is not limited to feedback from miners after an accident, changes in the mine environment, and equipment conditions.

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