A coal mine disaster simulation and prediction system based on multi-dimensional data collection

Abstract

The present invention discloses a coal mine disaster simulation prediction system based on multi-dimensional data collection, which relates to the field of coal mine safety technology. A variety of monitoring equipment such as geological radar and microseismic monitoring are integrated to build a real-time monitoring network covering key areas of coal mines, and comprehensively collect multi-dimensional data. The system includes modules such as data preprocessing, model construction and training, prediction and decision-making, early warning and emergency response, and user interaction interface. Data is processed through data cleaning, filtering, standardization and fusion technology, and disaster prediction is performed using a CNN and LSTM hybrid model to output disaster probability and risk areas, and automatically trigger early warning and emergency plans. The user interaction interface displays real-time monitoring, prediction results and early warning information, providing a comprehensive and intelligent solution for coal mine disaster prevention and control.

Description

Technical Field

[0001] The invention belongs to the technical field of coal mine safety, and in particular relates to a coal mine disaster simulation prediction system based on multi-dimensional data collection. Background Art

[0002] As one of the important pillars of the global economy, coal mine safety issues have always attracted much attention. However, due to the complex geological conditions of coal mines, the harsh mining environment, and human factors, coal mine disasters and accidents occur frequently, causing huge losses to people's lives and property. Traditional coal mine disaster early warning methods mainly rely on experience judgment and single monitoring data, such as relying only on gas concentration monitoring or geological condition analysis. These methods are difficult to fully and accurately reflect the actual situation of coal mine disasters, and there are problems such as untimely warnings and insufficient accuracy.

[0003] In recent years, with the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, new technical means have been provided for coal mine disaster warning. Some existing patent documents have also attempted to apply these technologies to coal mine disaster warning systems. For example, patent document CN106092095A proposes a coal mine safety monitoring system based on the Internet of Things. The system monitors the coal mine environment in real time by integrating multiple sensors, but it mainly focuses on data collection and display, and lacks in-depth data analysis and disaster prediction functions. For another example, patent document CN107702571A proposes a coal mine gas disaster warning method based on deep learning. Although deep learning technology is used to predict disasters, only the single-dimensional data of gas concentration is considered, ignoring other factors that may affect the occurrence of disasters.

[0004] In order to overcome the shortcomings of the existing technology, the present invention proposes a coal mine disaster simulation prediction system based on multi-dimensional data collection. The system not only integrates a variety of monitoring equipment to achieve multi-dimensional real-time monitoring of the coal mine environment, but also uses advanced technologies such as deep learning to conduct in-depth analysis and processing of the collected data to achieve accurate prediction and effective prevention and control of coal mine disasters. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a coal mine disaster simulation prediction system based on multi-dimensional data collection. The system integrates multiple monitoring devices to build a real-time monitoring network covering key areas of coal mines, and realizes the comprehensive collection of multi-dimensional data. At the same time, the collected data is processed and analyzed using a deep learning model to predict the probability of disaster occurrence and potential risk areas, providing a scientific basis for disaster prevention and control.

[0006] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0007] The present invention is a coal mine disaster simulation prediction system based on multi-dimensional data collection, comprising:

[0008] The multi-dimensional data collection module integrates a variety of monitoring devices to build a real-time monitoring network covering all key areas of the coal mine, realizing the comprehensive collection of multi-dimensional data;

[0009] The data preprocessing module uses data cleaning algorithms to remove redundant and erroneous data, applies filtering technology to reduce noise interference, uses standardization methods to unify data formats and dimensions, and integrates data from different sources into a unified data set through data fusion technology;

[0010] Model building and training module: select the deep learning framework, build a hybrid model based on convolutional neural network (CNN) and long short-term memory network (LSTM), and use historical disaster data and real-time monitoring data to train and verify the model;

[0011] The prediction and decision-making module inputs the preprocessed data into the trained prediction model, outputs the probability of disaster occurrence and potential risk areas, and formulates disaster prevention and control measures and emergency plans based on expert knowledge and the actual situation of the coal mine;

[0012] The early warning and emergency response module automatically triggers the early warning system and initiates the corresponding emergency plan based on the prediction results when the probability of a disaster exceeds the preset threshold;

[0013] The user interaction interface displays real-time monitoring data, prediction results, warning information and emergency plans. As a preferred technical solution of the present invention, the multiple detection equipment includes but is not limited to multiple sensors including geological radar, microseismic monitoring, gas detection and video monitoring, and the multi-dimensional data collected includes but is not limited to geological structure, stress distribution, gas concentration, equipment status, and personnel location during coal mine production.

[0014] As a preferred technical solution of the present invention, when the multi-dimensional data collection module is arranged, sensors are deployed in multiple levels according to different disaster types and risk levels to achieve refined management of key areas. At the same time, a sensor linkage mechanism is established. When a sensor is abnormal, other related sensors are automatically triggered for collaborative monitoring.

[0015] As a preferred technical solution of the present invention, the multidimensional data collection module also includes the selection and optimization of the sensor deployment position, and the adjustment of the monitoring range and sensitivity of the sensor according to the monitoring requirements, wherein the optimization of the sensor deployment position follows the following objective function:

[0016]

[0017] P represents the set of sensor deployment locations, N is the number of sensors, wi is the weight of sensor i, d(Pi,Si) is the distance between the deployment location Pi of sensor i and its monitoring target Si, σi is the standard deviation of the monitoring range of sensor i, and this objective function aims to minimize the sum of the weighted distances between the sensor and the target to optimize monitoring coverage and accuracy.

[0018] As a preferred technical solution of the present invention, the data preprocessing module also includes data quality control and data integrity verification to ensure the accuracy and reliability of the data, wherein the data quality control adopts the following outlier detection algorithm:

[0019]

[0020] Among them, xi is the data point, μ is the mean of the data set, σ is the standard deviation, and when Score(xi) exceeds the preset threshold, xi is considered to be an outlier.

[0021] As a preferred technical solution of the present invention, the data preprocessing module further includes:

[0022] Confirm the weight of multi-dimensional data by using statistical methods, machine learning algorithms or expert experience to perform risk shadowing and weight confirmation on multi-dimensional data;

[0023] Confirmation of data collection frequency: According to the actual situation of the coal mine and the early warning needs of gas disasters, set a reasonable data collection order and frequency based on the real-time, stability and importance of the data.

[0024] As a preferred technical solution of the present invention, the model building and training module also includes model verification and model evaluation, wherein the model verification adopts a cross-validation method and the evaluation indicator adopts a weighted F1 score evaluation.

[0025] As a preferred technical solution of the present invention, the model construction and training module also includes model iteration: according to the model's prediction results and actual disaster conditions, the model is iteratively optimized, and the model parameters and structure are continuously adjusted to improve the model's prediction accuracy and stability.

[0026] As a preferred technical solution of the present invention, in the early warning and emergency response module, when the probability of a disaster exceeding a preset threshold, data reconfirmation is required before the corresponding emergency plan is initiated. The specific data reconfirmation includes:

[0027] Related data collection: when a key data deviates, other data related to the data should be collected immediately for comprehensive analysis and judgment;

[0028] Increase the frequency of data collection. When data deviates from the threshold or abnormal data is detected, increase the frequency of data collection to obtain data change information in a more timely manner.

[0029] Data quality check: When new data is re-collected, strict quality checks should be carried out on data integrity, consistency, and accuracy.

[0030] As a preferred technical solution of the present invention, it also includes a data security and real-time guarantee module, specifically:

[0031] During data transmission and storage, advanced encryption technology is used to ensure data security.

[0032] Establish data backup and recovery mechanisms to prevent data loss or damage,

[0033] By optimizing data collection, transmission and processing processes, the real-time nature of data can be ensured so that potential risks and problems can be discovered in a timely manner.

[0034] The present invention has the following beneficial effects:

[0035] The present invention integrates multiple sensors such as geological radar and microseismic monitoring to comprehensively and in real time collect multi-dimensional data of the coal mine environment, thus overcoming the problem of incomplete early warning caused by a single data source; the deep learning model is used to deeply analyze multi-dimensional data, which significantly improves the accuracy and stability of disaster prediction and realizes the accurate identification of potential risk areas; at the same time, the system can automatically trigger early warnings and start emergency plans, effectively preventing and reducing the occurrence of coal mine disasters and ensuring safe production in coal mines. This technical solution not only optimizes the deployment of sensors and the preprocessing process of data, but also ensures the security and real-time nature of data, providing a more comprehensive, reliable and intelligent solution for coal mine disaster prevention and control.

[0036] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0038] Figure 1 This is a schematic diagram of the structure of this system. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Embodiment 1

[0041] A coal mine disaster simulation prediction system based on multi-dimensional data collection includes: a multi-dimensional data collection module, which integrates multiple monitoring devices to build a real-time monitoring network covering key areas of coal mines and realizes comprehensive collection of multi-dimensional data; a data preprocessing module, which uses a data cleaning algorithm to remove redundant and erroneous data, applies filtering technology to reduce noise interference, uses a standardized method to unify data formats and dimensions, and integrates data from different sources into a unified data set through data fusion technology; a model construction and training module, which selects a deep learning framework to build a hybrid model based on a convolutional neural network (CNN) and a long short-term memory network (LSTM), and uses historical disaster data and real-time monitoring data to train and verify the model; a prediction and decision module, which inputs the preprocessed data into a trained prediction model, outputs the probability of disaster occurrence and potential risk areas, and formulates disaster prevention and control measures and emergency plans in combination with expert knowledge and actual coal mine conditions; an early warning and emergency response module, which automatically triggers an early warning system and starts a corresponding emergency plan according to the prediction results when the probability of disaster occurrence exceeds a preset threshold; and a user interaction interface, which displays real-time monitoring data, prediction results, early warning information and emergency plans.

[0042] The coal mine disaster simulation prediction system in this embodiment realizes effective prediction and management of coal mine disasters through the following key modules:

[0043] Multidimensional data collection module: This module is the data foundation of the entire system. It builds a comprehensive monitoring network by integrating monitoring equipment such as earthquake monitoring, gas detection, hydrological observation, and video monitoring. This network can collect environmental parameters and activity status of key areas in coal mines in real time, providing raw data for subsequent data analysis and model training.

[0044] Data preprocessing module: After collecting a large amount of data, the data preprocessing module is responsible for cleaning and organizing the data. The data cleaning algorithm can remove invalid and erroneous data, the filtering technology is used to reduce the random noise in the monitoring data, and the standardization method ensures the consistency of all data in format and dimension. Data fusion technology integrates data from different monitoring devices into a unified data set, providing high-quality data input for model training.

[0045] Model building and training module: This module uses a deep learning framework that combines the advantages of convolutional neural networks (CNN) and long short-term memory networks (LSTM). CNN is good at processing spatial features, while LSTM is suitable for analyzing time series data. By using historical disaster data and real-time monitoring data, the system trains and verifies the model to improve the model's prediction accuracy.

[0046] Prediction and decision-making module: The preprocessed data is input into the trained model, and the model outputs the probability of disaster occurrence and potential risk areas. Combining expert experience and the specific conditions of the coal mine, this module can formulate corresponding disaster prevention and control measures and emergency plans.

[0047] Early warning and emergency response module: When the prediction results show that the probability of a disaster exceeds the safety threshold, this module will automatically trigger the early warning system and activate the pre-set emergency plan, thereby achieving a rapid response to the disaster.

[0048] User interaction interface: This interface provides an intuitive monitoring platform for coal mine managers, which can display monitoring data, prediction results, early warning information and emergency plans in real time, so that managers can make timely decisions.

[0049] Through the implementation of this system, coal mining enterprises can more scientifically and accurately predict and prevent disaster risks, protect the lives of miners, and ensure safe production in coal mines.

[0050] Embodiment 2

[0051] Based on the first embodiment, the difference of this embodiment is that:

[0052] The various detection equipment includes but is not limited to geological radar, microseismic monitoring, gas detection and video surveillance, and other sensors. The multi-dimensional data collected includes but is not limited to geological structure, stress distribution, gas concentration, equipment status, and personnel location during the coal mine production process.

[0053] When deploying the multi-dimensional data collection module, sensors are deployed in multiple levels according to different disaster types and risk levels to achieve refined management of key areas. At the same time, a sensor linkage mechanism is established. When a sensor is abnormal, other related sensors are automatically triggered for coordinated monitoring. The multi-dimensional data collection module also includes the selection and optimization of sensor deployment locations, as well as the adjustment of sensor monitoring range and sensitivity according to monitoring requirements. The optimization of sensor deployment locations follows the following objective function:

[0054]

[0055] P represents the set of sensor deployment locations, N is the number of sensors, wi is the weight of sensor i, d(Pi,Si) is the distance between the deployment location Pi of sensor i and its monitoring target Si, σi is the standard deviation of the monitoring range of sensor i. This objective function aims to minimize the sum of the weighted squares of the distances between the sensor and the target to optimize the monitoring coverage and accuracy.

[0056] In the coal mine production process, the accuracy and reliability of the disaster monitoring system are crucial to ensuring production safety. Example 1 has introduced a coal mine disaster simulation prediction system based on multidimensional data collection, and Example 2 further optimizes the multidimensional data collection module on this basis to improve the monitoring effect of the system. The specific implementation details are as follows:

[0057] 1. Integration of multiple testing equipment

[0058] The multi-dimensional data collection module in the second embodiment integrates a variety of detection equipment, including but not limited to geological radar, microseismic monitor, gas sensor, video surveillance camera, etc. These devices can collect multi-dimensional data in the coal mine production process in real time through built-in sensors.

[0059] Geological radar: used to detect underground geological structures in coal mines, such as faults and folds, to assess potential geological disaster risks;

[0060] Microseismic monitor: used to monitor the tiny vibrations generated during coal mining to analyze changes in rock stress and possible disasters such as rock bursts;

[0061] Gas sensor: used to monitor gas concentrations in coal mines, such as methane and carbon monoxide, to prevent gas explosions and poisoning accidents;

[0062] Video surveillance camera: used to monitor personnel activities, equipment status, etc. at the coal mine production site in real time to detect potential safety hazards.

[0063] 2. Collection of Multidimensional Data

[0064] Through the above detection equipment, the multi-dimensional data collection module can collect multi-dimensional data including but not limited to geological structure, stress distribution, gas concentration, equipment status and personnel location, etc. These data provide a comprehensive information basis for subsequent disaster warning and emergency response.

[0065] Geological structure data: reflects the geological characteristics of the underground coal mine and helps to assess the risk of geological disasters;

[0066] Stress distribution data: reflects the changes in rock stress and helps predict the occurrence of disasters such as rock bursts;

[0067] Gas concentration data: reflects the gas concentration in coal mines in real time, which helps prevent gas explosions and poisoning accidents;

[0068] Equipment status data: reflects the operating status of coal mine production equipment, which helps to timely discover equipment failures and safety hazards;

[0069] Personnel location data: The real-time location of coal mine workers is located, which helps in emergency evacuation and rescue when a disaster occurs.

[0070] 3. Multi-level hierarchical deployment and sensor linkage mechanism

[0071] In order to further improve the monitoring effect, the multi-dimensional data collection module in the second embodiment adopts a multi-level hierarchical deployment and sensor linkage mechanism.

[0072] Multi-level deployment: Sensors are deployed in multiple levels according to different disaster types and risk levels. More sensors are deployed in high-risk areas to achieve refined management of key areas. At the same time, the monitoring range and sensitivity of the sensors are adjusted to ensure the optimization of the monitoring effect.

[0073] Sensor linkage mechanism: When a sensor is abnormal, it automatically triggers other related sensors for coordinated monitoring. For example, when a gas sensor detects an abnormal gas concentration, it can trigger a video surveillance camera for real-time monitoring to confirm whether there are any safety hazards. This linkage mechanism can enhance the system's response speed and accuracy, and detect and deal with potential disasters in a timely manner.

[0074] 4. Selection and Optimization of Sensor Deployment Locations

[0075] The selection and optimization of the sensor deployment position is the key to ensure the monitoring effect. The multi-dimensional data collection module in the second embodiment uses an objective function to optimize the sensor deployment position.

[0076] Objective function: It takes into account the number of sensors, sensor weights, the distance between the sensor deployment location and its monitoring target, and the standard deviation of the sensor's monitoring range, aiming to minimize the weighted sum of the squares of the distance between the sensor and the target. By adjusting these parameters, the optimal sensor deployment solution can be found to achieve the best monitoring effect.

[0077] Through the above optimization measures, the multidimensional data collection module in Example 2 can more accurately collect multidimensional data in the coal mine production process, providing more comprehensive information support for subsequent disaster warning and emergency response. At the same time, the application of multi-level hierarchical deployment and sensor linkage mechanism also improves the response speed and accuracy of the system, helps to timely discover and deal with potential disasters, and ensure the safety and stability of coal mine production.

[0078] Embodiment 3

[0079] The data preprocessing module also includes data quality control and data integrity verification to ensure the accuracy and reliability of the data. The data quality control uses the following outlier detection algorithm:

[0080]

[0081] Among them, xi is the data point, μ is the mean of the data set, σ is the standard deviation, and when Score(xi) exceeds the preset threshold, xi is considered to be an outlier.

[0082] The data preprocessing module also includes: weight confirmation of multidimensional data, using statistical methods, machine learning algorithms or expert experience to confirm the risk shadow and weight of multidimensional data; data collection frequency confirmation: according to the actual situation of the coal mine and the early warning needs of gas disasters, set a reasonable data collection sequence and frequency according to the real-time, stability and importance of the data. The model building and training module also includes model verification and model evaluation, in which the model verification adopts the cross-validation method and the evaluation indicator adopts the weighted F1 score evaluation. The model building and training module also includes model iteration: according to the model's prediction results and actual disaster conditions, the model is iteratively optimized, and the model parameters and structure are continuously adjusted to improve the model's prediction accuracy and stability.

[0083] In the coal mine disaster monitoring system, data preprocessing and model building are two crucial links. Data preprocessing can ensure that the data input into the model is accurate and reliable, while model building determines whether the system can accurately predict and warn disasters. Example 3 further optimizes the data preprocessing module and the model building and training module on the basis of the previous one.

[0084] 1. Optimization of data preprocessing module

[0085] Data quality control:

[0086] In order to ensure the accuracy and reliability of the data, the data preprocessing module introduces a data quality control link, which uses an outlier detection algorithm to identify and process outliers in the data set.

[0087] The outlier detection algorithm is based on the statistical characteristics of data points. For each data point, the algorithm calculates its distance from the mean of the data set (in units of standard deviation), that is, the Score(xi) value. When the Score(xi) value exceeds the preset threshold, the data point is considered an outlier and may be deleted, replaced, or corrected.

[0088] Data integrity check: Data integrity check is used to ensure that there are no missing or duplicate values ​​in the data set to ensure data consistency and integrity. During the verification process, the system will check the existence, uniqueness and rationality of each data point to ensure that the data set does not contain invalid or redundant information.

[0089] Weight confirmation of multidimensional data:

[0090] Multidimensional data have different importance in disaster prediction. In order to make full use of these data, the data preprocessing module uses statistical methods, machine learning algorithms or expert experience to determine the risk shadow (that is, its contribution to disaster prediction) and weight of each data dimension; through weight confirmation, the system can more accurately evaluate the impact of different data dimensions on disaster prediction, thereby improving the accuracy of the prediction.

[0091] Confirmation of data collection frequency: The frequency of data collection is crucial to the real-time and accuracy of disaster warning. According to the actual situation of the coal mine and the warning needs of gas disasters, the data preprocessing module will set a reasonable data collection sequence and frequency according to the real-time, stability and importance of the data. By optimizing the frequency of data collection, the system can obtain key data more efficiently and provide timely and accurate information support for disaster warning.

[0092] 2. Model construction and optimization of training modules

[0093] Model Validation and Model Evaluation:

[0094] In the process of model construction and training, model verification and evaluation are key steps to ensure model performance. Example 3 uses a cross-validation method to verify the stability and generalization ability of the model.

[0095] The evaluation indicator uses the weighted F1 score, which comprehensively considers the precision and recall of the model and can more comprehensively evaluate the performance of the model.

[0096] Through model verification and evaluation, the system can promptly identify problems with the model and make adjustments to improve the model's prediction accuracy and stability.

[0097] The model building and training module in Example 3 includes a model iteration step. Based on the model's prediction results and actual disaster situations, the system will iteratively optimize the model and continuously adjust the model parameters and structure to improve prediction accuracy. Through continuous model iteration, the system can continuously learn and adapt to new data and environmental changes, thereby improving the accuracy and reliability of disaster warnings.

[0098] Through the above optimization measures, the coal mine disaster monitoring system in Example 3 can more accurately collect and process multidimensional data and build a prediction model with better performance. This not only improves the prediction accuracy and stability of the system, but also provides a stronger technical guarantee for coal mine production safety. At the same time, with the continuous iteration and optimization of the system, its adaptability and robustness will be further improved.

[0099] Embodiment 4

[0100] Based on the first embodiment, the difference of this embodiment is that:

[0101] In the early warning and emergency response module, when the probability of a disaster exceeds the preset threshold, data reconfirmation is required before the corresponding emergency plan is initiated. The specific data reconfirmation includes:

[0102] Related data collection: when a key data deviates, other data related to the data should be collected immediately for comprehensive analysis and judgment; increase the collection frequency: when the data deviates from the threshold or abnormal data is detected, increase the collection frequency of related data to obtain data change information more timely; data quality check: when the new data is re-collected, strict data integrity, consistency, and accuracy quality checks should be carried out.

[0103] At the same time, the present invention also includes a data security and real-time guarantee module. Specifically: in the process of data transmission and storage, advanced encryption technology is used to ensure data security, and a data backup and recovery mechanism is established to prevent data loss or damage. By optimizing the data collection, transmission and processing processes, the real-time nature of the data is ensured so as to timely discover potential risks and problems.

[0104] In the coal mine disaster monitoring system, the early warning and emergency response module is one of the core functions of the system. It can issue early warnings in time before a disaster occurs, buying precious time for emergency response. At the same time, the security and real-time performance of data are also important guarantees for the stable operation of the system. Based on the previous implementation, the early warning and emergency response module is optimized, and a data security and real-time performance guarantee module is added to improve the overall performance and reliability of the system.

[0105] 1. Optimization of early warning and emergency response modules

[0106] Data reconfirmation mechanism: When the probability of a disaster exceeds a preset threshold and the system is ready to initiate the corresponding emergency plan, the fourth embodiment introduces a data reconfirmation mechanism. This mechanism is intended to ensure the accuracy and reliability of early warning information and avoid false alarms or missed alarms.

[0107] Data reconfirmation includes three steps: related data collection, increasing the collection frequency, and data quality inspection. Related data collection requires that when a key data deviates, other data related to the data should be collected immediately for comprehensive analysis and judgment. Increasing the collection frequency means increasing the collection frequency of related data when the data deviates from the threshold or abnormal data is detected, so as to obtain data change information more timely. Data quality inspection is to conduct strict data integrity, consistency, and accuracy quality inspection on the re-collected data to ensure the accuracy and reliability of the data.

[0108] Activation of emergency plan: After data reconfirmation, if the probability of disaster occurrence is still higher than the preset threshold, the system will automatically activate the corresponding emergency plan. The emergency plan includes disaster type identification, disaster level determination, emergency measures formulation and emergency resource allocation to ensure rapid and effective response when a disaster occurs.

[0109] 2. Data security real-time guarantee module

[0110] Security of data transmission and storage: In the process of data transmission and storage, Example 4 uses advanced encryption technologies, such as SSL / TLS protocol, AES encryption algorithm, etc., to ensure data security. These encryption technologies can prevent data from being stolen or tampered with during transmission, and ensure the confidentiality and integrity of data.

[0111] Data backup and recovery mechanism: In order to prevent data loss or damage, Example 4 establishes a complete data backup and recovery mechanism. Important data in the system is backed up regularly and stored in a safe and reliable location. When data is lost or damaged, it can be quickly restored to ensure the normal operation of the system.

[0112] Data real-time guarantee: Data real-time is one of the important performance indicators of coal mine disaster monitoring systems. Example 4 ensures data real-time by optimizing data collection, transmission and processing processes. For example, efficient data collection technology and transmission protocols are used to reduce data transmission delays; parallel processing and data caching technologies are used to improve the speed and efficiency of data processing. These measures can ensure that the system can detect potential risks and problems in a timely manner, and provide timely and accurate information support for early warning and emergency response.

[0113] Through the above optimization measures, the coal mine disaster monitoring system in Example 4 not only improves the accuracy and reliability of early warning and emergency response, but also enhances the security and real-time nature of data. This helps to timely discover potential risks and problems and provide more powerful technical guarantees for coal mine production safety. At the same time, with the continuous optimization and upgrading of the system, its adaptability and robustness will also be further improved.

[0114] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0115] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A coal mine disaster simulation prediction system based on multidimensional data collection, characterized in that: include: The multi-dimensional data collection module integrates a variety of monitoring devices to build a real-time monitoring network covering all key areas of the coal mine, realizing the comprehensive collection of multi-dimensional data; The data preprocessing module uses data cleaning algorithms to remove redundant and erroneous data, applies filtering technology to reduce noise interference, uses standardization methods to unify data formats and dimensions, and integrates data from different sources into a unified data set through data fusion technology; Model building and training module: select the deep learning framework, build a hybrid model based on convolutional neural network (CNN) and long short-term memory network (LSTM), and use historical disaster data and real-time monitoring data to train and verify the model; The prediction and decision-making module inputs the preprocessed data into the trained prediction model, outputs the probability of disaster occurrence and potential risk areas, and formulates disaster prevention and control measures and emergency plans based on expert knowledge and the actual situation of the coal mine; The early warning and emergency response module automatically triggers the early warning system and initiates the corresponding emergency plan based on the prediction results when the probability of a disaster exceeds the preset threshold; The user interaction interface displays real-time monitoring data, forecast results, warning information and emergency plans.

2. A coal mine disaster simulation prediction system based on multidimensional data collection according to claim 1, characterized in that: The various detection equipment include but are not limited to various sensors including geological radar, microseismic monitoring, gas detection and video surveillance, and the collected multi-dimensional data include but are not limited to geological structure, stress distribution, gas concentration, equipment status, and personnel location during the coal mine production process.

3. A coal mine disaster simulation prediction system based on multidimensional data collection according to claim 2, characterized in that: When the multi-dimensional data collection module is deployed, sensors are deployed in multiple levels according to different disaster types and risk levels to achieve refined management of key areas. At the same time, a sensor linkage mechanism is established. When a sensor is abnormal, other related sensors are automatically triggered for coordinated monitoring.

4. A coal mine disaster simulation prediction system based on multidimensional data collection according to claim 3, characterized in that: The multi-dimensional data collection module also includes the selection and optimization of the sensor deployment location, and the adjustment of the monitoring range and sensitivity of the sensor according to the monitoring requirements, wherein the optimization of the sensor deployment location follows the following objective function: P represents the set of sensor deployment locations, N is the number of sensors, wi is the weight of sensor i, d(Pi,Si) is the distance between the deployment location Pi of sensor i and its monitoring target Si, σi is the standard deviation of the monitoring range of sensor i. This objective function aims to minimize the sum of the weighted squares of the distances between the sensor and the target to optimize the monitoring coverage and accuracy.

5. A coal mine disaster simulation prediction system based on multidimensional data collection according to claim 1, characterized in that: The data preprocessing module also includes data quality control and data integrity verification to ensure the accuracy and reliability of the data, wherein the data quality control adopts the following outlier detection algorithm: Among them, xi is the data point, u is the mean of the data set, σ is the standard deviation, and when Score(xi) exceeds the preset threshold, xi is considered to be an outlier.

6. A coal mine disaster simulation prediction system based on multidimensional data collection according to claim 1, characterized in that: The data preprocessing module also includes: Weight confirmation of multi-dimensional data: Use statistical methods, machine learning algorithms or expert experience to perform risk shadowing and weight confirmation on multi-dimensional data; Confirmation of data collection frequency: According to the actual situation of the coal mine and the early warning needs of gas disasters, set a reasonable data collection order and frequency based on the real-time, stability and importance of the data.

7. A coal mine disaster simulation prediction system based on multidimensional data collection according to claim 1, characterized in that: The model building and training module also includes model validation and model evaluation, wherein the model validation adopts the cross-validation method and the evaluation indicator adopts the weighted F1 score evaluation.

8. A coal mine disaster simulation prediction system based on multi-dimensional data collection according to claim 7, characterized in that: The model building and training module also includes model iteration: according to the model's prediction results and actual disaster conditions, the model is iteratively optimized, and the model parameters and structure are continuously adjusted to improve the model's prediction accuracy and stability.

9. The coal mine disaster simulation prediction system based on multi-dimensional data collection according to claim 1, characterized in that: In the early warning and emergency response module, when the probability of a disaster exceeds a preset threshold, data reconfirmation is required before the corresponding emergency plan is initiated. The specific data reconfirmation includes: Related data collection: when a key data deviates, other data related to the data should be collected immediately for comprehensive analysis and judgment; Increase the frequency of data collection. When data deviates from the threshold or abnormal data is detected, increase the frequency of data collection to obtain data change information in a more timely manner. Data quality check: When new data is re-collected, strict quality checks should be carried out on data integrity, consistency, and accuracy.

10. The coal mine disaster simulation prediction system based on multi-dimensional data collection according to claim 1, characterized in that: It also includes data security and real-time guarantee modules, specifically: During data transmission and storage, advanced encryption technology is used to ensure data security; Establish data backup and recovery mechanisms to prevent data loss or damage; By optimizing data collection, transmission and processing processes, the real-time nature of data can be ensured so that potential risks and problems can be discovered in a timely manner.

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