A distributed methane monitoring system for coal mine safety

Abstract

The present invention provides a distributed methane monitoring system for coal mine safety, which relates to the technical field of methane monitoring and includes: a monitoring device layout module for distributing multiple methane monitoring device groups in a distributed manner in the coal mine mining area; a continuous monitoring module for obtaining multiple methane monitoring data sets; a prediction model construction module; a prediction result acquisition module for obtaining multiple methane risk prediction results; an abnormal point positioning module for positioning abnormal monitoring points; a diffusion area analysis module for obtaining the methane diffusion hazard area; and a warning information generation module for obtaining the real-time position information of the operators and generating methane risk warning information through visual analysis. The present invention solves the technical problem that traditional coal mine safety measures focus on using fixed methane monitoring stations for fixed-point monitoring, which is difficult to meet the real-time monitoring requirements for the dynamic changes of methane in the entire mining area, resulting in insufficient coal mine safety management effects.

Description

Technical Field

[0001] The present invention relates to the technical field of methane monitoring, and particularly relates to a distributed methane monitoring system for coal mine safety. Background Art

[0002] Methane is one of the common gas in coal mines. When its leakage and accumulation reach a certain concentration, it can trigger explosion or asphyxiation accidents, posing a serious threat to the safety of miners. Traditional coal mine safety measures focus on using fixed methane monitoring stations for fixed-point monitoring. Although this method can monitor methane concentration to a certain extent, it has limitations in terms of coverage and real-time performance. With the complex and changeable coal mine operation environment, a single or local monitoring system is difficult to meet the real-time monitoring requirements for the dynamic changes of methane concentration in the entire mining area. Therefore, the coal mine safety monitoring technology urgently needs to improve its real-time performance, accuracy and coverage to more effectively prevent and respond to methane-related safety risks. Summary of the Invention

[0003] This application aims to solve the technical problem that traditional coal mine safety measures focus on using fixed methane monitoring stations for fixed-point monitoring, which is difficult to meet the real-time monitoring requirements for the dynamic changes of methane in the entire mining area, resulting in insufficient coal mine safety management effect, by providing a distributed methane monitoring system for coal mine safety.

[0004] This application discloses a distributed methane monitoring system for coal mine safety. The system includes: a monitoring device layout module for distributing multiple groups of methane monitoring devices in a distributed manner in the coal mine mining area, where the multiple groups of methane monitoring devices have multiple monitoring point identifiers; a continuous monitoring module for continuously monitoring within a preset time range through the multiple groups of methane monitoring devices to obtain multiple methane monitoring data sets, where the multiple methane monitoring data sets include multiple methane monitoring data sequences and multiple environmental monitoring data sequences; a prediction model construction module for constructing a distributed methane risk prediction model based on the multiple monitoring point identifiers, where the distributed methane anomaly prediction model includes multiple methane risk prediction units corresponding to the multiple monitoring point identifiers; a prediction result acquisition module for respectively inputting the multiple methane monitoring data sequences into the multiple methane risk prediction units to obtain multiple methane risk prediction results; an abnormal point positioning module for positioning abnormal monitoring points based on the multiple methane risk prediction results; a diffusion area analysis module for performing methane diffusion area analysis of the abnormal monitoring points based on the multiple environmental monitoring data sequences to obtain a methane diffusion dangerous area; and a warning information generation module for obtaining the real-time location information of the operating personnel in the coal mine mining area, performing visual analysis on the methane diffusion dangerous area and the real-time location information, and generating methane risk warning information.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0006] By distributing multiple methane monitoring device groups in the coal mining area, comprehensive monitoring of key areas is achieved. This layout enables the monitoring system to cover a wider area, providing continuous monitoring and data recording. Based on the distributed methane risk prediction model at the monitoring points, the data of each monitoring point is analyzed to generate accurate risk predictions. This distributed risk assessment method makes the prediction more targeted and locally accurate, improving the efficiency of risk management. Through the analysis of environmental monitoring data, the diffusion path and diffusion speed of methane are evaluated. Combining real-time monitoring data, the distribution and diffusion trend of methane in the mining area can be dynamically predicted, realizing real-time visual analysis of the methane diffusion danger area. Combining the real-time location information of miners, miners can be notified in time to avoid or evacuate from the dangerous area, significantly improving the safety and health protection of miners. The generation and transmission mechanism of early warning information ensures that all relevant personnel can obtain key information in the first time, reducing accidents and injuries that may be caused by methane leakage, improving the safety management ability of the coal mine, reducing the risks caused by methane, protecting the lives of miners, and at the same time providing important technical support for the continuous operation of the mining area.

[0007] The above description is only an overview of the technical solutions of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specific embodiments of this application are specifically given. Description of the Drawings

[0008] Figure 1 It is a schematic structural diagram of a distributed methane monitoring system for coal mine safety provided by an embodiment of this application.

[0009] Figure 2 It is a schematic structural diagram of a monitoring device layout module in a distributed methane monitoring system for coal mine safety provided by an embodiment of this application.

[0010] Description of the reference numerals: Monitoring device layout module 10, Continuous monitoring module 20, Prediction model construction module 30, Prediction result acquisition module 40, Abnormal point location module 50, Diffusion area analysis module 60, Early warning information generation module 70, Initial point preset channel 11, Device performance data acquisition channel 12, Monitoring range coverage analysis channel 13, Distributed layout channel 14. Detailed Embodiments

[0011] By providing a distributed methane monitoring system for coal mine safety in the embodiments of the present application, the technical problem that traditional coal mine safety measures focus on using fixed methane monitoring sites for fixed-point monitoring, which is difficult to meet the real-time monitoring requirements for the dynamic changes of methane in the entire mining area and results in insufficient coal mine safety management effects is solved.

[0012] After introducing the basic principle of the present application, various non-limiting implementation manners of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0013] As Figure 1 shown, the embodiments of the present application provide a distributed methane monitoring system for coal mine safety, and the system includes:

[0014] A monitoring device layout module 10, configured to distribute multiple methane monitoring device groups in a distributed manner in a coal mine mining area, where the multiple methane monitoring device groups have multiple monitoring point identifiers.

[0015] In the coal mine mining area, according to the layout and mining conditions of the mining area, the methane monitoring device groups are distributed in a distributed manner. The distributed methane monitoring device groups cover the main working faces and dangerous areas of the coal mine, ensuring that all potential risk areas in the mine are effectively monitored. Each monitoring point of each monitoring device group has a unique identifier, so that the monitoring data of each point can be clearly identified during data collection and analysis.

[0016] A continuous monitoring module 20, configured to perform continuous monitoring within a preset time range through the multiple methane monitoring device groups to obtain multiple methane monitoring data sets, where the multiple methane monitoring data sets include multiple methane monitoring data sequences and multiple environmental monitoring data sequences.

[0017] The preset time range is set according to the monitoring requirements, such as every minute, every hour, etc. Within the preset time range, each methane monitoring device continuously monitors the methane monitoring data and related environmental monitoring data of the corresponding monitoring point in the coal mine mining area. The methane monitoring data includes static data (such as methane concentration) and dynamic data (such as the instantaneous change rate of methane), and these data provide a basis for subsequent risk assessment; the environmental monitoring data includes wind speed, wind direction, temperature, humidity, etc., and is used for subsequent methane dispersion analysis. The data collected by the multiple monitoring device groups are integrated to form a complete methane monitoring data set.

[0018] A prediction model construction module 30, configured to construct a distributed methane risk prediction model based on the multiple monitoring point identifiers, where the distributed methane anomaly prediction model includes multiple methane risk prediction units corresponding to the multiple monitoring point identifiers.

[0019] The construction of the distributed methane risk prediction model is based on the identification and relevant data of each monitoring point. Each monitoring point corresponds to a methane risk prediction unit, which can provide customized risk assessment for each specific location. The methane risk prediction unit includes a methane concentration risk prediction branch and a methane instantaneous change risk prediction branch. The model uses machine learning methods, such as feedforward neural networks, support vector machines, or random forests, etc., to learn the change pattern and instantaneous change rate of methane concentration from historical data, so as to predict the future risk level.

[0020] The prediction result acquisition module 40 is used to input the multiple methane monitoring data sequences into the multiple methane risk prediction units respectively to obtain multiple methane risk prediction results.

[0021] Input the multiple methane monitoring data sequences into the methane risk prediction unit corresponding to each monitoring point. Each methane risk prediction unit processes the input data and uses its internal model to predict the current risk probability of the monitoring point. The risk prediction result can be a risk level, such as high, medium, low, or a specific probability score, indicating the possibility that the methane concentration reaches the dangerous threshold. Further, the methane risk prediction results of all monitoring points can be integrated to provide a comprehensive methane risk map of the coal mine area, thereby effectively improving the safety management and response capabilities of the mining area.

[0022] The abnormal point positioning module 50 is used to locate abnormal monitoring points based on the multiple methane risk prediction results.

[0023] Analyze the methane risk prediction results of all monitoring points to identify those monitoring points whose risk levels are higher than the normal threshold as abnormal monitoring points. These points indicate the existence of abnormal methane concentration or other related risks. These abnormal monitoring points can be visualized through a map, enabling relevant personnel to intuitively see the specific locations of the risk points.

[0024] The dispersion area analysis module 60 is used to perform methane dispersion area analysis of the abnormal monitoring points based on the multiple environmental monitoring data sequences to obtain the methane dispersion dangerous area.

[0025] Utilize the collected environmental monitoring data, including wind direction, wind speed, temperature, humidity, etc., to analyze the dispersion path and dispersion speed of methane in the coal mine mining area. For example, use hydrodynamic simulation or other related models to simulate the dispersion pattern of methane gas. This simulation combined with real-time or historical environmental data can provide a detailed view of the methane gas diffusion. Combine the methane dispersion model and the data of the monitoring points to determine the methane dispersion dangerous area.

[0026] The early warning information generation module 70 is used to obtain the real-time location information of the operating personnel in the coal mining area, perform visual analysis on the methane dispersion danger area and the real-time location information, and generate methane risk early warning information.

[0027] Use relevant technologies such as RFID (Radio Frequency Identification), Wi-Fi positioning, and underground positioning systems to track and obtain the real-time location information of all operating personnel in the coal mining area. Integrate the obtained methane dispersion danger area with the real-time location information of the operating personnel on a unified visualization platform. For example, use a GIS system to map the methane dispersion danger area and the real-time location information onto a 3D map of the coal mine. Analyze the relationship between the positions of the operating personnel and the identified methane dispersion danger area, evaluate which miners are in potentially dangerous areas or may enter these areas. According to the analysis results, generate targeted methane risk early warning information, which should include the specific locations of the operating personnel, the methane concentration levels in the areas where they are located, recommended safety measures, etc. Once the early warning information is issued, relevant rescue and safety teams can take prompt actions, thereby enhancing the personal safety of the operating personnel and improving the overall emergency response efficiency of the mining area.

[0028] Furthermore, as Figure 2 shown, the monitoring device layout module 10 includes:

[0029] The initial point preset channel 11 is used to preset a number of initial monitoring points based on the layout information of the coal mining area; the device performance data acquisition channel 12 is used to acquire the device performance data of the methane monitoring device, where the device performance data includes the monitoring range and the monitoring accuracy; the monitoring range coverage analysis channel 13 is used to perform monitoring range coverage analysis on the number of initial monitoring points according to the device performance data to obtain a number of monitoring blind spots; the distributed layout channel 14 is used to set a number of new monitoring points according to the number of monitoring blind spots, and combine the number of initial monitoring points to perform distributed layout of the number of methane monitoring device groups.

[0030] Analyze the layout information of the coal mining area, including the ventilation system, known methane generation sources, geological structures, and the common working routes of miners, etc. Preset initial monitoring points in key areas, such as working faces, ventilation shafts, and known methane accumulation areas, etc. The selection of these points aims to maximize the coverage of potential high-risk areas and those areas that are crucial for safety monitoring.

[0031] Obtain the technical specifications of each methane monitoring device, including the monitoring range and the monitoring accuracy, where the monitoring range is the maximum and minimum distances that the device can detect, and the monitoring accuracy is the measurement error of the concentration.

[0032] According to the device performance data, analyze the coverage range after combining the initial monitoring points with the performance parameters of the methane monitoring device, and determine whether there are blind spots within the monitoring range, that is, those areas that are not effectively covered due to device performance limitations or geographical location factors.

[0033] Based on the identified blind spots, set up new monitoring points to fill these blind spots, ensuring that there are no dead spots in the monitoring coverage throughout the coal mine area. The setting of new monitoring points should consider the actual operational feasibility, such as the power requirements of the equipment, the installation of communication equipment, etc. Combining the initial monitoring points and the new monitoring points, conduct the final distributed layout of the methane monitoring device group to ensure comprehensive and effective methane monitoring within the coal mine area. By reasonably setting the monitoring points, the safety and efficiency of methane management in the mining area can be greatly improved.

[0034] Furthermore, the continuous monitoring module 20 includes:

[0035] The multiple methane monitoring device groups include multiple methane monitoring devices and multiple environmental monitoring devices; a methane continuous monitoring channel for continuously monitoring methane at corresponding monitoring points by the multiple methane monitoring devices according to preset methane monitoring indicators within a preset time range to obtain multiple methane monitoring data sequences; an environmental continuous monitoring channel for continuously monitoring the environment at corresponding monitoring points by the multiple environmental monitoring devices within a preset time range to obtain multiple environmental monitoring data sequences; a data set acquisition channel for integrating the multiple methane monitoring data sequences and the multiple environmental monitoring data sequences according to the multiple monitoring point identifiers to obtain the multiple methane monitoring data sets.

[0036] Each methane monitoring device group includes a methane monitoring device and an environmental monitoring device. Among them, the methane monitoring device is used to monitor the concentration of methane gas, and the environmental monitoring device is used to monitor environmental data related to the distribution and dispersion of methane, such as temperature, humidity, wind speed, and wind direction, etc.

[0037] The preset methane monitoring indicators are the key indicators for monitoring methane, including concentration thresholds and instantaneous change rates. The methane monitoring device continuously collects methane concentration data at corresponding monitoring points according to the preset methane monitoring indicators within the set time range, and arranges the data in chronological order to obtain multiple methane monitoring data sequences.

[0038] The environmental monitoring device continuously monitors the key environmental variables that affect methane distribution, including wind direction, wind speed, temperature, humidity, etc., within the set time range, and arranges the data in chronological order to obtain multiple environmental monitoring data sequences. These data are crucial for analyzing how methane moves and disperses within the mining area.

[0039] Integrate the methane data and environmental data from different monitoring points according to the monitoring point identification, and perform necessary preprocessing on the data, such as cleaning, denoising, standardization, etc., to ensure the data quality, and obtain multiple methane monitoring data sets. This process ensures the integrity and timeliness of the data and provides key data support for the safety management of the mining area.

[0040] Furthermore, the preset methane monitoring indicators include preset methane static indicators and preset methane dynamic indicators. Among them, the preset methane static indicators include methane concentration, and the preset methane dynamic indicators include methane instantaneous change rate.

[0041] The preset methane static indicator refers to the methane concentration measured at a specific time point, which provides the methane gas level at a certain time point. This is a basic indicator for evaluating the risk level of methane gas in a coal mine. Among them, the methane concentration is usually expressed as a percentage or parts per million (ppm). According to safety regulations, the methane concentration should not exceed a certain threshold. The monitoring of the methane static indicator is used to ensure that the gas concentration in the mining area does not exceed the safety threshold, which helps to avoid explosion or fire accidents caused by methane.

[0042] The preset methane dynamic indicator involves the change rate of methane concentration, that is, the increase or decrease of methane concentration in a short period of time. This indicator can help predict the trend and speed of methane leakage and is a supplement to the static indicator. The methane instantaneous change rate measures the speed of change of methane concentration within a specific time and can be used to warn of a rapid increase or decrease in methane concentration, indicating signs of a large amount of methane leakage or other dynamic environmental changes. The dynamic indicator can provide early warnings about the dynamics of methane leakage, thus allowing preventive measures to be taken to avoid potential safety accidents.

[0043] Furthermore, the prediction model construction module 30 includes:

[0044] A prediction unit construction channel for constructing a first methane risk prediction unit based on the first monitoring point identification, where the first monitoring point identification is any one of the multiple monitoring point identifications, and the first methane risk prediction unit includes a first methane concentration risk prediction branch and a first methane instantaneous change risk prediction branch; a model construction channel for continuing to construct multiple methane risk prediction units based on other multiple monitoring point identifications to obtain the distributed methane risk prediction model.

[0045] Randomly select one of the multiple monitoring points as the analysis object, which is the first monitoring point. Using the methane concentration data collected at this first monitoring point, combined with historical concentration data, a machine learning model is used to predict the future changes in methane concentration and the potential risk level, and a first methane concentration risk prediction branch is established; analyze the change rate of methane concentration, construct a model to predict the short-term change trend of methane concentration and potential risk events, and establish a first methane instantaneous change risk prediction branch. Integrate the first methane concentration risk prediction branch and the first methane instantaneous change risk prediction branch to obtain the first methane risk prediction unit.

[0046] Repeat the above process for other monitoring points in the area, establish corresponding methane risk prediction units for each point, including setting risk prediction branches for methane concentration and methane instantaneous change rate for each point, and integrate the risk prediction units of all monitoring points into a comprehensive distributed methane risk prediction model, which can achieve comprehensive monitoring and analysis of methane risks in the entire mining area.

[0047] Furthermore, the prediction unit construction channel includes:

[0048] Point attribute extraction node, used to extract point attributes based on the first monitoring point identifier to obtain the first monitoring point attribute information; same-attribute retrieval source setting node, used to set the first same-attribute retrieval source of the first monitoring point based on the first monitoring point attribute information, where the first same-attribute retrieval source includes multiple same-attribute monitoring points corresponding to the first monitoring point; retrieval node, used to retrieve and obtain multiple sample first methane concentration data and multiple sample first methane instantaneous change rate data within the historical time range based on the first same-attribute retrieval source; calculation node, used to calculate multiple first methane concentration anomaly probabilities and multiple methane instantaneous change anomaly probabilities according to the first warning methane concentration and the first warning methane instantaneous change rate of the first same-attribute retrieval source; first supervised training node, used to use the multiple sample first methane concentration data and the multiple first methane concentration anomaly probabilities as construction data, and based on the feedforward neural network, construct and supervise the training to obtain a first methane concentration risk prediction branch that meets the preset accuracy rate; second supervised training node, used to use the multiple sample first methane instantaneous change rate data and the multiple methane instantaneous change anomaly probabilities as construction data, and based on the feedforward neural network, construct and supervise the training to obtain a first methane instantaneous change risk prediction branch that meets the preset accuracy rate; prediction unit acquisition node, used to obtain the first methane risk prediction unit based on the first methane concentration risk prediction branch and the first methane instantaneous change risk prediction branch.

[0049] Obtain the point attribute information of the first monitoring point, including the geographical location, installation depth, historical methane concentration records, environmental conditions (such as temperature, humidity), etc. These attributes help to determine the environmental and operating conditions of the monitoring point, thus providing background information for risk analysis.

[0050] According to the attribute information of the first monitoring point, define monitoring points with the same attributes. Monitoring points with the same attributes refer to those with similar geographical locations, environmental conditions or other relevant characteristics. Set up a retrieval source, which includes all other monitoring points that meet the attributes of the first monitoring point. This can facilitate the retrieval of historical data required according to these monitoring points with the same attributes.

[0051] Retrieve data within the historical time range from the first retrieval source with the same attributes, including methane concentration and methane instantaneous change rate data. The historical time range can be set according to the analysis requirements, such as the past year, three years or longer. Organize the retrieved historical data to obtain multiple sample first methane concentration data and multiple sample first methane instantaneous change rate data. These data will be used to train and validate the risk prediction model.

[0052] Determine the first warning methane concentration and the first warning methane instantaneous change rate, that is, the thresholds of methane concentration and methane instantaneous change rate. These thresholds are set based on historical data and safety standards and are used to identify potential dangerous states. The warning thresholds can be fixed values or dynamically adjusted according to environmental conditions. Calculate the deviation between the methane concentration and the instantaneous change rate and the warning thresholds to calculate the probability of abnormal occurrence. Obtain multiple first methane concentration abnormal probabilities and multiple methane instantaneous change abnormal probabilities according to the calculation results. The abnormal probability reflects the possibility that the data of the monitoring point exceeds the normal range under given conditions.

[0053] Design a feedforward neural network model, which includes an input layer, several hidden layers and an output layer. Use multiple sample first methane concentration data and their corresponding multiple first methane concentration abnormal probabilities as training data. Use the supervised learning method to train the neural network. Optimize the model through methods such as cross-validation and adjusting hyperparameters to ensure that the accuracy and generalization ability of the prediction meet the preset accuracy standard. Finally, obtain the first methane concentration risk prediction branch, which can accurately predict the probability of methane concentration abnormality, thus providing real-time risk assessment based on actual measurement data.

[0054] The construction and training process of the first methane instantaneous change risk prediction branch is similar to that of the first methane concentration risk prediction branch. For the sake of brevity of the specification, it will not be elaborated here. Integrate the trained first methane concentration risk prediction branch and the first methane instantaneous change risk prediction branch to obtain the first methane risk prediction unit. This data-driven prediction method helps the coal mine safety monitoring system to respond to potential methane leakage events in a timely manner and improve the safety management level of the mining area.

[0055] Furthermore, the diffusion area analysis module 60 includes:

[0056] The first environmental analysis channel is used to analyze multiple wind direction sequences and multiple wind speed sequences in the multiple environmental monitoring data sequences to obtain methane diffusion movement characteristics; the second environmental analysis channel is used to analyze multiple temperature sequences and multiple humidity sequences in the multiple environmental monitoring data sequences to obtain methane diffusion distribution characteristics; the diffusion area analysis channel is used to combine the methane diffusion movement characteristics and the methane diffusion distribution characteristics to perform methane diffusion area analysis of the abnormal monitoring points to obtain the methane diffusion danger area.

[0057] Collect wind direction and wind speed data sequences of multiple monitoring points from the environmental monitoring device. These data help to determine the air flow pattern in the mining area and provide direct influencing factors for how methane diffuses inside the mine. Use a fluid dynamics model to analyze the wind direction and wind speed data, analyze the time variation trends of wind speed and wind direction, and how they affect the movement of methane gas, determine the possible propagation paths and speeds of methane in the mining area, and obtain methane diffusion movement characteristics according to the analysis results.

[0058] Collect temperature and humidity data sequences of multiple monitoring points from the environmental monitoring device. Temperature and humidity have a significant impact on the density and diffusion rate of methane gas. Analyze how temperature and humidity data affect the distribution of methane gas. For example, an increase in temperature will increase the diffusion rate of methane gas, while high humidity will reduce the upward speed of methane. Use relevant physical models to evaluate the specific impact of temperature and humidity changes on methane diffusion behavior, and obtain methane diffusion distribution characteristics according to the analysis results.

[0059] Combine the analyzed methane diffusion movement characteristics with the methane diffusion distribution characteristics to comprehensively evaluate the diffusion behavior of methane in the mining area. For example, use simulation software or mathematical models to predict the diffusion path and range of methane under specific environmental conditions, identify areas where methane may accumulate or exist at high concentrations, and determine the methane diffusion danger area.

[0060] Furthermore, the system further includes an intelligent early warning module, including:

[0061] Early warning device layout channels are used to deploy intelligent early warning devices in the coal mining area; targeted early warning channels are used to conduct targeted early warning of the methane risk warning information based on the intelligent early warning devices.

[0062] Intelligent early warning devices are deployed in the coal mining area, and the deployment locations should cover all key areas, especially those areas with high historical methane concentrations or poor ventilation. Once methane risk warning information is generated, the intelligent early warning devices immediately issue warnings automatically, which can be conveyed to the workers and managers in the mining area through various means such as sound and light signals, so as to ensure a rapid response when the methane concentration is abnormal and effectively protect the lives of miners and the safe operation of the mining area.

[0063] In summary, the distributed methane monitoring system for coal mine safety provided by the embodiments of the present application has the following technical effects:

[0064] By distributing and deploying multiple methane monitoring device groups in the coal mining area, comprehensive monitoring of key areas is achieved. This layout enables the monitoring system to cover a wider area and provide continuous monitoring and data recording; based on the distributed methane risk prediction model at the monitoring points, the data of each monitoring point is analyzed to generate accurate risk predictions. This distributed risk assessment method makes the prediction more targeted and locally accurate, improving the efficiency of risk management; through environmental monitoring data analysis, the dispersion path and dispersion speed of methane are evaluated. Combining with real-time monitoring data, the distribution and diffusion trend of methane in the mining area can be dynamically predicted, realizing real-time visual analysis of the methane dispersion dangerous area. Combining with the real-time location information of miners, miners can be notified in time to avoid or evacuate from the dangerous area, significantly improving the safety and health protection of miners; the generation and transmission mechanism of warning information ensures that all relevant personnel can obtain key information in the first time, reducing accidents and injuries that may be caused by methane leakage, improving the safety management ability of the coal mine, reducing the risks caused by methane, protecting the lives of miners, and at the same time providing important technical support for the continuous operation of the mining area.

[0065] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A distributed methane monitoring system for coal mine safety, characterized in that, The system includes: A monitoring device deployment module for distributing and deploying multiple methane monitoring device groups in a coal mining area, where the multiple methane monitoring device groups have multiple monitoring point identifiers; A continuous monitoring module for continuously monitoring within a preset time range through the multiple methane monitoring device groups to obtain multiple methane monitoring data sets, where the multiple methane monitoring data sets include multiple methane monitoring data sequences and multiple environmental monitoring data sequences; A prediction model construction module for constructing a distributed methane risk prediction model based on the multiple monitoring point identifiers, where the distributed methane anomaly prediction model includes multiple methane risk prediction units corresponding to the multiple monitoring point identifiers; A prediction result acquisition module for respectively inputting the multiple methane monitoring data sequences into the multiple methane risk prediction units to obtain multiple methane risk prediction results; An abnormal point positioning module for positioning abnormal monitoring points based on the multiple methane risk prediction results; A dispersion area analysis module for analyzing the methane dispersion area of the abnormal monitoring points based on the multiple environmental monitoring data sequences to obtain a methane dispersion dangerous area; A warning information generation module for obtaining the real-time location information of the operators in the coal mining area, visually analyzing the methane dispersion dangerous area and the real-time location information, and generating methane risk warning information; The prediction model construction module includes: A prediction unit construction channel for constructing a first methane risk prediction unit based on a first monitoring point identifier, where the first monitoring point identifier is any one of the multiple monitoring point identifiers, and the first methane risk prediction unit includes a first methane concentration risk prediction branch and a first methane instantaneous change risk prediction branch; A model construction channel for continuously constructing multiple methane risk prediction units based on other multiple monitoring point identifiers to obtain the distributed methane risk prediction model; The prediction unit construction channel includes: A point attribute extraction node for extracting point attributes based on the first monitoring point identifier to obtain first monitoring point attribute information; A same-attribute retrieval source setting node for setting a first same-attribute retrieval source of the first monitoring point based on the first monitoring point attribute information, where the first same-attribute retrieval source includes multiple same-attribute monitoring points corresponding to the first monitoring point; A retrieval node for retrieving and obtaining multiple sample first methane concentration data and multiple sample first methane instantaneous change rate data within a historical time range based on the first same-attribute retrieval source; A calculation node for calculating multiple first methane concentration anomaly probabilities and multiple methane instantaneous change anomaly probabilities according to the first warning methane concentration and the first warning methane instantaneous change rate of the first same-attribute retrieval source; A first supervised training node for using the multiple sample first methane concentration data and the multiple first methane concentration anomaly probabilities as construction data, and constructing and supervising the training based on a feedforward neural network to obtain a first methane concentration risk prediction branch that meets a preset accuracy rate; The second supervised training node is used to construct and supervise the training of the first methane instantaneous change risk prediction branch that meets the preset accuracy rate based on the feedforward neural network by using the multiple sample first methane instantaneous change rate data and the multiple methane instantaneous change abnormal probabilities as the construction data; The prediction unit acquisition node is used to obtain the first methane risk prediction unit based on the first methane concentration risk prediction branch and the first methane instantaneous change risk prediction branch.

2. The distributed methane monitoring system for coal mine safety according to claim 1, characterized in that, The monitoring device layout module includes: The initial point preset channel is used to preset a number of initial monitoring points based on the layout information of the coal mine mining area; The device performance data acquisition channel is used to acquire the device performance data of the methane monitoring device, where the device performance data includes the monitoring range and the monitoring accuracy; The monitoring range coverage analysis channel is used to perform monitoring range coverage analysis on the number of initial monitoring points according to the device performance data to obtain a number of monitoring blind areas; The distributed layout channel is used to set a number of new monitoring points according to the number of monitoring blind areas, and combine the number of initial monitoring points to perform distributed layout of the number of methane monitoring device groups.

3. A distributed methane monitoring system for coal mine safety according to claim 1, characterized in that, The continuous monitoring module includes: The number of methane monitoring device groups includes a number of methane monitoring devices and a number of environmental monitoring devices; The methane continuous monitoring channel is used to perform continuous methane monitoring of the corresponding monitoring points by the number of methane monitoring devices within a preset time range to obtain a number of methane monitoring data sequences; The environmental continuous monitoring channel is used to perform continuous environmental monitoring of the corresponding monitoring points by the number of environmental monitoring devices within a preset time range to obtain a number of environmental monitoring data sequences; The data set acquisition channel is used to integrate the number of methane monitoring data sequences and the number of environmental monitoring data sequences according to the number of monitoring point identifiers to obtain the number of methane monitoring data sets.

4. A distributed methane monitoring system for coal mine safety according to claim 3, characterized in that, The preset methane monitoring indicators include a preset methane static indicator and a preset methane dynamic indicator, where the preset methane static indicator includes the methane concentration, and the preset methane dynamic indicator includes the methane instantaneous change rate.

5. A distributed methane monitoring system for coal mine safety according to claim 1, characterized in that, The diffusion area analysis module includes: The first environmental analysis channel is used to analyze the number of wind direction sequences and the number of wind speed sequences in the number of environmental monitoring data sequences to obtain the methane diffusion movement characteristics; The second environmental analysis channel is used to analyze the number of temperature sequences and the number of humidity sequences in the number of environmental monitoring data sequences to obtain the methane diffusion distribution characteristics; The diffusion area analysis channel is used to combine the methane diffusion movement characteristics and the methane diffusion distribution characteristics to perform methane diffusion area analysis on the abnormal monitoring points to obtain the methane diffusion dangerous area.

6. A distributed methane monitoring system for coal mine safety according to claim 1, characterized in that, The system further includes an intelligent early warning module, including: The early warning device layout channel is used to layout intelligent early warning devices in the coal mine mining area; The targeted early warning channel is used to perform targeted early warning of the methane risk early warning information according to the intelligent early warning device.

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