Distributed methane monitoring system for coal mine safety

Abstract

The invention provides a distributed methane monitoring system for coal mine safety, and relates to the technical field of methane monitoring, and the system comprises a monitoring device laying module which is used for laying a plurality of methane monitoring device groups in a coal mining area in a distributed manner; the continuous monitoring module is used for obtaining a plurality of methane monitoring data sets; a prediction model construction module; the prediction result acquisition module is used for acquiring a plurality of methane risk prediction results; the abnormal point location positioning module is used for positioning an abnormal monitoring point location; the dispersion area analysis module is used for obtaining a methane dispersion dangerous area; and the early warning information generation module is used for acquiring the real-time position information of the operating personnel and carrying out visual analysis to generate methane risk early warning information. The technical problem that the coal mine safety management effect is insufficient due to the fact that traditional coal mine safety measures focus on fixed-point monitoring by using a fixed methane monitoring station and the requirement for real-time monitoring of the dynamic change of methane in a whole mining area is difficult to meet is solved.

Description

Technical Field

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

[0002] Methane is one of the common gases in coal mines. When it leaks and accumulates to a certain concentration, it can cause explosions or suffocation 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 concentrations to a certain extent, it has limitations in coverage and real-time performance. With the complexity and changeability of the coal mine operating environment, a single or local monitoring system is difficult to meet the real-time monitoring needs of the dynamic changes in methane concentrations in the entire mine area. Therefore, 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 provides a distributed methane monitoring system for coal mine safety, aiming 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 needs of the dynamic changes of methane in the entire mining area, resulting in insufficient coal mine safety management effects.

[0004] The present application discloses a distributed methane monitoring system for coal mine safety, the system comprising: a monitoring device deployment module, used for distributing multiple methane monitoring device groups in a coal mining area, wherein the multiple methane monitoring device groups have multiple monitoring point identifiers; a continuous monitoring module, used for performing continuous monitoring within a preset time range through the multiple methane monitoring device groups to obtain multiple methane monitoring data sets, wherein the multiple methane monitoring data sets include multiple methane monitoring data sequences and multiple environmental monitoring data sequences; a prediction model construction module, used for constructing a distributed methane risk prediction model based on the multiple monitoring point identifiers, wherein the distributed methane anomaly prediction model includes corresponding A plurality of methane risk prediction units marked with the plurality of monitoring points; a prediction result acquisition module, used for inputting the plurality of methane monitoring data sequences into the plurality of methane risk prediction units respectively, to obtain a plurality of methane risk prediction results; an abnormal point positioning module, used for positioning abnormal monitoring points based on the plurality of methane risk prediction results; a diffusion area analysis module, used for performing methane diffusion area analysis of the abnormal monitoring points based on the plurality of environmental monitoring data sequences, to obtain methane diffusion danger areas; an early warning information generation module, used for obtaining real-time location information of operating personnel in the coal mining area, performing visual analysis on the methane diffusion danger areas and the real-time location information, to generate methane risk early warning information.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages: 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 and provide continuous monitoring and data recording. Based on the distributed methane risk prediction model of 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 diffusion path and diffusion speed of methane are evaluated. Combined with real-time monitoring data, the distribution and diffusion trend of methane in the mining area can be dynamically predicted, and real-time visual analysis of methane diffusion danger areas can be achieved. Combined with the real-time location information of miners, miners are notified in time to avoid or evacuate dangerous areas, 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 at the first time, reduce accidents and injuries that may be caused by methane leaks, improve the safety management capabilities of coal mines, reduce the risks caused by methane, protect the lives of miners, and also provide important technical support for the continuous operation of mining areas.

[0006] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 A schematic diagram of the structure of a distributed methane monitoring system for coal mine safety provided in an embodiment of the present application.

[0008] Figure 2 A structural schematic diagram of a monitoring device deployment module in a distributed methane monitoring system for coal mine safety provided in an embodiment of the present application.

[0009] Explanation of the accompanying drawings: monitoring device deployment module 10, continuous monitoring module 20, prediction model construction module 30, prediction result acquisition module 40, abnormal point positioning 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 deployment channel 14. DETAILED DESCRIPTION

[0010] The embodiment of the present application provides a distributed methane monitoring system for coal mine safety, which 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 needs of the dynamic changes of methane in the entire mining area, resulting in insufficient coal mine safety management effects.

[0011] After introducing the basic principles of the present application, various non-limiting implementation methods 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.

[0012] like Figure 1 As shown, the embodiment of the present application provides a distributed methane monitoring system for coal mine safety, the system comprising: The monitoring device deployment module 10 is used to deploy a plurality of methane monitoring device groups in a distributed manner in a coal mining area, wherein the plurality of methane monitoring device groups have a plurality of monitoring point identifiers.

[0013] In the coal mining area, methane monitoring device groups are distributedly deployed according to the layout of the mining area and the mining conditions. The deployed methane monitoring device groups cover the main working faces and dangerous areas of the coal mine, ensuring effective monitoring coverage of all potential risk areas in the mine. The monitoring points of each monitoring device group have a unique identification so that the monitoring data of each point can be clearly identified during data collection and analysis.

[0014] The continuous monitoring module 20 is used to perform continuous monitoring within a preset time range through the multiple methane monitoring device groups to obtain multiple methane monitoring data sets, wherein the multiple methane monitoring data sets include multiple methane monitoring data sequences and multiple environmental monitoring data sequences.

[0015] 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 points in the coal mining area. The methane monitoring data includes static data (such as methane concentration) and dynamic data (such as the instantaneous change rate of methane), which provide the basis for subsequent risk assessment; environmental monitoring data includes wind speed, wind direction, temperature, humidity, etc., which are used for subsequent methane diffusion analysis. The data collected by multiple monitoring device groups are integrated to form a complete methane monitoring data set.

[0016] The prediction model construction module 30 is used to construct a distributed methane risk prediction model based on the multiple monitoring point identifiers, wherein the distributed methane anomaly prediction model includes multiple methane risk prediction units corresponding to the multiple monitoring point identifiers.

[0017] The construction of the distributed methane risk prediction model is based on the identification and related data of each monitoring point. Each monitoring point corresponds to a methane risk prediction unit, so that a customized risk assessment can be provided 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, to learn the change pattern and instantaneous change rate of methane concentration based on historical data, thereby predicting future risk levels.

[0018] 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.

[0019] Multiple methane monitoring data sequences are input 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, or low, or a specific probability score, indicating the possibility of methane concentration reaching a dangerous threshold. Furthermore, the methane risk prediction results of all monitoring points can be integrated to provide a comprehensive methane risk map for coal mine areas, thereby effectively improving the safety management and response capabilities of the mining area.

[0020] The abnormal point positioning module 50 is used to locate abnormal monitoring points based on the multiple methane risk prediction results.

[0021] The methane risk prediction results of all monitoring points are analyzed to identify those monitoring points with risk levels higher than the normal threshold as abnormal monitoring points. These points indicate abnormal methane concentrations or other related risks. These abnormal monitoring points can be visualized through maps so that relevant personnel can intuitively see the specific locations of risk points.

[0022] The diffusion area analysis module 60 is used to perform methane diffusion area analysis of the abnormal monitoring points based on the multiple environmental monitoring data sequences to obtain methane diffusion danger areas.

[0023] Utilize the collected environmental monitoring data, including wind direction, wind speed, temperature, humidity, etc., to analyze the diffusion path and diffusion speed of methane in the coal mining area. For example, fluid dynamics simulation or other related models are used to simulate the diffusion pattern of methane gas. This simulation combined with real-time or historical environmental data can provide a detailed view of the diffusion of methane gas. Combined with the methane diffusion model and the data from the monitoring points, the dangerous areas of methane diffusion can be determined.

[0024] The warning information generation module 70 is used to obtain the real-time location information of the workers in the coal mining area, perform visual analysis on the methane diffusion danger area and the real-time location information, and generate methane risk warning information.

[0025] Use RFID (radio frequency identification), Wi-Fi positioning, underground positioning systems and other related technologies to track and obtain the real-time location information of all workers in the coal mining area, and integrate the obtained methane diffusion hazard areas and the real-time location information of the workers on a unified visualization platform. For example, use the GIS system to map the methane diffusion hazard areas and real-time location information onto the three-dimensional map of the coal mine, analyze the relationship between the positions of the workers and the identified methane diffusion hazard areas, and evaluate which miners are in potential danger areas or may enter these areas. Based on the analysis results, generate targeted methane risk warning information. This information should include the specific location of the workers, the methane concentration level in the area, recommended safety measures, etc. Once the warning information is issued, the relevant rescue and safety teams can take quick action to improve the personal safety of the workers and improve the overall emergency response efficiency of the mining area.

[0026] Furthermore, if Figure 2 As shown, the monitoring device deployment module 10 includes: An 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; a device performance data acquisition channel 12 is used to obtain the device performance data of the methane monitoring device, wherein the device performance data includes a monitoring range and a monitoring accuracy; a monitoring range coverage analysis channel 13 is used to perform a monitoring range coverage analysis on the several initial monitoring points according to the device performance data to obtain a plurality of monitoring blind areas; a distributed deployment channel 14 is used to set a number of new monitoring points according to the multiple monitoring blind areas, and to perform distributed deployment of the multiple methane monitoring device groups in combination with the several initial monitoring points.

[0027] Analyze the layout information of the coal mining area, including the ventilation system, known methane sources, geological structure, and the common working routes of miners, and preset initial monitoring points in key areas such as working faces, ventilation shafts, and known methane accumulation areas. The selection of these points is aimed at maximizing the coverage of potential high-risk areas and those areas that are critical to safety monitoring.

[0028] Obtain the technical specifications of each methane monitoring device, including monitoring range and monitoring accuracy, where the monitoring range is the maximum and minimum distances the device can detect and the monitoring accuracy is the measurement error of the concentration.

[0029] Based on the device performance data, the coverage of the initial monitoring points combined with the performance parameters of the methane monitoring device is analyzed to determine whether there are blind spots in the monitoring range, that is, areas that are not effectively covered due to device performance limitations or geographical location factors.

[0030] According to the blind spots identified, additional monitoring points are set up to fill these blind spots to ensure that the monitoring coverage in the entire coal mine area is complete. The setting of additional monitoring points should take into account the actual operational feasibility, such as the power supply requirements of the equipment, the installation of communication equipment, etc. Combined with the initial monitoring points and the additional monitoring points, the final methane monitoring device group is distributedly deployed to ensure that the methane monitoring in the coal mine area is comprehensive and effective. By setting the monitoring points reasonably, the safety and efficiency of methane management in the mine area can be greatly improved.

[0031] Furthermore, the continuous monitoring module 20 includes: The plurality of methane monitoring device groups include a plurality of methane monitoring devices and a plurality of environmental monitoring devices; a methane continuous monitoring channel, which is used for the plurality of methane monitoring devices to continuously monitor the methane at corresponding monitoring points according to preset methane monitoring indicators within a preset time range, and obtain a plurality of methane monitoring data sequences; an environmental continuous monitoring channel, which is used for the plurality of environmental monitoring devices to continuously monitor the environment at corresponding monitoring points within a preset time range, and obtain a plurality of environmental monitoring data sequences; a data set acquisition channel, which is used for integrating the plurality of methane monitoring data sequences and the plurality of environmental monitoring data sequences according to the plurality of monitoring point identifiers, and obtain the plurality of methane monitoring data sets.

[0032] Each methane monitoring device group includes a methane monitoring device and an environmental monitoring device, wherein 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 methane distribution and diffusion, such as temperature, humidity, wind speed and wind direction, etc.

[0033] The preset methane monitoring indicators are key indicators for monitoring methane, including concentration thresholds and instantaneous change rates. The methane monitoring device continuously collects methane concentration data of corresponding monitoring points within a set time range according to the preset methane monitoring indicators, and arranges the data in chronological order to obtain multiple methane monitoring data sequences.

[0034] Within a set time range, the environmental monitoring device continuously monitors key environmental variables that affect methane distribution, including wind direction, wind speed, temperature, humidity, etc., and arranges the data in chronological order to obtain multiple environmental monitoring data sequences, which are crucial for analyzing how methane moves and disperses within the mining area.

[0035] The methane data and environmental data from different monitoring points are integrated according to the monitoring point identification, and the data are preprocessed as necessary, such as cleaning, denoising, and standardization, to ensure 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 mine safety management.

[0036] Furthermore, the preset methane monitoring index includes a preset methane static index and a preset methane dynamic index, wherein the preset methane static index includes methane concentration, and the preset methane dynamic index includes methane instantaneous change rate.

[0037] The preset methane static indicator refers to the methane concentration measured at a specific point in time. It provides the methane gas level at a point in time, which is a basic indicator for assessing the degree of danger of methane gas in coal mines. 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 methane static indicators is used to ensure that the gas concentration in the mining area does not exceed the safety threshold, which helps to avoid explosions or fire accidents caused by methane.

[0038] The preset methane dynamic indicator involves the rate of change 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 in a specific time. It can be used to warn of rapid increases or decreases in methane concentration, characterizing signs of large-scale methane leakage or other dynamic environmental changes. Dynamic indicators can provide early warnings about the dynamics of methane leakage, allowing preventive measures to be taken to avoid potential safety accidents.

[0039] Furthermore, the prediction model building module 30 includes: A prediction unit construction channel is used to construct a first methane risk prediction unit based on a first monitoring point identifier, wherein 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 is used to continue to construct multiple methane risk prediction units based on other multiple monitoring point identifiers to obtain the distributed methane risk prediction model.

[0040] Randomly select one of the multiple monitoring points as the analysis object as the first monitoring point. Utilize the methane concentration data collected by the first monitoring point, combine with the historical concentration data, use the machine learning model to predict the future changes of methane concentration and potential risk levels, and establish the first methane concentration risk prediction branch; analyze the rate of change of methane concentration, build a model to predict the change trend of methane concentration in a short period of time and potential risk events, and establish the 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.

[0041] Repeat the above process for other monitoring points in the area, and establish a corresponding methane risk prediction unit for each point, including setting up risk prediction branches for methane concentration and methane instantaneous change rate for each point, and integrating the risk prediction units of all monitoring points into a comprehensive distributed methane risk prediction model. This model can realize comprehensive monitoring and analysis of methane risks in the entire mining area.

[0042] Furthermore, the prediction unit constructs a channel, including: A point attribute extraction node is used to extract point attributes based on the first monitoring point identifier to obtain the first monitoring point attribute information; a same attribute retrieval source setting node is used to set the first same attribute retrieval source of the first monitoring point based on the first monitoring point attribute information, wherein the first same attribute retrieval source includes multiple same attribute monitoring points corresponding to the first monitoring point; a retrieval node is used to retrieve and obtain 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 is used to calculate and obtain multiple first methane concentration abnormal probabilities, multiple methane concentration abnormality probabilities, and multiple methane concentration abnormality probability according to the first warning methane concentration and the first warning methane instantaneous change rate of the first same attribute retrieval source. Instantaneous change abnormal probability; a first supervised training node, used to use the first methane concentration data of the multiple samples and the multiple first methane concentration abnormal probabilities as construction data, based on a feedforward neural network, to construct and supervise the training to obtain a first methane concentration risk prediction branch that meets the preset accuracy; a second supervised training node, used to use the first methane instantaneous change rate data of the multiple samples and the multiple methane instantaneous change abnormal probabilities as construction data, based on a feedforward neural network, to construct and supervise the training to obtain a first methane instantaneous change risk prediction branch that meets the preset accuracy; a 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.

[0043] 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. of the point. These attributes help determine the environmental and operating conditions of the monitoring point, thereby providing background information for risk analysis.

[0044] Based on the attribute information of the first monitoring point, monitoring points with the same attributes are defined. Monitoring points with the same attributes refer to monitoring points with similar geographical locations, environmental conditions or other relevant characteristics. A retrieval source is set, which includes all other monitoring points that meet the attributes of the first monitoring point. This makes it convenient to retrieve the required historical data based on these points with the same attributes.

[0045] Data within a historical time range is retrieved from the first homologous retrieval source, including methane concentration and methane instantaneous change rate data. The historical time range can be set according to analysis requirements, such as the past year, three years or longer. The retrieved historical data is sorted 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 verify the risk prediction model.

[0046] 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 to identify potential dangerous conditions. The warning threshold can be a fixed value or dynamically adjusted according to environmental conditions. Calculate the deviation between the methane concentration and instantaneous change rate and the warning threshold to calculate the probability of abnormal occurrence. According to the calculation results, multiple first methane concentration abnormal probabilities and multiple methane instantaneous change abnormal probabilities are obtained. The abnormal probability reflects the possibility that the data at the monitoring point exceeds the normal range under given conditions.

[0047] A feedforward neural network model is designed, which includes an input layer, several hidden layers and an output layer. Multiple sample first methane concentration data and their corresponding multiple first methane concentration abnormal probabilities are used as training data. The neural network is trained using a supervised learning method. The model is optimized by cross-validation, adjusting hyperparameters and other methods to ensure that the prediction accuracy and generalization ability meet the preset accuracy standards. Finally, the first methane concentration risk prediction branch is obtained, which can accurately predict the probability of methane concentration abnormality, thereby providing real-time risk assessment based on actual measurement data.

[0048] 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, it will not be repeated here. The first methane concentration risk prediction branch and the first methane instantaneous change risk prediction branch that have been trained are integrated 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 incidents in a timely manner and improve the safety management level of the mining area.

[0049] Furthermore, the diffuse region analysis module 60 includes: The first environmental analysis channel is used to obtain methane diffusion movement characteristics by analyzing multiple wind direction sequences and multiple wind speed sequences in the multiple environmental monitoring data sequences; the second environmental analysis channel is used to obtain methane diffusion distribution characteristics by analyzing multiple temperature sequences and multiple humidity sequences in the multiple environmental monitoring data sequences; the diffusion area analysis channel is used to perform methane diffusion area analysis of the abnormal monitoring point in combination with the methane diffusion movement characteristics and the methane diffusion distribution characteristics to obtain the methane diffusion danger area.

[0050] Wind direction and speed data sequences from multiple monitoring points are collected from environmental monitoring devices. These data help determine the pattern of air flow within the mining area and provide a direct influencing factor on how methane diffuses within the mine. Fluid dynamics models are used to analyze wind direction and speed data, including the temporal trends of wind speed and direction, and how they affect the movement of methane gas. The possible propagation paths and speeds of methane in the mining area are determined, and the methane diffusion movement characteristics are obtained based on the analysis results.

[0051] Temperature and humidity data sequences from multiple monitoring points are collected from environmental monitoring devices. Temperature and humidity have a significant impact on the density and diffusion rate of methane gas. How the temperature and humidity data affect the distribution of methane gas is analyzed. For example, increased temperature will increase the diffusion rate of methane gas, while high humidity will reduce the rising speed of methane. Relevant physical models are used to evaluate the specific effects of temperature and humidity changes on the diffusion behavior of methane, and the methane diffusion distribution characteristics are obtained based on the analysis results.

[0052] 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 in high concentrations, and determine methane diffusion danger areas.

[0053] Furthermore, the system also includes an intelligent early warning module, including: The early warning device deployment channel is used to deploy intelligent early warning devices in the coal mining area; the targeted early warning channel is used to carry out targeted early warning of the methane risk early warning information according to the intelligent early warning device.

[0054] Intelligent early warning devices are deployed in coal mining areas, and the deployment locations should cover all key areas, especially those areas with historically high methane concentrations or poor ventilation. Once methane risk warning information is generated, the intelligent early warning device will automatically issue an early warning immediately, which can be conveyed to the staff and managers in the mining area through various means such as sound and light signals, thereby ensuring that a quick response can be made when the methane concentration is abnormal, effectively protecting the lives of miners and the safe operation of the mining area.

[0055] In summary, the distributed methane monitoring system for coal mine safety provided by the embodiment of the present application has the following technical effects: 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 and provide continuous monitoring and data recording. Based on the distributed methane risk prediction model of 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 diffusion path and diffusion speed of methane are evaluated. Combined with real-time monitoring data, the distribution and diffusion trend of methane in the mining area can be dynamically predicted, and real-time visual analysis of methane diffusion danger areas can be achieved. Combined with the real-time location information of miners, miners are notified in time to avoid or evacuate dangerous areas, 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 at the first time, reduce accidents and injuries that may be caused by methane leaks, improve the safety management capabilities of coal mines, reduce the risks caused by methane, protect the lives of miners, and also provide important technical support for the continuous operation of mining areas.

[0056] 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 apparent to those skilled in the art, and the general principles defined herein may 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 the embodiments shown herein, but will conform to 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 comprises: A monitoring device deployment module, used for distributing a plurality of methane monitoring device groups in a coal mining area, wherein the plurality of methane monitoring device groups have a plurality of monitoring point identifiers; A continuous monitoring module, configured to perform continuous monitoring within a preset time range through the multiple methane monitoring device groups to obtain multiple methane monitoring data sets, wherein the multiple methane monitoring data sets include multiple methane monitoring data sequences and multiple environmental monitoring data sequences; A prediction model construction module, used to construct a distributed methane risk prediction model based on the multiple monitoring point identifiers, wherein the distributed methane anomaly prediction model includes multiple methane risk prediction units corresponding to the multiple monitoring point identifiers; A prediction result acquisition module, used to input the multiple methane monitoring data sequences into the multiple methane risk prediction units respectively to obtain multiple methane risk prediction results; An abnormal point positioning module, used for locating abnormal monitoring points based on the multiple methane risk prediction results; A diffusion area analysis module, used to perform methane diffusion area analysis of the abnormal monitoring points based on the multiple environmental monitoring data sequences to obtain methane diffusion danger areas; The early warning information generation module is used to obtain the real-time location information of the operating personnel in the coal mining area, perform visual analysis on the methane diffusion danger area and the real-time location information, and generate methane risk early warning information.

2. A distributed methane monitoring system for coal mine safety as claimed in claim 1, characterized in that: The monitoring device deployment module includes: An initial point preset channel is used to preset a number of initial monitoring points based on the layout information of the coal mining area; A device performance data acquisition channel, used to acquire device performance data of the methane monitoring device, wherein the device performance data includes a monitoring range and a monitoring accuracy; A monitoring range coverage analysis channel, used to perform monitoring range coverage analysis on the several initial monitoring points according to the device performance data to obtain multiple monitoring blind areas; The distributed deployment channel is used to set up a number of new monitoring points according to the multiple monitoring blind areas, and to carry out distributed deployment of the multiple methane monitoring device groups in combination with the multiple initial monitoring points.

3. A distributed methane monitoring system for coal mine safety as claimed in claim 1, characterized in that: The continuous monitoring module comprises: The plurality of methane monitoring device groups include a plurality of methane monitoring devices and a plurality of environmental monitoring devices; A methane continuous monitoring channel is used for the multiple methane monitoring devices to continuously monitor the methane at corresponding monitoring points according to preset methane monitoring indicators within a preset time range to obtain multiple methane monitoring data sequences; The continuous environmental monitoring channel is used for the multiple environmental monitoring devices to continuously monitor the environment of corresponding monitoring points within a preset time range to obtain multiple environmental monitoring data sequences; The data set acquisition channel is used to integrate 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.

4. A distributed methane monitoring system for coal mine safety as claimed in claim 3, characterized in that: The preset methane monitoring index includes a preset methane static index and a preset methane dynamic index, wherein the preset methane static index includes methane concentration, and the preset methane dynamic index includes methane instantaneous change rate.

5. A distributed methane monitoring system for coal mine safety as claimed in claim 1, characterized in that: The prediction model building module includes: The prediction unit constructs a channel for constructing a first methane risk prediction unit based on a first monitoring point identifier, wherein 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; The model building channel is used to continue to build multiple methane risk prediction units based on multiple other monitoring point identifiers to obtain the distributed methane risk prediction model.

6. A distributed methane monitoring system for coal mine safety as claimed in claim 5, characterized in that: The prediction unit constructs a channel, including: A point attribute extraction node, used to extract point attributes based on the first monitoring point identifier to obtain first monitoring point attribute information; A same attribute search source setting node, used for setting a first same attribute search source for a first monitoring point based on the attribute information of the first monitoring point, wherein the first same attribute search source includes a plurality of same attribute monitoring points corresponding to the first monitoring point; A search node, used for searching and acquiring a plurality of sample first methane concentration data and a plurality of sample first methane instantaneous change rate data within a historical time range based on the first same attribute search source; A calculation node, used for calculating a first warning methane concentration and a first warning methane instantaneous change rate of the first homologous attribute retrieval source, and obtaining a plurality of first methane concentration abnormal probabilities and a plurality of methane instantaneous change abnormal probabilities by calculation; A first supervised training node is used to use the first methane concentration data of the multiple samples and the multiple first methane concentration abnormal probabilities as construction data, and to construct and supervise the training based on a feedforward neural network to obtain a first methane concentration risk prediction branch that meets a preset accuracy rate; A second supervised training node is used to use the multiple sample first methane instantaneous change rate data and the multiple methane instantaneous change abnormal probabilities as construction data, and to construct and supervise the training based on a feedforward neural network to obtain a first methane instantaneous change risk prediction branch that meets a preset accuracy rate; 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.

7. A distributed methane monitoring system for coal mine safety as claimed in claim 1, characterized in that: The diffuse area analysis module comprises: A first environmental analysis channel is used to obtain methane diffusion movement characteristics by analyzing multiple wind direction sequences and multiple wind speed sequences in the multiple environmental monitoring data sequences; A second environmental analysis channel is used to obtain methane diffusion distribution characteristics by analyzing multiple temperature sequences and multiple humidity sequences in the multiple environmental monitoring data sequences; 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 at the abnormal monitoring point to obtain the methane diffusion danger area.

8. A distributed methane monitoring system for coal mine safety as claimed in claim 1, characterized in that: The system also includes an intelligent early warning module, including: An early warning device deployment channel is used to deploy intelligent early warning devices in the coal mining area; A targeted early warning channel is used to provide targeted early warning of the methane risk early warning information according to the intelligent early warning device.

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