Coal mine safety supervision method and system

Abstract

The invention relates to the technical field of coal mine safety supervision, and provides a coal mine safety supervision method, which comprises the following steps of data acquisition, data transmission, data analysis, artificial intelligence judgment, risk early warning, safety decision and event recording. According to the comprehensive coal mine safety supervision method and system, the safety management level of a coal mine is greatly improved, the occurrence probability of accidents is reduced, and the sense of safety and the working efficiency of miners are improved. In addition, through continuous data collection and analysis, the system can be continuously self-optimized to adapt to the change of a coal mine environment, and long-term safety production is realized. The popularization and application of the system undoubtedly bring revolutionary improvement to the safety management of the whole coal mine industry.

Description

Technical Field

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

[0002] In the coal mining industry, production safety has always been a key issue. Due to the complexity of the coal mining operating environment and its inherent dangers, such as methane and carbon monoxide gas leakage, dust explosions, mine collapse, etc., ensuring the safety and health of miners is one of the most important tasks in coal mine operations. Therefore, the development of coal mine safety supervision technology and systems has always been a research hotspot in the field of coal mine safety management.

[0003] However, although the existing coal mine safety supervision methods and systems have improved the safety level of coal mines to a certain extent, there are still some shortcomings, mainly including: Limitations of sensor technology: Most existing coal mine safety monitoring systems rely on traditional sensor technology. Although these sensors can monitor specific environmental parameters, they often do not respond quickly enough to the detection of key indicators such as methane and dust, and are easily affected by environmental factors such as temperature and humidity, and lack accuracy and stability.

[0004] Limitations of data transmission: Many coal mine safety supervision systems rely on wired communication technology, which is prone to physical damage or interference in the complex underground environment of coal mines, affecting the stability and real-time performance of data transmission. In addition, traditional wireless technologies such as Wi-Fi have poor coverage in deep coal mines.

[0005] Insufficient data processing and analysis capabilities: Despite the collection of large amounts of data, existing systems are often limited to basic data recording and display in terms of data processing and analysis capabilities. They lack in-depth data analysis and real-time risk assessment functions, and are unable to effectively predict and prevent potential security risks.

[0006] Primary application of artificial intelligence: Although some advanced coal mine safety monitoring systems have begun to try to introduce artificial intelligence technology, most of them are still in the initial stage. They lack the ability to conduct in-depth learning and pattern recognition of complex data, making it difficult to achieve accurate risk prediction and intelligent decision support.

[0007] Imperfection of early warning system: The existing early warning system can only issue warnings when an accident is about to happen. It lacks sufficient foresight and prevention, and cannot provide miners and managers with sufficient time to take risk avoidance measures.

[0008] Therefore, this invention proposes a coal mine safety supervision method and system to solve the above problems. Summary of the invention

[0009] In order to overcome the defects of the prior art, the object of the present invention is to provide a coal mine safety supervision method and system.

[0010] To achieve the above object, the technical solution of the present invention is implemented as follows: A coal mine safety supervision method comprises the following steps: a. Data collection: Multiple sensors are deployed in the coal mine to collect real-time data such as gas concentration (such as methane, carbon monoxide, oxygen, etc.), temperature, humidity, dust concentration, miner location and movement status in the coal mine environment; b. Data transmission: The collected data is transmitted to the ground monitoring center through wireless communication technology to ensure the real-time and stability of data transmission; c. Data analysis: Use big data analysis technology and machine learning algorithms to conduct real-time analysis of data transmitted to the monitoring center, including but not limited to trend analysis, anomaly detection and predictive analysis, to identify potential safety hazards; d. Artificial intelligence judgment: Apply artificial intelligence modules to further evaluate and judge the data analysis results and generate more accurate risk assessment reports, including classification and priority ranking of different types of risks; e. Risk warning: Based on the results of AI judgment, early warning information is issued to coal mine managers and miners through sound and light alarms, SMS notifications, APP push, etc. The early warning information includes specific risk types, severity and recommended response measures; f. Safety decision-making: Based on the early warning information, coal mine managers formulate and implement corresponding safety measures and initiate emergency plans when necessary to ensure the safety of the coal mine site; g. Event records: Keep detailed records of each warning and corresponding treatment measures to form an event log, including the time of the event, warning content, measures taken and final results, for subsequent analysis and improvement.

[0011] Preferably, the risk early warning step includes: a. Warning level classification: According to the severity of potential safety hazards, the warning information is divided into different levels (such as low, medium, high, and emergency), and each level corresponds to a different response strategy; b. Multi-channel notification: According to the warning level, warning information is sent to relevant personnel through multiple channels (such as sound and light alarm, SMS notification, APP push, email, etc.) to ensure that the warning information can be conveyed in time.

[0012] Preferably, it includes: a. Multiple sensors are used to collect data such as gas concentration, temperature, humidity, dust concentration, miners' position and movement status in the coal mine environment; b. Wireless communication module, used to transmit the collected data to the ground monitoring center to ensure the real-time and stability of data transmission; c. Data analysis module, installed in the ground monitoring center, is used to perform real-time analysis of the transmitted data, including trend analysis, anomaly detection and predictive analysis, to identify potential safety hazards; d. Artificial intelligence judgment module, which is used to further evaluate and judge the data analysis results and generate more accurate risk assessment reports, including classification and priority ranking of different types of risks; e. Risk warning module, which is used to issue early warning information to coal mine managers and miners based on the results of artificial intelligence judgment. The early warning information includes specific risk types, severity and recommended response measures; f. Safety decision support module, which is used to assist coal mine managers in formulating and implementing corresponding safety measures and, when necessary, launching emergency plans to ensure the safety of the coal mine site; g. Event recording module, used to record each warning and corresponding treatment measures, forming an event log, including the time of event occurrence, warning content, measures taken and final results, for subsequent analysis and improvement.

[0013] Preferably, the sensor comprises: a. Gas sensor: used to detect the concentration of methane, carbon monoxide and oxygen in coal mine environment; b. Temperature and humidity sensor: used to measure temperature and humidity in coal mine environment; c. Dust sensor: used to detect dust concentration in coal mine air; d. Positioning sensor: used to determine the specific location of miners in the coal mine; e. Motion sensor: used to monitor the movement status of miners, including walking, standing still and falling.

[0014] Preferably, the wireless communication module supports one or more of the following communication technologies: a.Wi-Fi: used for short-distance, high-bandwidth data transmission; b. ZigBee: for low-power, low-data-rate wireless communications; c.LoRa: used for long-distance, low-power data transmission; d.NB-IoT: for wide-area coverage and low-power IoT communications; e.5G: For high-bandwidth, low-latency, and high-reliability wireless communications.

[0015] Preferably, the data analysis module includes: a. Data cleaning submodule: denoise, complete and normalize the collected data to ensure the accuracy and consistency of the data; b. Feature extraction submodule: extract key feature parameters from the cleaned data, such as gas concentration change rate, temperature gradient, etc.; c. Model training submodule: Based on historical data and expert knowledge, the risk assessment model is trained using supervised learning, unsupervised learning or semi-supervised learning methods; d. Real-time evaluation submodule: Use the trained model to evaluate real-time data, identify safety hazards, and generate risk scores.

[0016] Preferably, the artificial intelligence judgment module includes: a. Deep learning submodule: Use deep learning algorithms (such as convolutional neural networks, recursive neural networks, etc.) to further process data analysis results and identify more complex safety hazards; b. Pattern recognition submodule: predicts possible security incidents through pattern recognition technology (such as cluster analysis, pattern matching, etc.); c. Risk scoring submodule: Generates risk scores based on a comprehensive assessment of multiple factors to quantify the severity of different safety hazards.

[0017] Preferably, the risk warning module includes: a. Warning level classification submodule: According to the severity of potential safety hazards, the warning information is divided into different levels (such as low, medium, high, and emergency), and each level corresponds to a different response strategy; b. Multi-channel notification submodule: According to the warning level, send warning information to relevant personnel through multiple channels (such as sound and light alarm, SMS notification, APP push, email, etc.) to ensure that the warning information can be conveyed in time.

[0018] The beneficial effects of the present invention are embodied in: Real-time monitoring and early warning system: By deploying a variety of sensors to monitor key indicators in the coal mine environment such as gas concentration, temperature and humidity, dust, and miners' position and movement status in real time, the system can capture potential safety hazards in a timely manner. By analyzing these data through artificial intelligence algorithms, it can more accurately identify and predict potential safety issues, and issue early warnings in a timely manner, effectively reducing the risk of accidents.

[0019] Data-driven decision support: The data analysis module and artificial intelligence judgment module in the system conduct in-depth analysis of the collected data, which can not only achieve real-time evaluation, but also provide more complex safety hazard identification through advanced algorithms such as deep learning and pattern recognition. This provides coal mine managers with data-supported decision-making tools to help them formulate more effective response measures.

[0020] Multi-channel communication and emergency response: The system ensures that all warning information can be transmitted to ground monitoring personnel and miners in a timely and accurate manner through multi-channel notification submodules. The setting of this communication mechanism ensures that in an emergency, relevant personnel can respond quickly and initiate necessary emergency plans.

[0021] Subsequent analysis and improvement: Through the event recording module, each safety incident and its handling measures will be recorded in detail, providing valuable data for subsequent analysis and improvement. These records help to discover the root causes of the incident and further optimize safety supervision measures and work processes.

[0022] In general, this comprehensive coal mine safety supervision method and system has greatly improved the safety management level of coal mines, reduced the probability of accidents, and improved the miners' sense of security and work efficiency. In addition, through continuous data collection and analysis, the system can continuously optimize itself, adapt to changes in the coal mine environment, and achieve long-term safe production. The promotion and application of this system will undoubtedly bring revolutionary improvements to the safety management of the entire coal mining industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In the attached picture: Figure 1 It is a schematic diagram of the overall system structure of the present invention; Figure 2 is a schematic structural diagram of a sensor module of the present invention; Figure 3 It is a structural schematic diagram of the data analysis module of the present invention; Figure 4 It is a structural schematic diagram of the artificial intelligence judgment module of the present invention; Figure 5 It is a schematic diagram of the structure of the risk warning module of the present invention; Figure 6 It is a schematic diagram of the operation steps of the present invention; Figure 7 It is a schematic diagram of the risk warning steps of the present invention. DETAILED DESCRIPTION

[0024] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. Obviously, the described embodiments are only part of the embodiments of the invention, rather than all of the embodiments. In the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other. Based on the embodiments in the invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the invention.

[0025] It should be noted that if the embodiments of the invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0026] In addition, "multiple" means more than two. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection required by the invention.

[0027] Please refer to the instruction manual Figure 1-Figure 7 , the present invention provides a coal mine safety supervision method and system: Implementation of coal mine safety supervision methods: The coal mine safety supervision method of the present invention can be widely applied in various coal mine environments to ensure the safety of miners and equipment. The following are the detailed implementation steps: a. Data collection: Multiple sensors are deployed in coal mines, including gas sensors, temperature and humidity sensors, dust sensors, positioning sensors, and motion sensors. a. Gas sensors: Electrochemical or infrared gas sensors are used to detect the concentration of methane, carbon monoxide, and oxygen in the coal mine environment. These sensors have high sensitivity and fast response time.

[0028] Temperature and humidity sensors: semiconductor or thermistor sensors are used to measure the temperature and humidity in the coal mine environment. These sensors have high accuracy and stability.

[0029] Dust sensors: Light scattering or laser dust sensors are used to detect dust concentration in coal mine air. These sensors can monitor dust concentration in real time to ensure miners’ breathing health.

[0030] Positioning sensors: Use UWB (ultra-wideband) or RFID (radio frequency identification) technology to determine the miner's specific location in the coal mine. These technologies have high accuracy and anti-interference capabilities.

[0031] Motion sensors: MEMS sensors such as accelerometers and gyroscopes are used to monitor the miners’ motion status, including walking, standing still, and falling. These sensors can detect the miners’ activity status in real time and provide support for safety warnings.

[0032] b. Data transmission: The data collected by the sensor is transmitted to the ground monitoring center through wireless communication technologies (such as Wi-Fi, ZigBee, LoRa, NB-IoT, 5G, etc.) to ensure the real-time and stability of data transmission.

[0033] c. Data analysis: In the ground monitoring center, the data analysis module is used to analyze the transmitted data in real time. The data analysis module includes a data cleaning submodule, a feature extraction submodule, a model training submodule, and a real-time evaluation submodule. These submodules work together to denoise, complete, and normalize the data, extract key feature parameters, train the risk assessment model, and use the trained model to evaluate the real-time data, identify safety hazards, and generate risk scores.

[0034] d. Artificial Intelligence Judgment: Apply the artificial intelligence judgment module to further evaluate and judge the data analysis results. This module includes deep learning submodules, pattern recognition submodules, and risk scoring submodules. Deep learning submodule: Use deep learning algorithms such as convolutional neural networks (CNN) and recurrent neural networks (RNN) to further process data analysis results and identify more complex safety hazards.

[0035] Pattern recognition submodule: predicts possible security incidents through cluster analysis, pattern matching and other pattern recognition technologies.

[0036] Risk scoring submodule: Based on a comprehensive assessment of multiple factors (such as gas concentration, temperature, humidity, dust concentration, etc.), a risk score is generated to quantify the severity of different safety hazards.

[0037] e. Risk warning: Based on the results of artificial intelligence judgment, the risk warning module sends warning information to coal mine managers and miners. The risk warning module includes a warning level division submodule and a multi-channel notification submodule. The warning level division submodule divides the warning information into different levels (such as low, medium, high, and emergency) according to the severity of the safety hazards, and each level corresponds to a different response strategy. The multi-channel notification submodule sends warning information to relevant personnel through multiple channels such as sound and light alarms, SMS notifications, APP push, and emails according to the warning level to ensure that the warning information can be conveyed in a timely manner.

[0038] f. Safety decision-making: The safety decision support module provides tools such as decision trees and rule engines to assist coal mine managers in formulating and implementing corresponding safety measures based on early warning information and initiating emergency plans when necessary.

[0039] g. Event records: Keep detailed records of each warning and corresponding treatment measures to form an event log, including the time of the event, warning content, measures taken and final results, for subsequent analysis and improvement.

[0040] Implementation of coal mine safety supervision system: The coal mine safety supervision system of the present invention can be widely used in various coal mine environments to achieve real-time supervision of coal mine safety. The following are detailed implementation components: a. Multiple sensors: including gas sensors, temperature and humidity sensors, dust sensors, positioning sensors and motion sensors. These sensors are responsible for collecting various key data in the coal mine environment.

[0041] b. Wireless communication module: Using Wi-Fi, ZigBee, LoRa, NB-IoT, 5G and other communication technologies, the data collected by the sensor is transmitted to the ground monitoring center in real time and stably.

[0042] c. Data analysis module: installed in the ground monitoring center, it performs real-time analysis on the transmitted data, including data cleaning, feature extraction, model training and real-time evaluation to identify potential safety hazards.

[0043] d. Artificial Intelligence Judgment Module: Further evaluates and judges the data analysis results to generate a more accurate risk assessment report, including the classification and priority ranking of different types of risks.

[0044] e. Risk warning module: Issues warning information to coal mine managers and miners based on the results of artificial intelligence judgment. The warning information includes specific risk types, severity and recommended response measures.

[0045] f. Safety decision support module: assist coal mine managers in formulating and implementing corresponding safety measures, and initiate emergency plans when necessary to ensure the safety of the coal mine site.

[0046] g. Event recording module: records each warning and corresponding treatment measures to form an event log, including the time of event occurrence, warning content, measures taken and final results, for subsequent analysis and improvement.

[0047] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0048] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0049] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A coal mine safety supervision method, comprising the following steps: a. Data collection: Multiple sensors are deployed in the coal mine to collect real-time data such as gas concentration (such as methane, carbon monoxide, oxygen, etc.), temperature, humidity, dust concentration, miner location and movement status in the coal mine environment; b. Data transmission: The collected data is transmitted to the ground monitoring center through wireless communication technology to ensure the real-time and stability of data transmission; c. Data analysis: Use big data analysis technology and machine learning algorithms to conduct real-time analysis of data transmitted to the monitoring center, including but not limited to trend analysis, anomaly detection and predictive analysis, to identify potential safety hazards; d. Artificial intelligence judgment: Apply artificial intelligence modules to further evaluate and judge the data analysis results and generate more accurate risk assessment reports, including classification and priority ranking of different types of risks; e. Risk warning: Based on the results of AI judgment, early warning information is issued to coal mine managers and miners through sound and light alarms, SMS notifications, APP push, etc. The early warning information includes specific risk types, severity and recommended response measures; f. Safety decision-making: Based on the early warning information, coal mine managers formulate and implement corresponding safety measures and initiate emergency plans when necessary to ensure the safety of the coal mine site; g. Event records: Keep detailed records of each warning and corresponding treatment measures to form an event log, including the time of the event, warning content, measures taken and final results, for subsequent analysis and improvement.

2. A coal mine safety supervision method and system according to claim 1, characterized in that: The risk warning steps include: a. Warning level classification: According to the severity of potential safety hazards, the warning information is divided into different levels (such as low, medium, high, and emergency), and each level corresponds to a different response strategy; b. Multi-channel notification: According to the warning level, warning information is sent to relevant personnel through multiple channels (such as sound and light alarm, SMS notification, APP push, email, etc.) to ensure that the warning information can be conveyed in time.

3. A coal mine safety supervision system, comprising: a. Multiple sensors are used to collect data such as gas concentration, temperature, humidity, dust concentration, miners' position and movement status in the coal mine environment; b. Wireless communication module, used to transmit the collected data to the ground monitoring center to ensure the real-time and stability of data transmission; c. Data analysis module, installed in the ground monitoring center, is used to perform real-time analysis of the transmitted data, including trend analysis, anomaly detection and predictive analysis, to identify potential safety hazards; d. Artificial intelligence judgment module, which is used to further evaluate and judge the data analysis results and generate more accurate risk assessment reports, including classification and priority ranking of different types of risks; e. Risk warning module, which is used to issue early warning information to coal mine managers and miners based on the results of artificial intelligence judgment. The early warning information includes specific risk types, severity and recommended response measures; f. Safety decision support module, which is used to assist coal mine managers in formulating and implementing corresponding safety measures and, when necessary, launching emergency plans to ensure the safety of the coal mine site; g. Event recording module, used to record each warning and corresponding treatment measures, forming an event log, including the time of event occurrence, warning content, measures taken and final results, for subsequent analysis and improvement.

4. A coal mine safety supervision system according to claim 3, characterized in that: The sensor comprises: a. Gas sensor: used to detect the concentration of methane, carbon monoxide and oxygen in coal mine environment; b. Temperature and humidity sensor: used to measure temperature and humidity in coal mine environment; c. Dust sensor: used to detect dust concentration in coal mine air; d. Positioning sensor: used to determine the specific location of miners in the coal mine; e. Motion sensor: used to monitor the movement status of miners, including walking, standing still and falling.

5. A coal mine safety supervision system according to claim 3, characterized in that: The wireless communication module supports one or more of the following communication technologies: a.Wi-Fi: used for short-distance, high-bandwidth data transmission; b. ZigBee: for low-power, low-data-rate wireless communications; c.LoRa: used for long-distance, low-power data transmission; d.NB-IoT: for wide-area coverage and low-power IoT communications; e.5G: For high-bandwidth, low-latency, and high-reliability wireless communications.

6. A coal mine safety supervision system according to claim 3, characterized in that: The data analysis module includes: a. Data cleaning submodule: denoise, complete and normalize the collected data to ensure the accuracy and consistency of the data; b. Feature extraction submodule: extract key feature parameters from the cleaned data, such as gas concentration change rate, temperature gradient, etc.; c. Model training submodule: Based on historical data and expert knowledge, the risk assessment model is trained using supervised learning, unsupervised learning or semi-supervised learning methods; d. Real-time evaluation submodule: Use the trained model to evaluate real-time data, identify safety hazards, and generate risk scores.

7. A coal mine safety supervision system according to claim 3, characterized in that: The artificial intelligence judgment module includes: a. Deep learning submodule: Use deep learning algorithms (such as convolutional neural networks, recursive neural networks, etc.) to further process data analysis results and identify more complex safety hazards; b. Pattern recognition submodule: predicts possible security incidents through pattern recognition technology (such as cluster analysis, pattern matching, etc.); c. Risk scoring submodule: Generates risk scores based on a comprehensive assessment of multiple factors to quantify the severity of different safety hazards.

8. A coal mine safety supervision system according to claim 3, characterized in that: The risk warning module includes: a. Warning level classification submodule: According to the severity of potential safety hazards, the warning information is divided into different levels (such as low, medium, high, and emergency), and each level corresponds to a different response strategy; b. Multi-channel notification submodule: According to the warning level, send warning information to relevant personnel through multiple channels (such as sound and light alarm, SMS notification, APP push, email, etc.) to ensure that the warning information can be conveyed in time.

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