Coal mine safety information intelligent monitoring system applying industrial internet

Abstract

The invention discloses a coal mine safety information intelligent monitoring system applying an industrial internet, and relates to the technical field of intelligent monitoring. During operation of the system, multi-source data are collected from equipment and subjected to primary processing, and data from different sources are subjected to space-time matching and association by using a data fusion technology; and obtaining a coal mine safety information early warning index Yjzs after calculation, analyzing the stress change and risk of the underground rock stratum, and simulating and predicting the chemical risk in the mine by processing the hydrogen sulfide diffusion rate and nitrogen oxide concentration gradient data and adopting a computational fluid dynamics (CFD) chemical reaction rate model. And monitoring and evaluating behaviors of coal miners and operation conditions of equipment, comparing a coal mine safety information early warning index Yjzs with a preset warning threshold value M and a danger threshold value N, generating a corresponding strategy scheme according to a comparison result, and dynamically adjusting the threshold value to adapt to different mine conditions and safety standards.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring technology, and specifically to a coal mine safety information intelligent monitoring system using the industrial Internet. Background Art

[0002] Coal mine safety monitoring is an important link that cannot be ignored in the mining industry. It is a key means to ensure the safety of mine operations and avoid accidents. Traditional coal mine safety monitoring mainly relies on manual inspections and fixed monitoring equipment. However, with the increase in the depth of mines and the complexity of the working environment, a single monitoring method can no longer meet the needs. Traditional monitoring systems rely on fixed monitoring points, and data coverage is not comprehensive, which makes it difficult to conduct real-time monitoring in some hidden areas or high-risk operation areas. At the same time, due to equipment performance and technical limitations, the early warning capabilities of existing systems are weak, and it is difficult to effectively predict the risk of accidents. In addition, there are many manual interventions and operation processes, and there is a greater risk of operational errors, and it is impossible to form an automated early warning and response strategy.

[0003] The shortcomings and deficiencies in the existing coal mine safety monitoring system not only affect the safety of daily mine operations, but also increase the probability of mine accidents and accidents. Especially in extreme environments such as high stress, high temperature, and high humidity, the accuracy of equipment monitoring and the data response speed are low, making it difficult to identify and control risks in the early stages. Once an emergency occurs, such as rock instability, chemical gas leakage, equipment failure, or uncontrolled worker behavior, the existing system cannot respond and handle it quickly, which may cause serious casualties and economic losses. The occurrence of the above abnormal situations not only increases the safety risks of mine operations, but may also cause larger-scale secondary disasters. Therefore, there is an urgent need for an intelligent and comprehensive safety monitoring system that can quickly and accurately identify, warn, and respond to risks to ensure the safety and stability of coal mine operations. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a coal mine safety information intelligent monitoring system using the industrial Internet, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent monitoring system for coal mine safety information using the industrial Internet, comprising a multi-source data acquisition module, a data processing module, a geological stress intelligent analysis module, a chemical environment risk assessment module, an operation monitoring module and an intelligent early warning module;

[0006] The multi-source data acquisition module is used to collect multi-source data from various sensors, monitoring devices and smart wearable devices to obtain geological monitoring data sets, chemical gas monitoring data sets and personnel behavior monitoring data sets;

[0007] The data processing module is used to perform preliminary processing on the collected multi-source data, including data cleaning, denoising, completion and normalization, eliminate redundancy and noise in the data, use data fusion technology to match and associate data from different sources in time and space, and obtain after calculation: coal mine safety information early warning index Yjzs;

[0008] The geological stress intelligent analysis module is used to analyze the stress changes and risks of underground rock formations, and predict the risk of rock formation instability through geological models and machine learning algorithms using real-time microseismic frequency and stress deformation rate;

[0009] The chemical environmental risk assessment module is used to perform real-time analysis and assessment of the diffusion and reaction risks of chemical gases in the mine. By processing the hydrogen sulfide diffusion rate and nitrogen oxide concentration gradient data, computational fluid dynamics (CFD) and chemical reaction rate models are used to simulate and predict the chemical risks in the mine.

[0010] The operation monitoring module is used to monitor and evaluate the behavior of coal miners and the operating status of equipment through intelligent video analysis and wearable devices, detect high-risk behaviors, illegal operations and abnormal equipment status, and reduce safety accidents caused by human factors;

[0011] The intelligent early warning module is used to compare the coal mine safety information early warning index Yjzs with the preset warning threshold M and danger threshold N. According to the comparison results, the intelligent early warning module will generate a corresponding strategy plan, and dynamically adjust the threshold to adapt to different mine conditions and safety standards according to changes in historical data and actual conditions.

[0012] Preferably, the multi-source data acquisition module includes a geological monitoring data acquisition unit, a chemical gas monitoring data acquisition unit and a personnel behavior monitoring data acquisition unit;

[0013] The geological monitoring data acquisition unit is used to collect and process geological data in the mine through microseismic monitoring sensors, fiber grating sensors and seismic wave sensors, including microseismic frequency data, rock formation stress and deformation data, seismic wave attenuation data, rock mass stability index, rock formation temperature and humidity change data, and obtain: rock formation microseismic frequency F ms , rock stress deformation rate D rate , seismic wave attenuation coefficient A sw , Rock fracture precursor energy accumulation index E acc and mine pressure fluctuation index P mf , forming a geological monitoring data set.

[0014] Preferably, the chemical gas monitoring data acquisition unit is used to measure the diffusion rate of toxic and harmful gases in the mine and the potential safety hazards they bring, the chemical reactions and diffusion processes in the coal mine working environment, and obtain: the hydrogen sulfide diffusion rate DH2S , nitrogen oxide concentration gradient G NOx , Volatile Organic Compound Emission Index I VOC , carbon oxide deposition rate R COx and the gas reaction rate constant k gas , forming a chemical gas monitoring data set.

[0015] Preferably, the personnel behavior monitoring data acquisition unit is used to collect the behavior data of the mine operators, monitor the activities of the personnel in the mine and their operation conditions, obtain the personnel behavior data through the smart wearable device and the smart video analysis system, analyze the safety behavior and status of the operators, and obtain: fatigue index F fatigue , High-risk action times C hr 、Frequency of abnormal operation of equipment abn , reaction delay time T delay and the incidence rate of non-normative behavior R nonstd , forming a personnel behavior monitoring data set.

[0016] Preferably, the data processing module includes a data preprocessing unit and a data fusion unit;

[0017] The data preprocessing unit is used to clean, denoise and normalize the geological monitoring data set, the chemical gas monitoring data set and the personnel behavior monitoring data set to eliminate redundancy, errors and noise in the data, and convert data of different dimensions into the same scale range by normalizing the data.

[0018] Preferably, the data fusion unit is used to fuse and perform spatiotemporal correlation analysis on the cleaned and normalized data, unify the data from different sources into a high-dimensional data set through multi-sensor data fusion technology, and obtain the coal mine safety information warning index Y after calculation: jzs , geological stress variation coefficient C geo , chemical gas diffusion coefficient C chem and high-risk operation behavior identification coefficient C hrb .

[0019] Preferably, the geological stress intelligent analysis module includes a real-time stress data analysis unit and a geological model prediction unit;

[0020] The real-time stress data analysis unit is used to analyze the real-time stress data from the rock formation, including microseismic frequency and stress deformation rate, and use advanced data processing technology to conduct in-depth analysis of these stress data to evaluate the stress state and potential risks of the rock formation;

[0021] The geological model prediction unit is used to identify potential risks of rock instability and continuously learn and optimize the risk identification model. By using deep learning and reinforcement learning algorithms, the prediction model is automatically adjusted and improved, new features are extracted from real-time data, and unknown risk patterns are identified.

[0022] Preferably, the chemical environment risk assessment module includes a gas diffusion simulation unit and a chemical reaction risk prediction unit;

[0023] The gas diffusion simulation unit is used to simulate the diffusion behavior of toxic and harmful gases in the mine. The diffusion rate, flow path and concentration change of hydrogen sulfide and nitrogen oxide gases are simulated and visualized in detail by using computational fluid dynamics (CFD) technology. By modeling the ventilation conditions of the mine, the gas generation source and the physical properties of the diffusion medium, potential high-concentration areas are identified, and the time and range of gas diffusion under different conditions are predicted.

[0024] The chemical reaction risk prediction unit is used to evaluate the chemical reaction risks between toxic gases in the mine environment. Through the chemical reaction rate model, it analyzes the reaction rate and product generation of gases under different temperatures, humidity and pressures. It can also simulate the risk characteristics of chemical reaction processes and reaction products to help estimate the generation of hazardous chemicals and their potential impacts.

[0025] Preferably, the operation monitoring module includes an intelligent behavior monitoring unit and an equipment status evaluation unit;

[0026] The intelligent behavior monitoring unit is used to monitor and analyze the behavior of coal miners in real time through intelligent video analysis technology and computer vision algorithms. It can identify high-risk behaviors of workers, including not wearing safety helmets, entering dangerous areas, and signs of fatigue caused by long working hours. By establishing a behavior pattern library and preset rules, it can also detect illegal operations and issue warnings;

[0027] The equipment status evaluation unit is used to monitor the operating conditions of various types of equipment in the mine, and uses wearable devices and IoT sensors to collect data in real time, including the vibration frequency, temperature changes and current load of the equipment. Through data analysis and anomaly detection algorithms, the equipment status evaluation unit can identify abnormal conditions of the equipment, including overheating, abnormal vibration or reduced operating efficiency, and promptly remind maintenance personnel to conduct inspections and maintenance.

[0028] Preferably, the intelligent early warning module includes an early warning strategy generation unit and an evaluation report unit;

[0029] The early warning strategy generation unit is used to compare the coal mine safety information early warning index Yjzs with the preset warning threshold M and danger threshold N, and generate a corresponding early warning strategy according to the comparison result:

[0030] When Yjzs < M, the coal mine safety is in a safe state, the overall safety condition of the mine is stable, the operation status of the ventilation system is regularly checked daily, the smoothness and fault-free operation of all ventilation equipment and pipelines are monitored, and the wind speed, air pressure and current data of the ventilation equipment are recorded. The ventilation systems of the main shaft and auxiliary shaft of the coal mine are inspected to ensure the oxygen supply and the effective discharge of harmful gases;

[0031] When M ≤ Yjzs < N, the coal mine safety is in a warning state, there are potential risks, and preventive measures need to be taken to avoid risk escalation. A preventive strategy plan is generated, and it is recommended to inspect the risk areas or operations, including strengthening ventilation and reducing high-risk operations, and increasing the monitoring frequency;

[0032] When Yjzs ≥ N, the coal mine safety is in a dangerous state, and emergency measures must be taken immediately to avoid accidents or disasters, including evacuating personnel urgently, closing specific areas and stopping operations, and notifying the relevant emergency teams;

[0033] The evaluation report unit is used to generate a report including the current safety status, early warning strategies and future trend predictions, providing decision-making support for the mine safety management personnel.

[0034] The present invention provides an intelligent monitoring system for coal mine safety information applying industrial Internet, having the following beneficial effects:

[0035] (1) During the operation of the system, by collecting multi-source data from the devices and performing preliminary processing, using data fusion technology to perform spatio-temporal matching and correlation on the data from different sources, and obtaining after calculation: the coal mine safety information early warning index Yjzs, analyzing the stress changes and risks of the underground rock strata, through the processing of the hydrogen sulfide diffusion rate and nitrogen oxide concentration gradient data, adopting the computational fluid dynamics CFD chemical reaction rate model to simulate and predict the chemical risks in the mine, monitoring and evaluating the behaviors of coal mine workers and the operation status of the devices, comparing the coal mine safety information early warning index Yjzs with the preset warning threshold M and danger threshold N, and generating corresponding strategy plans according to the comparison results, dynamically adjusting the thresholds to adapt to different mine conditions and safety standards.

[0036] (2) The coal mine safety information intelligent monitoring system based on the industrial Internet completes the comprehensive monitoring and management of the coal mine operating environment through the comprehensive synergy of the multi-source data acquisition module, data processing module, geological stress intelligent analysis module, chemical environment risk assessment module, operation monitoring module and intelligent early warning module. The multi-source data acquisition module collects key data inside the mine in real time from multiple angles such as geological monitoring, chemical gas monitoring and personnel behavior monitoring. The data processing module processes these multi-source data uniformly and generates the coal mine safety information early warning index Yjzs through data cleaning, denoising and fusion. Through comprehensive data analysis and calculation of early warning index, the system can accurately identify potential risks and provide scientific decision-making basis for mine management.

[0037] (3) Compared with traditional coal mine safety monitoring methods, the system introduces a geological stress intelligent analysis module and a chemical environment risk assessment module, which greatly improves the ability to predict various risks in the mine environment. The geological stress intelligent analysis module achieves efficient prediction and early warning of potential risks such as rock instability through real-time stress data analysis and geological model prediction; the chemical environment risk assessment module is based on computational fluid dynamics (CFD) and chemical reaction rate models, which can simulate the diffusion behavior and chemical reaction process of harmful gases, providing an important reference for preventing toxic gas leaks and chemical accidents. Through the application of these two modules, the system can timely predict and handle abnormal conditions in the geological and chemical environments, significantly improving the overall safety of the mine.

[0038] (4) The introduction of the operation monitoring module and the intelligent early warning module further improves the system's safety management capabilities for mine operations. The operation monitoring module uses intelligent video analysis and wearable device monitoring technology to achieve real-time monitoring and evaluation of workers' high-risk behaviors and equipment status, which can effectively reduce safety accidents caused by human errors or equipment failures. The intelligent early warning module automatically generates corresponding early warning strategies based on the comparison results between the early warning index and the threshold, and can dynamically adjust the early warning threshold according to changes in mine conditions and safety standards. Compared with traditional manual judgment and simple rule setting, this intelligent and adaptive early warning method greatly improves the system's response speed and response capabilities, providing more accurate and flexible safety guarantees for coal mine operations. Overall, through the comprehensive upgrade of technical means, the system has achieved a leap from static, single monitoring methods to dynamic, intelligent monitoring and early warning for coal mine safety monitoring, effectively improving the level and efficiency of coal mine safety management. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a block diagram of a coal mine safety information intelligent monitoring system using the Industrial Internet according to the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Example 1

[0042] The present invention provides a coal mine safety information intelligent monitoring system using the industrial Internet. Figure 1 , including multi-source data acquisition module, data processing module, geological stress intelligent analysis module, chemical environment risk assessment module, operation monitoring module and intelligent early warning module;

[0043] The multi-source data acquisition module is used to collect multi-source data from various sensors, monitoring devices and smart wearable devices to obtain geological monitoring data sets, chemical gas monitoring data sets and personnel behavior monitoring data sets;

[0044] The data processing module is used to perform preliminary processing on the collected multi-source data, including data cleaning, denoising, completion and normalization, eliminate redundancy and noise in the data, use data fusion technology to match and associate data from different sources in time and space, and obtain after calculation: coal mine safety information early warning index Yjzs;

[0045] The geological stress intelligent analysis module is used to analyze the stress changes and risks of underground rock formations, and predict the risk of rock formation instability through geological models and machine learning algorithms using real-time microseismic frequency and stress deformation rate;

[0046] The chemical environmental risk assessment module is used to perform real-time analysis and assessment of the diffusion and reaction risks of chemical gases in the mine. By processing the hydrogen sulfide diffusion rate and nitrogen oxide concentration gradient data, computational fluid dynamics (CFD) and chemical reaction rate models are used to simulate and predict the chemical risks in the mine.

[0047] The operation monitoring module is used to monitor and evaluate the behavior of coal miners and the operating status of equipment through intelligent video analysis and wearable devices, detect high-risk behaviors, illegal operations and abnormal equipment status, and reduce safety accidents caused by human factors;

[0048] The intelligent early warning module is used to compare the coal mine safety information early warning index Yjzs with the preset warning threshold M and danger threshold N. According to the comparison results, the intelligent early warning module will generate a corresponding strategy plan, and dynamically adjust the threshold to adapt to different mine conditions and safety standards according to changes in historical data and actual conditions.

[0049] In this embodiment, multi-source data is collected from the equipment and preliminarily processed, and data fusion technology is used to match and associate data from different sources in time and space, and the following is obtained after calculation: coal mine safety information early warning index Yjzs, analyze the stress changes and risks of underground rock formations, and simulate and predict the chemical risks in the mine by processing the hydrogen sulfide diffusion rate and nitrogen oxide concentration gradient data and using the computational fluid dynamics (CFD) chemical reaction rate model. The behavior of coal miners and the operating conditions of equipment are monitored and evaluated, and the coal mine safety information early warning index Yjzs is compared with the preset warning threshold M and the danger threshold N. According to the comparison results, a corresponding strategy is generated to dynamically adjust the threshold to adapt to different mine conditions and safety standards.

[0050] Example 2

[0051] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the multi-source data acquisition module includes a geological monitoring data acquisition unit, a chemical gas monitoring data acquisition unit and a personnel behavior monitoring data acquisition unit;

[0052] The geological monitoring data acquisition unit is used to collect and process geological data in the mine through microseismic monitoring sensors, fiber grating sensors and seismic wave sensors, including microseismic frequency data, rock formation stress and deformation data, seismic wave attenuation data, rock mass stability index, rock formation temperature and humidity change data, and obtain: rock formation microseismic frequency F ms , rock stress deformation rate D rate , seismic wave attenuation coefficient A sw , Rock fracture precursor energy accumulation index E acc and mine pressure fluctuation index P mf , forming a geological monitoring data set;

[0053] The chemical gas monitoring data acquisition unit is used to measure the diffusion rate of toxic and harmful gases in the mine and the potential safety hazards they bring, the chemical reactions and diffusion processes in the coal mine working environment, and obtain: the hydrogen sulfide diffusion rate D H2S , nitrogen oxide concentration gradient G NOx , Volatile Organic Compound Emission Index I VOC , carbon oxide deposition rate R COx and the gas reaction rate constant k gas , forming a chemical gas monitoring data set;

[0054] The personnel behavior monitoring data acquisition unit is used to collect the behavior data of mine operators, monitor the activities of personnel in the mine and their operation conditions, obtain the personnel behavior data through smart wearable devices and smart video analysis systems, analyze the safety behavior and status of operators, and obtain: fatigue index F fatigue, High-risk action times C hr 、Frequency of abnormal operation of equipment abn , reaction delay time T delay and the incidence rate of non-normative behavior R nonstd , forming a personnel behavior monitoring data set.

[0055] The data processing module includes a data preprocessing unit and a data fusion unit;

[0056] The data preprocessing unit is used to clean, denoise and normalize the geological monitoring data set, the chemical gas monitoring data set and the personnel behavior monitoring data set, eliminate redundancy, errors and noise in the data, and convert data of different dimensions into the same scale range by normalizing the data;

[0057] The data fusion unit is used to fuse and analyze the spatiotemporal correlation of the cleaned and normalized data. Through multi-sensor data fusion technology, data from different sources are unified into a high-dimensional data set. After calculation, the following coal mine safety information early warning index Y is obtained: jzs , geological stress variation coefficient C geo , chemical gas diffusion coefficient C chem and high-risk operation behavior identification coefficient C hrb ;

[0058] The coal mine safety information early warning index Y jzs Calculated by the following formula:

[0059] Yjzs=w geo *C geo +w chem *C chem +w hrb *C hrb ;

[0060] Where Y jzs represents the coal mine safety information early warning index, C geo represents the geological stress variation coefficient, C chem represents the chemical gas diffusion coefficient, C hrb represents the high-risk operation behavior identification coefficient, w geo 、w chem and w hrb Represents the geological stress variation coefficient C geo , chemical gas diffusion coefficient C chem and high-risk operation behavior identification coefficient C hrb The proportionality factor.

[0061] The geological stress variation coefficient C geo Calculated by the following formula:

[0062]

[0063] In the formula, F ms represents the frequency of microseismicity in rock formation, D rate A represents the stress-deformation rate of rock formation, sw Represents the seismic wave attenuation coefficient, E acc represents the energy accumulation index of rock failure precursor, P mf represents the mine pressure fluctuation index, α 1 , α 2 , α 3 , α 4 and β 1 Represent the rock microseismic frequency F ms , rock stress deformation rate D rate , seismic wave attenuation coefficient A sw , Rock fracture precursor energy accumulation index E acc and mine pressure fluctuation index P mf The proportionality factor.

[0064] The chemical gas diffusion coefficient C chem Calculated by the following formula:

[0065]

[0066] Where D H2S represents the diffusion rate of hydrogen sulfide, G NOx represents the nitrogen oxide concentration gradient, I VOC represents the volatile organic compound dispersion index, R COx represents the carbon oxide deposition rate, k gas is the gas reaction rate constant, γ 1 , γ 2 , γ 3 and γ 4 Denote the hydrogen sulfide diffusion rate D H2S , nitrogen oxide concentration gradient G NOx , Volatile Organic Compound Emission Index I VOC , carbon oxide deposition rate R COx and the gas reaction rate constant k gas The proportionality factor.

[0067] The high-risk operation behavior identification coefficient C hrb Calculated by the following formula:

[0068]

[0069] In the formula, F fatigue represents fatigue index, C hr Indicates the number of high-risk actions, F abnIndicates the frequency of abnormal operation of the equipment, T delay Represents the reaction delay time, R nonstd represents the incidence of non-normative behavior, δ 1 , δ 2 , δ 3 and δ 4 Represents fatigue index F fatigue , High-risk action times C hr 、Frequency of abnormal operation of equipment abn , reaction delay time T delay and the incidence rate of non-normative behavior R nonstd The proportionality factor.

[0070] In this embodiment, the multi-source data acquisition module comprehensively improves the coverage and accuracy of coal mine operating environment monitoring by integrating three data units: geological monitoring, chemical gas monitoring, and personnel behavior monitoring. The geological monitoring data acquisition unit uses microseismic monitoring sensors, fiber grating sensors, and seismic wave sensors to collect multi-dimensional geological data such as rock stratum microseismic frequency, stress deformation rate, seismic wave attenuation coefficient, energy accumulation index, and mine pressure fluctuation in real time to ensure accurate grasp of changes in the stability of the mine rock mass; the chemical gas monitoring data acquisition unit evaluates the potential threat of chemical hazards in the operating environment by analyzing the diffusion rate and reaction speed of toxic gases; the personnel behavior monitoring data acquisition unit relies on smart wearable devices and video analysis systems to monitor the fatigue level, high-risk operation behavior, and abnormal reactions of miners to ensure the safety of the operation process. Through the real-time collection and processing of these data, the system constructs a multi-level and multi-dimensional coal mine safety monitoring information network, which can provide timely warnings in emergencies and improve the overall safety factor of coal mines. Compared with traditional single data acquisition methods, this module can perform real-time monitoring from multiple angles and in all directions, providing strong support for subsequent data processing and risk assessment, and significantly improving the accuracy and reliability of data acquisition.

[0071] Example 3

[0072] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the geological stress intelligent analysis module includes a real-time stress data analysis unit and a geological model prediction unit;

[0073] The real-time stress data analysis unit is used to analyze the real-time stress data from the rock formation, including microseismic frequency and stress deformation rate, and use advanced data processing technology to conduct in-depth analysis of these stress data to evaluate the stress state and potential risks of the rock formation;

[0074] The geological model prediction unit is used to identify potential risks of rock instability and continuously learn and optimize the risk identification model. By using deep learning and reinforcement learning algorithms, the prediction model is automatically adjusted and improved, new features are extracted from real-time data, and unknown risk patterns are identified.

[0075] The chemical environment risk assessment module includes a gas diffusion simulation unit and a chemical reaction risk prediction unit;

[0076] The gas diffusion simulation unit is used to simulate the diffusion behavior of toxic and harmful gases in the mine. The diffusion rate, flow path and concentration change of hydrogen sulfide and nitrogen oxide gases are simulated and visualized in detail by using computational fluid dynamics (CFD) technology. By modeling the ventilation conditions of the mine, the gas generation source and the physical properties of the diffusion medium, potential high-concentration areas are identified, and the time and range of gas diffusion under different conditions are predicted.

[0077] The chemical reaction risk prediction unit is used to evaluate the chemical reaction risks between toxic gases in the mine environment. Through the chemical reaction rate model, it analyzes the reaction rate and product generation of gases under different temperatures, humidity and pressures. It can also simulate the risk characteristics of chemical reaction processes and reaction products to help estimate the generation of hazardous chemicals and their potential impacts.

[0078] In this embodiment, the geological stress intelligent analysis module and the chemical environment risk assessment module are combined to significantly improve the comprehensive safety monitoring capability of the mine environment. The geological stress intelligent analysis module uses advanced data processing technology to deeply analyze the microseismic frequency and stress deformation rate of the rock formation through the real-time stress data analysis unit, and can evaluate the stress state and potential instability risk of the rock formation in real time. The geological model prediction unit uses deep learning and reinforcement learning algorithms to continuously optimize the prediction model, which can extract new features from real-time data, identify potential unknown risk patterns, and achieve early warning and prevention of geological disasters. At the same time, the chemical environment risk assessment module uses CFD technology through the gas diffusion simulation unit to accurately simulate and visualize the diffusion behavior of toxic and harmful gases in the mine, identify potential high-concentration areas, and predict the diffusion range and time of gases under different conditions. The chemical reaction risk prediction unit evaluates the reaction risk between toxic gases based on the chemical reaction rate model, analyzes the speed and product generation of chemical reactions under different environments, and helps predict the generation of hazardous chemicals and their possible impacts. Compared with traditional single monitoring or simple prediction methods, this multi-dimensional stress analysis and chemical risk assessment model has greatly improved the ability to predict and prevent mine environmental risks, can effectively prevent emergencies, and improve the overall safety level of coal mine operations.

[0079] Example 4

[0080] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the operation monitoring module includes an intelligent behavior monitoring unit and an equipment status evaluation unit;

[0081] The intelligent behavior monitoring unit is used to monitor and analyze the behavior of coal miners in real time through intelligent video analysis technology and computer vision algorithms. It can identify high-risk behaviors of workers, including not wearing safety helmets, entering dangerous areas, and signs of fatigue caused by long working hours. By establishing a behavior pattern library and preset rules, it can also detect illegal operations and issue warnings;

[0082] The equipment status evaluation unit is used to monitor the operating conditions of various types of equipment in the mine, and uses wearable devices and IoT sensors to collect data in real time, including the vibration frequency, temperature changes and current load of the equipment. Through data analysis and anomaly detection algorithms, the equipment status evaluation unit can identify abnormal conditions of the equipment, including overheating, abnormal vibration or reduced operating efficiency, and promptly remind maintenance personnel to conduct inspections and maintenance.

[0083] In this embodiment, the operation monitoring module effectively improves the real-time monitoring and early warning capabilities of coal mine safety production through the collaborative work of the intelligent behavior monitoring unit and the equipment status evaluation unit. The intelligent behavior monitoring unit uses intelligent video analysis technology and computer vision algorithms to monitor the behavior of workers in real time and identify high-risk behaviors such as not wearing a helmet and entering a dangerous area. At the same time, it also monitors the fatigue signs of workers working for a long time, establishes a behavior pattern library and preset rules, can detect illegal operations and issue warnings in time, and help prevent the occurrence of safety accidents. The equipment status evaluation unit uses wearable devices and IoT sensors to collect equipment operation data in real time, such as vibration frequency, temperature change, and current load, and identifies abnormal equipment conditions such as overheating, abnormal vibration, or reduced operating efficiency through data analysis and anomaly detection algorithms, and promptly issues inspection and maintenance prompts to maintenance personnel. Compared with the traditional method of relying on manual inspections and post-processing, this module can achieve all-weather monitoring and intelligent early warning of operations and equipment status, reduce safety hazards caused by equipment failures and human operating errors, improve the safety and efficiency of coal mine production, and help build a safer and more intelligent coal mine operating environment.

[0084] Example 5

[0085] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the intelligent warning module includes a warning strategy generating unit and an evaluation report unit;

[0086] The early warning strategy generation unit is used to compare the coal mine safety information early warning index Yjzs with the preset warning threshold M and danger threshold N, and generate corresponding early warning strategies according to the comparison results:

[0087] When Yjzs < M, the coal mine safety is in a safe state, the overall safety condition of the mine is stable. Regularly check the operation status of the ventilation system every day, monitor the smoothness and fault-free operation of all ventilation equipment and pipelines, and record the wind speed, air pressure and current data of the ventilation equipment. Check the ventilation systems of the main shaft and auxiliary shaft of the coal mine to ensure the oxygen supply and the effective discharge of harmful gases. Conduct safety training for operators once every two weeks, including emergency evacuation procedures, handling of abnormal equipment operations, identification of common high-risk behaviors and their avoidance measures;

[0088] When M ≤ Yjzs < N, the coal mine safety is in a warning state, there are potential risks, and preventive measures need to be taken to avoid risk escalation. Generate a preventive strategy plan, recommend checking the risk areas or operations, including strengthening ventilation and reducing high-risk operations, and increasing the monitoring frequency. When the warning state is activated, immediately start the auxiliary ventilation system, increase the air volume and ventilation speed, especially in the areas where the diffusion rate of hydrogen sulfide H 2 S is relatively high. For key areas, monitor the gas concentration level every 4 hours, and deploy temporary gas filtration equipment at key points in the mine to reduce the retention of toxic and harmful gases. For the areas with a high rock stress deformation rate D rate High, strengthen the manual inspection efforts, conduct a comprehensive inspection every 2 hours, focusing on checking the rock deformation, crack expansion and mine pressure fluctuation conditions. For the identified high-risk areas, use portable microseismic monitoring instruments for on-site verification. In potential risk areas, implement personnel zoning management, restrict the entry of non-essential personnel, and require the entering personnel to wear intelligent wearable devices to monitor their vital signs and behavior status in real time.

[0089] When Yjzs ≥ N, the coal mine safety is in a dangerous state, and immediate emergency measures must be taken. Immediately issue an emergency evacuation notice to all personnel in the mine, and quickly evacuate to a safe area according to the pre-determined evacuation route and order. Guide the personnel to evacuate safely through the mine broadcast system and emergency lighting indicators, and conduct real-time roll calls for the personnel leaving the well to ensure that none are left behind. For the mine sections determined to be high-risk areas, immediately close all channels leading to these areas, and stop all operations and equipment operations in these areas. Cut off the power supply of the relevant areas, and strictly control the coal dust, gas and other potential risk factors in these areas to prevent the expansion of the accident;

[0090] The evaluation report unit is used to generate a report containing the current safety status, early warning strategies and future trend predictions, providing decision-making support for the mine safety management personnel.

[0091] In this embodiment, the intelligent early warning module significantly improves the prevention capability and emergency response efficiency of coal mine safety management through the combination of the early warning strategy generation unit and the evaluation report unit. The early warning strategy generation unit can flexibly generate different early warning strategies under safe, alert and dangerous conditions based on the real-time monitoring data of the coal mine safety information early warning index Yjzs. In the safe state, the system recommends regular inspections of ventilation systems and equipment, and provides continuous safety training to maintain the overall safety and stability of the mine. In the alert state, the early warning strategy generation unit will recommend strengthening ventilation, frequently monitoring gas concentrations, and conducting manual inspections and on-site verification of portable microseismic monitoring instruments in high-risk areas, and reducing the escalation of potential risks through personnel zoning management and intelligent wearable device monitoring. In a dangerous state, the module can quickly issue an emergency evacuation notice, coordinate the safe evacuation of all personnel in the mine, and at the same time cut off the power supply and control potential risk sources such as coal dust and gas to minimize accident losses. The evaluation report unit provides mine safety managers with detailed safety status reports, early warning strategy implementation effect analysis and future trend forecasts to support accurate decision-making and risk management.

[0092] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent monitoring system for coal mine safety information using the industrial Internet, characterized in that: It includes multi-source data acquisition module, data processing module, geological stress intelligent analysis module, chemical environment risk assessment module, operation monitoring module and intelligent early warning module; The multi-source data acquisition module is used to collect multi-source data from various sensors, monitoring devices and smart wearable devices to obtain geological monitoring data sets, chemical gas monitoring data sets and personnel behavior monitoring data sets; The data processing module is used to perform preliminary processing on the collected multi-source data, including data cleaning, denoising, completion and normalization, eliminate redundancy and noise in the data, use data fusion technology to match and associate data from different sources in time and space, and obtain after calculation: coal mine safety information early warning index Yjzs; The geological stress intelligent analysis module is used to analyze the stress changes and risks of underground rock formations, and predict the risk of rock formation instability through geological models and machine learning algorithms using real-time microseismic frequency and stress deformation rate; The chemical environmental risk assessment module is used to perform real-time analysis and assessment of the diffusion and reaction risks of chemical gases in the mine. By processing the hydrogen sulfide diffusion rate and nitrogen oxide concentration gradient data, computational fluid dynamics (CFD) and chemical reaction rate models are used to simulate and predict the chemical risks in the mine. The operation monitoring module is used to monitor and evaluate the behavior of coal miners and the operating status of equipment through intelligent video analysis and wearable devices, detect high-risk behaviors, illegal operations and abnormal equipment status, and reduce safety accidents caused by human factors; The intelligent early warning module is used to compare the coal mine safety information early warning index Yjzs with the preset warning threshold M and danger threshold N. According to the comparison results, the intelligent early warning module will generate a corresponding strategy plan, and dynamically adjust the threshold to adapt to different mine conditions and safety standards according to changes in historical data and actual conditions.

2. According to claim 1, a coal mine safety information intelligent monitoring system using the industrial Internet is characterized in that: The multi-source data acquisition module includes a geological monitoring data acquisition unit, a chemical gas monitoring data acquisition unit and a personnel behavior monitoring data acquisition unit; The geological monitoring data acquisition unit is used to collect and process geological data in the mine through microseismic monitoring sensors, fiber grating sensors and seismic wave sensors, including microseismic frequency data, rock formation stress and deformation data, seismic wave attenuation data, rock mass stability index, rock formation temperature and humidity change data, and obtain: rock formation microseismic frequency F ms , rock stress deformation rate D rate , seismic wave attenuation coefficient A sw , Rock fracture precursor energy accumulation index E acc and mine pressure fluctuation index P mf , forming a geological monitoring data set.

3. According to claim 2, a coal mine safety information intelligent monitoring system using the industrial Internet is characterized in that: The chemical gas monitoring data acquisition unit is used to measure the diffusion rate of toxic and harmful gases in the mine and the potential safety hazards they bring, the chemical reactions and diffusion processes in the coal mine working environment, and obtain: the hydrogen sulfide diffusion rate D H2S , nitrogen oxide concentration gradient G NOx , Volatile Organic Compound Emission Index I VOC , carbon oxide deposition rate R COx and the gas reaction rate constant k gas , forming a chemical gas monitoring data set.

4. According to claim 2, a coal mine safety information intelligent monitoring system using the industrial Internet is characterized in that: The personnel behavior monitoring data acquisition unit is used to collect the behavior data of mine operators, monitor the activities of personnel in the mine and their operation conditions, obtain the personnel behavior data through smart wearable devices and smart video analysis systems, analyze the safety behavior and status of operators, and obtain: fatigue index F fatigue , High-risk action times C hr 、Frequency of abnormal operation of equipment abn , reaction delay time T delay and the incidence rate of non-normative behavior R nonstd , forming a personnel behavior monitoring data set.

5. According to claim 3, a coal mine safety information intelligent monitoring system using industrial Internet is characterized in that: The data processing module includes a data preprocessing unit and a data fusion unit; The data preprocessing unit is used to clean, denoise and normalize the geological monitoring data set, the chemical gas monitoring data set and the personnel behavior monitoring data set to eliminate redundancy, errors and noise in the data, and convert data of different dimensions into the same scale range by normalizing the data.

6. According to claim 5, a coal mine safety information intelligent monitoring system using the industrial Internet is characterized in that: The data fusion unit is used to fuse and analyze the spatiotemporal correlation of the cleaned and normalized data. Through multi-sensor data fusion technology, data from different sources are unified into a high-dimensional data set. After calculation, the following coal mine safety information early warning index Y is obtained: jzs , geological stress variation coefficient C geo , chemical gas diffusion coefficient C chem and high-risk operation behavior identification coefficient C hrb .

7. According to claim 1, a coal mine safety information intelligent monitoring system using the industrial Internet is characterized in that: The geological stress intelligent analysis module includes a real-time stress data analysis unit and a geological model prediction unit; The real-time stress data analysis unit is used to analyze the real-time stress data from the rock formation, including microseismic frequency and stress deformation rate, and use advanced data processing technology to conduct in-depth analysis of these stress data to evaluate the stress state and potential risks of the rock formation; The geological model prediction unit is used to identify potential risks of rock instability and continuously learn and optimize the risk identification model. By using deep learning and reinforcement learning algorithms, the prediction model is automatically adjusted and improved, new features are extracted from real-time data, and unknown risk patterns are identified.

8. According to claim 1, a coal mine safety information intelligent monitoring system using the industrial Internet is characterized in that: The chemical environment risk assessment module includes a gas diffusion simulation unit and a chemical reaction risk prediction unit; The gas diffusion simulation unit is used to simulate the diffusion behavior of toxic and harmful gases in the mine. The diffusion rate, flow path and concentration change of hydrogen sulfide and nitrogen oxide gases are simulated and visualized in detail by using computational fluid dynamics (CFD) technology. By modeling the ventilation conditions of the mine, the gas generation source and the physical properties of the diffusion medium, potential high-concentration areas are identified, and the time and range of gas diffusion under different conditions are predicted. The chemical reaction risk prediction unit is used to evaluate the chemical reaction risks between toxic gases in the mine environment. Through the chemical reaction rate model, it analyzes the reaction rate and product generation of gases under different temperatures, humidity and pressures. It can also simulate the risk characteristics of chemical reaction processes and reaction products to help estimate the generation of hazardous chemicals and their potential impacts.

9. According to claim 1, a coal mine safety information intelligent monitoring system using the industrial Internet is characterized in that: The operation monitoring module includes an intelligent behavior monitoring unit and an equipment status evaluation unit; The intelligent behavior monitoring unit is used to monitor and analyze the behavior of coal miners in real time through intelligent video analysis technology and computer vision algorithms. It can identify high-risk behaviors of workers, including not wearing safety helmets, entering dangerous areas, and signs of fatigue caused by long working hours. By establishing a behavior pattern library and preset rules, it can also detect illegal operations and issue warnings; The equipment status evaluation unit is used to monitor the operating conditions of various types of equipment in the mine, and uses wearable devices and IoT sensors to collect data in real time, including the vibration frequency, temperature changes and current load of the equipment. Through data analysis and anomaly detection algorithms, the equipment status evaluation unit can identify abnormal conditions of the equipment, including overheating, abnormal vibration or reduced operating efficiency, and promptly remind maintenance personnel to conduct inspections and maintenance.

10. The coal mine safety information intelligent monitoring system using the industrial Internet according to claim 1 is characterized by: The intelligent early warning module includes an early warning strategy generation unit and an evaluation report unit; The early warning strategy generation unit is used to compare the coal mine safety information early warning index Yjzs with the preset warning threshold M and danger threshold N, and generate a corresponding early warning strategy according to the comparison result: When the coal mine safety information early warning index Yjzs < the preset warning threshold M, the coal mine is in a safe state and the overall safety status of the mine is stable. The operation status of the ventilation system is checked regularly every day, the patency and trouble-free operation of all ventilation equipment and pipelines are monitored, and the wind speed, wind pressure and current data of the ventilation equipment are recorded. The ventilation systems of the main and auxiliary shafts of the coal mine are checked to ensure the oxygen supply and effective discharge of harmful gases. When the preset warning threshold M≤coal mine safety information early warning index Yjzs<danger threshold N, the coal mine safety is in a state of alert, there is a potential risk, and preventive measures need to be taken to avoid risk escalation, generate preventive strategy plans, and recommend inspections of risk areas or operations, including strengthening ventilation and reducing high-risk operations, and increasing the monitoring frequency; When the coal mine safety information warning index Yjzs ≥ the danger threshold N, the coal mine safety is in a dangerous state, and emergency measures must be taken immediately to avoid accidents or disasters, including emergency evacuation of personnel, closure of specific areas, and suspension of operations, and notification of relevant emergency teams; The evaluation and reporting unit is used to generate a report including current safety status, early warning strategies and future trend forecasts, providing decision support for mine safety managers.

Images