A real-time safety monitoring system for coal mine ventilation

Abstract

The present invention discloses a real-time safety monitoring system for coal mine ventilation, which relates to the field of safe construction technology. The system uses a mixed effect analysis module to analyze the stability of the sensor response when multiple harmful gases are mixed in the mine according to the sensor response fluctuation Xbd, and can automatically determine whether to start the investigation mechanism. Once the gas response is abnormal or unstable, the system will quickly start the investigation module and conduct a detailed analysis of the mine ventilation system to ensure that effective measures are taken in the shortest time. The investigation module extracts ventilation status information from real-time data and combines the influence of non-uniform gas distribution to generate an influence coefficient Yxs, providing an accurate basis for the optimization of the ventilation system. Based on this influence coefficient, the safety management module optimizes the ventilation state of the ventilation system, adjusts airflow and gas emissions, ensures that the gas distribution in the mine is more uniform, and reduces the instability and inaccuracy of sensor monitoring caused by excessive non-uniform distribution of harmful gases.

Description

Technical Field

[0001] The present invention relates to the technical field of safe construction, and in particular to a real-time safety monitoring system for coal mine ventilation. Background Art

[0002] Coal mine safety has always been a crucial area in the mining industry. With technological advancements, coal mine safety monitoring has gradually shifted from manual inspections to intelligent monitoring. Coal mine ventilation is a key factor in ensuring mine safety. A well-designed ventilation system not only effectively removes harmful gases but also prevents the accumulation of toxic gases, which can cause catastrophic accidents such as explosions. With increasing mine depths and increasingly complex mining environments, coal mine operations are facing increasing challenges with air pollution and harmful gas concentrations. In particular, during coal mine ventilation, various harmful gases such as methane, carbon monoxide, and sulfur dioxide often interact with each other due to changes in airflow and gas concentrations, posing a significant challenge to mine safety. Therefore, real-time safety monitoring of coal mine ventilation, particularly the analysis of the interference of various harmful gases in mine passageways on sensor responses, has become a key component in improving coal mine ventilation management and safety monitoring systems.

[0003] While existing coal mine ventilation safety monitoring systems can monitor harmful gas concentrations and ventilation status in real time, they suffer from several significant shortcomings. First, mine environments are complex, with multiple harmful gases often coexisting and exhibiting strong cross-sensitivity. Traditional mine ventilation safety monitoring methods typically employ single-gas monitoring methods while ignoring the interactions between gases. This makes it difficult for sensors to accurately distinguish and monitor the interference effects of multiple gases, increasing the error in the response signal and affecting the accuracy of the monitoring data. Second, mine ventilation systems may experience uneven airflow and gas diffusion at different times. This non-uniformity can affect sensor stability and response. Traditional monitoring methods lack effective dynamic adjustment and adaptive mechanisms, making it difficult to reflect the ventilation system's impact on the distribution of different harmful gases in real time, making it difficult to promptly detect and control safety risks. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention provides a real-time safety monitoring system for coal mine ventilation, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a real-time safety monitoring system for coal mine ventilation, including a signal acquisition module, a signal preprocessing module, a mixed effect analysis module, a troubleshooting module and a safety management module;

[0006] The signal acquisition module is used to monitor the response of various harmful gases in the mine channel in real time through the sensor group to obtain relevant response signals, and at the same time use the ventilation system to obtain relevant ventilation status information;

[0007] The signal preprocessing module is used to preprocess the relevant response signal and the relevant ventilation status information, obtain a real-time data set through noise removal, outlier elimination and missing value repair operations, and perform dimensionless processing on the real-time data set using dimensionless technology;

[0008] The mixed effect analysis module will extract relevant response signals from the real-time data set based on the signal preprocessing module, and analyze the stability of the sensor group response when multiple harmful gases are mixed based on the relevant response signals to obtain the response fluctuation Xbd. According to the value of the response fluctuation Xbd, the activation of the troubleshooting mechanism is determined;

[0009] The troubleshooting module is used to activate the troubleshooting mechanism, extract relevant ventilation status information from the real-time data set, and combine the characteristics of multiple harmful gases to analyze the impact of ventilation status on the uneven gas distribution caused by multiple harmful gases, and construct an impact coefficient Yxs;

[0010] The safety management module is used to optimize the ventilation state of the ventilation system based on the influence coefficient Yxs.

[0011] Preferably, the signal acquisition module includes a deployment unit, a gas dynamic monitoring unit and a ventilation acquisition unit;

[0012] The deployment unit is used to deploy a sensor group in the mine passage in advance, wherein the sensor group includes an electrochemical sensor, an infrared sensor, a metal oxide semiconductor sensor, and a wind speed sensor;

[0013] The gas dynamic monitoring unit is used to monitor the response of various harmful gases in the mine channel in real time based on the sensor group to obtain relevant response signals, wherein the relevant response signals include a response value Xtz collected by the sensor matching the corresponding harmful gas when it is not interfered with, a baseline response value Xyz0 of the sensor matching the corresponding harmful gas when the concentration of the corresponding harmful gas is zero, and a sensitivity Lmz of the sensor matching the corresponding harmful gas to the corresponding harmful gas when it is not interfered with;

[0014] The ventilation collection unit is used to collect ventilation conditions in the mine passage according to the ventilation system to obtain relevant ventilation status information, wherein the relevant ventilation status information includes the ventilation speed Fsz and ventilation direction at each monitoring moment, wherein the ventilation system includes a main air duct, a branch air duct, an air door and an air valve.

[0015] Preferably, the signal preprocessing module includes a preprocessing unit and a signal conversion unit;

[0016] The preprocessing unit is used to preprocess the relevant response signal and the relevant ventilation status information, and the preprocessing includes removing noise, filling missing values, eliminating outliers and data smoothing operations, wherein the missing value filling methods include mean filling, median filling, interpolation filling and regression filling;

[0017] The signal conversion unit is used to convert the response values Xyz collected by various sensors in the pre-processed relevant response signals into gas concentrations to obtain the gas concentration values Qnd of various harmful gases when they are not interfered with, which are specifically obtained according to the following formula:

[0018]

[0019] In the formula, Qnd a (t) represents the gas concentration value of the a-th harmful gas when it is not disturbed; Xyz a (t) represents the response value collected by the sensor matching the a-th harmful gas when it is not interfered with; Xyz a0 It is expressed as the reference response value of the sensor matching the a-th harmful gas when the concentration of the corresponding harmful gas is zero; Lmz a It is expressed as the sensitivity of the sensor matched with the a-th harmful gas to the a-th harmful gas when it is not interfered with.

[0020] Preferably, the mixed effect analysis module includes a cross-sensitivity analysis unit, a response stability analysis unit and a preliminary judgment unit;

[0021] The cross-sensitivity analysis unit is used to pre-monitor the response of the corresponding sensor in multiple harmful gas environments to establish a cross-sensitivity matrix S. Based on the gas concentration values Qnd of various harmful gases when not disturbed and the cross-sensitivity matrix S, the response changes of the corresponding sensors in different harmful gas environments are analyzed to obtain the response value Gxyz collected by the sensor when disturbed. Specifically, the response value Gxyz collected by the sensor when disturbed is obtained in the following manner:

[0022]

[0023] Where Gxyz(t) represents the response value collected by the sensor at time t when it is disturbed; N represents the number of harmful gases; a and b are the numbers of the corresponding harmful gases; Lmz a It represents the sensitivity of the sensor matched with the a-th harmful gas to the a-th harmful gas when it is not interfered with; Qnd a(t) represents the gas concentration value of the a-th harmful gas when it is not disturbed; Qnd b (t) represents the gas concentration value of the bth harmful gas when it is not disturbed; β ab Expressed as the cross-sensitivity coefficient between type a harmful gas and type b harmful gas.

[0024] Preferably, the response stability analysis unit is used to analyze the stability of the sensor group response when multiple harmful gases are mixed based on the response value Gxyz collected by the sensor when it is interfered with and obtained by the cross-sensitivity analysis unit, so as to calculate the response fluctuation Xbd. The response fluctuation Xbd is obtained by the following formula:

[0025]

[0026] Where T represents the monitoring period, t = 1, 2, 3, ..., T, Gxyz(t) represents the response value collected by the sensor at time t under the interference state, Expressed as mean response value.

[0027] Preferably, the preliminary judgment unit is used to pre-set a safety threshold Q and compare the safety threshold Q with the response fluctuation Xbd to determine whether to start the troubleshooting mechanism. The specific judgment content is as follows:

[0028] If the response fluctuation Xbd falls within the safety threshold Q, it is determined that the stability of the corresponding sensor in the current mine channel is in a normal state. At this time, the troubleshooting mechanism will not be activated temporarily, and the troubleshooting instruction will not be triggered temporarily;

[0029] If the response fluctuation Xbd does not fall within the safety threshold Q, it is determined that the stability of the corresponding sensor in the current mine channel is not in a normal state. At this time, the troubleshooting mechanism will be started and the troubleshooting instruction will be triggered.

[0030] Preferably, the troubleshooting module includes a feature acquisition unit and a ventilation impact unit;

[0031] The feature acquisition unit is used to retrieve the number N of harmful gases from the monitoring period T after receiving the troubleshooting instruction triggered by the preliminary judgment unit, and obtain the characteristics of multiple harmful gases at different monitoring times based on the number N of harmful gases, and construct a gas feature set, which includes the average gas density Qm and gas diffusion factor Qk of multiple harmful gases.

[0032] Preferably, the ventilation influencing unit is used to calculate and obtain a distribution attribute coefficient Fsxs based on the gas feature set by correlating the average gas density Qm of multiple harmful gases with the gas diffusion factor Qk and performing dimensionless processing. The distribution attribute coefficient Fsxs is obtained by the following formula:

[0033]

[0034] Where F1 and F2 represent the weighted values of the average gas density Qm and gas diffusion factor Qk of multiple harmful gases, respectively. The specific values of F1 and F2 are set by the user according to the situation.

[0035] Preferably, the ventilation impact unit is further used to analyze the degree of influence of the ventilation state on the uneven gas distribution caused by multiple harmful gases based on the relevant ventilation status information and gas feature set in the real-time data set, and construct an influence coefficient Yxs. The influence coefficient Yxs is obtained by the following formula:

[0036]

[0037] Where Fsz(t) is the ventilation velocity at time t, Fsxs(t) is the distribution property coefficient at time t, and Fsz(t) is the ventilation velocity at time t. σ Expressed as the standard deviation of ventilation speed during the monitoring period, Fsxs(t) σ It is expressed as the standard deviation of the distribution attribute coefficient within the monitoring period, and cov(Fsz(t), Fsxs(t)) is expressed as the covariance of Fsz(t) and Fsxs(t);

[0038] An evaluation threshold W is preset, and the influence coefficient Yxs is compared with the evaluation threshold W to determine the degree of influence of the current ventilation state on the uneven distribution of gas caused by various harmful gases. The specific contents are as follows:

[0039] If the influence coefficient Yxs ≥ the evaluation threshold W, it is determined that the influence of the current ventilation state on the uneven distribution of gas caused by the multiple harmful gases is in an abnormal state, and a first evaluation result is generated;

[0040] If the influence coefficient Yxs is less than the evaluation threshold W, it is determined that the influence of the current ventilation state on the uneven gas distribution caused by the multiple harmful gases is not in an abnormal state, and a second evaluation result is generated.

[0041] Preferably, the safety management module is used to optimize the ventilation state of the ventilation system according to the corresponding evaluation results issued by the ventilation impact unit. The specific optimization management contents are as follows:

[0042] If the first evaluation result is generated, the ventilation speed Fsz is lowered by one level;

[0043] If the second evaluation result is generated, the current ventilation speed Fsz will be maintained.

[0044] The present invention provides a real-time safety monitoring system for coal mine ventilation, which has the following beneficial effects:

[0045] (1) By real-time monitoring of the harmful gas response and ventilation status information in the mine passage, the system can promptly detect changes in gas concentration and abnormal ventilation status, thereby effectively improving the ventilation safety of the coal mine and reducing safety accidents caused by harmful gas leakage or poor ventilation. The signal preprocessing module ensures the accuracy and reliability of the monitoring data through steps such as noise removal, outlier elimination and missing value repair. In addition, the dimensionless technology is used to perform dimensionless processing on the data, which effectively avoids measurement errors between different monitoring equipment and further improves the uniformity and accuracy of data processing. The mixed effect analysis module can analyze the stability of the sensor group in a complex gas mixture environment by extracting the response signals of multiple harmful gases, calculate the response fluctuation Xbd, and determine whether to start the screening mechanism based on the fluctuation. This provides a more sensitive and accurate real-time monitoring method for coal mine ventilation management, thereby effectively preventing the omission of sudden gas concentration increases or other dangerous signals. The troubleshooting module extracts ventilation status information from real-time data sets and analyzes the impact of ventilation status on gas distribution based on the characteristics of various harmful gases. By constructing the influence coefficient Yxs, it optimizes the ventilation status of the ventilation system. This not only improves the ventilation effect, but also makes the coal mine ventilation system more adaptable to different mine environments, thereby reducing the instability of sensor monitoring caused by the uneven distribution of harmful gases. The safety management module automatically adjusts the working status of the ventilation system based on the influence coefficient Yxs to ensure that the ventilation effect is relatively good, thereby improving the automation and intelligence level of coal mine safety management. In short, the application of this system can adjust the ventilation system in time before danger occurs in the mine, effectively reducing the lack of timely intervention in harmful gases due to sensor instability and inaccuracy, and reducing the risk of accidents in coal mines.

[0046] (2) Capturing cross-sensitivity: The cross-sensitivity analysis unit can effectively establish a cross-sensitivity matrix S by monitoring the response of the sensor in a variety of harmful gas environments. This matrix captures the interaction of multiple harmful gases in the same environment, especially the joint impact on the sensor response when the harmful gases are present at the same time. By analyzing the cross-sensitivity between different harmful gases, the response changes of the sensor can be accurately understood, the response error in a single gas environment can be reduced, and the accuracy of harmful gas monitoring in mines can be improved. Efficiently identify cross-influence: The cross-sensitivity coefficient effectively reflects the concentration interaction effect between harmful gases, which may cause abnormal sensor response, especially when the gas concentration is high. By utilizing the cross-sensitivity coefficient, the system can identify and quantify the impact of the coexistence of multiple harmful gases on the sensor response, thereby more accurately evaluating the harmful gas concentration in the mine environment and providing a scientific basis for subsequent safety warnings and risk assessments. Dealing with nonlinear responses: There is usually a nonlinear relationship between the concentration of multiple harmful gases and the sensor response. Traditional sensor response analysis methods often find it difficult to capture this nonlinear characteristic.

[0047] (3) Improve the accuracy of sensor response stability monitoring: The response stability analysis unit can accurately measure the response stability of the sensor in a mixed environment of multiple harmful gases by calculating the response fluctuation Xbd. The calculation of this fluctuation takes into account the sensor response data within different monitoring cycles and can effectively identify whether the sensor has abnormal fluctuations in a multi-gas environment, thereby avoiding monitoring errors caused by sensor instability or slow response, and improving the accuracy of mine safety monitoring. At the same time, the system can ensure that potential safety risks such as abnormal gas concentration or ventilation failure can be quickly identified by promptly activating the troubleshooting mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a block diagram of a real-time safety monitoring system for coal mine ventilation according to the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0050] Example 1

[0051] See also Figure 1 , the present invention provides a real-time safety monitoring system for coal mine ventilation, including a signal acquisition module, a signal preprocessing module, a mixed effect analysis module, a troubleshooting module and a safety management module;

[0052] The signal acquisition module is used to monitor the response of various harmful gases in the mine channel in real time through the sensor group to obtain relevant response signals, and at the same time use the ventilation system to obtain relevant ventilation status information;

[0053] The signal preprocessing module is used to preprocess the relevant response signal and the relevant ventilation status information, obtain a real-time data set through noise removal, outlier elimination and missing value repair operations, and perform dimensionless processing on the real-time data set using dimensionless technology;

[0054] The mixed effect analysis module will extract relevant response signals from the real-time data set based on the signal preprocessing module, and analyze the stability of the sensor group response when multiple harmful gases are mixed based on the relevant response signals to obtain the response fluctuation Xbd. According to the value of the response fluctuation Xbd, the activation of the troubleshooting mechanism is determined;

[0055] The troubleshooting module is used to activate the troubleshooting mechanism, extract relevant ventilation status information from the real-time data set, and combine the characteristics of multiple harmful gases to analyze the impact of ventilation status on the uneven gas distribution caused by multiple harmful gases, and construct an impact coefficient Yxs;

[0056] The safety management module is used to optimize the ventilation state of the ventilation system based on the influence coefficient Yxs.

[0057] During operation, the system monitors the concentration of harmful gases and ventilation status within the mine in real time to ensure that gas distribution within the mine remains within a safe range. The signal acquisition module and the signal preprocessing module work together to collect and process harmful gas response signals in the mine in real time, promptly identifying sensor fluctuations. The system uses the mixed effect analysis module to analyze the stability of the sensor response when multiple harmful gases are mixed in the mine based on the sensor response fluctuation Xbd. This module automatically determines whether to activate the troubleshooting mechanism. If an abnormal or unstable gas response is detected, the system quickly activates the troubleshooting module and conducts a detailed analysis of the mine ventilation system, ensuring that effective measures are taken in the shortest possible time. The troubleshooting module extracts ventilation status information from real-time data and, combined with the effects of uneven gas distribution, generates an impact coefficient Yxs, providing a precise basis for optimizing the ventilation system. Based on this influence coefficient, the safety management module optimizes the ventilation system's ventilation status, regulates airflow and gas emissions, ensures more uniform gas distribution within the mine, and reduces sensor monitoring instability and inaccuracy caused by excessively uneven distribution of hazardous gases. By utilizing dimensionless technology to process real-time data sets, the system effectively eliminates unit differences in the data, unifies the outputs of different acquisition devices and sensors, and improves the accuracy and reliability of data processing. This processing approach enables the system to maintain consistent performance across different mines and environments, adapting to varying gas types and climatic conditions. The system integrates multiple information sources (such as hazardous gas concentrations and ventilation conditions), combines mixed effect analysis with influence coefficient calculation, and provides coal mine managers with more scientific and accurate decision support, helping them promptly identify safety hazards and implement appropriate preventive measures. In summary, the real-time safety monitoring system provided by this system, through the coordinated work of efficient signal acquisition, data processing, effect analysis, and safety management modules, can effectively improve the safety, stability, and emergency response capabilities of coal mine ventilation systems, reduce safety risks in coal mine operations, and enhance the safety of mine workers.

[0058] Example 2

[0059] Please refer to Figure 1 ,Specifically: the signal acquisition module includes a deployment unit, a gas dynamic monitoring unit and a ventilation acquisition unit;

[0060] The deployment unit is used to deploy a sensor group in the mine passage in advance, wherein the sensor group includes an electrochemical sensor, an infrared sensor, a metal oxide semiconductor sensor, and a wind speed sensor;

[0061] The gas dynamic monitoring unit is used to monitor the response of various harmful gases in the mine channel in real time based on the sensor group to obtain relevant response signals, wherein the relevant response signals include a response value Xyz collected by the sensor matching the corresponding harmful gas when it is not interfered with, a baseline response value Xyz0 of the sensor matching the corresponding harmful gas when the concentration of the corresponding harmful gas is zero, and a sensitivity Lmz of the sensor matching the corresponding harmful gas to the corresponding harmful gas when it is not interfered with;

[0062] The ventilation collection unit is used to collect ventilation conditions in the mine passage according to the ventilation system to obtain relevant ventilation status information, wherein the relevant ventilation status information includes the ventilation speed Fsz and ventilation direction at each monitoring moment, wherein the ventilation system includes a main air duct, a branch air duct, an air door and an air valve.

[0063] The signal preprocessing module includes a preprocessing unit and a signal conversion unit;

[0064] The preprocessing unit is used to preprocess the relevant response signal and the relevant ventilation status information, and the preprocessing includes removing noise, filling missing values, eliminating outliers and data smoothing operations, wherein the missing value filling methods include mean filling, median filling, interpolation filling and regression filling;

[0065] The signal conversion unit is used to convert the response values Xyz collected by various sensors in the pre-processed relevant response signals into gas concentrations to obtain the gas concentration values Qnd of various harmful gases when they are not interfered with, which are specifically obtained according to the following formula:

[0066]

[0067] In the formula, Qnd a (t) represents the gas concentration value of the a-th harmful gas when it is not disturbed; Xyz a (t) represents the response value collected by the sensor matching the a-th harmful gas when it is not interfered with; Xyz a0 It is expressed as the reference response value of the sensor matching the a-th harmful gas when the concentration of the corresponding harmful gas is zero; Lmz a It represents the sensitivity of the sensor matched with the a-th harmful gas to the a-th harmful gas when it is not interfered with. The value depends on the type and material of the sensor. It describes the reaction intensity of the sensor to the corresponding gas. The greater the sensitivity, the stronger the sensor's response to the gas. The sensitivity is generally obtained from the sensor's technical data sheet.

[0068] The response value Xyz collected by the sensor matching the corresponding harmful gas when it is not interfered with refers to the raw response data collected by the sensor under undisturbed conditions, which is used as a reference value for subsequent evaluation of sensor performance and stability. Its value can be used to detect each harmful gas through a gas sensor (such as an electrochemical sensor, an infrared sensor, or a metal oxide semiconductor sensor);

[0069] The sensor's technical data sheet contains various performance indicators and operating parameters of the sensor, such as sensitivity, detection range, response time, operating temperature, accuracy, and lifespan. This data can help determine the sensor's suitability and reliability in different operating environments. The sensor's technical data sheet is provided by the manufacturer and can usually be obtained from the sensor's product instructions, manuals, or technical documentation.

[0070] In this embodiment, the signal acquisition module uses a gas dynamics monitoring unit to monitor the response of various harmful gases in the mine passages in real time, ensuring accurate capture of changes in harmful gas concentrations within the mine. By analyzing the sensor response signals and combining them with baseline response values and sensitivity data, the system can convert them into accurate gas concentration values (Qnd). This process, through precise gas concentration conversion formulas, effectively eliminates external interference, improves the reliability and accuracy of gas monitoring data, and provides data support for mine safety. The ventilation acquisition unit collects the status of the mine ventilation system in real time, providing a comprehensive understanding of the ventilation conditions in the mine passages. The monitored ventilation status information helps the system analyze the distribution of gases within the mine, promptly identify potential problems with the ventilation system, and promptly adjust or optimize the ventilation strategy to ensure the effective removal of harmful gases from the mine. The signal preprocessing module ensures the quality of the collected signal data by removing noise, filling missing values, removing outliers, and performing data smoothing. This preprocessing process effectively improves the accuracy of the data, ensuring more reliable data for subsequent analysis and further preventing erroneous decisions caused by abnormal data. Especially in complex environments such as coal mines, data preprocessing can significantly reduce the interference of equipment failures or external environment on monitoring signals and improve the stability of the system.

[0071] Example 3

[0072] Please refer to Figure 1 ,Specifically: the mixed effect analysis module includes a cross-sensitivity analysis unit, a response stability analysis unit and a preliminary judgment unit;

[0073] The cross-sensitivity analysis unit is used to pre-monitor the response of the corresponding sensor in multiple harmful gas environments to establish a cross-sensitivity matrix S. Based on the gas concentration values Qnd of various harmful gases when not disturbed and the cross-sensitivity matrix S, the response changes of the corresponding sensors in different harmful gas environments are analyzed to obtain the response value Gxyz collected by the sensor when disturbed. Specifically, the response value Gxyz collected by the sensor when disturbed is obtained in the following manner:

[0074]

[0075] Where Gxyz(t) represents the response value collected by the sensor at time t when it is disturbed; N represents the number of harmful gases; a and b are the numbers of the corresponding harmful gases; Lmz a It represents the sensitivity of the sensor matched with the a-th harmful gas to the a-th harmful gas when it is not interfered with; Qnd a (t) represents the gas concentration value of the a-th harmful gas when it is not disturbed; Qnd b (t) represents the gas concentration value of the bth harmful gas when it is not disturbed; β ab Expressed as the cross-sensitivity coefficient between harmful gas a and harmful gas b, this coefficient captures the impact of the interaction between harmful gases, especially the impact of harmful gas a and harmful gas b on the sensor when they exist at the same time;

[0076] log[Qnd a (t)+1] indicates that in the crossover process between various harmful gases, the concentration of harmful gases and the sensor response usually present a nonlinear relationship. A logarithmic function is used to represent the sensor response (to avoid meaningless calculations when the concentration is zero). This expression represents the sensor response at a given gas concentration;

[0077] log[Qnd a (t)+1]*log[Qnd b (t)+1] represents the concentration interaction effect between the a-th harmful gas and the b-th harmful gas;

[0078] The cross-sensitivity matrix S described above describes the interactions between multiple gas sensors when responding to different gases. In practical applications, sensors often respond not only to their target gas but also to other gases to some extent. This phenomenon is called cross-sensitivity. The process of obtaining a cross-sensitivity matrix typically involves several steps: experimentation, data acquisition, and analytical modeling. The following is a detailed description of how to obtain the cross-sensitivity matrix:

[0079] 1. To obtain the cross-sensitivity matrix, we first need to design an experiment to measure the response of the sensor in a variety of gas environments. The experimental steps include: (1) Selecting test gases: Select gases that may affect the sensor response according to the application scenario. For example, in a coal mine, gases such as methane and carbon monoxide may exist at the same time. (2) Controlling gas concentration: Using a gas generator or a gas mixing device, the concentration of each gas is precisely controlled. These gas concentrations are usually gradually increased (for example, set to different concentration points), and the sensor response values at these concentrations are recorded. (3) Environmental control: Ensure that the temperature, humidity, and pressure of the laboratory environment remain constant, as these environmental factors will also affect the response of the sensor. (4) Sensor response recording: Use the sensor to test in a variety of gas environments with different concentrations, and record the concentration of each gas and the corresponding sensor response value. The experiment should cover the situation where different gases exist alone, as well as the mixed state of different gases (that is, the response when multiple gases coexist).

[0080] 2. During the experiment, the collected data usually includes the concentration of each gas and the response signal of the sensor at each moment. The data may include the following: the concentration of the corresponding harmful gas in the sensor area and the response value of the sensor to the corresponding harmful gas at time t (such as voltage, current, resistance, etc.), and are plotted into a table to establish a cross-sensitivity matrix S, where each element in the cross-sensitivity matrix S represents the degree of influence of different gases on the sensor. Assuming that there are N kinds of harmful gases, the size of the cross-sensitivity matrix is N*N, where each element β ab It represents the cross-sensitivity coefficient between type a harmful gas and type b harmful gas.

[0081] In this embodiment, the system, through a cross-sensitivity analysis unit, can pre-monitor the sensor's response in multiple hazardous gas environments, establish a cross-sensitivity matrix S, and conduct an in-depth analysis of changes in the sensor's response. This analysis effectively identifies the interactions between different hazardous gases, particularly the combined effects of different gases on the sensor when multiple gases are present simultaneously. By calculating cross-sensitivity coefficients, the system can accurately capture these cross-effects, helping to ensure accurate and reliable sensor responses in complex gas environments. The cross-sensitivity matrix S quantifies the interactions between multiple hazardous gases, particularly their impact on sensor response when gas concentrations vary. Based on this matrix, the system can analyze and adjust the sensor response value Gxyz under interference based on the gas concentration Qnd and the cross-sensitivity coefficients. In coal mining environments, hazardous gases often coexist and interact with each other. Traditional single-gas monitoring methods can be affected by multi-gas interference, resulting in inaccurate data. Through cross-sensitivity analysis, the system can accurately analyze the interactions between gases and their impact on the sensor when multiple hazardous gases are present simultaneously, avoiding data bias caused by gas cross-interference, thereby improving the reliability and accuracy of the gas monitoring system. By combining the concentrations of multiple hazardous gases and their cross-sensitivity effects, the system can predict sensor response changes in real time under multi-gas environments. This predictive capability provides important support for optimizing mine ventilation systems, ensuring that the system can identify potential dangerous gas concentration fluctuations in real time and respond promptly to various hazardous gas combinations. Using cross-sensitivity analysis, the system can more accurately identify concentration changes of multiple hazardous gases within coal mines, especially concentration fluctuations caused by the combined effects of multiple gases, thereby predicting and warning of potential safety risks in advance.

[0082] Example 4

[0083] Please refer to Figure 1 Specifically, the response stability analysis unit is used to analyze the stability of the sensor group response when multiple harmful gases are mixed based on the response value Gxyz collected by the sensor when it is interfered with by the cross-sensitivity analysis unit, so as to calculate the response fluctuation Xbd. The response fluctuation Xbd is obtained by the following formula:

[0084]

[0085] Where T represents the monitoring period, t = 1, 2, 3, ..., T, Gxyz(t) represents the response value collected by the sensor at time t under the interference state, Expressed as mean response value.

[0086] The preliminary judgment unit is used to pre-set a safety threshold Q and compare the safety threshold Q with the response fluctuation Xbd to determine whether to start the troubleshooting mechanism. The specific judgment content is as follows:

[0087] If the response fluctuation Xbd falls within the safety threshold Q, it is determined that the stability of the corresponding sensor in the current mine channel is in a normal state. At this time, the troubleshooting mechanism will not be activated temporarily, and the troubleshooting instruction will not be triggered temporarily;

[0088] If the response fluctuation Xbd does not fall within the safety threshold Q, it is determined that the stability of the corresponding sensor in the current mine channel is not in a normal state. At this time, the troubleshooting mechanism will be started and the troubleshooting instruction will be triggered.

[0089] In this embodiment, the response stability analysis unit evaluates the stability of the sensor group in a mixed environment with multiple hazardous gases by calculating the response fluctuation Xbd. This method effectively captures sensor response fluctuations that may occur in complex gas environments, avoiding erroneous data caused by gas mixture interference, thereby improving the accuracy and stability of hazardous gas monitoring in mines. Quantifying sensor response fluctuation: By analyzing multiple monitoring data sets, the response fluctuation Xbd is calculated. This technology provides a quantitative indicator of sensor stability in mines. Response fluctuation Xbd reflects the sensor's response stability under the interference of multiple hazardous gases, allowing the system to accurately determine whether the sensor is in a normal state, providing data support for further safety assessments and decision-making. Preventing the risk of false positives and missed detections: The preliminary judgment unit compares the response fluctuation Xbd with a preset safety threshold Q, effectively avoiding false positives and missed detections caused by unstable sensor response. If the response fluctuation falls within the safe range, the system determines the sensor is stable, eliminating the need to initiate a troubleshooting mechanism, reducing unnecessary troubleshooting instructions and avoiding wasted resources. On the contrary, if the response fluctuation exceeds the safe range, the system will promptly activate the troubleshooting mechanism to ensure rapid response and processing when the sensor has an abnormality.

[0090] Example 5

[0091] Please refer to Figure 1 ,Specifically: the investigation module includes a feature acquisition unit and a ventilation ,impact unit;

[0092] The feature acquisition unit is used to retrieve the number N of harmful gases from the monitoring period T after receiving the troubleshooting instruction triggered by the preliminary judgment unit, and obtain the characteristics of multiple harmful gases at different monitoring times based on the number N of harmful gases, and construct a gas feature set, which includes the average gas density Qm and gas diffusion factor Qk of multiple harmful gases.

[0093] The ventilation influencing unit is used to calculate the distribution attribute coefficient Fsxs based on the gas feature set by correlating the average gas density Qm of multiple harmful gases with the gas diffusion factor Qk and performing dimensionless processing. The distribution attribute coefficient Fsxs is obtained by the following formula:

[0094]

[0095] Where F1 and F2 represent the weighted values of the average gas density Qm and gas diffusion factor Qk of multiple harmful gases, respectively, where 0<F1<1, 0<F2<1. The specific values are set by the user according to the situation.

[0096] The average gas density Qm of multiple harmful gases refers to the average density of multiple harmful gases during the monitoring period, usually expressed in kilograms per cubic meter (kg / m 3 ) indicates that it reflects the overall distribution level of harmful gases in the mine and can be monitored and obtained through electrochemical sensors, infrared sensors, optical sensors or metal oxide semiconductor sensors;

[0097] The gas diffusion factor Qk refers to the diffusion rate of harmful gases in the air, which affects the distribution of harmful gases. The diffusion coefficient table in the "CRC Handbook of Chemistry and Physics" by Erich W. Weisstein et al. provides the diffusion coefficients of various gases at different temperatures and pressures.

[0098] In this embodiment, the feature acquisition unit retrieves data from the monitoring period T. Based on the types N of multiple hazardous gases, the system can obtain gas characteristics at different monitoring moments and construct a gas feature set consisting of the average gas density Qm and the gas diffusion factor Qk. This process helps the monitoring system comprehensively understand the types, distribution, and changing trends of hazardous gases in the mine environment, providing an accurate basis for subsequent investigations. When the preliminary judgment unit triggers an investigation instruction, the feature acquisition unit obtains the average density and diffusion factor of the hazardous gas, enabling it to quickly retrieve relevant data upon receiving the instruction. This ensures that investigations can be carried out swiftly after accurately identifying the gas characteristics. This helps improve the efficiency and accuracy of investigations and avoids misjudgments caused by inaccurate gas type and concentration changes. The gas feature set, by combining the average gas density Qm and diffusion factor Qk, reflects the distribution of hazardous gases in the mine passageways. Combining this feature information enables more intelligent monitoring and control, thereby improving the automation and responsiveness of the mine gas monitoring system. The system can effectively adjust monitoring and investigation strategies based on the characteristics of different gases. By obtaining the distribution attribute coefficient Fsxs, this coefficient can be used to assess the actual impact of the ventilation system on the distribution of harmful gases. Especially in complex gas environments, the system can identify the changing trends of gases in the ventilation ducts, determine whether the ventilation system is functioning properly, and significantly affect gas concentrations. After dimensionless processing, the calculated distribution attribute coefficient Fsxs can standardize the characteristics of different harmful gases, allowing the impact of gas characteristics in different environments and different ventilation conditions to be uniformly measured. This allows the system to more accurately assess changes in gas distribution when performing inspection tasks, avoid missing potentially dangerous gases or misjudging gas concentrations, and ensure the comprehensiveness and accuracy of the inspection work.

[0099] Example 6

[0100] Please refer to Figure 1 Specifically, the ventilation impact unit is further used to analyze the influence of the ventilation state on the uneven gas distribution caused by multiple harmful gases based on the relevant ventilation status information and gas feature set in the real-time data set, and construct an influence coefficient Yxs. The influence coefficient Yxs is obtained by the following formula:

[0101]

[0102] Where Fsz(t) is the ventilation velocity at time t, Fsxs(t) is the distribution property coefficient at time t, and Fsz(t) is the ventilation velocity at time t. σ Expressed as the standard deviation of ventilation speed during the monitoring period, Fsxs(t) σ It is expressed as the standard deviation of the distribution attribute coefficient within the monitoring period, and cov(Fsz(t), Fsxs(t)) is expressed as the covariance of Fsz(t) and Fsxs(t);

[0103] Among them, the ventilation speed Fsz refers to the speed of air flow in the mine, usually expressed in meters per second (m / s). It determines the flow and distribution of gas in the mine, affecting the concentration and distribution uniformity of harmful gases. Wind speed sensors (such as hot wire anemometers, ultrasonic anemometers, and Pitot tube anemometers) can be used to measure the ventilation speed in the mine.

[0104] An evaluation threshold W is preset, and the influence coefficient Yxs is compared with the evaluation threshold W to determine the degree of influence of the current ventilation state on the uneven distribution of gas caused by various harmful gases. The specific contents are as follows:

[0105] If the influence coefficient Yxs ≥ the evaluation threshold W, it is determined that the influence of the current ventilation state on the uneven distribution of gas caused by the multiple harmful gases is in an abnormal state, and a first evaluation result is generated;

[0106] If the influence coefficient Yxs is less than the evaluation threshold W, it is determined that the influence of the current ventilation state on the uneven gas distribution caused by the multiple harmful gases is not in an abnormal state, and a second evaluation result is generated.

[0107] The safety management module is used to optimize the ventilation status of the ventilation system according to the corresponding evaluation results issued by the ventilation impact unit. The specific optimization management contents are as follows:

[0108] If the first evaluation result is generated, it is determined that the current ventilation state has caused significant non-uniformity in the distribution of multiple harmful gases. When the distribution of multiple harmful gases is significantly non-uniform, the stability and accuracy of the sensor signal monitoring are further aggravated. Therefore, the ventilation speed Fsz is reduced by one level to improve the sensor instability caused by the severe non-uniform distribution of harmful gases.

[0109] If the second evaluation result is generated, it will be considered that the impact of the current ventilation state on the gas distribution is within the normal range, and the current ventilation speed Fsz is maintained.

[0110] In this embodiment, the ventilation impact unit analyzes the impact of ventilation status on the non-uniform distribution of hazardous gases by combining ventilation status information and gas signatures from real-time data sets. By constructing an impact coefficient Yxs, it quantifies the degree of ventilation's impact on gas distribution and identifies instances of uneven gas distribution. Using ventilation speed, distribution attribute coefficients, standard deviation, and covariance, it comprehensively assesses gas distribution non-uniformity and its impact on sensor response, providing a scientific basis for precise ventilation system adjustment. By comparing the impact coefficient Yxs with an evaluation threshold W, the system can determine in real time whether the current ventilation status results in significant non-uniform gas distribution. If the impact coefficient exceeds the threshold, the system determines that the gas distribution is abnormal; otherwise, the ventilation status is considered normal. This real-time response mechanism provides instant assessment and adjustment feedback for the mine ventilation system, ensuring a rapid response to potential hazards. If the ventilation status is determined to have significantly non-uniform gas distribution, the safety management module automatically adjusts the ventilation speed. By lowering the ventilation speed, it mitigates the impact of uneven gas distribution on sensor stability and accuracy. This not only effectively reduces sensor false positives and false negatives, but also improves the accuracy and reliability of monitoring data, avoiding misjudgments caused by uneven gas distribution. The safety management module can optimize the working status of the ventilation system based on the evaluation results. In the first evaluation result, the system will realize that the uneven distribution of gas may affect the monitoring accuracy, so it will actively adjust the ventilation speed. Through this intelligent optimization, the system can automatically adjust according to real-time data without manual intervention, further improving the intelligence of the system. In a mine environment, the uneven distribution of gas may cause excessively high gas concentrations in certain areas, increasing safety hazards. By accurately monitoring the influence coefficient Yxs, the system can identify abnormal gas distribution and improve the uneven distribution by adjusting the ventilation speed, thereby reducing the risk of excessive concentration of harmful gases and ensuring the safety of the working environment in the mine. The system significantly improves the monitoring stability and accuracy of the sensor by dynamically adjusting the ventilation speed and optimizing the uniformity of gas distribution.

[0111] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A real-time safety monitoring system for coal mine ventilation, characterized by: Including signal acquisition module, signal preprocessing module, mixed effect analysis module, troubleshooting module and security management module; The signal acquisition module is used to monitor the response of various harmful gases in the mine channel in real time through the sensor group to obtain relevant response signals. At the same time, it uses the ventilation system to obtain relevant ventilation status information. The relevant ventilation status information includes the ventilation speed Fsz and ventilation direction at each monitoring moment; The signal preprocessing module is used to preprocess the relevant response signals and related ventilation status information. After noise removal, outlier elimination and missing value repair operations, the real-time data set is obtained and dimensionless processing is performed on the real-time data set using dimensionless technology. The mixed effect analysis module will extract relevant response signals from the real-time data set based on the signal preprocessing module. Based on the relevant response signals, it will analyze the stability of the sensor group response when multiple harmful gases are mixed to obtain the response fluctuation Xbd. According to the response fluctuation Xbd value, it will determine the activation of the troubleshooting mechanism. The troubleshooting module is used to activate the troubleshooting mechanism, extract relevant ventilation status information from the real-time data set, and construct a gas feature set based on the characteristics of multiple harmful gases. This is used to analyze the impact of ventilation status on the uneven distribution of gas caused by multiple harmful gases and construct the impact coefficient Yxs. The gas feature set includes the average gas density Qm and gas diffusion factor Qk of multiple harmful gases. The troubleshooting module includes a ventilation impact unit. The ventilation impact unit is used to calculate the distribution attribute coefficient Fsxs by correlating the average gas density Qm of multiple harmful gases with the gas diffusion factor Qk based on the gas feature set and performing dimensionless processing. The distribution attribute coefficient Fsxs is obtained using the following formula: Where F1 and F2 represent the weighted values of the average gas density Qm and gas diffusion factor Qk of various harmful gases, respectively. The specific values of F1 and F2 are set by the user according to the actual situation. Based on the relevant ventilation status information and gas feature set in the real-time data set, the influence of the ventilation status on the uneven gas distribution caused by various harmful gases is analyzed, and the influence coefficient Yxs is constructed. The influence coefficient Yxs is obtained by the following formula: Where Fsz(t) represents the ventilation speed at time t, Fsxs(t) represents the distribution property coefficient at time t, and Fsz(t) σ Indicates the standard deviation of ventilation speed during the monitoring period, Fsxs(t) σ represents the standard deviation of the distribution attribute coefficient within the monitoring period, and cov(Fsz(t), Fsxs(t)) represents the covariance of Fsz(t) and Fsxs(t); The safety management module is used to determine whether the current ventilation state is in an abnormal state in terms of the uneven distribution of gas caused by various harmful gases based on the influence coefficient Yxs, so as to optimize the ventilation state of the ventilation system.

2. A real-time safety monitoring system for coal mine ventilation according to claim 1, characterized in that: The signal acquisition module includes a deployment unit, a gas dynamic monitoring unit and a ventilation acquisition unit; The deployment unit is used to deploy a sensor group in the mine passage in advance, and the sensor group includes an electrochemical sensor, an infrared sensor, a metal oxide semiconductor sensor and a wind speed sensor; The gas dynamic monitoring unit is used to monitor the response of various harmful gases in the mine channel in real time based on the sensor group to obtain relevant response signals, wherein the relevant response signals include a response value Xyz collected by the sensor matching the corresponding harmful gas when it is not interfered with, a baseline response value Xyz0 of the sensor matching the corresponding harmful gas when the concentration of the corresponding harmful gas is zero, and a sensitivity Lmz of the sensor matching the corresponding harmful gas to the corresponding harmful gas when it is not interfered with; The ventilation collection unit is used to collect ventilation conditions in the mine passage according to the ventilation system to obtain relevant ventilation status information, wherein the ventilation system includes a main air duct, branch air ducts, air doors and air valves.

3. The real-time safety monitoring system for coal mine ventilation according to claim 2, characterized in that: The signal preprocessing module includes a preprocessing unit and a signal conversion unit; The preprocessing unit is used to preprocess the relevant response signal and the relevant ventilation status information, and the preprocessing includes removing noise, filling missing values, eliminating outliers and data smoothing operations, wherein the missing value filling methods include mean filling, median filling, interpolation filling and regression filling; The signal conversion unit is used to convert the response values Xyz collected by various sensors in the pre-processed relevant response signals into gas concentrations to obtain the gas concentration values Qnd of various harmful gases when they are not interfered with, which are specifically obtained according to the following formula: In the formula, Qnd a (t) represents the gas concentration value of the a-th harmful gas when it is not disturbed; Xyz a (t) represents the response value collected by the sensor matching the a-th harmful gas when it is not interfered with; Xyz a0 It is expressed as the reference response value of the sensor matching the a-th harmful gas when the concentration of the corresponding harmful gas is zero; Lmz a It is expressed as the sensitivity of the sensor matched with the a-th harmful gas to the a-th harmful gas when it is not interfered with.

4. A real-time safety monitoring system for coal mine ventilation according to claim 3, characterized in that: The mixed effect analysis module includes a cross-sensitivity analysis unit, a response stability analysis unit and a preliminary judgment unit; The cross-sensitivity analysis unit is used to pre-monitor the response of the corresponding sensor in multiple harmful gas environments to establish a cross-sensitivity matrix S. Based on the gas concentration values Qnd of various harmful gases when not disturbed and the cross-sensitivity matrix S, the response changes of the corresponding sensors in different harmful gas environments are analyzed to obtain the response value Gxyz collected by the sensor when disturbed. Specifically, the response value Gxyz collected by the sensor when disturbed is obtained in the following manner: Where Gxyz(t) represents the response value collected by the sensor at time t when it is disturbed; N represents the number of harmful gases; a and b are the numbers of the corresponding harmful gases; Lmz a It represents the sensitivity of the sensor matched with the a-th harmful gas to the a-th harmful gas when it is not interfered with; Qnd a (t) represents the gas concentration value of the a-th harmful gas when it is not disturbed; Qnd b (t) represents the gas concentration value of the bth harmful gas when it is not disturbed; β ab Expressed as the cross-sensitivity coefficient between type a harmful gas and type b harmful gas.

5. The real-time safety monitoring system for coal mine ventilation according to claim 4, characterized in that: The response stability analysis unit is used to analyze the stability of the sensor group response when multiple harmful gases are mixed based on the response value Gxyz collected by the sensor when it is interfered with by the cross-sensitivity analysis unit, so as to calculate the response fluctuation Xbd. The response fluctuation Xbd is obtained by the following formula: Where T represents the monitoring period, t = 1, 2, 3, ..., T, Gxyz(t) represents the response value collected by the sensor at time t under the interference state, Expressed as mean response value.

6. The real-time safety monitoring system for coal mine ventilation according to claim 5, characterized in that: The preliminary judgment unit is used to pre-set a safety threshold Q and compare the safety threshold Q with the response fluctuation Xbd to determine whether to start the troubleshooting mechanism. The specific judgment content is as follows: If the response fluctuation Xbd falls within the safety threshold Q, it is determined that the stability of the corresponding sensor in the current mine channel is in a normal state. At this time, the troubleshooting mechanism will not be activated temporarily, and the troubleshooting instruction will not be triggered temporarily; If the response fluctuation Xbd does not fall within the safety threshold Q, it is determined that the stability of the corresponding sensor in the current mine channel is not in a normal state. At this time, the troubleshooting mechanism will be started and the troubleshooting instruction will be triggered.

7. The real-time safety monitoring system for coal mine ventilation according to claim 1, characterized in that: The troubleshooting module also includes a feature acquisition unit; The feature acquisition unit is used to retrieve the number N of harmful gases from the monitoring period T after receiving the screening instruction triggered by the preliminary judgment unit, and obtain the characteristics of multiple harmful gases at different monitoring times based on the number N of harmful gases, and construct a gas feature set.

8. The real-time safety monitoring system for coal mine ventilation according to claim 1, characterized in that: The ventilation impact unit is further configured to pre-set an evaluation threshold W, and to determine the degree of influence of the current ventilation state on the uneven distribution of gas caused by various harmful gases by comparing the impact coefficient Yxs with the evaluation threshold W. The specific contents are as follows: If the influence coefficient Yxs ≥ the evaluation threshold W, it is determined that the influence of the current ventilation state on the uneven distribution of gas caused by the multiple harmful gases is in an abnormal state, and a first evaluation result is generated; If the influence coefficient Yxs is less than the evaluation threshold W, it is determined that the influence of the current ventilation state on the uneven gas distribution caused by the multiple harmful gases is not in an abnormal state, and a second evaluation result is generated.

9. The real-time safety monitoring system for coal mine ventilation according to claim 8, characterized in that: The safety management module is used to optimize the ventilation status of the ventilation system according to the corresponding evaluation results issued by the ventilation impact unit. The specific optimization management contents are as follows: If the first evaluation result is generated, the ventilation speed Fsz is lowered by one level; If the second evaluation result is generated, the current ventilation speed Fsz will be maintained.

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