Real-time safety monitoring system for coal mine ventilation

Abstract

The invention discloses a real-time safety monitoring system for coal mine ventilation, and relates to the technical field of safety construction, the system analyzes the response stability of a sensor according to the response fluctuation Xbd of the sensor during mixing of various harmful gases in a mine through a mixing effect analysis module, and can automatically judge whether to start a troubleshooting mechanism or not; once the gas response is abnormal or unstable, the system can quickly start the checking module to carry out detailed analysis on the mine ventilation system so as to ensure that effective measures are taken in the shortest time. And the troubleshooting module generates an influence coefficient Yxs by extracting ventilation situation information in real-time data and combining the influence of non-uniform gas distribution, so that a precise basis is provided for optimization of a ventilation system. Based on the influence coefficient, the safety management module optimizes the ventilation state of the ventilation system, adjusts airflow and gas emission, ensures that gas distribution in the mine is more uniform, and reduces unstable and inaccurate sensor monitoring caused by excessive non-uniform distribution of harmful gas.

Description

Technical Field

[0001] The 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 an important area in the mining industry. With the advancement of technology, the way of coal mine safety monitoring has gradually shifted from manual inspections to intelligent monitoring. Coal mine ventilation is one of the core factors to ensure mine safety. A reasonable ventilation system can not only effectively remove harmful gases, but also prevent toxic gases from accumulating and causing catastrophic accidents such as explosions. With the continuous increase in the depth of mines and the increasing complexity of the mining environment, the problems of air pollution and harmful gas concentration faced by coal mine operations are gradually increasing. Especially in the process of coal mine ventilation, various harmful gases such as methane, carbon monoxide, sulfur dioxide, etc. often produce complex cross-effects due to changes in airflow and gas concentration, which brings huge challenges to mine safety. Therefore, real-time safety monitoring in coal mine ventilation, especially the analysis of the interference of various harmful gases in mine passages on sensor response, has become a key link in improving coal mine ventilation management and safety monitoring systems.

[0003] Although the existing coal mine ventilation safety monitoring system can monitor the concentration of harmful gases and ventilation status in the mine in real time, it has several important shortcomings. First, the mine environment is complex, and multiple harmful gases often exist at the same time, and these gases have strong cross-sensitivity. The traditional mine ventilation safety monitoring method usually adopts a single gas monitoring method, but ignores the interaction between gases, making it difficult for the sensor to accurately distinguish and monitor the interference effect of multiple gases, resulting in an increase in the error of the response signal and affecting the accuracy of the monitoring data. Secondly, the mine ventilation system may have uneven airflow and uneven gas diffusion in different time periods. This non-uniformity will affect the stability and response effect of the sensor. The traditional monitoring method lacks effective dynamic adjustment and adaptive mechanisms, and it is difficult to reflect the impact of the ventilation system on the distribution of different harmful gases in real time, resulting in safety risks that are difficult to detect and control in a timely manner. 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 situation 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 dimension 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 in the case of a mixture of multiple harmful gases based on the relevant response signals to obtain the response fluctuation Xbd, and determine the start of the troubleshooting mechanism according to the value of the response fluctuation Xbd;

[0009] The troubleshooting module is used to start 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 influence of ventilation status on the non-uniform gas distribution caused by multiple harmful gases, and construct the influence 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, and 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 according to the sensor group to obtain relevant response signals, wherein the relevant response signals include the response value Xtz collected by the sensor matching the corresponding harmful gas when it is not disturbed, the reference response value Xyz0 of the sensor matching the corresponding harmful gas when the concentration of the corresponding harmful gas is zero, and the sensitivity Lmz of the sensor matching the corresponding harmful gas to the corresponding harmful gas when it is not disturbed;

[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, removing 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 gas concentration values ​​Qnd of various harmful gases when they are not disturbed, 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 disturbed; Xyz a0 It is expressed as the reference response value of the sensor matching the ath 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 monitor the response of the corresponding sensor in various harmful gas environments in advance to establish a cross-sensitivity matrix S. According to 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 disturbed; N represents the number of harmful gases; a and b are the numbers of the corresponding harmful gases; 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; Qnd a(t) is 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 the ath harmful gas and the bth 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 according to the response value Gxyz collected by the sensor when disturbed by the cross-sensitivity analysis unit, so as to calculate the response fluctuation Xbd, and 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 judge the start of 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, and the troubleshooting mechanism will not be started at this time, and the troubleshooting instruction will not be triggered;

[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 the distribution attribute coefficient Fsxs according to the gas feature set by associating 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] In the formula, F1 and F2 represent the weight values ​​of the average gas density Qm and the gas diffusion factor Qk of various harmful gases, respectively, and the specific values ​​of F1 and F2 are set by the user according to the situation.

[0035] Preferably, the ventilation influencing unit is further used to analyze the influence of the ventilation state on the non-uniform gas distribution caused by various harmful gases according to the relevant ventilation status information and gas feature set in the real-time data set, and construct an influence coefficient Yxs, and 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 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 coefficients 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 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 various 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 timely detect changes in gas concentration and abnormal ventilation status, thereby effectively improving the ventilation safety of coal mines and reducing safety accidents caused by harmful gas leakage or poor ventilation. The signal preprocessing module ensures the accuracy and reliability of monitoring data through steps such as noise removal, outlier elimination and missing value repair. In addition, the dimensionless technology is used to process the data dimensionally, which effectively avoids the 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 troubleshooting 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 the real-time data set and analyzes the impact of ventilation status on gas distribution in combination with the characteristics of multiple harmful gases. By constructing the influence coefficient Yxs, the ventilation status of the ventilation system is optimized, which not only improves the ventilation effect, but also makes the coal mine ventilation system more adaptable in 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 state of the ventilation system according to 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 the mine is in danger, effectively reduce the untimely intervention of harmful gases caused by the instability and inaccuracy of sensors, and reduce 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. The matrix captures the interaction of multiple harmful gases in the same environment, especially the common influence on the sensor response when the harmful gases exist 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. Efficient identification of 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 using the cross-sensitivity coefficient, the system can identify and quantify the impact of multiple harmful gases on the sensor response when they coexist, and then more accurately evaluate the concentration of harmful gases in the mine environment, 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 monitoring accuracy of sensor response stability: 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 in 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 timely launching the troubleshooting mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0049] 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.

[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 situation 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 dimension 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 in the case of a mixture of multiple harmful gases based on the relevant response signals to obtain the response fluctuation Xbd, and determine the start of the troubleshooting mechanism according to the value of the response fluctuation Xbd;

[0055] The troubleshooting module is used to start 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 influence of ventilation status on the non-uniform gas distribution caused by multiple harmful gases, and construct the influence 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 the operation of this system, the system monitors the concentration of harmful gases and ventilation status in the mine in real time to ensure that the gas distribution in the mine is within a safe range. Through the joint work of the signal acquisition module and the signal preprocessing module, the harmful gas response signals in the mine can be collected and processed in real time, and the sensor fluctuations can be identified in time. Through the mixed effect analysis module, the system analyzes 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 troubleshooting mechanism. Once the gas response is abnormal or unstable, the system will quickly start the troubleshooting module and conduct a detailed analysis of the mine ventilation system to ensure that effective measures are taken in the shortest time. The troubleshooting module extracts the ventilation status information in the real-time data and combines the influence of uneven gas distribution to generate the 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 status of the ventilation system, adjusts the airflow and gas emissions, ensures a more uniform gas distribution in the mine, and reduces the instability and inaccuracy of sensor monitoring caused by excessive uneven distribution of harmful gases; by using dimensionless technology to process real-time data sets, the system can effectively eliminate unit differences in the data, unify the output results of different acquisition devices and sensors, and improve the accuracy and reliability of data processing. This processing method enables the system to maintain consistent performance in different mines and environments, and adapt to different gas types and climatic conditions. The system integrates multiple information sources (such as harmful gas concentration, ventilation conditions, etc.), combines mixed effect analysis and influence coefficient calculation, and provides more scientific and accurate decision support for coal mine managers, helping managers to identify safety hazards in a timely manner and take reasonable preventive measures. In short, the real-time safety monitoring system provided by the system can effectively improve the safety, stability and emergency response capabilities of the coal mine ventilation system through the coordinated work of efficient signal acquisition, data processing, effect analysis and safety management modules, reduce safety risks in coal mine operations, and improve 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, and 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 according to the sensor group to obtain relevant response signals, wherein the relevant response signals include the response value Xyz collected by the sensor matching the corresponding harmful gas when it is not interfered with, the reference response value Xyz0 of the sensor matching the corresponding harmful gas when the concentration of the corresponding harmful gas is zero, and the 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, removing 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 gas concentration values ​​Qnd of various harmful gases when they are not disturbed, 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 disturbed; Xyz a0 It is expressed as the reference response value of the sensor matching the ath 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 ath harmful gas to the ath 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 technical data sheet of the sensor.

[0068] Among them, the response value Xyz collected by the sensor matching the corresponding harmful gas when it is not disturbed refers to the original response data collected by the sensor under the condition of no interference, 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, a metal oxide semiconductor sensor);

[0069] The technical data sheet of the above-mentioned sensor contains various performance indicators and working parameters of the sensor, such as sensitivity, detection range, response time, operating temperature, accuracy and life, etc. These data can help determine the applicability and reliability of the sensor in different working environments. The technical data sheet of the sensor is provided by the manufacturer and can usually be obtained from the instruction manual, manual or technical documentation of the sensor product.

[0070] In this embodiment, the signal acquisition module monitors the response of various harmful gases in the mine channel in real time through the gas dynamic monitoring unit to ensure that the concentration changes of harmful gases in the mine can be accurately captured. By analyzing the sensor response signal, combined with the reference response value and sensitivity data, the system can convert it into an accurate gas concentration value Qnd. This process can effectively eliminate external interference through an accurate gas concentration conversion formula, improve the reliability and accuracy of gas monitoring data, and provide data support for mine safety. The ventilation acquisition unit can understand the ventilation conditions of the mine channel in many aspects by collecting the status of the ventilation system in the mine in real time. The monitored ventilation situation information helps the system analyze the distribution of gas in the mine, promptly discover possible problems with the ventilation system, and thus adjust or optimize the ventilation strategy in time to ensure that harmful gases in the mine are effectively discharged; the signal preprocessing module ensures the quality of the collected signal data by removing noise, filling missing values, eliminating outliers, and data smoothing operations. This preprocessing process can effectively improve the accuracy of the data, ensure that the data used in subsequent analysis is more reliable, and further avoid wrong decisions due to 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 monitor the response of the corresponding sensor in various harmful gas environments in advance to establish a cross-sensitivity matrix S. According to 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 disturbed; N represents the number of harmful gases; a and b are the numbers of the corresponding harmful gases; 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; Qnd a (t) is 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 It is expressed as the cross-sensitivity coefficient between the a-th harmful gas and the b-th harmful gas. This coefficient captures the impact of the interaction between harmful gases, especially the impact on the sensor when harmful gas a and harmful gas b exist at the same time;

[0076] log[Qnd a (t)+1] indicates that in the process of crossover 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 is a matrix used to describe the mutual influence of multiple gas sensors when responding to different gases. In practical applications, sensors usually respond not only to their target gas, but also to other gases to a certain extent. This phenomenon is called cross-sensitivity. The process of obtaining the cross-sensitivity matrix usually includes several steps: experiment, data collection, and analysis modeling. The following is a detailed process of how to obtain the cross-sensitivity matrix:

[0079] 1. To obtain the cross-sensitivity matrix, we first need to measure the response of the sensor in a variety of gas environments through experimental design. The experimental steps include: (1) Select the test gas: Select the gas 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) Control the gas concentration: Use a gas generator or a gas mixing device to accurately control the concentration of each gas. These gas concentrations are usually increased step by step (for example, set to different concentration points), and record the sensor response values ​​at these concentrations. (3) Environmental control: Ensure that the temperature, humidity and pressure of the laboratory environment remain constant, because 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 is drawn 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 we have 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 the ath harmful gas and the bth harmful gas.

[0081] In this embodiment, through the cross-sensitivity analysis unit, the system can monitor the response of the sensor in a variety of harmful gas environments in advance, establish a cross-sensitivity matrix S, and then conduct an in-depth analysis of the response changes of the sensor. This analysis can effectively identify the interaction between different harmful gases, especially when multiple harmful gases exist at the same time, the common influence of different gases on the sensor. Through the calculation of the cross-sensitivity coefficient, the system can accurately capture this cross effect, helping to ensure that the response of the sensor in a complex gas environment is still accurate and reliable. The cross-sensitivity matrix S can quantify the interaction between multiple harmful gases, especially the impact on the sensor response when the gas concentration changes. Based on this matrix, the system can analyze and adjust the sensor response value Gxyz when disturbed according to the gas concentration Qnd and the cross-sensitivity coefficient. In the coal mine operating environment, harmful gases often exist at the same time and interact with each other. The traditional single gas monitoring method may be affected by the interference of multiple gases, resulting in inaccurate data. Through cross-sensitivity analysis, the system can accurately analyze the interaction between gases and their influence on the sensor when multiple harmful gases exist at the same time, avoiding data deviation caused by gas cross-interference, thereby improving the reliability and accuracy of the gas monitoring system. By combining the concentrations of multiple harmful gases and cross-sensitivity effects, the system can predict the response changes of sensors in real time under multi-gas environments. This predictive capability provides important support for the optimization of mine ventilation systems, ensuring that the system can identify potential fluctuations in dangerous gas concentrations in real time and respond in a timely manner under various combinations of harmful gases. Using cross-sensitivity analysis, the system can more accurately identify changes in the concentrations of multiple harmful gases in coal mines, especially concentration fluctuations under the combined action 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 according to the response value Gxyz collected by the sensor when disturbed by the cross-sensitivity analysis unit, so as to calculate the response fluctuation Xbd, and 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 judge the start of 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, and the troubleshooting mechanism will not be started at this time, and the troubleshooting instruction will not be triggered;

[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 of multiple harmful gases by calculating the response fluctuation Xbd. This method can effectively capture the response fluctuations that may occur in the sensor in a complex gas environment, avoid erroneous data caused by gas mixture interference, and thus improve the accuracy and stability of harmful gas monitoring in mines. Quantify sensor response fluctuations: By analyzing multiple monitoring data, the response fluctuation Xbd is calculated. This technology provides a quantitative indicator for the stability of sensors in mines. The response fluctuation Xbd reflects the response stability of the sensor under the interference of multiple harmful gases, so that the system can accurately determine whether the sensor is in a normal state, and provide data support for further safety assessment and decision-making. Prevent the risk of misjudgment and missed judgment: The preliminary judgment unit can effectively avoid misjudgment and missed judgment caused by unstable sensor response by comparing the response fluctuation Xbd with the preset safety threshold Q. If the response fluctuation falls within the safety range, the system will judge that the sensor is stable, and there is no need to start the troubleshooting mechanism, which reduces unnecessary troubleshooting instructions and avoids waste of 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 and obtain the distribution attribute coefficient Fsxs according to the gas feature set by associating 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] In the formula, F1 and F2 represent the weight values ​​of the average gas density Qm and the gas diffusion factor Qk of various harmful gases, respectively, where 0<F1<1, 0<F2<1, and 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 the data in the monitoring period T. Based on the types N of multiple harmful gases, the system can obtain the gas characteristics at different monitoring times and construct a gas feature set including the average gas density Qm and the gas diffusion factor Qk. This process can help the monitoring system fully understand the types, distribution and change trends of harmful gases in the mine environment, and provide accurate basis for subsequent investigation. When the preliminary judgment unit triggers the investigation instruction, the feature acquisition unit can quickly retrieve relevant data after receiving the instruction by obtaining the average density and diffusion factor of the harmful gas, ensuring that the investigation work is carried out quickly after accurately identifying the gas characteristics, which helps to improve the efficiency and accuracy of the investigation and avoid misjudgment caused by inaccurate changes in gas types and concentrations. The gas feature set reflects the distribution of harmful gases in the mine channel by combining the average density Qm and diffusion factor Qk of the gas. Combined with these characteristic information, more intelligent monitoring and control can be achieved, thereby improving the automation and responsiveness of the mine gas monitoring system. The system can effectively adjust the monitoring and investigation strategies according to the characteristics of different gases. By obtaining the distribution attribute coefficient Fsxs, this coefficient can be used to evaluate the actual impact of the ventilation system on the distribution of harmful gases, especially in complex gas environments. The system can identify the changing trend of gases in the ventilation ducts, determine whether the ventilation system is working properly and have a significant impact on the gas concentration. After dimensionless processing, the calculated distribution attribute coefficient Fsxs can standardize the characteristics of different harmful gases, so that the impact of gas characteristics in different environments and different ventilation conditions can be uniformly measured. In this way, when performing troubleshooting tasks, the system can more accurately evaluate changes in gas distribution, avoid missing potential dangerous gases or misjudging gas concentrations, and ensure the comprehensiveness and accuracy of the troubleshooting work.

[0099] Example 6

[0100] Please refer to Figure 1 Specifically: the ventilation impact unit is also used to analyze the influence of ventilation status on the non-uniform gas distribution caused by various harmful gases according to the relevant ventilation status information and gas feature set in the real-time data set, and construct an influence coefficient Yxs, and 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 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 coefficients 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, affects the concentration and distribution uniformity of harmful gases, and can use wind speed sensors (such as hot wire anemometers, ultrasonic anemometers, pitot tube anemometers) 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 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 various 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 judged that the current ventilation state leads to 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 adjusted down by one gear to improve the sensor instability caused by the serious 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 non-uniform influence of ventilation status on the distribution of harmful gases by combining the ventilation status information and gas feature set in the real-time data set. By constructing the influence coefficient Yxs, the influence degree of ventilation on gas distribution can be quantified, and the situation of uneven gas distribution can be identified. Using ventilation speed, distribution attribute coefficient, standard deviation and covariance, the non-uniformity of gas distribution and its influence on sensor response can be comprehensively evaluated, which provides a scientific basis for accurately adjusting the ventilation system. By comparing the influence coefficient Yxs with the evaluation threshold W, the system can judge in real time whether the current ventilation state leads to significant non-uniformity of gas distribution. When the influence coefficient exceeds the threshold, the system will judge that there is an abnormality in gas distribution, otherwise it is considered that the ventilation state is normal. This real-time response mechanism provides instant evaluation and adjustment feedback for the mine ventilation system, ensuring that it can respond quickly to possible dangers. If it is judged that the ventilation state brings significant non-uniformity to gas distribution, the safety management module will automatically adjust the ventilation speed. By lowering the ventilation speed, the influence of uneven gas distribution on sensor stability and accuracy can be improved. This can not only effectively reduce sensor false alarms and missed alarms, but also improve the accuracy and reliability of monitoring data, and avoid misjudgment caused by uneven gas distribution. The safety management module can optimize the working state of the ventilation system according to 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 excessive gas concentration 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] 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. A real-time safety monitoring system for coal mine ventilation, characterized by: It includes 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, and at the same time use the ventilation system to obtain relevant ventilation situation information; 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 dimension processing 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, and analyze the stability of the sensor group response in the case of a mixture of multiple harmful gases based on the relevant response signals to obtain the response fluctuation Xbd, and determine the start of the troubleshooting mechanism according to the value of the response fluctuation Xbd; The troubleshooting module is used to start 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 influence of ventilation status on the non-uniform gas distribution caused by multiple harmful gases, and construct the influence coefficient Yxs; The safety management module is used to optimize the ventilation state of the ventilation system based on the influence coefficient Yxs.

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 according to the sensor group to obtain relevant response signals, wherein the relevant response signals include the response value Xyz collected by the sensor matching the corresponding harmful gas when it is not interfered with, the reference response value Xyz0 of the sensor matching the corresponding harmful gas when the concentration of the corresponding harmful gas is zero, and the 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 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.

3. A 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, removing 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 gas concentration values ​​Qnd of various harmful gases when they are not disturbed, 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 disturbed; Xyz a0 It is expressed as the reference response value of the sensor matching the ath 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 monitor the response of the corresponding sensor in various harmful gas environments in advance to establish a cross-sensitivity matrix S. According to 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 disturbed; N represents the number of harmful gases; a and b are the numbers of the corresponding harmful gases; 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; Qnd a (t) is 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 the ath harmful gas and the bth harmful gas.

5. A 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 according to the response value Gxyz collected by the sensor when disturbed 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. A 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 judge the start of 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, and the troubleshooting mechanism will not be started at this time, and the troubleshooting instruction will not be triggered; 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. A real-time safety monitoring system for coal mine ventilation according to claim 1, characterized in that: The troubleshooting module includes a feature acquisition unit and a ventilation impact unit; 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.

8. A real-time safety monitoring system for coal mine ventilation according to claim 7, characterized in that: The ventilation influencing unit is used to calculate and obtain the distribution attribute coefficient Fsxs according to the gas feature set by associating 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: In the formula, F1 and F2 represent the weight values ​​of the average gas density Qm and the gas diffusion factor Qk of various harmful gases, respectively, and the specific values ​​of F1 and F2 are set by the user according to the situation.

9. A real-time safety monitoring system for coal mine ventilation according to claim 8, characterized in that: The ventilation influencing unit is also used to analyze the influence of the ventilation state on the non-uniform gas distribution caused by various harmful gases according to the relevant ventilation status information and gas feature set in the real-time data set, and construct an influence coefficient Yxs, which is obtained by the following formula: Where Fsz(t) is the ventilation velocity at time t, Fsxs(t) is the distribution property coefficient at time t, and Fsz(t) is 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 coefficients within the monitoring period, and cov(Fsz(t), Fsxs(t)) is expressed as the covariance of Fsz(t) and Fsxs(t); An evaluation threshold W is preset, and the influence coefficient Yxs is compared with the evaluation threshold W to determine the influence of the current ventilation state on the uneven distribution of gas caused by various harmful gases. 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 various harmful gases is not in an abnormal state, and a second evaluation result is generated.

10. A real-time safety monitoring system for coal mine ventilation according to claim 9, 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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