Intelligent real-time monitoring regulation and control system for coal mine ventilation

Abstract

The invention discloses an intelligent real-time monitoring regulation and control system for coal mine ventilation, and relates to the technical field of coal mine ventilation, through a ventilation duct layout analysis module, the system can construct pressure loss of each branch in a coal mine ventilation duct, and further analyze the relevance between the pressure loss and ventilation data information of a corresponding area; the analysis provides detailed air flow distribution and ventilation duct pressure loss data, accurate decisions can be made on layout optimization, pressure regulation and air flow distribution of the mine ventilation duct, and stability of air flow and balance of pressure in the mine are ensured. And dynamic prediction and evaluation of a mine stability index: the system fits and outputs the mine stability index through a mine performance analysis module in combination with the trained ventilation stability prediction model, and compares the mine stability index with a set stability threshold value to evaluate the stability of the mine in real time. When the mine ventilation system faces an unstable risk, the system can give out early warning in time.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine ventilation, and particularly to an intelligent real-time monitoring and control system for coal mine ventilation. Background Art

[0002] Coal mines are an important part of energy production, and it is crucial to ensure the safety and efficient operation of coal mine operations. Coal mine ventilation, as an important part of mine safety management, is related to the health and safety of workers inside the mine, production efficiency, and the rational exploitation of coal mine resources. In the field of coal mine ventilation, the flow of air, changes in temperature and humidity, and the emission of harmful gases are important factors affecting mine safety. Especially in the deep part of the mine, due to its special physical conditions, the air flow and pressure distribution are uneven, which easily causes abnormal air flow in local areas or too high concentration of harmful gases, thus leading to the instability of the ventilation system.

[0003] At present, traditional coal mine ventilation management mainly relies on manual operation and regular inspections, and the adjustment is often based on experience. This management method is difficult to monitor and adjust the air flow changes in the mine in real time. Especially when facing deep well operations and complex mine ventilation networks, it is difficult to grasp the air pressure changes inside the mine in real time. In addition, existing ventilation systems often ignore the details of the pressure distribution in the ventilation ducts, resulting in uneven air flow or insufficient fan efficiency, thus affecting the overall stability of the ventilation system. Although some coal mines have adopted simple sensors to monitor air flow and air pressure, most systems lack in-depth data analysis and dynamic control functions, and it is difficult to accurately predict the change trend of air flow under real-time monitoring, resulting in unstable air flow in the mine, affecting the working environment of workers, and even bringing potential safety hazards. Therefore, there are still great deficiencies in the existing systems for real-time monitoring and control of coal mine air pressure stability. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides an intelligent real-time monitoring and control system for coal mine ventilation, which solves the problems in the above background art.

[0005] To achieve the above object, the present invention is realized through the following technical solutions: An intelligent real-time monitoring and control system for coal mine ventilation includes a data acquisition module, a fan status analysis module, a ventilation duct layout analysis module, an in-mine performance analysis module, and a feedback and control module;

[0006] The data acquisition module is used to deploy several groups of monitoring sensors inside the coal mine to real-time monitor the ventilation status and the pressure change trend inside the coal mine, generate relevant ventilation data information and relevant air pressure change data information, and after data preprocessing, construct a coal mine data set;

[0007] The fan status analysis module is used to analyze the working efficiency of the fans in the coal mine based on the relevant ventilation data information in the coal mine dataset after feature extraction, so as to obtain the fan efficiency Fjx. Based on the value of the fan efficiency Fjx, it preliminarily judges whether it is necessary to start the inspection mechanism;

[0008] The ventilation duct layout analysis module is used to receive and start the inspection mechanism. According to the layout of the ventilation ducts in the coal mine and combined with the relevant air pressure change data information in the coal mine dataset, it constructs the pressure loss Yss of each branch in the ventilation duct, and analyzes the correlation between the pressure loss Yss of each branch and the relevant ventilation data information of the corresponding area in the coal mine, so as to obtain the correlation coefficient Xgxs;

[0009] The in-mine performance analysis module is used to analyze and calculate the pressure distribution influence coefficient Yfz of the air flow in the coal mine according to the value of the correlation coefficient Xgxs, and combined with the trained ventilation stability prediction model, it fits and outputs the in-mine stability index Kwzs;

[0010] The feedback and regulation module is used to preset the stability threshold K. By comparing the in-mine stability index Kwzs with the stability threshold K, it comprehensively judges the current stability situation in the coal mine, and takes adaptive regulation measures according to the judgment result.

[0011] Preferably, the data acquisition module includes an acquisition unit and a processing unit;

[0012] The acquisition unit is used to deploy several groups of monitoring sensors in each area of the coal mine in advance to monitor the ventilation status and the pressure change trend in the coal mine in real time, and generate relevant ventilation data information and relevant air pressure change data information. Among them, several groups of monitoring sensors include power sensors, fluid mechanics sensors, laser scanners, gas density sensors, pitot tubes, depth sensors, gas analyzers and temperature sensors; the relevant ventilation data information includes the input power P of the fan input and the power P provided by the fan output ; the relevant air pressure change data information includes the gas density ρ in the ventilation duct, the air flow velocity Qjd, the friction factor Mcyz, the moving pressure Dyz and the initial pressure value Yyz in the coal mine area corresponding to each branch;

[0013] The processing unit is used to preprocess the data information obtained in the acquisition unit to identify and remove noise, fill in missing values, and perform data smoothing operations, and use dimensionless processing technology to perform dimensional analysis on the preprocessed data information to remove units and construct a coal mine dataset. The coal mine dataset includes relevant ventilation data information, relevant air pressure change data information, and relevant environmental data information after preprocessing. Among them, the relevant environmental data information includes the length Tcd of the ventilation duct, the diameter Tzj of the ventilation duct, the depth Sd of each coal mine area from the mine mouth, the density Hqmd of the mixed gas in each coal mine area, the variable difference Bc in the coal mine area, and the temperature difference Wc between each coal mine area and the adjacent coal mine area.

[0014] Preferably, the fan status analysis module includes a condition analysis unit and a trigger unit;

[0015] The condition analysis unit is used to extract features from the relevant ventilation data information in the coal mine dataset to obtain the input power P of the fan input and the power P provided by the fan output , and based on the input power P of the fan input and the power P provided by the fan output , analyze the working efficiency of the fan in the coal mine to obtain the fan efficiency Fjx. The fan efficiency Fjx is obtained through the following formula:

[0016]

[0017] In the formula, P output represents the power provided by the fan, and P input represents the input power of the fan.

[0018] Preferably, the trigger unit is used to preset an efficiency requirement range and compare the fan efficiency Fjx with the efficiency requirement range to preliminarily determine whether to start the inspection mechanism. The specific content is as follows:

[0019] If the fan efficiency Fjx falls within the efficiency requirement range, it is preliminarily determined that there is no need to start the inspection mechanism at this time, and the monitoring operation continues;

[0020] If the fan efficiency Fjx does not fall within the efficiency requirement range, it is preliminarily determined that it is necessary to start the inspection mechanism at this time, and the instruction to start the inspection mechanism is transmitted to the ventilation duct layout analysis module.

[0021] Preferably, the ventilation duct layout analysis module includes a layout information unit, a loss unit, and an impact analysis unit;

[0022] The layout information unit is used to pre-acquire the topographic distribution map in the coal mine, identify the distribution positions of the ventilation ducts in the coal mine from the topographic distribution map in the coal mine. The distribution positions of the ventilation ducts include the distribution positions of the main ventilation ducts and the branch ventilation ducts. According to the distribution positions of the ventilation ducts, several groups of coal mine areas are divided at the distribution positions of the ventilation ducts, and the several groups of coal mine areas are sequentially marked as the first coal mine area I1, the second coal mine area I2, the third coal mine area I3,..., the nth coal mine area I n .

[0023] Preferably, the loss unit is used to receive and activate the inspection mechanism in the trigger unit, and construct the pressure loss Yss of each branch in the ventilation duct according to the distribution position of the ventilation duct and in combination with the relevant air pressure change data information in the coal mine data set. The pressure loss Yss of each branch in the ventilation duct is obtained through the following formula:

[0024]

[0025] In the formula, Mcyz represents the friction factor, Tcd represents the length of the ventilation duct, Tzj represents the diameter of the ventilation duct, ρ represents the gas density, and Qjd represents the air flow velocity.

[0026] Preferably, the influence analysis unit is used to determine the coal mine area corresponding to each branch according to the positions of each branch in the ventilation duct, so as to obtain the relevant air pressure change data information in the coal mine area corresponding to each branch. After feature extraction, the moving pressure Dyz and the initial pressure value Yyz in the coal mine area corresponding to each branch are obtained. Based on the moving pressure Dyz and the initial pressure value Yyz in the coal mine area corresponding to each branch, the pressure difference Djc is obtained. The pressure difference Djc is obtained through the following formula: Djc = |Dyz - Djc|;

[0027] Based on the pressure difference Djc and the pressure loss Yss of each branch in the ventilation duct, analyze the correlation between the pressure difference Djc and the pressure loss Yss of each branch in the ventilation duct to obtain the correlation coefficient Xgxs in each monitoring period, which is specifically obtained through the following formula:

[0028]

[0029] In the formula, Djc j represents the pressure difference in the jth coal mine area, Yss j represents the pressure loss of the branch corresponding to the jth coal mine area, σDjc represents the standard deviation of the pressure difference, σYss represents the standard deviation of the pressure loss, cov(Djc j , Yss j ) represents the covariance of Djc and Yss j ;

[0030] Set a monitoring period to obtain the correlation coefficient Xgxs in each monitoring time period within the monitoring period, and analyze whether it tends to a positive correlation state based on the numerical change trend of multiple groups of correlation coefficients Xgxs. If the value of the correlation coefficient Xgxs continuously tends to 1 within the monitoring period, it is judged that within the current monitoring period, the pressure difference Djc and the pressure loss Yss of each branch in the ventilation duct gradually tend to a positive correlation state, and at this time, a barometric pressure analysis instruction will be triggered; if the value of the correlation coefficient Xgxs continuously tends to -1 within the monitoring period, it is judged that within the current monitoring period, the pressure difference Djc and the pressure loss Yss of each branch in the ventilation duct gradually tend to a negative correlation state, and at this time, a barometric pressure analysis instruction will be triggered; if the value of the correlation coefficient Xgxs continuously tends to 0 within the monitoring period, it is judged that within the current monitoring period, there is no linear correlation between the pressure difference Djc and the pressure loss Yss of each branch in the ventilation duct, and at this time, the barometric pressure analysis instruction will not be triggered temporarily.

[0031] Preferably, the in-mine performance analysis module includes a distribution unit and a comprehensive analysis unit;

[0032] The distribution unit is used to trigger a barometric pressure analysis instruction based on the magnitude of the correlation coefficient Xgxs value in the influence analysis unit, receive the barometric pressure analysis instruction, analyze and calculate the pressure distribution influence coefficient Yfz of the airflow in the coal mine according to the pressure loss Yss of each branch in the ventilation duct, in combination with relevant environmental data information, and after dimensionless processing. The pressure distribution influence coefficient Yfz of the airflow in the coal mine is obtained through the following formula:

[0033]

[0034] In the formula, Yfz j represents the pressure distribution influence coefficient of the airflow in the jth coal mine area, Sd j represents the depth of the jth coal mine area from the mine entrance, Hqmd j represents the density of the mixed gas in the jth coal mine area, Yss j represents the pressure loss of each branch in the jth coal mine area, and a1, a2, and a3 all represent weight values. The specific values of a1, a2, and a3 are set by the user according to the situation.

[0035] Preferably, the comprehensive analysis unit is used to construct an initial model by using deep learning technology and combining the coal mine data set. By dividing the data in the coal mine data set into a training set and a validation set, training and testing are carried out, and the trained initial model is used as a feature recognition model. The feature information in the feature recognition model is obtained respectively, and the obtained feature information is used to train and test the feature recognition model. At the same time, combined with an inspection mechanism, the trained feature recognition model is used as a ventilation stability prediction model. Through the ventilation stability prediction model and after dimensionless processing, the in-mine stability index Kwzs is fitted and output. The in-mine stability index Kwzs is obtained through the following formula:

[0036]

[0037] In the formula, Yfz j represents the influence coefficient of the air pressure distribution in the jth coal mine area, Bc j represents the variable difference in the jth coal mine area, Wc j represents the temperature difference between the jth coal mine area and the adjacent coal mine area, Yfz avg represents the average value of the influence coefficient of the air pressure distribution, Bc avg represents the average value of the variable difference, Wc avg represents the average value of the temperature difference, α, β and γ all represent, n represents, j = 1, 2, 3,..., n.

[0038] Preferably, the feedback and regulation module is used to comprehensively judge the stability situation in the current coal mine by comparing the in-mine stability index Kwzs with the stability threshold K. The specific content is as follows:

[0039] If the in-mine stability index Kwzs exceeds the stability threshold K, it is comprehensively judged that the current coal mine is not in a stable state. At this time, the frequency converter is used to adjust the speed of the fan to 120% of the original fan speed to increase the air flow, and smooth regulation is carried out on the temperature fluctuation and humidity fluctuation in the coal mine;

[0040] If the in-mine stability index Kwzs does not exceed the stability threshold K, it is comprehensively judged that the current coal mine is in a stable state. At this time, regular inspection and maintenance of the fan, valve and air purification equipment in the coal mine will continue.

[0041] The present invention provides an intelligent real-time monitoring and regulation system for coal mine ventilation, which has the following beneficial effects:

[0042] (1) The system deploys several groups of monitoring sensors inside the coal mine to obtain the ventilation status and pressure change trends inside the mine in real time. This real-time data acquisition function ensures the dynamic monitoring of the mine ventilation status, can promptly detect abnormal air pressure changes and poorly ventilated areas, and further reduces potential safety hazards. Fan efficiency analysis and optimization: Through the fan status analysis module, the system can extract the working efficiency of the fan based on the ventilation data information in the coal mine dataset. When the fan efficiency deviates from the normal range, the system can pre-judge whether it is necessary to start the inspection mechanism. This function not only improves the operating efficiency of the fan but also avoids energy consumption waste and poor ventilation caused by the decline in fan performance. Precise pressure loss and air flow analysis: Through the ventilation duct layout analysis module, the system can construct the pressure loss of each branch in the coal mine ventilation duct and further analyze the correlation between the pressure loss and the ventilation data information in the corresponding area. This analysis provides detailed air flow distribution and ventilation duct pressure loss data, which helps to make precise decisions in the optimization of the mine ventilation duct layout, pressure regulation, and air flow distribution, ensuring the stability of the air flow and the balance of the pressure inside the mine. Dynamic prediction and evaluation of the mine stability index: The system, through the in-mine performance analysis module, combines the trained ventilation stability prediction model, fits and outputs the in-mine stability index, and compares it with the set stability threshold to evaluate the stability of the mine in real time. When the mine ventilation system faces unstable risks, the system can issue early warnings in a timely manner to ensure that the air flow and ventilation conditions in the mine always remain within the safe range. In short, through intelligent real-time monitoring and control means, the system realizes the dynamic management of the mine air pressure stability, further improves the safety, stability, and energy use efficiency of the coal mine ventilation system, reduces the risks caused by poor ventilation or fan failures, and enhances the safety guarantee ability of coal mine production.

[0043] (2) By mastering the distribution of the main ventilation ducts and branch ventilation ducts, the system can divide the coal mine area into multiple regions, label each region with a number, provide basic data for subsequent pressure loss calculation and air flow analysis, and help optimize the distribution of ventilation ducts. Under the action of the loss unit, the system can calculate the pressure loss of each branch in the ventilation duct according to the air pressure change information in the coal mine dataset and the distribution position of the ventilation duct, and can further accurately evaluate the loss of air flow in the ventilation duct. By calculating the real-time pressure loss of each branch, the system can identify possible ventilation problems, such as poor air flow, excessive or too little pressure, etc., and then guide the optimization of the ventilation duct layout. Optimization of ventilation duct layout and air flow distribution: By analyzing the pressure loss of each branch in the ventilation duct, the system can further optimize the design and layout of the ventilation duct. When it is found that the pressure loss of some ventilation ducts is too large, the system can adjust the size of the ventilation duct, increase or decrease the number of branches, or take other measures according to the pressure loss data to balance the air flow distribution in each region. This optimization process ensures the uniform distribution of air flow in the coal mine, helps reduce poor ventilation in local areas, and improves the overall ventilation efficiency and safety of the mine. Under the action of the inspection mechanism, the system can monitor and analyze the layout and pressure loss of the ventilation ducts in the coal mine in real time. When an abnormality is found, the trigger unit will issue an instruction to start the inspection. Through this feedback mechanism, the system can timely discover potential problems in the coal mine ventilation system and take effective measures for adjustment or repair.

[0044] (3) The impact analysis unit calculates the dynamic pressure and static pressure in each coal mine area to obtain a pressure difference. This pressure difference can reflect the difference between the dynamic characteristics and static characteristics of the air flow in the ventilation duct. This difference is of great significance to the stability of the coal mine ventilation system. By analyzing the correlation between the pressure difference and the pressure loss of each branch, abnormal fluctuations of the air flow in the coal mine can be more accurately identified, providing a scientific basis for the optimization and adjustment of the coal mine ventilation system. Real-time monitoring and abnormal detection: The system sets a monitoring period, continuously tracks and calculates the correlation coefficient in each monitoring period, and judges the correlation state between the pressure difference and the pressure loss based on the trend of its numerical change.

[0045] (4) The distribution unit triggers a barometric analysis instruction according to the value of the correlation coefficient in the impact analysis unit, and further combines the pressure loss of each branch and relevant environmental data information to calculate the pressure distribution impact coefficient of the air flow in the coal mine. This impact coefficient can comprehensively reflect the air flow pressure distribution status in different regions of the coal mine, help the system identify weak links in the internal ventilation of the mine, and provide a reference basis for optimizing the ventilation design. Especially under the action of factors such as depth, gas density, and pressure loss, the pressure change in each region can be accurately judged to ensure the optimization of the ventilation effect. Description of the Drawings

[0046] Figure 1 This is a block diagram of an intelligent real-time monitoring and control system for coal mine ventilation according to the present invention. Specific implementation manner

[0047] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0048] Embodiment 1

[0049] Please refer to Figure 1 , the present invention provides an intelligent real-time monitoring and control system for coal mine ventilation, including a data acquisition module, a fan status analysis module, a ventilation duct layout analysis module, an in-mine performance analysis module, and a feedback and control module;

[0050] The data acquisition module is used to deploy a number of groups of monitoring sensors inside the coal mine to real-time monitor the ventilation status and pressure change trend inside the coal mine, generate relevant ventilation data information and relevant air pressure change data information, and construct a coal mine data set after data preprocessing;

[0051] The fan status analysis module is used to analyze the working efficiency of the fans inside the coal mine based on the relevant ventilation data information in the coal mine data set after feature extraction to obtain the fan efficiency Fjx, and preliminarily determine whether to start the inspection mechanism based on the value of the fan efficiency Fjx;

[0052] The ventilation duct layout analysis module is used to receive and start the inspection mechanism, construct the pressure loss Yss of each branch in the ventilation duct according to the layout of the ventilation ducts inside the coal mine and in combination with the relevant air pressure change data information in the coal mine data set, and analyze the correlation between the pressure loss Yss of each branch and the relevant ventilation data information of the corresponding area inside the coal mine to obtain the correlation coefficient Xgxs;

[0053] The in-mine performance analysis module is used to analyze and calculate the pressure distribution influence coefficient Yfz of the air flow inside the coal mine according to the value of the correlation coefficient Xgxs, and fit and output the in-mine stability index Kwzs in combination with the trained ventilation stability prediction model;

[0054] The feedback and control module is used to preset a stability threshold K, compare the in-mine stability index Kwzs with the stability threshold K to comprehensively judge the current stability situation inside the coal mine, and take adaptive control measures according to the judgment result.

[0055] During the operation of this system, the data acquisition module monitors the ventilation status and pressure change trend in the coal mine in real time by deploying multiple groups of monitoring sensors. This real-time data collection mechanism can not only help monitor the air flow distribution and pressure conditions in the coal mine, but also provide high-precision real-time data information, laying a solid data foundation for subsequent analysis and ensuring that the ventilation system can respond promptly according to real-time changes. The fan status analysis module can evaluate the working efficiency of the fan in real time by extracting and analyzing the features of the ventilation data in the coal mine dataset. Based on the analysis of the fan efficiency, the system can determine whether there is a decrease in fan performance or unstable operation, and promptly start the inspection mechanism to further avoid the long-term operation of the fan in an inefficient state, reduce energy consumption and maintenance costs, improve the working efficiency of the fan, and ensure that the fan always maintains a relatively good operating state. The ventilation duct layout analysis module combines the air pressure change data information in the coal mine dataset to accurately calculate and analyze the pressure loss of each branch of the ventilation duct, further explore the relationship between the pressure loss and the regional air flow, and obtain the correlation coefficient. This analysis helps the coal mine accurately grasp the air flow distribution and the magnitude of the pressure loss inside the ventilation duct, making the regulation of the ventilation duct more accurate and further avoiding potential safety hazards caused by uneven air flow distribution and pressure fluctuations. The in-mine performance analysis module conducts in-depth analysis based on the correlation coefficient and pressure distribution, combines the ventilation stability prediction model, and fits and outputs the in-mine stability index. This index provides a quantitative indicator for coal mine managers to monitor the stability of the air flow in the mine in real time and promptly detect unstable areas. This model can effectively predict the change trend of the mine air flow, enabling the ventilation system to make dynamic adjustments according to the actual situation of the mine to ensure that the air flow remains stable in each area. The feedback and regulation module compares the in-mine stability index with the preset stability threshold to judge the current ventilation status of the mine and automatically activates adaptive regulation measures. Whether it is to improve the fan efficiency, adjust the opening degree of the ventilation duct valve, or optimize the temperature and humidity control system, the system can make adaptive adjustments according to the real-time situation to avoid resource waste and unnecessary equipment operation.

[0056] Embodiment 2

[0057] Please refer to Figure 1 , specifically: The data acquisition module includes an acquisition unit and a processing unit;

[0058] The acquisition unit is used to pre-deploy several groups of monitoring sensors in various areas of the coal mine to monitor the ventilation status and pressure change trend in the coal mine in real time, generate relevant ventilation data information and relevant air pressure change data information. Among them, several groups of monitoring sensors include power sensors, fluid mechanics sensors, laser scanners, gas density sensors, pitot tubes, depth sensors, gas analyzers, and temperature sensors; the relevant ventilation data information includes the input power P of the fan inpu

[0059] and the power P provided by the fan output ; the relevant air pressure change data information includes the gas density ρ in the ventilation duct, the air flow velocity Qjd, the friction factor Mcyz, the moving pressure Dyz and the initial pressure value Yyz in the coal mine areas corresponding to each branch;

[0060] The processing unit is used to preprocess the data information obtained in the acquisition unit to identify and remove noise, fill in missing values, and perform data smoothing operations. Among them, the methods for filling in missing values include mean filling, median filling, interpolation method filling, and regression method filling, and the dimensionless processing technology is used to perform dimensional analysis on the preprocessed data information to remove units and construct a coal mine data set. The coal mine data set includes the relevant ventilation data information, relevant air pressure change data information, and relevant environmental data information after preprocessing. Among them, the relevant environmental data information includes the length Tcd of the ventilation duct, the diameter Tzj of the ventilation duct, the depth Sd of each coal mine area from the mine mouth, the density Hqmd of the mixed gas in each coal mine area, the variable difference Bc in the coal mine area, and the temperature difference Wc between each coal mine area and the adjacent coal mine area.

[0061] In this embodiment, real-time data collection in multiple aspects: The data collection module deploys several groups of monitoring sensors in each area of the coal mine to monitor the ventilation status and air pressure change trend in the coal mine in real time. This function ensures that the air flow and pressure changes inside the coal mine can be reflected in real time, covering multiple key parameters such as the input power of the fan, the power provided by the fan, the gas density in the ventilation duct, the air flow velocity, the friction factor, and the initial pressure value. The real-time monitoring of these data provides comprehensive data support for the safety and production regulation of the coal mine. Efficient data preprocessing and noise removal: The processing unit effectively preprocesses the original data obtained by the acquisition unit, including noise removal, missing value filling, and data smoothing operations. Multiple methods such as mean filling, median filling, interpolation method filling, and regression method filling are used for missing value filling to ensure the integrity and accuracy of the data. This preprocessing function effectively eliminates data outliers and inaccurate information, ensuring the reliability and effectiveness of subsequent analysis results. Dimensionless processing and unit removal: The dimensionless processing technology is used to perform dimensional analysis on the preprocessed data information to remove the units in the data, so that various physical quantities can be compared and processed under a unified standard during subsequent calculations and analyses. The dimensionless processing not only improves the universality of the data, but also enables the system to perform consistent comparisons across regions and time periods, avoiding errors caused by inconsistent units.

[0062] Embodiment 3

[0063] Please refer to Figure 1 , specifically: The fan status analysis module includes a condition analysis unit and a trigger unit;

[0064] The condition analysis unit is used to extract features from relevant ventilation data information in the coal mine data set to obtain the input power P of the fan input and the power P provided by the fan output , and based on the input power P of the fan input and the power P provided by the fan output , analyze the working efficiency of the fan in the coal mine to obtain the fan efficiency Fjx. The fan efficiency Fjx is obtained through the following formula:

[0065]

[0066] In the formula, P output represents the power provided by the fan, that is, the mechanical power output by the fan, and P input represents the input power of the fan.

[0067] The above-mentioned power P provided by the fan outout and the input power P of the fan input can be monitored and obtained through a power sensor (power meter);

[0068] The trigger unit is used to preset the efficiency requirement range and compare the fan efficiency Fjx with the efficiency requirement range to preliminarily judge whether it is necessary to start the inspection mechanism. The specific content is as follows:

[0069] If the fan efficiency Fjx falls within the efficiency requirement range, it is preliminarily judged that there is no need to start the inspection mechanism at this time, and the monitoring operation continues;

[0070] If the fan efficiency Fjx does not fall within the efficiency requirement range, it is preliminarily judged that it is necessary to start the inspection mechanism at this time, and the instruction to start the inspection mechanism is transmitted to the ventilation duct layout analysis module.

[0071] The numerical fluctuation of the fan efficiency Fjx may mean that the fan is running unstably.

[0072] In this embodiment, for accurate fan efficiency evaluation: The fan status analysis module extracts ventilation data information from the coal mine dataset through the condition analysis unit, analyzes the input power and provided power of the fan, and calculates the fan efficiency. This calculation reflects the working efficiency of the fan through an accurate power ratio, which helps to promptly identify whether the fan is operating under a relatively optimal condition. When the fan efficiency is within a predetermined range, the system can confirm that the fan is operating normally; if the fan efficiency deviates from the normal range, it indicates that there may be a risk of fan failure or efficiency decline. For dynamic fan efficiency monitoring and real-time judgment: The system sets the required range of fan efficiency through the trigger unit and compares the fan efficiency with this required range in real time. If the fan efficiency falls within the normal range, the system determines that there is no need to start the inspection mechanism, and the ventilation system can continue to operate stably. This automated judgment process avoids unnecessary manual intervention and reduces the monitoring workload; if the fan efficiency exceeds the normal range, the system will trigger the inspection mechanism and transmit the command to the ventilation duct layout analysis module for further inspection and adjustment to ensure that the system takes remedial measures in a timely manner and avoids ventilation problems caused by fan failures.

[0073] Embodiment 4

[0074] Please refer to Figure 1 , specifically: The ventilation duct layout analysis module includes a layout information unit, a loss unit, and an impact analysis unit;

[0075] The layout information unit is used to pre-obtain the topographic distribution map within the coal mine and identify the distribution positions of the ventilation ducts within the coal mine from the topographic distribution map within the coal mine. The distribution positions of the ventilation ducts include the distribution positions of the main ventilation ducts and branch ventilation ducts. Based on the distribution positions of the ventilation ducts, several groups of coal mine areas are divided at the distribution positions of the ventilation ducts, and the several groups of coal mine areas are sequentially marked as the first coal mine area I1, the second coal mine area I2, the third coal mine area I3,..., the nth coal mine area I n .

[0076] The loss unit is used to receive and activate the inspection mechanism in the trigger unit, and based on the distribution positions of the ventilation ducts, combined with the relevant air pressure change data information in the coal mine dataset, construct the pressure loss Yss of each branch within the ventilation duct. The pressure loss Yss of each branch within the ventilation duct is obtained through the following formula:

[0077]

[0078] In the formula, Mcyz represents the friction factor, Tcd represents the length of the ventilation duct, Tzj represents the diameter of the ventilation duct, ρ represents the gas density, and Qjd represents the air flow velocity.

[0079] The above-mentioned friction factor Mcyz can be monitored and obtained through a fluid mechanics sensor;

[0080] The length Tcd of the ventilation duct and the diameter Tzj of the ventilation duct can be monitored and obtained through a Geographic Information System (GIS) or a laser scanner;

[0081] The gas density ρ can be monitored and obtained through a gas density sensor;

[0082] The air flow velocity Qjd can be monitored and obtained through a pitot tube or a thermal anemometer sensor;

[0083] In this embodiment, accurate identification of ventilation duct distribution and coal mine area division: The layout information unit of the ventilation duct layout analysis module obtains the topographic map of the coal mine and identifies the distribution positions of the ventilation ducts in the coal mine from it, further dividing the coal mine into multiple areas. Through this accurate geographical information processing, the system can clearly understand the distribution of the main ventilation duct and each branch ventilation duct, and subdivide the coal mine area into several marked areas (such as the first, second, third coal mine areas, etc.), providing a clear spatial division and data support for subsequent analysis of ventilation duct pressure loss and air flow control. Real-time pressure loss monitoring and optimization: The loss unit can receive the inspection mechanism started by the trigger unit and calculate the pressure loss of each branch in the ventilation duct in real time according to the air pressure change information in the coal mine data set. By using parameters such as the friction factor, the length, diameter, gas density, and air flow velocity of the ventilation duct, the system can accurately calculate the pressure loss of each branch according to the ventilation duct conditions in different areas. This accurate calculation of pressure loss can timely reflect the ventilation conditions in different areas of the coal mine, helping the system to make corresponding adjustments and optimizations, so as to ensure the stable distribution and efficient transmission of air flow in the ventilation duct. Dynamic adjustment and feedback mechanism: When an abnormal pressure loss occurs in the ventilation duct, the loss unit will trigger corresponding adjustment measures. By real-time monitoring of the pressure loss in each coal mine area, the system can dynamically adjust the air flow distribution, the operating state of the fan, or other ventilation system parameters to ensure that the air flow distribution in the mine is always in a relatively optimal state. Based on the feedback mechanism of pressure loss, the system can timely discover and correct the deficiencies in the ventilation duct layout design, reduce energy consumption, and reduce ventilation system failures or air pressure instability problems caused by uneven air flow. Improve the ventilation safety of coal mines: Through accurate ventilation duct layout and pressure loss analysis, the system can timely identify potential problems in the ventilation duct (such as too high or too low pressure, unbalanced air flow, etc.) and take effective measures for optimization or adjustment. This can not only improve the ventilation efficiency in the coal mine, but also reduce the accumulation of harmful gases, ensure the safety of the miners' working environment, and reduce the risk of safety accidents caused by poor ventilation. In short, through accurate geographical information identification, pressure loss monitoring, and real-time feedback mechanism, the ventilation duct layout analysis module can effectively improve the stability of air flow in the coal mine and the operating efficiency of the ventilation system, thereby improving the safety and production efficiency of coal mine operations.

[0084] Example 5

[0085] Please refer to Figure 1 , specifically: the impact analysis unit is used to determine the coal mine areas corresponding to each branch according to the positions of each branch in the ventilation duct, so as to obtain the relevant air pressure change data information in the coal mine areas corresponding to each branch. After feature extraction, the moving pressure Dyz and the initial pressure value Yyz in the coal mine areas corresponding to each branch are obtained. Based on the moving pressure Dyz and the initial pressure value Yyz in the coal mine areas corresponding to each branch, the pressure difference Djc is obtained. The pressure difference Djc is obtained through the following formula: Djc = |Dyz - Djc|; where, the initial pressure value Yyz refers to the static pressure value, and the moving pressure Dyz refers to the dynamic pressure value; and the differential pressure sensor can be used to measure the static pressure and dynamic pressure of the air flow in the ventilation duct. These sensors provide the static-dynamic pressure difference data by monitoring the difference between the static pressure and the dynamic pressure.

[0086] Based on the pressure difference Djc and the pressure loss Yss of each branch in the ventilation duct, analyze the correlation between the pressure difference Djc and the pressure loss Yss of each branch in the ventilation duct to obtain the correlation coefficient Xgxs in each monitoring period, which is specifically obtained through the following formula:

[0087]

[0088] In the formula, Djc j represents the pressure difference in the jth coal mine area, Yss j represents the pressure loss of the branch corresponding to the jth coal mine area, σDjc represents the standard deviation of the pressure difference, σYss represents the standard deviation of the pressure loss, and cov(Djc j , Yss j ) represents the covariance of Djc

[0089] and Yss j .

[0090] Set a monitoring period to obtain the correlation coefficient Xgxs in each monitoring time period within the monitoring period, and based on the numerical change trend of multiple groups of correlation coefficients Xgxs, analyze whether it tends to a positive correlation state. If the value of the correlation coefficient Xgxs continuously tends to 1 within the monitoring period, it is determined that within the current monitoring period, the pressure difference Djc and the pressure loss Yss of each branch in the ventilation duct gradually tend to a positive correlation state, and at this time, a barometric pressure analysis instruction will be triggered; if the value of the correlation coefficient Xgxs continuously tends to -1 within the monitoring period, it is determined that within the current monitoring period, the pressure difference Djc and the pressure loss Yss of each branch in the ventilation duct gradually tend to a negative correlation state, and at this time, a barometric pressure analysis instruction will be triggered; if the value of the correlation coefficient Xgxs continuously tends to 0 within the monitoring period, it is determined that within the current monitoring period, there is no linear correlation between the pressure difference Djc and the pressure loss Yss of each branch in the ventilation duct, and at this time, the barometric pressure analysis instruction will not be triggered temporarily.

[0091] In this embodiment, the precise correlation analysis of the dynamic and static pressure difference and the pressure loss: The system obtains the dynamic pressure and the initial pressure value of the area corresponding to each branch ventilation duct in the coal mine through the influence analysis unit, calculates the pressure difference based on these values, and further analyzes the correlation between the dynamic and static pressure difference and the pressure loss through the pressure loss. This process can capture the change trend of the air flow in different areas in real time. Especially in the ventilation conditions of complex terrains and different coal mine areas, it can clearly reveal the relationship between the pressure loss and the dynamic and static pressure difference of the air flow, providing a basis for optimizing the mine ventilation management. Dynamic monitoring and real-time response: By obtaining and analyzing the correlation coefficient between the pressure difference and the pressure loss, the system can monitor the change of the air flow in the coal mine ventilation duct in real time. If the value of the correlation coefficient continuously tends to a positive correlation (close to 1), it indicates that the correlation between the pressure difference and the pressure loss gradually increases, and the air flow tends to be stable; if the correlation coefficient tends to a negative correlation

[0092] If it approaches -1, it indicates that the air flow may be affected by unstable factors, resulting in blocked air flow; if the coefficient approaches 0, it means that the relationship between air flows in different regions does not have an obvious linear trend and is in a dynamic change state. Based on these changes, the system can trigger a pressure analysis instruction for targeted regulation to optimize the ventilation efficiency. Early warning and optimization regulation: By monitoring the changing trend of consecutive correlation coefficients within a monitoring period, the system can identify potential ventilation problems in advance. For example, when it continuously tends to be positively correlated, it indicates that the air flow in the ventilation duct is becoming stable, and the system can maintain the existing ventilation strategy; while when it continuously tends to be negatively correlated, the system will automatically trigger a pressure analysis instruction, indicating that measures need to be taken to adjust the fan operation or regulate the ventilation duct layout to avoid ventilation failures caused by poor air flow. Through this dynamic warning mechanism, the coal mine ventilation system can achieve real-time and automatic adjustment to ensure the stability and safety of the air flow in the coal mine. Improving the intelligence and automation level of the ventilation system: The system can realize the intelligent regulation of the coal mine ventilation system by continuously monitoring and automatically analyzing the relationship between the static and dynamic pressure differences and pressure losses in various regions of the coal mine. When an abnormal air flow state is detected, the system can automatically trigger the corresponding pressure analysis instruction and timely adjust the fan operation state, ventilation duct layout, and even start an inspection mechanism to ensure that the coal mine ventilation system always remains in a relatively optimal state. This highly automated regulation ability can significantly improve the ventilation efficiency and safety of the coal mine and avoid potential risks caused by lagging or misjudgment of manual intervention. In summary, through accurate analysis of static and dynamic pressure differences and pressure losses, combined with a dynamic monitoring and automatic feedback mechanism, the system can effectively improve the intelligence level of the coal mine ventilation system, ensure stable air flow distribution, prevent potential ventilation failures, and optimize the operation efficiency of the ventilation duct, effectively reducing the ventilation safety hazards in the mine.

[0093] Example 6

[0094] Please refer to Figure 1 , specifically: The in-mine performance analysis module includes a distribution unit and a comprehensive analysis unit;

[0095] The distribution unit is used to trigger a pressure analysis instruction based on the magnitude of the correlation coefficient Xgxs in the impact analysis unit, receive the pressure analysis instruction, and based on the pressure loss Yss of each branch in the ventilation duct, combined with relevant environmental data information, and after dimensionless processing, analyze and calculate the pressure distribution influence coefficient Yfz of the air flow in the coal mine. The pressure distribution influence coefficient Yfz of the air flow in the coal mine is obtained through the following formula:

[0096]

[0097] In the formula, Yfz j represents the pressure distribution influence coefficient of the air flow in the jth coal mine area, Sd jIt represents the depth of the j-th coal mine area from the mine mouth, Hqmd j It represents the density of the mixed gas in the j-th coal mine area, Yss j It represents the pressure loss of each branch in the j-th coal mine area. a1, a2, and a3 all represent weight values, where 0 < a1 < 1, 0 < a2 < 1, 0 < a3 < 1. The specific values of a1, a2, and a3 are set by the user according to the situation.

[0098] The depth Sd of each of the above-mentioned coal mine areas from the mine mouth can be monitored and obtained through a depth sensor or a pressure sensor;

[0099] The density Hqmd of the mixed gas in each coal mine area can analyze the gas components in the coal mine through a gas analyzer (such as an infrared gas analyzer or a gas chromatograph). Combining its environmental parameters such as temperature and pressure, the density of the mixed gas can be calculated;

[0100] In this embodiment, the pressure distribution impact of the airflow in the coal mine is accurately analyzed. By the distribution unit, based on the change of the correlation coefficient value in the impact analysis unit, the air pressure analysis instruction is triggered in a timely manner. Combining the pressure loss of each branch in the coal mine, environmental data information, and the data after dimensionless processing, the pressure distribution impact coefficient of the airflow in the coal mine can be accurately calculated. This impact coefficient reflects the flow state of the airflow in different regions and can indicate potential uneven or unstable airflow distribution areas. Through these data, coal mine managers can have a deeper understanding of the pressure distribution of the airflow, so as to formulate more scientific and reasonable ventilation strategies. Improve the precise control ability of the ventilation system: By calculating the pressure distribution impact coefficient of the airflow in the coal mine and combining multi-dimensional environmental factors such as the depth, gas density, and pressure loss of each branch in the coal mine area, the system can dynamically identify and analyze the airflow distribution in the coal mine ventilation duct. This multi-factor based analysis method enables the coal mine ventilation system to be more precisely controlled under complex environmental conditions. Whether it is the depth of the mine, the friction factor of the airflow, or the change of gas density, the system can process in real time and output corresponding adjustment instructions to ensure the stability and uniformity of the airflow inside the coal mine. Customization and flexibility: The impact coefficient calculation formula in the system includes multiple weight values (such as depth, gas density, pressure loss, etc.), and these weight values can be set according to the actual situation. This customized processing method enables the system to flexibly adjust various parameters according to different geological conditions and ventilation requirements of the coal mine, so as to achieve a relatively better ventilation control effect. Coal mine managers can flexibly adjust these weight values according to different mine characteristics and operation requirements, so that the system can exert better performance in different working environments. Real-time response and optimize ventilation effect: Through the dimensionless processing technology, the calculation of the pressure distribution impact coefficient of the airflow in the coal mine can eliminate the impact caused by different dimensions, making the coal mine ventilation system maintain consistency and comparability under various complex environments. This technology not only optimizes the ventilation effect of the coal mine, but also ensures the stable operation of the system under various complex conditions such as mine depth and gas density change. The real-time monitoring and analysis of the airflow pressure distribution can help coal mine managers timely discover the risks of uneven airflow or poor ventilation, and quickly adjust the layout of fans, ventilation ducts and other ventilation parameters, so as to ensure the continuous and efficient operation of the ventilation system in the mine.

[0101] Example 7

[0102] Please refer to Figure 1, specifically: the comprehensive analysis unit is used to utilize deep learning technology and construct an initial model in combination with the coal mine dataset. By dividing the data in the coal mine dataset into a training set and a validation set, training and testing are carried out, and the trained initial model is used as a feature recognition model. The feature information in the feature recognition model is obtained respectively, and the obtained feature information is used to train and test the feature recognition model. At the same time, in combination with an inspection mechanism, the trained feature recognition model is used as a ventilation stability prediction model. Through the ventilation stability prediction model and after dimensionless processing, the in-mine stability index Kwzs is fitted and output. The in-mine stability index Kwzs is obtained through the following formula:

[0103]

[0104] In the formula, Yfz j represents the influence coefficient of the air pressure distribution in the jth coal mine area, Bc j represents the variable difference in the jth coal mine area, Wc j represents the temperature difference between the jth coal mine area and the adjacent coal mine area, Yfz avg represents the average value of the influence coefficient of the air pressure distribution, Bc avg represents the average value of the variable difference, Wc avg represents the average value of the temperature difference. α, β, and γ all represent, n represents, and j = 1, 2, 3,..., n.

[0105] The variable difference Bc in each of the above-mentioned coal mine areas refers to the difference between the static air pressure in each coal mine area and the static air pressure at the mine entrance. The magnitude of the static air pressure will change with factors such as the depth of the mine, the flow condition of the air current, and temperature and humidity. The larger the static air pressure difference, it may mean that the resistance or pressure loss of the air current flow in the ventilation duct is larger, which will affect the ventilation efficiency and air flow stability of the entire coal mine.

[0106] The temperature difference Wc between each coal mine area and the adjacent coal mine area is monitored and obtained through a temperature sensor.

[0107] The feedback and regulation module is used to comprehensively judge the current stability situation in the coal mine by comparing the in-mine stability index Kwzs with the stability threshold K. The specific content is as follows:

[0108] If the in-mine stability index Kwzs exceeds the stability threshold K, it is comprehensively judged that the current coal mine is not in a stable state. At this time, the frequency converter is used to adjust the speed of the fan to 120% of the original fan speed to increase the air flow, thereby reducing the pressure difference, improving the ventilation efficiency of the mine, and smoothly regulating the temperature fluctuation and humidity fluctuation in the coal mine to avoid the drastic change of temperature and humidity affecting the air flow stability;

[0109] When the in - mine stability index Kwzs does not exceed the stability threshold K, it is comprehensively judged that the current coal mine is in a stable state. At this time, regular inspections and maintenance of the fans, valves, and air purification equipment in the coal mine will continue to ensure the long - term stable operation of the equipment.

[0110] In this embodiment, intelligent ventilation stability prediction and real - time monitoring: The comprehensive analysis unit uses deep - learning technology. By combining the coal - mine data set, an initial model is constructed, and through the training and verification process, an accurate feature recognition model is obtained. This process can extract key information such as the air - flow pressure distribution, variable difference, and temperature difference in each area of the coal mine, and then predict the ventilation stability in the coal mine. By applying the trained feature recognition model to the in - mine ventilation system, the in - mine stability index can be evaluated in real time, providing data support for coal - mine ventilation management, reducing errors caused by human judgment, and thus optimizing the ventilation management of the mine. Dynamic regulation through the stability index: Based on the stability index, the feedback and regulation module can respond to the ventilation state of the mine in a timely manner. When the in - mine stability index exceeds the preset stability threshold, the system can automatically trigger the adjustment of the fan speed to increase the air - flow rate, thereby reducing the air - flow pressure difference and improving the ventilation efficiency of the mine. This intelligent regulation scheme can effectively avoid poor ventilation or air - flow disorder in the coal mine and ensure the stability of the air quality inside the mine. At the same time, smooth regulation of the temperature and humidity fluctuations is carried out to avoid drastic changes affecting the stability of the air - flow and improve the safety and production efficiency of the coal mine. Regular maintenance and long - term stability guarantee: When the in - mine stability index is in a stable state, the system can, according to the actual situation, recommend regular inspections and maintenance of the fans, valves, and air purification equipment in the mine. This strategy not only helps to ensure the long - term stable operation of the equipment, but also can effectively reduce the occurrence of equipment failures, improve the reliability of the coal - mine ventilation system, and guarantee the safety of the personnel in the mine.

[0111] Long - term stable operation and effective equipment management can extend the service life of the equipment and reduce the operating costs. Flexible adjustment and real - time response ability: The system can dynamically adjust the working state of the fan by using a frequency converter to regulate the fan speed according to the real - time monitored stability index, achieving fast response and flexible regulation. By precisely controlling the fan speed and adjusting the ventilation state of the mine, the system can effectively improve the ventilation efficiency of the coal mine and reduce energy consumption. At the same time, by regulating factors such as temperature, humidity, and pressure, the influence of air - flow fluctuations on the ventilation effect of the mine can be reduced, thereby improving the air quality and ventilation safety in the mine and providing a more stable and safe working environment for the mine personnel. In summary, the system not only helps to improve the production efficiency of the coal mine, but also can effectively guarantee the long - term stability and safety of the mine.

[0112] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art will appreciate that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent real-time monitoring and control system for coal mine ventilation, characterized in that: It includes data acquisition module, fan status analysis module, ventilation duct layout analysis module, mine performance analysis module and feedback and control module; The data acquisition module is used to deploy several groups of monitoring sensors in the coal mine to monitor the ventilation status and pressure change trend in the coal mine in real time, generate relevant ventilation data information and relevant air pressure change data information, and construct a coal mine data set after data preprocessing; The fan status analysis module is used to analyze the working efficiency of the fans in the coal mine according to the relevant ventilation data information in the coal mine data set after feature extraction to obtain the fan efficiency Fjx, and based on the fan efficiency Fjx value, preliminarily determine whether the inspection mechanism needs to be started; The ventilation duct layout analysis module is used to receive and start the inspection mechanism, and construct the pressure loss Yss of each branch in the ventilation duct according to the layout of the ventilation duct in the coal mine and the relevant air pressure change data information in the coal mine data set, and analyze the correlation between the pressure loss Yss of each branch and the relevant ventilation data information of the corresponding area in the coal mine according to the relevant ventilation data information of the corresponding area, so as to obtain the correlation coefficient Xgxs; The mine performance analysis module is used to analyze and calculate the pressure distribution influence coefficient Yfz of the airflow in the coal mine according to the numerical value of the correlation coefficient Xgxs, and fit the output of the mine stability index Kwzs in combination with the trained ventilation stability prediction model; The feedback and control module is used to pre-set the stability threshold K, and compare the stability index Kwzs in the mine with the stability threshold K to comprehensively judge the stability situation in the current coal mine, and take adaptive control measures according to the judgment result.

2. The intelligent real-time monitoring and control system for coal mine ventilation according to claim 1 is characterized in that: The data acquisition module includes an acquisition unit and a processing unit; The acquisition unit is used to deploy several groups of monitoring sensors in various areas of the coal mine in advance to monitor the ventilation status and pressure change trend in the coal mine in real time, and generate relevant ventilation data information and relevant air pressure change data information, wherein the several groups of monitoring sensors include power sensors, fluid mechanics sensors, laser scanners, gas density sensors, pitot tubes, depth sensors, gas analyzers and temperature sensors; the relevant ventilation data information includes the input power P of the fan inpu And the power P provided by the fan output ; The relevant air pressure change data information includes the gas density ρ in the ventilation duct, the air flow velocity Qjd, the friction factor Mcyz, the movement pressure Dyz and the initial pressure value Yyz in the coal mine area corresponding to each branch; The processing unit is used to preprocess the data information obtained by the acquisition unit to identify and remove noise, fill missing values ​​and perform data smoothing operations, and use dimensionless processing technology to perform dimensional analysis on the preprocessed data information to remove units and construct a coal mine data set. The coal mine data set includes relevant ventilation data information, relevant air pressure change data information and relevant environmental data information after preprocessing, wherein the relevant environmental data information includes the length Tcd of the ventilation duct, the diameter Tzj of the ventilation duct, the depth Sd of each coal mine area from the mine entrance, the density Hqmd of the mixed gas in each coal mine area, the variable difference Bc in the coal mine area and the temperature difference Wc between each coal mine area and the adjacent coal mine area.

3. The intelligent real-time monitoring and control system for coal mine ventilation according to claim 2 is characterized in that: The wind turbine status analysis module includes a condition analysis unit and a trigger unit; The condition analysis unit is used to extract the features of the relevant ventilation data information in the coal mine data set to obtain the input power P of the fan. input And the power P provided by the fan output , based on the fan input power P input And the power P provided by the fan output , analyze the working efficiency of the fan in the coal mine to obtain the fan efficiency Fjx, and the fan efficiency Fjx is obtained by the following formula: Where P output Indicates the power provided by the fan, P input Indicates the input power of the fan.

4. The intelligent real-time monitoring and control system for coal mine ventilation according to claim 3 is characterized in that: The trigger unit is used to pre-set the efficiency requirement range, and compare the fan efficiency Fjx with the efficiency requirement range to preliminarily determine whether the inspection mechanism needs to be started. The specific contents are as follows: If the fan efficiency Fjx falls within the efficiency requirement range, it is preliminarily determined that there is no need to start the inspection mechanism at this time, and the monitoring operation continues; If the fan efficiency Fjx does not fall within the efficiency requirement range, it is preliminarily determined that a check mechanism needs to be started at this time, and an instruction to start the check mechanism is transmitted to the ventilation duct layout analysis module.

5. The intelligent real-time monitoring and control system for coal mine ventilation according to claim 2 is characterized in that: The ventilation duct layout analysis module includes a layout information unit, a loss unit and an impact analysis unit; The layout information unit is used to pre-acquire a topographic distribution map of the coal mine, and identify the distribution positions of ventilation ducts in the coal mine from the topographic distribution map of the coal mine, wherein the distribution positions of the ventilation ducts include the distribution positions of the main ventilation ducts and the branch ventilation ducts, and according to the distribution positions of the ventilation ducts, a plurality of groups of coal mine areas are divided at the distribution positions of the ventilation ducts, and the plurality of groups of coal mine areas are marked in sequence as No. 1 coal mine area I1, No. 2 coal mine area I2, No. 3 coal mine area I3, ..., No. n coal mine area I n .

6. The intelligent real-time monitoring and control system for coal mine ventilation according to claim 5 is characterized in that: The loss unit is used to receive and start the inspection mechanism in the trigger unit, and construct the pressure loss Yss of each branch in the ventilation duct according to the distribution position of the ventilation duct and the relevant air pressure change data information in the coal mine data set. The pressure loss Yss of each branch in the ventilation duct is obtained by the following formula: Where Mcyz represents the friction factor, Tcd represents the length of the ventilation duct, Tzj represents the diameter of the ventilation duct, ρ represents the gas density, and Qjd represents the air flow velocity.

7. The intelligent real-time monitoring and control system for coal mine ventilation according to claim 4 is characterized in that: The impact analysis unit is used to determine the coal mine area corresponding to each branch according to the position of each branch in the ventilation duct, so as to obtain relevant air pressure change data information in the coal mine area corresponding to each branch, and obtain the motion pressure Dyz and initial pressure value Yyz in the coal mine area corresponding to each branch after feature extraction, and obtain the pressure difference Djc based on the motion pressure Dyz and initial pressure value Yyz in the coal mine area corresponding to each branch, and the pressure difference Djc is obtained by the following formula: Djc=|Dyz-Djc|; Based on the pressure difference Djc and the pressure loss Yss of each branch in the ventilation duct, the correlation between the pressure difference Djc and the pressure loss Yss of each branch in the ventilation duct is analyzed to obtain the correlation coefficient Xgxs in each monitoring period, which is specifically obtained by the following formula: In the formula, Djc j represents the pressure difference within the jth coal mine area, Yss j represents the pressure loss of the corresponding branch in the jth coal mine area, σDjc represents the standard deviation of the pressure difference, σYss represents the standard deviation of the pressure loss, cov(Djc j , Yss j ) indicates Djc and Yss j The covariance of A monitoring cycle is set to obtain the correlation coefficient Xgxs in each monitoring period within the monitoring cycle, and based on the numerical change trend of multiple groups of correlation coefficients Xgxs, it is analyzed whether they tend to be positively correlated. If the value of the correlation coefficient Xgxs within the monitoring cycle continuously tends to 1, it is judged that in the current monitoring cycle, the pressure difference Djc and the pressure loss Yss of each branch in the ventilation duct gradually tend to a positive correlation state, and the air pressure analysis instruction will be triggered at this time; if the value of the correlation coefficient Xgxs within the monitoring cycle continuously tends to -1, it is judged that in the current monitoring cycle, the pressure difference Djc and the pressure loss Yss of each branch in the ventilation duct gradually tend to a negative correlation state, and the air pressure analysis instruction will be triggered at this time; if the value of the correlation coefficient Xgxs within the monitoring cycle continuously tends to 0, it is judged that in the current monitoring cycle, the pressure difference Djc and the pressure loss Yss of each branch in the ventilation duct are in a non-linear correlation state, and the air pressure analysis instruction will not be triggered temporarily.

8. The intelligent real-time monitoring and control system for coal mine ventilation according to claim 7 is characterized in that: The in-mine performance analysis module includes a distribution unit and a comprehensive analysis unit; The distribution unit is used to trigger the air pressure analysis instruction based on the value of the correlation coefficient Xgxs in the impact analysis unit, and receive the air pressure analysis instruction, and analyze and calculate the pressure distribution influence coefficient Yfz of the airflow in the coal mine according to the pressure loss Yss of each branch in the ventilation duct, combined with relevant environmental data information, and after dimensionless processing. The pressure distribution influence coefficient Yfz of the airflow in the coal mine is obtained by the following formula: In the formula, Yfz j represents the pressure distribution influence coefficient of airflow in the jth coal mine area, Sd j Indicates the depth of the j-th coal mine area from the mine entrance, Hqmd j represents the density of the mixed gas in the jth coal mine area, Yss j It represents the pressure loss of each branch in the jth coal mine area. a1, a2 and a3 all represent weight values. The specific values ​​of a1, a2 and a3 are set by the user according to the situation.

9. The intelligent real-time monitoring and control system for coal mine ventilation according to claim 2 is characterized in that: The comprehensive analysis unit is used to use deep learning technology and combine with the coal mine data set to build an initial model, divide the data in the coal mine data set into a training set and a validation set for training and testing, and use the trained initial model as a feature recognition model to obtain feature information in the feature recognition model, and use the acquired feature information to train and test the feature recognition model. At the same time, combined with the inspection mechanism, the trained feature recognition model is used as a ventilation stability prediction model. Through the ventilation stability prediction model and after dimensionless processing, the mine stability index Kwzs is fitted and output. The mine stability index Kwzs is obtained by the following formula: In the formula, Yfz j represents the pressure distribution influence coefficient of airflow in the jth coal mine area, Bc j represents the variable difference within the jth coal mine area, Wc j represents the temperature difference between the jth coal mine area and the adjacent coal mine area, Yfz avg Indicates the average value of the pressure distribution coefficient of the airflow, Bc avg Represents the average value of the variable difference, Wc avg represents the average value of the temperature difference, α, β and γ all represent, n represents, j=1, 2, 3, ..., n.

10. The intelligent real-time monitoring and control system for coal mine ventilation according to claim 1, characterized in that: The feedback and control module is used to comprehensively judge the stability situation in the current coal mine by comparing the stability index Kwzs in the mine with the stability threshold K. The specific contents are as follows: If the stability index Kwzs in the mine exceeds the stability threshold K, it is comprehensively judged that the current coal mine is not in a stable state. At this time, the frequency converter will be used to adjust the fan speed to 120% of the original fan speed to increase the air flow and smoothly control the temperature fluctuation and humidity fluctuation in the coal mine; If the mine stability index Kwzs does not exceed the stability threshold K, it is comprehensively judged that the current coal mine is in a stable state. At this time, regular inspections and maintenance of fans, valves and air purification equipment in the coal mine will continue.

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