A mine main ventilation fan monitoring system

Abstract

The present invention relates to the field of ventilator monitoring systems, and particularly to a mine main ventilator monitoring system, which is used to solve the problems that the wind speed of the existing main ventilator monitoring system always remains unchanged. On the one hand, it will lead to unsatisfactory ventilation effects, and it is impossible to specify reasonable rotational speeds and blade angles of the main ventilator according to environmental requirements. On the other hand, it cannot achieve the effects of energy conservation and environmental protection. At the same time, the traditional system has relatively weak monitoring of the state of the ventilator itself, and it is impossible to timely detect potential equipment failure hazards through the air volume loss value. The main ventilator monitoring system includes: a sensor group, a host computer, and a PLC. The main ventilator monitoring system realizes the real-time adjustment of the rotational speed and blade angle of the main ventilator according to the comprehensive indicators of environmental factors, achieves the purpose of energy conservation and environmental protection while ensuring ventilation requirements, and at the same time combines the calculation of the air volume loss value and historical data to judge possible faults and reasons for maintenance of the main ventilator.

Description

Technical Field

[0001] The present invention relates to the field of ventilator monitoring systems, and particularly to a mine main ventilator monitoring system. Background Art

[0002] A ventilator is a device installed in a specific environment to increase the air flow rate and improve the air replacement rate. It mainly relies on an electric drive motor to work. The motor drives the blades to rotate to generate wind. Different installation environments of the ventilator require different controls. Therefore, a control monitoring system is needed for monitoring and control.

[0003] The traditional ventilator monitoring system lacks real-time and accurate response to the complex and changeable underground environment. It is difficult for the traditional system to flexibly adjust ventilation parameters according to the unique risk characteristics of different mine types. In terms of ventilator control, the traditional system mostly relies on manual experience or simple timing regulation, and cannot dynamically adjust the rotational speed and blade angle of the ventilator in real time according to the comprehensive changes of various environmental parameters such as gas, carbon monoxide, carbon dioxide, dust, temperature, and humidity. Usually, the working frequency of the ventilator is adjusted at fixed time or space, and the wind speed always remains unchanged. On the one hand, this will lead to an unsatisfactory ventilation effect, and it is impossible to specify a reasonable rotational speed and blade angle of the main ventilator according to the environmental requirements. On the other hand, it cannot achieve the effect of energy conservation and environmental protection. High-speed ventilation blindly results in a large amount of electric energy waste, and also affects the service life of the ventilator. At the same time, it is impossible to detect potential equipment failure hazards in time through the air volume loss value. Summary of the Invention

[0004] In order to overcome the above technical problems, the purpose of the present invention is to provide a mine main ventilator monitoring system: through this main ventilator monitoring system, the rotational speed and blade angle of the main ventilator are adjusted in real time according to the comprehensive indicators of environmental factors, achieving the purpose of energy conservation and environmental protection while ensuring ventilation requirements. At the same time, by calculating the air volume loss value and combining historical data to judge possible faults and reasons for maintenance of the main ventilator, it solves the problem that the wind speed of the existing main ventilator monitoring system always remains unchanged. On the one hand, it will lead to an unsatisfactory ventilation effect, and it is impossible to specify a reasonable rotational speed and blade angle of the main ventilator according to the environmental requirements. On the other hand, it cannot achieve the effect of energy conservation and environmental protection. High-speed ventilation blindly results in a large amount of electric energy waste, and also affects the service life of the ventilator. At the same time, the traditional system is also relatively weak in monitoring the state of the ventilator itself, and it is impossible to detect potential equipment failure hazards in time through the air volume loss value.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A mine main ventilation fan monitoring system, comprising: a sensor group, a host computer and a PLC. The sensor group includes a gas concentration sensor, a carbon monoxide concentration sensor, a carbon dioxide concentration sensor, a dust concentration sensor, a temperature sensor, and a humidity sensor. The host computer installs a wind flow simulation software, and the PLC installs an adaptive control system;

[0007] The sensor group is used to monitor the gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity in the ventilation environment where the main ventilation fan is to be installed in real time, and transmit the detected data to the PLC;

[0008] The host computer is used to run the wind flow simulation software. The software has a complete 3D model of the environment, covering detailed information such as the roadway orientation, section change, and obstacle distribution. According to the real-time ventilation parameters, it simulates the real dynamics of the wind flow. The ventilation parameters include the rotational speed of the main ventilation fan and the angle of its blades;

[0009] The PLC is used to execute the adaptive control system. The adaptive control system includes storing the data weights of the gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity in the environment, calculating a comprehensive index β according to each weight and the real-time monitoring data, and formulating an adaptive control strategy for the rotational speed of the main ventilation fan and an adaptive control strategy for the blade angle of the main ventilation fan according to the comprehensive index β and the wind flow simulation software. The PLC controls the rotational speed and blade angle of the main ventilation fan through the adaptive control strategy.

[0010] As a further solution of the present invention: The specific process of presetting each data weight in the adaptive control system is as follows:

[0011] According to the environmental type and industry regulations, weights are respectively set for the gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity, and the weights are set as K1, K2, K3, K4, K5, and K6 in sequence. According to different mine types, different weight coefficients are artificially set. The sum of the six weights is marked as K total, and K total = 1.

[0012] As a further solution of the present invention: The specific calculation formula for calculating the comprehensive index β according to each weight and the real-time monitoring data is as follows: ;

[0013] Among them, is the maximum allowable value of the gas concentration, is the maximum allowable value of the carbon monoxide concentration, is the maximum allowable value of the carbon dioxide concentration, is the maximum allowable value of the dust concentration, is the suitable temperature, is the highest allowable temperature, is the suitable humidity, is the maximum allowable humidity, all set in advance according to the environmental type, is the monitoring data of the gas concentration sensor, is the monitoring data of the carbon monoxide concentration sensor, is the monitoring data of the carbon dioxide concentration sensor, is the monitoring data of the dust concentration sensor, T is the monitoring data of the temperature sensor, and H is the monitoring data of the humidity sensor.

[0014] As a further solution of the present invention: The specific method for formulating the adaptive control strategy for the rotational speed of the main ventilator is as follows:

[0015] The β value is divided into four stages, corresponding to excellent environmental conditions where β ≤ 0.2, normal fluctuation conditions where 0.2 < β ≤ 0.5, deteriorating trend conditions where 0.5 < β ≤ 0.8, and emergency dangerous conditions where β > 0.8;

[0016] When β ≤ 0.2, the PLC controls the main ventilator to operate at the lowest working frequency, and sets this frequency as the basic frequency of the main ventilator;

[0017] When 0.2 < β ≤ 0.5, based on the basic frequency, for every 0.1 increase in the comprehensive index β, the working frequency of the main ventilator is adaptively adjusted according to the strategy of increasing the frequency by 10%. Then, within this stage, β = 0.5 belongs to the highest frequency of the main ventilator within this stage;

[0018] When 0.5 < β ≤ 0.8, the frequency corresponding to β = 0.5 in the previous stage is set as the basic frequency of this stage, and the working frequency of the main ventilator is adaptively adjusted according to the strategy of increasing the frequency by 15% for every 0.1 increase in the comprehensive index β;

[0019] When β > 0.8, the main ventilator is adjusted to the highest frequency;

[0020] Through the real-time monitored gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity data, the comprehensive index β is calculated in real time. The PLC adaptively controls the working frequency of the main ventilator according to this adaptive control strategy, and then adaptively controls the rotational speed of the main ventilator.

[0021] As a further solution of the present invention: The specific method for formulating the adaptive control strategy for the blade angle of the main ventilator is as follows:

[0022] Based on the four situations of β ≤ 0.2, 0.2 < β ≤ 0.5, 0.5 < β ≤ 0.8, and β > 0.8;

[0023] When β ≤ 0.2, the actual output air volume of the main ventilator is collected through the air volume sensor , the output air pressure P of the main ventilator is collected through the air pressure sensor, and then through the formula calculate the shaft power W, where represents the real-time efficiency of the main ventilation fan, , usually between 0.719 and 0.8, represents the mechanical transmission efficiency of the main ventilation fan, usually between 70% and 90%. According to the factory parameters of the main ventilation fan, and the roadway resistance coefficient R in the installation environment of the main ventilation fan is measured in advance. The actual output air volume , the output air pressure P of the main ventilation fan, the shaft power W, and the roadway resistance coefficient R parameters are introduced into the air flow simulation software. The dynamic air flow is simulated through the built-in environmental 3D model of the software, and then the blade angle core adjustment coefficient is set to zero initially, according to the formula:

[0024] ;

[0025] for accounting, where are all corresponding ideal values set in advance, is the maximum allowable value, is the weight coefficient customized according to the performance of the ventilation fan and the actual situation of the mine, and , after calculating , according to the formula of adjustment angle = , adjust the blade angle of the main ventilation fan, and limit the fine-tuning range to ±2°. And set this angle as the initial set angle. Thus, on the basis of energy conservation and noise reduction, optimize the blade angle in all aspects according to the real-time operation of the ventilation fan and the underground environment, so as to make the air flow uniform and efficient, and the ventilation effect leap;

[0026] When 0.2 < β ≤ 0.5, the real-time efficiency of the main ventilation fan and the surge margin S are synchronously introduced. According to the formula , where is the weight coefficient of the real-time efficiency of the main ventilation fan, taking values between 0 and 1, is the weight coefficient of the surge margin, taking values between 0 and 1, are all corresponding ideal values set in advance, are all allowable maximum values. Calculate the blade angle compensation coefficient G, and finally the blade angle is adjusted to , to ensure that the ventilation fan can operate efficiently and stably when dealing with working condition changes, and avoid unstable phenomena such as surging;

[0027] When 0.5 < β ≤ 0.8, recalculate the real-time efficiency of the main ventilation fan, the surge margin S, introduce the roadway temperature gradient , the specific heat capacity C of air, and set the blade optimization angle coefficient . According to the formula calculate the value, where It represents the weight coefficient considering the influence of temperature gradient on ventilation demand, with a value between 0 and 1. It represents the weight coefficient of the specific heat capacity difference of air, with a value between 0 and 1. It is the real-time efficiency weight coefficient of the main ventilator, with a value between 0 and 1. It is the surge margin weight coefficient, with a value between 0 and 1. They are all corresponding ideal values set in advance. They are all the allowable maximum values. After calculating the blade angle is adjusted to to adapt to the dynamic underground thermal environment and the change of the ventilator working conditions, and deepen the cooling and ventilation efficiency.

[0028] When β > 0.8, the smoke concentration gradient and the smoke particle size distribution D are introduced, and the blade adjustment coefficient is set. According to the formula calculate value, where respectively represent the weight coefficient of smoke concentration on the ventilation demand and the weight coefficient of smoke particle size distribution on the ventilation demand, both with values between 0 and 1. They are all corresponding ideal values set in advance. They are all the allowable maximum values. After calculating the blade angle is anchored at to ensure the efficient dispersion of the air current to disperse the smoke and clear the escape corridor.

[0029] As a further solution of the present invention: Store the inherent parameters of the main ventilator in the PLC, including the impeller diameter, blade shape, and hub ratio. Based on the adaptive control system, control the speed and blade angle of the main ventilator, and calculate the current theoretical air volume of the main ventilator The specific formula is as follows:

[0030] where are the structural parameters related to the ventilator, including the blade shape and hub ratio, which are fixed parameter values obtained by the ventilator manufacturer through testing and are between 0.05 and 0.15. n is the actual speed of the current main ventilator obtained. is the impeller diameter. is the current blade angle obtained.

[0031] After obtaining bring it into to calculate the air volume loss value .

[0032] As a further solution of the present invention: Based on the calculated air volume loss value store different grade thresholds for the loss value in the PLC, which are respectively , where ;

[0033] The PLC continuously collects the historical data of the long - term operation of the ventilator, including the ventilator speed n, blade angle , theoretical air volume , actual air volume under four different working conditions, the data of each environmental sensor and the corresponding timestamp information. Classify and organize these data, store them according to the working condition category and time series, construct a database, and use the decision tree algorithm to perform model training in the data analysis module built into the PLC. Take the air volume loss value as the target variable, and take the ventilator operation parameters, environmental parameters, and treatment measures taken at the corresponding historical moments as feature variables. Through repeated learning of historical data, the decision tree model gradually constructs the decision relationship between different air volume loss levels and various influencing factors, forming a diagnosis and decision model. During the actual operation of the ventilator, the PLC real - time collects the current operation parameters and environmental parameters, calculates the air volume loss value , and inputs it into the trained model. The model, based on the input parameter information and the learned decision rules, determines the level of air volume loss and the corresponding reasons.

[0034] As a further solution of the present invention: The specific processing steps of the system are as follows:

[0035] S1. Install several gas concentration sensors, carbon monoxide concentration sensors, carbon dioxide concentration sensors, dust concentration sensors, temperature sensors, and humidity sensors in the environment where the main ventilator is to be installed. The data of these sensors are collected, stored, and processed by the PLC;

[0036] S2. Calculate the comprehensive index β through a weight strategy based on the processed data of each sensor, and formulate an adaptive control strategy for the main ventilator speed and an adaptive control strategy for the main ventilator blade angle based on the comprehensive index β;

[0037] S3. The PLC adaptively adjusts the main ventilator speed and blade angle based on the adaptive control strategy for the main ventilator speed and the adaptive control strategy for the main ventilator blade angle in combination with the air flow simulation software in the upper computer;

[0038] S4. Based on the adjusted main ventilator speed and blade angle, introduce various factory parameters of the main ventilator and the resistance coefficient R of the roadway to be installed, and calculate the current theoretical air volume of the main ventilator ;

[0039] S5. Based on the calculated theoretical air volume and the actual air volume obtained by monitoring Perform calculations to obtain the lost air volume , and based on the lost air volume, combined with historical data and a decision tree model, determine the level of air volume loss and the corresponding reasons.

[0040] Advantages of the present invention:

[0041] By setting up an adaptive control system, the adaptive control system can calculate the comprehensive index in the installation environment of the ventilator through the data of each sensor collected in real time, and adaptively adjust the speed of the main ventilator according to the value of the comprehensive index in cooperation with the specified adaptive ventilation control strategy. On the one hand, this can always maintain an excellent ventilation environment in the environment, and there will be no problems of insufficient ventilation or excessive ventilation. On the other hand, it avoids phenomena such as power waste caused by long-term excessive ventilation and damage to the life of the main ventilator. Moreover, since the weight calculation method is adopted, people can preset weights according to the environmental type and requirements, so that the system can be applied to the monitoring work of the main ventilator in each environment;

[0042] By setting up the blade adaptive control function, the blade angle of the main ventilator can be adjusted in a timely manner according to the speed of the main ventilator controlled by the adaptive control system and the fixed parameters of the main ventilator within different β value ranges, so that the blades of the main ventilator can be at a suitable angle in cooperation with its speed and environmental requirements, deeply collect parameters such as the real-time air volume, air pressure, and shaft power of the ventilator, and combine the roadway specification parameters and the air flow simulation software, comprehensively consider factors such as air volume matching, air pressure matching, roadway resistance, ventilator efficiency, and surge margin, and accurately adjust the blade angle. For example, in an energy-saving oriented mine, the ventilator efficiency can be improved and the energy consumption can be reduced by adjusting the blade angle. In a mine sensitive to ventilation resistance fluctuations, the blade angle can be adjusted in a timely manner according to the change of roadway resistance to ensure ventilation stability. In different working conditions, such as in a conventional fluctuation working condition, the blade angle can be accurately adjusted according to factors such as gas concentration and fire hazards, taking into account ventilation, energy conservation, noise reduction, and prevention of various safety hazards. Through precise blade angle adjustment, the air flow is more evenly distributed in the underground roadway, reducing ventilation dead corners and turbulent flow phenomena. With the assistance of the air flow simulation software, the blade angle can be optimized according to information such as roadway orientation, cross-section change, and obstacle distribution to ensure stable air flow transmission and effectively improve the ventilation effect and ensure the safety of the underground working environment;

[0043] By setting up the loss value calculation function, using the main ventilator speed adjusted by the adaptive control system, the blade angle, and some fixed parameters of the main ventilator, the theoretical air volume of the main ventilator is calculated. Then, the loss value is obtained through the difference between the theoretical air volume and the actual air volume. An intelligent processing model is constructed in the PLC based on historical data. According to the air volume loss value and other operating parameters, the cause of the fault can be quickly judged. For example, if the model determines that moderate air volume loss may be caused by an increase in duct resistance, an instruction to check the duct can be automatically issued, or relevant equipment can be started for preliminary troubleshooting and cleaning. At the same time, a detailed maintenance work order is generated to improve the efficiency of fault handling and ensure the continuous and stable operation of the ventilator.

[0044] In summary, this main ventilator monitoring system realizes the real-time adjustment of the speed and blade angle of the main ventilator according to the comprehensive indicators of environmental factors, achieving the purpose of energy conservation and environmental protection while ensuring ventilation requirements. At the same time, by calculating the air volume loss value and combining historical data, possible faults and reasons for maintenance of the main ventilator can be judged. Brief Description of the Drawings

[0045] The present invention will be further described below with reference to the accompanying drawings.

[0046] Figure 1 It is a principle block diagram of a mine main ventilator monitoring system in the present invention. Detailed Embodiments

[0047] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of 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 As shown, this embodiment is a mine main ventilator monitoring system, including: a sensor group, a host computer, and a PLC. The sensor group includes a gas concentration sensor, a carbon monoxide concentration sensor, a carbon dioxide concentration sensor, a dust concentration sensor, a temperature sensor, and a humidity sensor. The host computer installs a wind flow simulation software, and the PLC installs an adaptive control system;

[0050] The sensor group is used to continuously monitor the gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity in the ventilation environment where the main ventilator is to be installed, and transmit the detected data to the PLC;

[0051] The upper computer is used to run the air flow simulation software. The software has a complete 3D model of the environment built-in, covering detailed information such as the roadway orientation, section change, and obstacle distribution. It simulates the real dynamics of the air flow based on real-time ventilation parameters, where the ventilation parameters include the main ventilator speed and its blade angle.

[0052] The PLC is used to execute the adaptive control system. The adaptive control system includes storing the data weights of the gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity in the environment, calculating the comprehensive index β according to each weight and the real-time monitoring data, and formulating the adaptive control strategy for the main ventilator speed and the adaptive control strategy for the main ventilator blade angle according to the comprehensive index β and the air flow simulation software. The PLC controls the main ventilator speed and the blade angle through the adaptive control strategy.

[0053] Preferably, the specific process of presetting each data weight in the adaptive control system is as follows:

[0054] According to the environment type and industry regulations, the gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity are respectively set with weights, which are set as K1, K2, K3, K4, K5, and K6 in sequence. The weight of the gas concentration K1 in the environment is set to 0.4, the weight of the carbon monoxide concentration K2 is set to 0.2, the weight of the carbon dioxide concentration K3 is set to 0.1, the weight of the dust concentration K4 is set to 0.1, the weight of the temperature K5 is set to 0.1, and the weight of the humidity K6 is set to 0.1. The sum of the six weights is marked as K total, and K total = 1.

[0055] It should be noted that for different types of environments, different values should be set for the weights, as shown in Table 1 specifically:

[0056] Table 1: Weight Table for Some Types of Mines

[0057]

[0058] Preferably, the specific calculation formula for calculating the comprehensive index β according to each weight and the real-time monitoring data is as follows: ;

[0059] Among them, is the maximum allowable value of the gas concentration, is the maximum allowable value of the carbon monoxide concentration, is the maximum allowable value of the carbon dioxide concentration, is the maximum allowable value of the dust concentration, is the suitable temperature, is the highest allowable temperature, is the suitable humidity, is the maximum allowable humidity, all of which are set in advance according to the environment type, is the monitoring data of the gas concentration sensor, It is the monitoring data of the carbon monoxide concentration sensor, It is the monitoring data of the carbon dioxide concentration sensor, It is the monitoring data of the dust concentration sensor, T is the monitoring data of the temperature sensor, and H is the monitoring data of the humidity sensor.

[0060] Preferably, the specific method for formulating the adaptive control strategy for the main ventilator speed is as follows:

[0061] The β value is divided into four stages, corresponding to the excellent environmental condition where β ≤ 0.2, the normal fluctuation condition where 0.2 < β ≤ 0.5, the deteriorating trend condition where 0.5 < β ≤ 0.8, and the emergency dangerous condition where β > 0.8;

[0062] When β ≤ 0.2, the PLC controls the main ventilator to operate at the lowest working frequency, and sets this frequency as the basic frequency of the main ventilator. At this time, the ventilator operates stably and all performance indicators are good;

[0063] When 0.2 < β ≤ 0.5, based on the basic frequency, for every 0.1 increase in the comprehensive index β, the working frequency of the main ventilator is adaptively adjusted according to the strategy of increasing the frequency by 10%. Then, within this stage, β = 0.5 belongs to the highest frequency of the main ventilator in this stage. At this time, when the speed of the ventilator increases, the air volume and air pressure will also change accordingly;

[0064] When 0.5 < β ≤ 0.8, the frequency corresponding to β = 0.5 in the previous stage is set as the basic frequency of this stage, and the working frequency of the main ventilator is adaptively adjusted according to the strategy of increasing the frequency by 15% for every 0.1 increase in the comprehensive index β;

[0065] When β > 0.8, the main ventilator is adjusted to the highest frequency. At this time, the ventilator outputs the air volume with the maximum capacity to ensure the rapid dilution of harmful gases underground and create conditions for personnel to escape and for emergency rescue and disaster relief;

[0066] Through the real-time monitored data of gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity, the comprehensive index β is calculated in real time. The PLC adaptively controls the working frequency of the main ventilator according to this adaptive control strategy, and then adaptively controls the speed of the main ventilator.

[0067] Preferably, the specific method for formulating the adaptive control strategy for the main ventilator blade angle is as follows:

[0068] Based on the four situations of β ≤ 0.2, 0.2 < β ≤ 0.5, 0.5 < β ≤ 0.8, and β > 0.8;

[0069] When β ≤ 0.2, the actual output air volume of the main ventilator is collected through the air volume sensor , the output air pressure P of the main ventilator is collected through the air pressure sensor, and then through the formula Calculate the shaft power W, where represents the real-time efficiency of the main ventilation fan, , usually between 0.719 - 0.8, represents the mechanical transmission efficiency of the main ventilation fan, usually between 70% - 90%. According to the factory parameters of the main ventilation fan, and the roadway resistance coefficient R in the installation environment of the main ventilation fan is measured in advance. The actual output air volume , the output air pressure P of the main ventilation fan, the shaft power W, and the roadway resistance coefficient R parameters are introduced into the air flow simulation software. Through the built-in environmental 3D model of the software, the air flow dynamics are simulated, and then the blade angle core adjustment coefficient is set to zero initially, according to the formula:

[0070] ;

[0071] Check and calculate, where are all corresponding ideal values set in advance, is the maximum allowable value, is the weight coefficient customized according to the performance of the ventilation fan and the actual situation of the mine, and , after calculating , according to the formula of adjustment angle = adjust the blade angle of the main ventilation fan, and limit the fine-tuning range to ±2°. And set this angle as the initial set angle. Thus, on the basis of energy conservation and noise reduction, the blade angle is optimized comprehensively according to the real-time operation of the ventilation fan and the underground environment, promoting the uniform and efficient air flow and improving the ventilation effect;

[0072] It should be noted that the air flow simulation software used is: "Mine 3D Ventilation Simulation System" launched by Jinma Software (Beijing) Co., Ltd.;

[0073] is the air volume matching influence coefficient, which reflects the influence degree of the deviation between the actual air volume of the current ventilation fan and the ideal air volume on the blade angle adjustment, and is used to ensure that the air volume provided by the ventilation fan meets the actual needs of the mine. If the deviation between the air volume and the ideal value is large, the blade angle needs to be adjusted significantly. For example, when the actual air volume is much less than the ideal air volume, if the value is 0.6, it will cause a large adjustment of the blade angle to increase the air volume;

[0074] is the air pressure matching influence coefficient, which reflects the weight of the difference between the real-time air pressure P of the ventilation fan and the ideal air pressure on the blade angle adjustment, and is used to ensure that the air pressure generated by the ventilation fan can overcome the roadway resistance and maintain a stable air flow. Insufficient air pressure will affect the ventilation effect. For example, in a long roadway with large ventilation resistance, if the air pressure is insufficient, Set it to 0.5, which can adjust the blade angle to increase the wind pressure and meet the ventilation requirements;

[0075] is the roadway resistance influence coefficient. Since the change in roadway resistance will affect the operation of the ventilator, by adjusting the blade angle to adapt to different resistance conditions, so represents the effect of the roadway resistance coefficient R on the blade angle adjustment. For example, when the resistance increases due to local collapse in the roadway, If it is 0.7, the system will quickly adjust the blade angle according to this coefficient to ensure that the ventilator overcomes the increased resistance and maintains normal ventilation;

[0076] is the shaft power matching influence coefficient, indicating the influence degree of the deviation between the shaft power W of the ventilator and the ideal shaft power on the blade angle adjustment, which is used to avoid excessive or too small shaft power of the ventilator, ensure the stable operation of the equipment, and prevent overload or inefficient operation. For example, when the shaft power W of the ventilator exceeds the ideal value, it may cause equipment damage, If it is 0.6, the blade angle will be adjusted to reduce the shaft power and ensure the safe operation of the ventilator.

[0077] When 0.2 < β ≤ 0.5, the real-time efficiency of the main ventilator and the surge margin S are synchronously introduced, according to the formula where is the weight coefficient of the real-time efficiency of the main ventilator, taking values between 0 and 1, is the weight coefficient of the surge margin, taking values between 0 and 1, are all pre-set corresponding ideal values, are all the allowable maximum values, calculate the blade angle compensation coefficient G, and finally the blade angle is adjusted to to ensure that the ventilator can operate efficiently and stably and reliably when dealing with working condition changes, and avoid unstable phenomena such as surging;

[0078] It should be noted that is a measure of the influence degree of the real-time efficiency of the ventilator on the blade angle adjustment, which is used to improve the operation efficiency of the ventilator and reduce energy consumption. When the efficiency is low, the operation is optimized by adjusting the blade angle. For example, in a mine with high energy-saving requirements, if the efficiency of the ventilator is lower than the ideal value, Taking 0.6 can promote the adjustment of the blade angle, improve the efficiency and reduce unnecessary energy consumption;

[0079] reflects the weight of the surge margin S of the ventilator on the blade angle adjustment, which is used to prevent the ventilator from entering the surge state and ensure the stability of the equipment operation. For example, in a mine where the ventilation system is prone to unstable working conditions, to prevent surging, Take 0.5. When the surge margin is small, the blade angle will be adjusted to increase the surge margin and ensure the stable operation of the ventilator.

[0080] When 0.5 < β ≤ 0.8, recalculate the real-time efficiency of the main ventilator , surge margin S, and introduce the temperature gradient of the intake airway , specific heat capacity of air C, and set the blade optimization angle coefficient , according to the formula calculate value, where represents the influence weight coefficient considering the impact of temperature gradient on ventilation demand, taking values between 0 and 1, represents the weight coefficient of specific heat capacity difference of air, taking values between 0 and 1, is the real-time efficiency weight coefficient of the main ventilator, taking values between 0 and 1, is the surge margin weight coefficient, taking values between 0 and 1, are all pre-set corresponding ideal values, are all the allowable maximum values. After calculating , the blade angle is adjusted to , adapting to the dynamic underground thermal environment and the changes in the operating conditions of the ventilator, deepening the cooling and ventilation efficiency;

[0081] It should be noted that reflects the influence weight of the temperature gradient of the intake airway on the blade angle adjustment. It is used in mines with obvious temperature changes. The temperature gradient affects the air flow distribution, and the blade angle needs to be adjusted to optimize the ventilation and cooling effect. For example, in mines with heat hazard problems, when the temperature gradient is large, is set to 0.5, and the system adjusts the blade angle according to this coefficient to strengthen ventilation and heat dissipation and improve the working environment;

[0082] reflects the role size of the specific heat capacity of air C on the blade angle adjustment. It is used for different specific heat capacities of air in different regions, which affect the ventilation effect. The blade angle is adjusted to adapt to the difference in air thermal properties. For example, in a mine, due to the difference in air composition, the specific heat capacity changes, is set to 0.4, and the system adjusts the blade angle according to this coefficient to optimize the ventilation effect;

[0083] The specific representation is the same.

[0084] When β > 0.8, introduce the smoke concentration gradient , smoke particle size distribution D, and set the blade adjustment coefficient , according to the formula calculate value, where respectively represent the weight coefficients of smoke concentration on the ventilation demand and the weight coefficients of smoke particle size distribution on the ventilation demand, both taking values between 0 and 1, both are the corresponding ideal values set in advance, both are the maximum allowable values. After calculation, the blade angle is anchored, ensuring that the air current efficiently disperses the smoke and clears the escape corridor;

[0085] It should be noted that, reflecting the effect of the smoke concentration gradient on the blade angle adjustment. For example, when a fire breaks out in the mine and the smoke concentration gradient is large, if it is 0.7, by adjusting the blades, the air current is guided to disperse the smoke, creating conditions for personnel to escape;

[0086] reflecting the influence degree of the smoke particle size distribution D on the blade angle adjustment. For example, when the smoke particles generated by the fire are large and not conducive to diffusion, if it is 0.5, by adjusting the blades, the air current state is changed to improve the smoke dispersion efficiency.

[0087] As a further solution of the present invention: store the inherent parameters of the main ventilator in the PLC, including the impeller diameter, blade shape, and hub ratio. Based on the adaptive control system, control the speed and blade angle of the main ventilator, and calculate the current theoretical air volume of the main ventilator , and the specific formula is as follows:

[0088] , where are the relevant structural parameters of the ventilator, including the blade shape and hub ratio, which are fixed parameter values obtained by the ventilator manufacturer through testing and are between 0.05 and 0.15. n is the actual speed of the currently obtained main ventilator, is the impeller diameter, is the currently obtained blade angle;

[0089] After obtaining , substitute it into to calculate the air volume loss value .

[0090] As a further solution of the present invention: based on the calculated air volume loss value , store the different-level thresholds for the loss value in the PLC, which are respectively , where ;

[0091] The PLC continuously collects the historical data of the long-term operation of the ventilator, including the ventilator speed n, blade angle under four different working conditions , theoretical air volume , actual air volume , the data of each environmental sensor and the corresponding timestamp information. Classify and organize these data, store them according to the working condition category and time series, construct a database, and use the decision tree algorithm to perform model training in the data analysis module built into the PLC. Take the air volume loss value as the target variable, and take the ventilator operation parameters, environmental parameters, and treatment measures taken at the corresponding historical moments as feature variables. Through repeated learning of historical data, the decision tree model gradually constructs the decision relationship between different air volume loss levels and various influencing factors, forming a diagnosis and decision model. During the actual operation of the ventilator, the PLC real-time collects the current operation parameters and environmental parameters, calculates the air volume loss value , and inputs it into the trained model. The model, based on the input parameter information and the learned decision rules, judges the level of air volume loss and the corresponding reasons.

[0092] As a further solution of the present invention: The specific processing steps of the system are as follows:

[0093] S1. Install several gas concentration sensors, carbon monoxide concentration sensors, carbon dioxide concentration sensors, dust concentration sensors, temperature sensors, and humidity sensors in the environment where the main ventilator is to be installed. The data of these sensors are collected, stored, and processed by the PLC;

[0094] S2. Calculate the comprehensive index β based on the processed data of each sensor through a weight strategy, and formulate an adaptive control strategy for the main ventilator speed and an adaptive control strategy for the main ventilator blade angle based on the comprehensive index β;

[0095] S3. The PLC adaptively adjusts the main ventilator speed and blade angle based on the adaptive control strategy for the main ventilator speed and the adaptive control strategy for the main ventilator blade angle in combination with the air flow simulation software in the upper computer;

[0096] S4. Based on the adjusted main ventilator speed and blade angle, introduce various factory parameters of the main ventilator and the resistance coefficient R of the roadway to be installed, and calculate the current theoretical air volume of the main ventilator ;

[0097] S5. Calculate based on the calculated theoretical air volume and the actual air volume obtained by monitoring to obtain the lost air volume , and judge the level of air volume loss and the corresponding reasons based on the lost air volume combined with historical data and the decision tree model.

[0098] Example 2:

[0099] Preferably, the inherent parameters of the main ventilator are stored in the PLC, including the impeller diameter, blade shape, and hub ratio. Based on the adaptive control system, the rotational speed and blade angle of the main ventilator are controlled, and the theoretical air volume of the current main ventilator is calculated , and the specific formula is as follows:

[0100] , where are the structural parameters related to the ventilator, including the blade shape and hub ratio, which are fixed parameter values obtained by the ventilator manufacturer through testing and are between 0.05 and 0.15. n is the actual rotational speed of the current main ventilator obtained is the impeller diameter is the current blade angle obtained

[0101] After obtaining , substitute it into to calculate the air volume loss value .

[0102] It should be noted that is a comprehensive coefficient related to the specific model and structure of the ventilator. It integrates various internal structure factors that affect the performance of the ventilator, including blade shape (such as whether the blade is forward-curved, backward-curved or radial, the degree of blade bending and torsion law, etc.), the number of blades, the blade installation method, and the hub ratio (the ratio of the hub diameter to the impeller diameter). For different ventilator designs, these structural parameters are different, resulting in differences in the flow characteristics of the air flow inside the ventilator, and it is precisely the comprehensive quantification of these differences; and is obtained through experimental calibration. After the ventilator is produced, the manufacturer will conduct a series of performance test experiments on it. Under different operating conditions such as rotational speed and blade angle, the actual air volume output of the ventilator is measured and compared with the theoretical calculation value. Through a large amount of experimental data fitting and optimization, the relatively accurate value of this model of ventilator under different operating conditions is finally determined. This value is usually recorded in the product technical data of the ventilator for reference during subsequent use and maintenance, and is usually between 0.05 and 0.15

[0103] Preferably, based on the calculated air volume loss value , different-level thresholds for the loss value are stored in the PLC, which are respectively , where ;

[0104] The PLC continuously collects the historical data of the long-term operation of the ventilator, including the rotational speed n of the ventilator and the blade angle under four different operating conditions , theoretical air volume , actual air volume , data of each environmental sensor and the corresponding timestamp information, classify and organize these data, store them according to the working condition category and time series, construct a database, and use the decision tree algorithm to perform model training in the data analysis module built into the PLC. Take the air volume loss value as the target variable, and take the ventilation fan operation parameters, environmental parameters, and treatment measures taken at the corresponding historical moments as feature variables. Through repeated learning of historical data, the decision tree model gradually constructs the decision relationship between different air volume loss levels and various influencing factors, forming a diagnosis and decision model. During the actual operation of the ventilation fan, the PLC real-time collects the current operation parameters and environmental parameters, calculates the air volume loss value , and inputs it into the trained model. The model, based on the input parameter information and the learned decision rules, determines the level of air volume loss and the corresponding reasons.

[0105] Preferably, the specific processing steps of the system are as follows:

[0106] S1. Install several gas concentration sensors, carbon monoxide concentration sensors, carbon dioxide concentration sensors, dust concentration sensors, temperature sensors, and humidity sensors in the environment where the main ventilation fan is to be installed. The data of these sensors are collected, stored, and processed by the PLC;

[0107] S2. Calculate the comprehensive index β based on the processed data of each sensor through the weight strategy, and based on the comprehensive index β, the main ventilation fan speed adaptive control strategy and the main ventilation fan blade angle adaptive control strategy;

[0108] S3. The PLC adaptively adjusts the main ventilation speed and blade angle based on the main ventilation fan speed adaptive control strategy and the main ventilation fan blade angle adaptive control strategy in combination with the air flow simulation software in the upper computer;

[0109] S4. Based on the adjusted main ventilation fan speed and blade angle, introduce various factory parameters of the main ventilation fan and the resistance coefficient R of the roadway to be installed, and calculate the current theoretical air volume of the main ventilation fan ;

[0110] S5. Calculate based on the calculated theoretical air volume and the actual air volume obtained by monitoring to obtain the lost air volume , and based on the lost air volume, combined with historical data and the decision tree model, determine the level of air volume loss and the corresponding reasons.

[0111] In the description of this specification, the descriptions referring to the terms "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0112] The above content is only an example and explanation of the present invention. Those skilled in the art of the present technology can make various modifications or supplements to the described specific embodiments or use similar ways to replace them. As long as they do not deviate from the invention or exceed the scope defined by the claims of the present invention, they should fall within the protection scope of the present invention.

Claims

1. A mine main ventilation fan monitoring system, comprising: Sensor group, host computer and PLC, characterized in that: the sensor group includes a gas concentration sensor, a carbon monoxide concentration sensor, a carbon dioxide concentration sensor, a dust concentration sensor, a temperature sensor, and a humidity sensor, the host computer installs a wind flow simulation software, and the PLC installs an adaptive control system; The sensor group is used to monitor the gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity in the ventilation environment to be installed by the main ventilator in real time, and transmit the detected data to the PLC; The host computer is used to run the wind flow simulation software. The software has a complete 3D model of the environment built-in, and simulates the real dynamics of the wind flow based on real-time ventilation parameters. The ventilation parameters include the rotational speed of the main ventilator and the angle of its blades; The PLC is used to execute the adaptive control system. The adaptive control system includes storing the data weights of the gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity in the environment, calculating a comprehensive index β based on each weight and the real-time monitoring data, formulating an adaptive control strategy for the rotational speed of the main ventilator and formulating an adaptive control strategy for the blade angle of the main ventilator according to the comprehensive index β and the wind flow simulation software. The PLC controls the rotational speed and blade angle of the main ventilator through the adaptive control strategy; The specific process of presetting each data weight in the adaptive control system is as follows: According to the environmental type and industry regulations, the gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity are respectively set with weights, and the weights are set as K1, K2, K3, K4, K5, and K6 in sequence. The sum of the six weights is marked as K total, and K total = 1; The specific calculation formula for calculating the comprehensive index β based on each weight and the real-time monitoring data is as follows: ； Among them, is the maximum allowable value of gas concentration, is the maximum allowable value of carbon monoxide concentration, is the maximum allowable value of carbon dioxide concentration, is the maximum allowable value of dust concentration, is the appropriate temperature, is the maximum allowable temperature, is the appropriate humidity, is the maximum allowable humidity, all of which are set in advance according to the environmental type, is the monitoring data of the gas concentration sensor, is the monitoring data of the carbon monoxide concentration sensor, is the monitoring data of the carbon dioxide concentration sensor, is the monitoring data of the dust concentration sensor, T is the monitoring data of the temperature sensor, and H is the monitoring data of the humidity sensor.

2. The mine main ventilator monitoring system according to claim 1, wherein The specific method for formulating the adaptive control strategy for the rotational speed of the main ventilator is as follows: The β value is divided into four stages, corresponding to excellent environmental conditions β ≤ 0.2, normal fluctuation conditions 0.2 < β ≤ 0.5, deteriorating trend conditions 0.5 < β ≤ 0.8, and emergency dangerous conditions β > 0.8; When β ≤ 0.2, the PLC controls the main ventilator to operate at the lowest working frequency, and sets this frequency as the basic frequency of the main ventilator; When 0.2 < β ≤ 0.5, based on the basic frequency, for every 0.1 increase in the comprehensive index β, the working frequency of the main ventilator is adaptively adjusted according to the strategy of increasing the frequency by 10%. Then, in this stage, β = 0.5 belongs to the highest frequency of the main ventilator in this stage; When 0.5 < β ≤ 0.8, set the frequency corresponding to β = 0.5 in the previous stage as the basic frequency of this stage, and adaptively adjust the working frequency of the main ventilator according to the strategy of increasing the frequency by 15% for every 0.1 increase in the comprehensive index β; When β > 0.8, the main ventilator is adjusted to the highest frequency; Through the real-time monitored data of gas concentration, carbon monoxide concentration, carbon dioxide concentration, dust concentration, temperature, and humidity, the comprehensive index β is calculated in real time. The PLC adaptively controls the working frequency of the main ventilator in real time according to this adaptive control strategy, and then adaptively controls the rotational speed of the main ventilator.

3. The mine main ventilation fan monitoring system according to claim 1, characterized in that, The specific method for formulating the adaptive control strategy for the blade angle of the main ventilator is as follows: Based on four cases of β ≤ 0.2, 0.2 < β ≤ 0.5, 0.5 < β ≤ 0.8, and β > 0.8; When β ≤ 0.2, the actual output air volume of the main ventilator is collected by the air volume sensor. , the output air pressure P of the main ventilator is collected by the air pressure sensor, the shaft power W is calculated, according to the rated value of the factory parameters of the main ventilator, and the roadway resistance coefficient R in the installation environment of the main ventilator is measured in advance. The actual output air volume , the output air pressure P of the main ventilator, the shaft power W, and the roadway resistance coefficient R parameters are introduced into the air flow simulation software. The dynamic air flow is simulated through the built-in environmental 3D model of the software, and then the core adjustment coefficient of the blade angle is set , after calculating , according to the formula of adjustment angle = , the blade angle of the main ventilator is adjusted, and the fine-tuning range is limited to ±2°, and this angle is set as the initial setting angle; When 0.2 < β ≤ 0.5, set the compensation coefficient as G and adjust the blade angle to ; When 0.5 < β ≤ 0.8, set the compensation coefficient to , and adjust the blade angle to ; When β > 0.8, set the compensation coefficient as , and anchor the blade angle .

4. The mine main ventilation fan monitoring system according to claim 1, wherein, Store the inherent parameters of the main ventilator in the PLC, including the impeller diameter, blade shape, and hub ratio. Control the rotational speed and blade angle of the main ventilator based on an adaptive control system, and calculate the current theoretical air volume of the main ventilator , obtain , then substitute it into to calculate the air volume loss value .

5. The mine main ventilation fan monitoring system according to claim 4, characterized in that, Based on the calculated air volume loss value , store different level thresholds for the loss value in the PLC, which are respectively , where ; The PLC continuously collects the historical data of the long-term operation of the ventilator, including the ventilator speed n, blade angle under four different working conditions , theoretical air volume , actual air volume , data of each environmental sensor and the corresponding timestamp information. These data are classified and sorted, stored according to the working condition category and time series, and a database is constructed. The decision tree algorithm is used to perform model training in the data analysis module built into the PLC. The air volume loss value is used as the target variable, and the ventilator operation parameters, environmental parameters, and treatment measures taken at the corresponding historical moments are used as feature variables. Through repeated learning of historical data, the decision tree model gradually constructs the decision relationship between different air volume loss levels and various influencing factors, forming a diagnosis and decision model. During the actual operation of the ventilator, the PLC real-time collects the current operation parameters and environmental parameters, calculates the air volume loss value , and inputs it into the trained model. The model, based on the input parameter information and the learned decision rules, determines the level of air volume loss and the corresponding reasons.

6. The mine main ventilation fan monitoring system according to any one of claims 1-5, characterized in that, The specific processing steps of the system are as follows: S1. Install a number of gas concentration sensors, carbon monoxide concentration sensors, carbon dioxide concentration sensors, dust concentration sensors, temperature sensors, and humidity sensors in the environment where the main ventilator is to be installed. The data of these sensors are collected, stored, and processed by the PLC; S2. Calculate the comprehensive index β through the weight strategy based on the processed data of each sensor, and formulate an adaptive control strategy for the main ventilator speed and an adaptive control strategy for the main ventilator blade angle based on the comprehensive index β; S3. The PLC adaptively adjusts the main ventilation speed and blade angle based on the adaptive control strategy for the main ventilator speed and the adaptive control strategy for the main ventilator blade angle in combination with the air flow simulation software in the upper computer; S4. Based on the adjusted main ventilator speed and blade angle, introduce various factory parameters of the main ventilator and the resistance coefficient R of the roadway to be installed, and calculate the theoretical air volume of the current main ventilator ; S5. Theoretical air volume based on calculation and the actual air volume obtained through monitoring are calculated to obtain the lost air volume . Based on the lost air volume, combined with historical data and using a decision tree model, the level of air volume loss and the corresponding reasons are determined.

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