Efficient and energy-saving mine multi-stage machine station ventilation monitoring system

Abstract

The invention discloses an efficient and energy-saving mine multi-stage machine station ventilation monitoring system, which is characterized in that a real-time data assimilation module is integrated in a ventilation network resolving program, direct data connection with an underground sensor network is established, and real-time acquisition of roadway wind speed, wind pressure and environmental parameters is automatically executed when each resolving cycle begins; and dynamically solving the actual wind resistance value of the roadway through reverse calculation based on the ventilation resistance law deltaP = R * Q, calculating the natural wind pressure value in real time according to the horizontal temperature and pressure data of the mine, and automatically writing the dynamic parameters into an original static data file and updating the original static data file, the resolving program performs iterative calculation based on the roadway wind resistance data file and the natural wind pressure data file which are updated in real time, so that the problem that a digital model and a physical entity are asynchronous due to dependence on manual updating of a static data file in the prior art is solved; the technical effects that the calculation result of the ventilation network is highly consistent with the real roadway working condition, and the regulation and control accuracy of the system is remarkably improved are achieved.

Description

Technical Field

[0001] This invention relates to mine ventilation technology, and in particular to a high-efficiency and energy-saving multi-level mine ventilation monitoring system. Background Technology

[0002] Mine ventilation systems are a crucial component in ensuring safe production in underground mines. Their core tasks are to continuously supply fresh air, dilute and remove toxic and harmful gases and dust, and regulate the underground climate. Traditional mine ventilation systems mainly adopt a centralized main fan ventilation mode. Although this mode meets basic ventilation needs to a certain extent, it generally suffers from inherent defects such as uneven ventilation pressure distribution, serious air leakage, low effective air volume ratio, and high energy consumption.

[0003] The existing publication CN102953745A discloses a high-efficiency and energy-saving multi-level ventilation monitoring technology system for mines. This system includes intake air stations in the intake air section, return air stations in the return air section, and user air stations in the user air section. A central monitoring room is located on the mine surface. The system develops a multi-level ventilation network calculation program to perform numerical calculations of ventilation parameters such as ventilation resistance, fan volume, air pressure, efficiency, and total power. It determines the number and location of the air stations, selects and adjusts fan types and volume, and establishes a remote centralized monitoring system for the multi-level air stations and an online monitoring system for the underground atmospheric environment. This system enables pressure equalization ventilation in each ventilation network section of the mine, reduces air leakage, and allows remote control of the start / stop of fans in the intake, return, and user air stations, as well as variable frequency speed regulation and system reversal. It also provides real-time monitoring of fan operating status, fan volume and air pressure, motor bearing temperature, power factor, and other parameters, and real-time online monitoring of the underground ventilation environment.

[0004] However, existing ventilation network calculation models heavily rely on preset, static basic parameters. Specifically, key input data necessary for the calculation program, such as the wind resistance and natural wind pressure values ​​of each roadway, are usually measured and entered into the system before production based on design drawings, and then repeatedly called as fixed data files. However, a mine is actually a dynamically changing environment; roadway wind resistance changes constantly due to mining activities, support deformation, or material stacking; natural wind pressure also fluctuates with seasons, day and night, and even weather conditions. This inherent contradiction between the "static model" and "dynamic reality" makes it difficult for the system's calculation results to accurately reflect the constantly evolving actual working conditions underground, thus leading to biases in decisions made based on this data, such as airflow adjustment and station control. Summary of the Invention

[0005] The purpose of this invention is to provide a highly efficient and energy-saving multi-level mine ventilation monitoring system, which solves the core technical problems of existing mine ventilation monitoring systems, such as the disconnect between digital models and physical entities caused by reliance on static data files, and the deterioration of calculation accuracy with changes in working conditions.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a high-efficiency and energy-saving multi-level ventilation monitoring system for mines, comprising intake fan stations in the intake section, return fan stations in the return air section, and user fan stations in the user air section; a mine surface monitoring room located on the mine surface; and a multi-level ventilation network calculation program executed by the mine surface monitoring room, wherein the calculation program can call fan parameter data files, station parameter data files, network parameter data files, roadway air resistance data files, and natural wind pressure data files. The system also includes a real-time data assimilation module; The execution flow of the multi-level station ventilation network calculation program includes the following steps: Step S1: Call the real-time data assimilation module to collect the wind speed and wind pressure monitoring values ​​of each roadway in real time through the wind speed and wind pressure sensor network in the mine, and collect the temperature and air pressure monitoring values ​​at different horizontal depths in the mine in real time through the temperature and air pressure sensor network. Step S2: Calculate the actual air volume of the roadway based on the wind speed monitoring value and the roadway cross-sectional area, and combine it with the wind pressure monitoring value. According to the ventilation resistance law ΔP=R·Q², solve the dynamic wind resistance value of the roadway in reverse, where ΔP is the wind pressure monitoring value, Q is the actual air volume of the roadway, and R is the dynamic wind resistance value. Step S3: Based on the temperature and air pressure monitoring values, calculate the dynamic natural wind pressure value using the air density formula and the hydrostatic formula; Step S4: Write the calculated dynamic wind resistance value and the dynamic natural wind pressure value into the roadway wind resistance data file and the natural wind pressure data file, respectively; Step S5: Call the updated roadway wind resistance data file and the natural wind pressure data file, and execute the subsequent air volume iteration calculation and parameter calculation process of the ventilation network solution program.

[0007] Furthermore, in step S1, the operating current, voltage, frequency, and bearing temperature data of each station's fan are collected in real time.

[0008] Furthermore, after step S4 and before step S5, the main program automatically skips the manual judgment logic regarding whether to manually input the roadway wind resistance and whether to consider natural wind pressure, and directly loads the updated data file.

[0009] Furthermore, the multi-level station ventilation network calculation program is configured to automatically restart and run according to a preset time period T, where the value of T ranges from 5 minutes to 60 minutes.

[0010] Furthermore, the real-time data assimilation module is implemented as an embedded subroutine of the multi-level station ventilation network calculation program.

[0011] Furthermore, the system also includes a fan controller that is communicatively connected to the mine surface general monitoring room (4); after the ventilation network calculation program completes the calculation, it outputs an air volume adjustment command to the fan controller; the fan controller performs start-stop operation on the fan of the corresponding station or adjusts its operating frequency through the frequency converter according to the air volume adjustment command.

[0012] Furthermore, the fan controller is also configured to: upon receiving a reverse air command, control the axial flow fans in the intake fan station, return fan station and user fan station to run in reverse.

[0013] Furthermore, the wind speed sensor and wind pressure sensor network, and the temperature sensor and air pressure sensor network, constitute the downhole atmospheric environment online monitoring system of the system.

[0014] Furthermore, the subsequent processes of the multi-level station ventilation network solution program include calling the self-circulating mesh sub-program, the air volume iterative calculation sub-program, the roadway fan station parameter calculation sub-program, and the node pressure calculation sub-program.

[0015] Furthermore, the contents of the wind turbine parameter data file include the wind turbine type, wind turbine pressure characteristic equation coefficients, and wind turbine efficiency characteristic equation coefficients.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention employs the following specific technical means: It integrates a real-time data assimilation module into the ventilation network calculation program, establishing a direct data connection with the underground sensor network. At the start of each calculation cycle, it automatically collects real-time data on roadway wind speed, wind pressure, and environmental parameters. Based on the ventilation resistance law ΔP=R·Q², it dynamically calculates the actual roadway wind resistance value through reverse calculation. Simultaneously, it calculates the natural wind pressure value in real-time based on temperature and pressure data at various mine levels. These dynamic parameters are automatically written into and update the original static data file. This allows the calculation program to perform iterative calculations based on the real-time updated roadway wind resistance and natural wind pressure data files. This solves the technical problems in existing technologies where the reliance on manually updated static data files leads to a disconnect between the digital model and the physical entity, and the deterioration of calculation accuracy due to changes in operating conditions. The invention achieves the technical effect of ensuring a high degree of consistency between the ventilation network calculation results and the actual roadway operating conditions, and significantly improving the accuracy of system control. Attached Figure Description

[0017] The present invention will be further described below with reference to the accompanying drawings: Figure 1 This is a system architecture diagram of a high-efficiency and energy-saving multi-level mine ventilation monitoring system according to the present invention; Figure 2 This is a schematic diagram of the steps of a high-efficiency and energy-saving multi-level mine ventilation monitoring system according to the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0019] The technical solution of the present invention will be described in detail below with reference to specific embodiments. The following specific embodiments may be combined or substituted with each other according to the actual situation, and the same or similar concepts or processes may not be described again in some embodiments.

[0020] like Figures 1 to 2 As shown, this invention provides a highly efficient and energy-saving multi-level ventilation monitoring system for mines. First, a complete monitoring network is deployed at key locations throughout the mine. KGF2 wind speed sensors and CYB10 differential pressure sensors are installed in the main roadways at -200m. The sensors are positioned at least 10 times the roadway diameter from the roadway entrance to avoid turbulence effects. Simultaneously, environmental monitoring stations are set up at the -100m, -200m, and -300m levels, equipped with PT100 temperature sensors and MPM480 barometric pressure sensors. All sensors are connected to an underground ring network switch via industrial Ethernet, and finally transmitted to the ground monitoring center via fiber optic cable. The monitoring center is equipped with a dual-redundant server system, using Intel Xeon Silver 4210 processors and 64GB of memory to ensure continuous and stable system operation.

[0021] In terms of software configuration, the real-time data assimilation module is compiled into a dynamic link library using Visual Studio 2019, and data exchange is achieved with the existing FORTRAN solver through a specially developed API interface. The system is set to run in 15-minute cycles and is triggered periodically via Windows Task Scheduler. At the beginning of each cycle, the data acquisition engine collects all sensor data in a multi-threaded manner. The fan operating parameters (current, voltage, frequency, and bearing temperature) required in claim 2 are directly read from the ABB ACS880 frequency converters at each station via the Modbus TCP protocol. These device status data are collected synchronously with the ventilation parameters, providing comprehensive monitoring of the system's operating status.

[0022] This invention systematically solves the core technical problems of existing mine ventilation monitoring systems, which rely on static data files, leading to a disconnect between the digital model and the physical entity, and a degradation in calculation accuracy with changing operating conditions. Its key technical feature lies in constructing a complete dynamic adaptive closed loop: First, in step S1, a multi-sensor network collects real-time data on wind speed, wind pressure, and ambient temperature and pressure in the roadway. The product of the airflow velocity monitored by the wind speed sensor and the roadway cross-sectional area is directly converted into real-time airflow Q, while paired differential pressure sensors accurately measure the pressure difference ΔP between the two ends of the roadway. This fundamental data provides a reliable input source for subsequent dynamic calculations. Then, in step S2, the reverse analysis process involves substituting the real-time acquired Q and ΔP into the ventilation resistance law ΔP=R·Q², and directly solving for the dynamic wind resistance value R, which characterizes the ease or difficulty of roadway ventilation, through the mathematical transformation R=ΔP / Q². This analytical process transforms the constant empirical value used in traditional design into a physical quantity that updates in real-time with the actual state of the roadway (such as deformation, blockage, and changes in surface roughness). The analytical results are directly expressed in numerical form. The process is reflected in the updated entries in the roadway air resistance data file; step S3 converts the temperature and pressure monitoring values ​​into air density parameters using the air density formula, and then calculates the weight difference of the air column inside and outside the mine using the hydrostatic formula, finally outputting the natural wind pressure value that changes in real time with climate conditions and mine depth; step S4 writes the air resistance value and natural wind pressure value obtained by the above dynamic analysis into the original static data file in a programmed manner and overwrites it. This automatic update mechanism ensures that the basic database is synchronized with the physical world in real time; finally, step S5 drives the solution program to perform iterative calculation of air volume based on the latest dynamic parameters. The dynamic air resistance value directly determines the air volume distribution ratio of each roadway, while the dynamic natural wind pressure value serves as an important input term in the system energy balance equation, affecting the calculation of the fan operating point. These precise input parameters enable the solution program to output fan control commands that closely match the actual situation. This complete technical approach ultimately achieved three breakthroughs: First, by dynamically updating parameters, the digital model and the physical entity are kept highly consistent, reducing the air volume calculation error of more than 15% caused by data lag in traditional methods to less than 5%. Second, a minute-level response closed loop from data acquisition to control execution was established, shortening the system's adjustment time to changes in working conditions such as roadway deformation from hours to less than 15 minutes. Third, by accurately matching working conditions, the total energy consumption of the system was further reduced on the original basis while ensuring ventilation effect. At the same time, the workload of manual data maintenance was reduced by introducing a real-time data assimilation module and establishing an automatic closed loop between the monitoring system and the calculation program, realizing the dynamic acquisition and automatic updating of roadway wind resistance and natural wind pressure data, and ensuring that the ventilation network calculation is always based on real-time working condition data.

[0023] In step S1, the three-phase current and voltage data of the motor are collected to calculate the motor input power and load rate in real time, and cross-validated with the frequency value output by the inverter. This process solves the problem that the actual load status of the fan cannot be determined by air volume and air pressure parameters alone, and can directly identify faults such as motor overload, undervoltage operation, or abnormal inverter output. Bearing temperature data is collected via a Pt100 thermal resistor embedded in the bearing housing, and the monitored data is uploaded to the PLC control system. This data serves as a direct indicator of the mechanical system's operating status, solving the problem of failures caused by poor lubrication or mechanical wear not being able to be predicted in advance. These equipment status parameters, together with ventilation parameters, form the basis for system decision-making. When the ventilation network calculation program outputs adjustment commands, the system first verifies the equipment status data of the target station: if the current is within the rated range and the bearing temperature is below the alarm threshold, the adjustment command is executed immediately; if abnormal current fluctuations or an upward trend in bearing temperature are detected, the system automatically downgrades the execution or triggers a maintenance alarm.

[0024] After step S4 and before step S5, the main program automatically skips the original manual judgment logic of whether to manually input the roadway wind resistance and whether to consider natural wind pressure, and directly loads the data file that has been updated by the real-time data assimilation module. By setting a flag, the judgment is forced to be skipped and the updated file is loaded directly. This eliminates the manual confirmation step, shortens the cycle time, and ensures that the calculation is based on the latest data. The effect is to reduce delays, avoid operational errors, and achieve unattended continuous operation.

[0025] In order to solve the problem of response uncertainty caused by traditional ventilation systems relying on manual triggering or single event triggering, the multi-level station ventilation network calculation program is configured to automatically restart and run according to a preset time period T during the construction of a complete real-time data assimilation system, where the value of T ranges from 5 minutes to 60 minutes. After the real-time data assimilation module completes data acquisition, dynamic calculation, and file updates, the periodic triggering mechanism ensures that the decomposition program can start on time, avoiding the system from entering a waiting state after completing a calculation and being unable to respond to subsequent changes in operating conditions in a timely manner.

[0026] In practice, the system periodically triggers the calculation process according to a set T value via Windows Task Scheduler or a dedicated timer thread. When T is small (e.g., 5 minutes), the system has a stronger ability to track dynamic conditions such as roadway deformation and wind resistance changes, making it suitable for mines with frequent mining activities and rapidly changing ventilation conditions. When T is large (e.g., 60 minutes), the system's computational load is significantly reduced, making it suitable for mines with relatively stable ventilation networks. This configurable periodicity allows the system to achieve an optimal balance between response speed and resource consumption. The preset time period T automatically restarts the system, working in conjunction with other components of the real-time data assimilation module to form a complete "acquisition-computation-update-solution" closed loop. When the periodically triggered calculation program runs, it automatically loads the updated dynamic data file, ensuring that each calculation is based on the latest operating parameters. This periodic automatic restart mechanism guarantees that the system can continuously track the dynamic changes of the mine ventilation system, forming a truly adaptive closed-loop control system together with data acquisition, dynamic calculation, and file updates, thus realizing a fundamental shift from static design to dynamic optimization.

[0027] To establish a unified memory space data sharing mechanism, replacing the original method of cross-process data transfer via files or networks, the real-time data assimilation module is compiled into a dynamic link library or static library and directly integrated into the main program's execution flow through function calls. This achieves a high degree of integration between the data assimilation function and the solving program, resolving the problems of poor system compatibility and low data exchange efficiency that existed when the real-time data assimilation module was run as an independent external program. Specifically, by implementing the real-time data assimilation module as an embedded subroutine, this integration method allows the data assimilation module to directly access the memory data structure of the solver, while the solver can also directly call the calculation results of the data assimilation module, avoiding format conversion errors and transmission delays that may occur when data is transmitted between different programs. In actual operation, the embedded subroutines share the same exception handling mechanism and resource management module as the main program. When anomalies occur during data acquisition or calculation, the system can maintain a unified error handling process, preventing system crashes caused by poor coordination between modules. At the same time, this integration method reduces the system's dependence on external interfaces, lowers the risk of system failures due to interface changes or compatibility issues, and ensures that the entire process from data acquisition to solution output can be executed efficiently, reliably, and continuously.

[0028] To address the disconnect between computational decision-making and on-site execution in traditional systems, the system architecture establishes a real-time communication connection between the fan controller, which communicates with the mine's surface central monitoring room, and the monitoring system via an industrial fieldbus (such as Profibus-DP) or industrial Ethernet. This allows the digital control commands output by the ventilation network calculation program to be converted into physical control signals for the on-site fans. In practical implementation, after the ventilation network calculation program completes the calculation, it transmits the airflow adjustment command to the fan controller at the corresponding station via a communication protocol. The controller executes control in two ways based on the received command: for operating conditions requiring start-stop control, it directly controls the fan contactor by outputting a relay signal via hard-wiring; for operating conditions requiring precise airflow adjustment, it sends a frequency setpoint to the inverter via analog output or bus communication to adjust the fan speed. This established control link ensures that the optimization strategy obtained based on real-time data assimilation and dynamic calculation can be accurately executed on the field equipment. When the system identifies changes in operating conditions through real-time data assimilation and calculates the optimization scheme through the calculation program, the fan controller ensures that these schemes are promptly and accurately translated into actions on the field equipment, thereby achieving dynamic adaptive adjustment of the ventilation system. Simultaneously, the controller feeds back the execution results to the monitoring system, forming a verification mechanism for the control effect and providing a data foundation for further system optimization. The establishment of this control loop enables seamless connection between all aspects of the ventilation system, from data acquisition to equipment control, effectively ensuring the real-time performance and control accuracy of the ventilation system.

[0029] To address the slow response and poor reliability issues of traditional reversing systems that rely on complex mechanical damper switching and manual on-site operation, the reversing control function of the fan controller combines preset reversing control logic with the torque control mode of the frequency converter. Specifically, upon receiving a reversing command from the monitoring system, the controller first uses the DC braking function of the frequency converter to reduce the fan speed to zero within a set time. Then, it initiates reverse operation according to a preset acceleration curve, simultaneously adjusting the blade installation angle to adapt to the reversing condition. When the environmental monitoring system detects abnormal gas levels or fire signals, it can automatically or manually issue a reversing command, and the fan controller immediately executes the preset reversing program. Precise torque control by the frequency converter ensures a smooth transition during fan reversal, avoiding impact on the power grid and mechanical systems. The reversing operation of fans at each station is strictly synchronized, ensuring rapid and reliable switching of airflow direction throughout the ventilation system.

[0030] To address the issue of bridging dynamic data updates with actual ventilation network calculations, in the subsequent processes of the multi-level station ventilation network solution program, the system sequentially calls a series of predetermined calculation modules to complete the solution. These modules include the self-circulating mesh sub-program, the air volume iterative calculation sub-program, the roadway fan station parameter calculation sub-program, and the node pressure calculation sub-program. This allows for accurate numerical solutions to the ventilation network using proven and mature algorithms, based on the dynamic parameter updates completed by the real-time data assimilation module.

[0031] The wind turbine parameter data file systematically stores three key parameters: wind turbine type, wind turbine pressure characteristic equation coefficients, and wind turbine efficiency characteristic equation coefficients. By establishing a standardized wind turbine characteristic database, parametric modeling methods are used to transform the aerodynamic performance and energy consumption characteristics of the wind turbine into a calculable mathematical model. Specifically, the fan pressure characteristic equation is typically in cubic form: H = a0 + a1Q + a2Q² + a3Q³, while the fan efficiency characteristic equation is in parabolic form: η = b0 + b1Q + b2Q². The coefficients a0-a3 and b0-b2 constitute the core content of the data file. This solves the problem of calculation errors caused by simplified fan models in traditional ventilation calculations. After the ventilation network calculation program determines the required airflow in the pressure subroutine of the calculation nodes, it can accurately calculate the corresponding fan's operating pressure and efficiency under that condition by querying the characteristic equation coefficients in the data file, providing accurate input data for subsequent variable frequency speed control and energy consumption optimization.

[0032] This technical solution works in close conjunction with a real-time data assimilation system: after dynamic wind resistance and natural wind pressure data are updated in real time, the calculation program, using these precise fan characteristic parameters, can calculate fan control parameters that perfectly match the current roadway conditions. Particularly in the fan selection and variable frequency speed control stages, the system automatically selects the most suitable fan model and operating frequency for the current conditions by comparing the characteristic equations of different fans, ensuring both accurate airflow control and maximizing operational efficiency. This parametric modeling method enables the system to adapt to mixed use scenarios with multiple fan models, providing a complete equipment characteristic foundation for the refined management and control of multi-level station ventilation systems.

[0033] In addition to the preferred embodiments described above, the present invention has other embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection claimed by the present invention.

Claims

1. A high-efficiency and energy-saving multi-stage machine station ventilation monitoring system for mine, comprising an intake air fan station arranged at an intake air section of the mine, a return air fan station arranged at a return air section of the mine, and a used air fan station arranged at a used air section of the mine, a mine ground general monitoring room arranged at a ground surface of the mine, and a multi-stage machine station ventilation network solving program executed by the mine ground general monitoring room, the solving program being capable of calling fan parameter data files, machine station parameter data files, network parameter data files, roadway air resistance data files, and natural air pressure data files, characterized in that: the system further comprises a real-time data assimilation module; an execution flow of the multi-stage machine station ventilation network solving program comprises the following steps: Step S1: calling the real-time data assimilation module, collecting, in real time, air speed monitoring values and air pressure monitoring values of each roadway through a network of air speed sensors and air pressure sensors under the mine, and collecting, in real time, temperature monitoring values and air pressure monitoring values at different horizontal depths in the mine through a network of temperature sensors and air pressure sensors; Step S2: calculating an actual air volume of the roadway based on the air speed monitoring values and a roadway cross-sectional area, and inversely solving a dynamic air resistance value of the roadway according to a ventilation resistance law ΔP = R·Q2 in combination with the air pressure monitoring values, wherein ΔP is the air pressure monitoring value, Q is the actual air volume of the roadway, and R is the dynamic air resistance value; Step S3: calculating a dynamic natural air pressure value through an air density formula and a hydrostatic formula based on the temperature monitoring values and the air pressure monitoring values; Step S4: writing and updating the dynamic air resistance value and the dynamic natural air pressure value into and in the roadway air resistance data files and the natural air pressure data files, respectively; Step S5: calling the updated roadway air resistance data files and the natural air pressure data files to execute a subsequent air volume iteration calculation and parameter calculation flow of the ventilation network solving program. In the step S1, running current, voltage, frequency, and bearing temperature data of each machine station fan are also collected in real time. After the step S4 and before the step S5, the main program automatically skips artificial judgment logic about whether to manually input a roadway air resistance and whether to consider a natural air pressure, and directly loads the updated data files. The multi-stage machine station ventilation network solving program is configured to automatically restart running at a preset time period T, wherein a value range of T is 5 minutes to 60 minutes. The real-time data assimilation module is implemented as an embedded subprogram of the multi-stage machine station ventilation network solving program. The system further comprises a fan controller in communication connection with the mine ground general monitoring room (4); after the ventilation network solving program completes calculation, an air volume adjustment instruction is output to the fan controller; the fan controller executes start-stop operation or adjusts a running frequency of a fan of a corresponding machine station through a frequency converter according to the air volume adjustment instruction. The fan controller is further configured to control axial fan blowers in the intake air fan station, the return air fan station, and the used air fan station to run reversely when receiving a reverse air instruction. The network of air speed sensors and air pressure sensors, and the network of temperature sensors and air pressure sensors constitute an on-line monitoring system of an underground atmospheric environment of the system.

2. The energy efficient mine multi-stage station ventilation monitoring system of claim 1, wherein, ​ 3. The energy efficient mine multi-stage station ventilation monitoring system of claim 1, wherein, ​ 4. The energy efficient mine multi-stage station ventilation monitoring system of claim 1, wherein, ​ 5. The energy efficient mine multi-stage station ventilation monitoring system of claim 1, wherein, ​ 6. The energy efficient mine multi-stage station ventilation monitoring system of claim 1, wherein, ​ 7. The energy efficient mine multi-stage station ventilation monitoring system of claim 1, wherein, ​ 8. The energy efficient mine multi-stage station ventilation monitoring system of claim 1, wherein, ​ 9. The energy efficient mine multi-stage station ventilation monitoring system of claim 1, wherein, The subsequent process of the multi-stage machine station ventilation network solver includes calling a self-loop network hole subroutine, a wind volume iterative calculation subroutine, a calculation of roadway fan machine station parameter subroutine and a calculation of node pressure subroutine.

10. The energy efficient mine multi-stage station ventilation monitoring system of claim 1, wherein, The content of the fan parameter data file includes fan type, fan wind pressure characteristic equation coefficient and fan efficiency characteristic equation coefficient.

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