Ventilation self-adaptive control system and method for high-altitude tunnel construction

Abstract

The invention discloses a ventilation self-adaptive control system and method for high-altitude tunnel construction, and the system comprises a mobile device and monitoring subsystem which is used for arranging a monitoring device capable of moving along with tunneling of a tunnel, and collecting dust concentration, CO concentration, air pressure, temperature and humidity data and mileage positions of measuring points; the ventilation working condition acquisition subsystem is used for acquiring the ventilation working condition to determine the ventilation flow; the data processing and source item inversion module is used for constructing a one-dimensional convection-dispersion discrete model of the segmented control body based on the monitoring data and inverting the time-varying release rate and the total yield of blasting pollutants; the ventilation self-adaptive control module is used for generating a staged ventilation strategy according to the inversion result; and the user interface and alarm module is used for displaying real-time data and a ventilation strategy and triggering an alarm when the real-time data and the ventilation strategy exceed the standard. According to the method, the blasting pollutant source item can be accurately inversed in real time, and the optimal ventilation strategy is dynamically generated according to the blasting pollutant source item, so that ventilation efficiency maximization and energy consumption minimization are realized, and construction safety is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of ventilation and environmental monitoring technology in tunnel construction, specifically to an adaptive ventilation control system and method for high-altitude tunnel construction. Background Technology

[0002] High-altitude tunnel construction is characterized by low air pressure, low air density, low oxygen content, and large fluctuations in temperature and humidity. Under such conditions, when drilling and blasting are carried out, the dust and harmful gases (represented by CO) generated after blasting can easily form a high-concentration accumulation zone near the tunnel face. The concentration decay pattern is affected by a variety of complex factors, such as ventilation volume, ventilation duct inlet location, disturbance from construction machinery, and air leakage in ventilation ducts.

[0003] Current ventilation management in high-altitude tunnels largely relies on monitoring at a few fixed points and setting ventilation volume and timing based on experience. The fundamental flaw in this method is the difficulty in quantifying the "actual total amount of pollutants (source term) generated by each blast." This leads to overly conservative ventilation plans, resulting in energy waste and prolonged waiting times; or insufficient ventilation, causing pollutant concentrations to exceed standards, endangering the health and safety of construction workers. Furthermore, the limited space near the tunnel face and poor post-blast safety make it difficult to establish stable, fixed monitoring points for extended periods. This further increases the difficulty of accurate monitoring and intelligent ventilation control. Summary of the Invention

[0004] To address the shortcomings of the existing technology, this invention provides a ventilation adaptive control system and method for high-altitude tunnel construction. This system can accurately and invert the source terms of blasting pollutants in real time and dynamically generate the optimal ventilation strategy accordingly, thereby maximizing ventilation efficiency, minimizing energy consumption, and ensuring construction safety.

[0005] To achieve the above-mentioned technical objectives, the technical solution adopted by the present invention is as follows: An adaptive ventilation control system for high-altitude tunnel construction includes: Mobile equipment and monitoring subsystems are used to deploy monitoring equipment that can move with the tunnel excavation to collect data on dust concentration, CO concentration, air pressure, temperature and humidity, and the location of measuring points. The ventilation condition acquisition subsystem communicates with the data processing and source term inversion module to acquire fan frequency, speed, operating status or key section wind speed in order to determine ventilation flow rate. The data processing and source term inversion module is used to construct a one-dimensional convection-dispersion discrete model of the segmented control volume based on monitoring data, and to invert the time-varying release rate and total output of blasting pollutants. The ventilation adaptive control module is used to generate a ventilation strategy based on the inversion results, including phased ventilation volume, fan frequency, and operation access time, and then distribute the ventilation strategy to the variable frequency fan controller. The user interface and alarm module are used to display real-time data, ventilation strategies, and trigger alarms when standards are exceeded.

[0006] Preferably, the mobile device and monitoring subsystem include: The movable measuring point unit is deployed on the trestle or trolley that advances with the tunneling within a 300m range of the tunnel face, maintaining a relatively stable distance from the tunnel face; The movable fixed measuring point unit is deployed in cross passages, passing tunnels, or stable support locations, and is periodically moved forward and its mileage position is recalibrated. Each measuring unit is equipped with a dust monitor, a CO monitor, a temperature and humidity sensor, and a barometric pressure sensor.

[0007] Preferably, the mobile device and monitoring subsystem further include: The positioning module uses at least one of UWB positioning, wheel speed odometer, RFID tag or inertial navigation IMU to acquire the mileage position of the measurement point in real time. The data communication module is used to send monitoring data to the data processing and source term inversion module via wireless transmission.

[0008] Preferably, the data processing and source term inversion module is used to: based on the one-dimensional convection-dispersion discrete model of the segmented control body within a preset distance range from the working face, use the concentration observation data of each monitoring point and ventilation flow constraints to invert the time-varying release rate of dust and CO for each blast and calculate the total output, and identify the equivalent attenuation parameters of dust settling or adhesion.

[0009] Preferably, the data processing and source term inversion module uses Kalman filtering or particle filtering algorithms to filter out noise from the monitoring data; The state variables of a Kalman filter form a state vector: State variables include pollutant concentration C(t), wind speed V(t), and pollutant source release rate S(t); The state equation is: , in For process noise, For control input; The weight update formula for particle filtering is: , in, For the first i The weight of each particle, The likelihood function is based on the measurement data, and the source term estimation is optimized by resampling.

[0010] Preferably, the ventilation adaptive control module is used to: generate a phased ventilation strategy based on the inverted total output and attenuation parameters, including ventilation volume, fan frequency, duct outlet position and operation access time; and send control commands to the variable frequency fan controller; The formula for adjusting air volume is: , in It is the real-time air volume. The current concentration, For safe concentration limits, This is the proportionality coefficient for the fan frequency.

[0011] Preferably, it also includes: a closed-loop verification subsystem, used to update the parameters of the one-dimensional convection-diffusion discrete model of the segmented control body by comparing the concentration decay data after ventilation with the model's expected value, including at least one of the diffusion coefficient, effective air volume correction coefficient, or dust decay parameter.

[0012] An adaptive ventilation control method for high-altitude tunnel construction, employing the aforementioned adaptive ventilation control system for high-altitude tunnel construction, includes the following steps: Step S1: Equipment deployment and positioning. Deploy movable measuring point units and movable fixed measuring point units within a 300m range of the working face, and collect the mileage position of the measuring points. Step S2: Data acquisition and transmission. Collect dust concentration, CO concentration and environmental parameters, and send the monitoring data to the data processing and source term inversion module via wireless communication. Step S3: Data Inversion and Source Term Estimation. The data processing and source term inversion module, based on the concentration changes and location of each monitoring point and the one-dimensional convection-dispersion discrete model of the segmented control volume, inverts the time-varying release rate and total output of blasting pollutants. Step S4: Adaptive generation of ventilation strategy. The ventilation strategy is automatically adjusted based on the inversion results, including ventilation volume, fan frequency, duct outlet location and work access time. Step S5: System feedback and optimization. After actual ventilation is performed, compare the actual concentration change data with the expected value and correct the model parameters to achieve closed-loop control.

[0013] Preferably, in step S3: Kalman filtering or particle filtering algorithms are used to filter out noise from the data, wherein particle filtering improves the inversion accuracy through weight updates and resampling. Preferably, in step S4: Ventilation volume according to formula The ventilation time is dynamically adjusted and set in stages according to the total amount of pollution sources.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention utilizes a movable measuring point unit that maintains a relative position to the tunnel face, enabling the acquisition of high-quality data in the tunnel face region (the core area for pollutant diffusion). This significantly improves the accuracy and stability of blast source term inversion and overcomes the problem of insufficient representativeness of fixed measuring point data under high altitude and low pressure conditions.

[0015] This invention directly uses the total dust and CO production obtained from the source term inversion based on the physical model for ventilation control, realizing the transformation from "experience-driven" to "data and model-driven". It can adaptively generate the optimal ventilation strategy and avoid over-ventilation or inefficient ventilation.

[0016] This invention optimizes ventilation strategies, precisely controls ventilation volume and duration, reduces energy consumption from excessive ventilation, lowers operating costs for tunnel construction, improves work efficiency, and reduces construction safety hazards. Furthermore, real-time alarm and emergency response mechanisms significantly enhance the safety of the construction environment. It can be expanded and adjusted according to the actual needs of tunnel construction to adapt to ventilation requirements under different construction conditions.

[0017] This invention employs a closed-loop verification mechanism to enable the system to adapt to changes in tunnel construction conditions, such as mileage advancement and environmental parameter fluctuations, thus ensuring long-term operational reliability. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the measurement point arrangement in the system of the present invention; Figure 3 This is a flowchart of data inversion and ventilation strategy generation; Figure 4 This is a schematic diagram of the adaptive control and feedback mechanism for ventilation strategies. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0021] An adaptive ventilation control system for high-altitude tunnel construction, such as Figures 1-4 As shown, it includes: Mobile equipment and monitoring subsystems are used to deploy monitoring equipment that can move with the tunnel excavation to collect data on dust concentration, CO concentration, air pressure, temperature and humidity, and the location of measuring points. The ventilation condition acquisition subsystem communicates with the data processing and source term inversion module to acquire fan frequency, speed, operating status or key section wind speed in order to determine ventilation flow rate. The data processing and source term inversion module is used to construct a one-dimensional convection-dispersion discrete model of the segmented control volume based on monitoring data, and to invert the time-varying release rate and total output of blasting pollutants. The ventilation adaptive control module is used to generate a ventilation strategy based on the inversion results, including phased ventilation volume, fan frequency, and operation access time, and then distribute the ventilation strategy to the variable frequency fan controller. The user interface and alarm module are used to display real-time data, ventilation strategies, and trigger alarms when standards are exceeded.

[0022] Specifically, the mobile device and monitoring subsystem are used to deploy monitoring points within the tunnel; including: a movable measuring point unit and a movable fixed measuring point unit; the movable measuring point unit is set on a trestle, trolley, or combination thereof within 300 m behind the tunnel face, advancing with the tunnel excavation and maintaining a relatively stable distance from the tunnel face, for collecting dust concentration, CO concentration, air pressure, temperature, and humidity data, and obtaining its own mileage location information; the movable fixed measuring point unit is set at cross passages, passing tunnels, or stable support locations, and can periodically move forward and recalibrate its mileage location during tunnel advancement, collecting dust concentration, CO concentration, air pressure, temperature, and humidity data. Each measuring point unit is equipped with a dust monitor, a CO monitor, a temperature and humidity sensor, and an air pressure sensor.

[0023] The mobile device and monitoring subsystem also include a positioning module and a data communication module. The positioning module uses at least one of UWB positioning, wheel speed odometer, RFID tag or inertial navigation IMU to acquire the mileage position of the measuring point in real time. The data communication module is used to send the monitoring data to the data processing and source term inversion module via wireless transmission.

[0024] Preferably, the data processing and source term inversion module is used to receive and process monitoring data, and to invert the time-varying release rate of dust and CO for each blast and calculate the total output based on the one-dimensional convection-dispersion discrete model of the segmented control body within the range from the working face to the preset distance, using the concentration observation data of the servo-movable measuring point unit and the movable fixed measuring point unit and the ventilation flow constraint, through constraint optimization or state estimation algorithms (such as Kalman filtering, particle filtering), and identify the equivalent attenuation parameter of dust settling or adhesion.

[0025] Inversion Algorithm Selection and Parameter Setting: Kalman filtering is used to filter and correct noise in sensor data. The state variables of the Kalman filter form a state vector: State variables include pollutant concentration C(t), wind speed V(t), and pollutant source release rate S(t); The state equation is: Where w(t) represents process noise, and u(t) represents control input (such as wind speed, temperature, and humidity). By optimizing the Kalman gain matrix, the state estimation error is minimized after each update.

[0026] Particle filtering handles nonlinear and noisy conditions, and can better capture complex changes in pollution sources. The weight update method for particle filtering is as follows: in, Let be the weight of the i-th particle. This is the likelihood function based on the measurement data. Through multiple resampling operations, an accurate estimate of the inversion source term is obtained.

[0027] Preferably, the ventilation adaptive control module is used to: automatically generate a phased ventilation strategy based on the inverted total dust and CO production and attenuation parameters, including ventilation volume, fan frequency, duct outlet position, and operation access time, and send control commands to the variable frequency fan controller.

[0028] The formula for adjusting air volume is: Where Q(t) is the real-time air volume and C(t) is the current concentration. K represents the safe concentration limit, and K is the proportionality coefficient of the fan frequency.

[0029] The ventilation strategy incorporates optimized delay and airflow distribution, adjusting fan start / stop and fan speed based on inversion results. For example, if the estimated pollution source release is too high, the system will prioritize increasing airflow and maintaining efficient operation of ventilation equipment to ensure that pollution concentration in the construction area drops to a safe level as quickly as possible.

[0030] The user interface and alarm module provide a real-time monitoring interface that displays concentration data, source term estimation results, and ventilation strategies at various monitoring points within the tunnel. When the monitoring data exceeds safety limits, an alarm is automatically triggered and an emergency ventilation procedure is initiated.

[0031] Preferably, the system also includes a closed-loop verification subsystem, used to compare the monitored concentration change data with the model's expected values ​​after the ventilation strategy is implemented, to correct model parameters such as the dispersion coefficient, effective air volume correction coefficient, or dust attenuation parameters, and to optimize the ventilation strategy after the next round of blasting. Continuous optimization of the ventilation strategy through the closed-loop control system ensures air quality and operational safety during tunnel construction.

[0032] An adaptive ventilation control method for high-altitude tunnel construction, employing the aforementioned adaptive ventilation control system for high-altitude tunnel construction, includes the following steps: Step S1: Equipment Deployment and Positioning. During tunnel excavation, movable and fixed measuring point units are deployed. Movable measuring point units are positioned on trestle bridges, trolleys, or other equipment within 300m behind the tunnel face, maintaining a relatively stable distance from the face. Fixed measuring point units are deployed in cross passages, passing tunnels, or stable support locations, and are periodically moved forward and their mileage recalibrated. All measuring points are equipped with monitoring devices such as dust sensors, CO sensors, temperature and humidity sensors, and barometric pressure sensors. UWB, wheel speed gauges, and RFID technologies are used for equipment positioning to ensure real-time location information for each monitoring point.

[0033] Step S2: Data Acquisition and Transmission. After the blasting operation is completed, dust concentration, CO concentration and environmental parameters are collected at each measuring point. The monitoring data is sent to the data processing and source term inversion module via wireless communication. During the data transmission process, data compression and error correction technologies are used to improve the efficiency and accuracy of data transmission.

[0034] Step S3: Data Inversion and Source Term Estimation. The data processing and source term inversion module, based on the concentration changes and location of each monitoring point and using a one-dimensional convection-dispersion discrete model of the segmented control volume, inverts the time-varying release rate and total yield of blasting pollutants. Kalman filtering or particle filtering algorithms are used to process the data, remove noise, and optimize the source term inversion results.

[0035] Step S4: Adaptive generation of ventilation strategy. Based on the total dust and CO production obtained from the inversion and the preset safe concentration threshold, the ventilation strategy is automatically generated, including ventilation volume, fan frequency, duct outlet location and operation access time; the ventilation strategy is adjusted according to the actual monitoring data to ensure that the air volume and ventilation time match the changes in the pollution source.

[0036] Step S5: Closed-loop verification and optimization. After actual ventilation is implemented, the monitored concentration change data is compared with the model's predicted values ​​to correct model parameters and optimize the ventilation plan after the next round of blasting. Closed-loop verification continuously optimizes the ventilation strategy to ensure air quality and operational safety during tunnel construction.

[0037] Of course, the present invention may have other various embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art can make various corresponding changes and modifications according to the present invention, but these corresponding changes and modifications should all fall within the protection scope of the appended claims.

Claims

1. A ventilation adaptive control system for high altitude tunnel construction, characterized by, Comprise: Mobile device and monitoring subsystem, for laying monitoring devices that can move with tunnel excavation, collecting dust concentration, CO concentration, air pressure, temperature and humidity data and measuring point mileage position; Ventilation working condition collection subsystem, in communication connection with data processing and source term inversion module, for obtaining fan frequency, rotating speed, running state or key section wind speed to determine ventilation flow; Data processing and source term inversion module, for constructing segmented control body one-dimensional convection-dispersion discrete model based on monitoring data, inverting time-varying release rate and total yield of blasting pollutants; Ventilation adaptive control module, for generating ventilation strategy of staged ventilation air volume, fan frequency and operation access time according to inversion result, and issuing ventilation strategy to variable frequency fan controller; User interface and alarm module, for displaying real-time data, ventilation strategy and triggering alarm when exceeding standard.

2. The ventilation adaptive control system for high altitude tunneling of claim 1, wherein, The mobile device and monitoring subsystem comprises: Movable measuring point unit, laid on the trestle or trolley within 300m of the working face and advancing with tunnel excavation, maintaining a relatively stable distance from the working face; Movable fixed measuring point unit, laid in the cross aisle, car avoidance hole or supporting stable position, periodically moving forward and recalibrating the mileage position; Each measuring point unit is equipped with a dust monitor, a CO monitor, a temperature and humidity sensor, and an air pressure sensor.

3. The ventilation adaptive control system for high altitude tunneling of claim 2, wherein, The mobile device and monitoring subsystem further comprises: Positioning module, using at least one of UWB positioning, wheel speed odometer, RFID tag or inertial navigation IMU, for real-time acquisition of measuring point mileage position; Data communication module, for sending monitoring data to the data processing and source term inversion module through wireless transmission.

4. The ventilation adaptive control system for high altitude tunneling of claim 1, wherein, The data processing and source term inversion module is used for: based on the one-dimensional convection-dispersion discrete model of the segmented control body within a preset distance range from the working face, using the concentration observation data of each monitoring point and the ventilation flow constraint, inverting the time-varying release rate of dust and CO for each blasting and calculating the total yield, and identifying the equivalent attenuation parameters of dust settlement or adhesion.

5. The ventilation adaptive control system for high altitude tunneling of claim 4, wherein, The data processing and source term inversion module uses Kalman filtering or particle filtering algorithm to filter noise of the monitoring data; The state variables of Kalman filtering constitute a state vector: The state variables include pollutant concentration C(t), wind speed V(t) and pollutant source release rate S(t), The state equation is: ， wherein is process noise, is a control input; The weight update formula of particle filtering is: ， wherein, is the weight of the i th particle, is the likelihood function based on the measurement data, which optimizes the source term estimate by re-sampling.

6. The ventilation adaptive control system for high altitude tunneling of claim 1, wherein, The ventilation adaptive control module is used for: generating staged ventilation strategy including ventilation air volume, fan frequency, air duct opening position and operation access time according to the total yield and attenuation parameters of the inversion result; and issuing control instructions to the variable frequency fan controller; The air volume adjustment formula is: ， wherein is the real-time wind volume, is the current concentration, is the safety concentration limit, is the proportional coefficient of the fan frequency.

7. The ventilation adaptive control system for high altitude tunneling of claim 1, wherein, Further comprising: Closed-loop checking subsystem, for comparing concentration attenuation data after ventilation execution with model expected values, updating one-dimensional convection-dispersion discrete model parameters of segmented control body, including at least one of dispersion coefficient, effective air volume correction coefficient or dust attenuation parameter.

8. A ventilation self-adaptive control method for high-altitude tunnel construction, using the ventilation self-adaptive control system for high-altitude tunnel construction according to any one of claims 1-7, characterized in that, The steps comprise: Step S1: device layout and positioning, laying movable measuring point unit and movable fixed measuring point unit, and collecting measuring point mileage position; Step S2: Data acquisition and transmission, collect dust concentration, CO concentration and environmental parameters, and send monitoring data to the data processing and source inversion module through wireless communication; Step S3: Data inversion and source estimation, the data processing and source inversion module inverses the time-varying release rate and total yield of the blasting pollutant based on the concentration change and location of each monitoring point and the one-dimensional convection-dispersion discrete model of the segmented control body; Step S4: Adaptive generation of ventilation strategy, automatically adjust the ventilation strategy according to the inversion results, including ventilation volume, fan frequency, air duct opening position and operation access time; Step S5: System feedback and optimization, after the actual ventilation is executed, compare the actual concentration change data with the expected value, and correct the model parameters to realize closed-loop control.

9. The method for adaptive control of ventilation for high altitude tunneling of claim 8, wherein, In step S3: Kalman filtering or particle filtering algorithm is used to filter noise from the data, and particle filtering improves the inversion accuracy through weight update and resampling.

10. The method for adaptive ventilation control for high altitude tunneling of claim 8, wherein, In step S4: Ventilation air volume according to the formula Dynamic adjustment, and according to the total amount of pollution sources to set the delay of phased ventilation.

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