High-performance ventilation system based on digital model design

Abstract

The invention belongs to the technical field of tunnel ventilation, and particularly relates to a high-performance ventilation system based on digital model design, and the ventilation system comprises a data analysis and processing module which carries out the real-time processing and analysis of data through a digital model, and predicts the ventilation demands. And the decision and control module controls start and stop of a fan and the air volume according to the analysis result to ensure the air quality. According to the high-performance ventilation system based on the digital model design, a plurality of modules such as a data acquisition module, an analysis and processing module and a decision control module are integrated, so that the system can dynamically adjust according to real-time data such as air quality, traffic flow and vehicle speed in a tunnel, and the ventilation flow and the running state of a fan are optimized; the intelligent adjustment can ensure that the air quality in the tunnel is always in a safe range, energy waste caused by excessive ventilation is avoided, the air quality is also improved, and the safety and comfort of personnel are guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tunnel ventilation, in particular to a high-performance ventilation system based on digital model design. BACKGROUND

[0002] Compared with the straight-through tunnel with single-point access, the urban underground road with multi-point access is influenced by the structure characteristics of the diverging and merging ramps, and the characteristics of air flow in the tunnel are much more complex than that of the urban underground road with single-point access. The urban underground road form and network linkage dynamic effect have significant changes, and the safe operation of the urban underground road has a very important influence on the safety of urban road traffic. Due to the setting of multiple ramps, the underground road has multiple nodes, multiple branches, and complex air flow organization, which has a great influence on the ventilation environment of the underground road. The longitudinal ventilation based on the piston effect and the non-blocking working condition smoke exhaust system are affected.

[0003] At present, the existing underground interchange technology mainly focuses on structure design and construction technology, and the research on the ventilation system is relatively lacking. Although many studies have proposed ventilation network analysis methods in underground interchange, they are still in the exploratory stage. Most of the literature relies on simple engineering structures, which are composed of a main tunnel and several straight-through ground ramps. The wind flow intersection points and inlets and outlets in the wind network are few, and the wind network structure is relatively simple. The research results are not suitable for complex underground interchange ventilation design. Therefore, how to develop a reasonable ventilation control scheme under different natural wind and traffic flow conditions and establish a digital model of the tunnel ventilation system to realize the on-demand flow of air flow in each tunnel section has become a key problem to be solved in the operation of underground interchange ventilation. SUMMARY

[0004] Based on the above technical problems, the present application proposes a high-performance ventilation system based on digital model design.

[0005] The high-performance ventilation system based on digital model design proposed by the present application comprises: a data collection module responsible for collecting vehicle data, air quality data and flow population information from the tunnel; a data analysis and processing module for real-time processing and analysis of the data using a digital model to predict ventilation demand; a decision and control module for controlling the start and stop of the fan, the air volume, and ensuring the air quality according to the analysis results, i.e. for adjusting the working state of the ventilation equipment including fan speed, air volume distribution, etc. according to the control signal provided by the data analysis and processing module through a control algorithm; a feedback and optimization module for feedback and optimization according to actual conditions and maintenance data; an environment simulation and prediction module for simulating and predicting the air quality and traffic flow in the tunnel in the future based on the digital model and historical data to provide optimization suggestions for the decision module.

[0006] Preferably, the data acquisition module includes vehicle type identification: combined with the laser radar installed at the entrance and middle of the tunnel and the high-definition camera, the vehicles in the tunnel are classified and identified by computer vision technology, and the speed is measured by radar technology, while the sensor equipped on each vehicle obtains the speed data through the lane monitoring system of the tunnel.

[0007] Passenger flow identification: through high-definition cameras and infrared sensors, the number of vehicles passing through the tunnel is identified, and the number of passengers is estimated.

[0008] Air quality identification: through 、 、 Gas concentration sensors monitor the air quality in the tunnel in real time and give pollutant concentration data, and then through the temperature and humidity sensors, the temperature and humidity in the tunnel are monitored in real time, and the data is used to evaluate the influence of air flow and exhaust demand.

[0009] Through the above technical solutions, the vehicle type is mainly divided into light vehicles, heavy vehicles and other special vehicles, and the vehicle type information identified will be used to analyze the ventilation demand.

[0010] Preferably, the data analysis and processing module includes,

[0011] a1, pollutant emission and vehicle speed relationship model: according to the combination of different types of vehicles and vehicle speed, a pollutant emission model is constructed, so as to control the ventilation flow to discharge these gases, the pollutant emission model is: ,

[0012] wherein, represents the amount of pollutants emitted by a single vehicle, and are coefficients related to the type of vehicle, to adjust the influence of vehicle speed on pollutant emission, represents the baseline pollutant emission amount specific to the type of vehicle, for example, if we consider two vehicle types, light vehicles and heavy vehicles , the formula can be expressed as: , ;

[0013] wherein, and are the speed coefficients of the respective vehicle types, and are the baseline emissions of the respective vehicle types.

[0014] a2, Accumulation effect of emissions: The emissions of vehicles are not only related to the vehicle speed, but also closely related to the accumulation effect of emissions, the factors of the accumulation effect include vehicle flow: the larger the vehicle flow, the higher the total amount of pollutant emissions, vehicle density: when the vehicle density is high, the amount of pollutant emissions per unit time increases, airflow effect: the airflow in the tunnel can affect the diffusion and accumulation of pollutants, and the airflow is weak at low speed, and the pollutants are not easy to be discharged quickly, and the pollutants discharged at low speed are easy to accumulate in the tunnel, thereby increasing the concentration of pollutants, and the formula of the accumulation effect is: ,

[0015] wherein, is the concentration of pollutants per unit volume, is the number of vehicles passing per unit time, is the volume of the tunnel, the formula reflects the influence of vehicle speed, vehicle flow, tunnel volume and other factors on the concentration of pollutants, the larger the vehicle flow and the smaller the tunnel volume, the higher the concentration of pollutants.

[0016] a3, Calculate the ventilation volume: The calculation of the ventilation volume is based on the concentration of emissions, and the formula is:

[0017] ; wherein, is the required ventilation volume, is a coefficient related to the efficiency of the ventilation equipment and the structure of the tunnel.

[0018] Through the above technical scheme, the data analysis and processing module considers various factors such as pollutant emissions, vehicle speed, traffic density, personnel flow, and automatically adjusts to optimize the air quality in the tunnel and ensure personnel safety.

[0019] Preferably, the decision and control module comprises, b1, fan start-stop control: it is mainly based on the real-time changes of vehicle speed, pollutant concentration and vehicle flow to adjust, the specific control logic is: when the concentration of pollutants exceeds the set threshold, that is, when the accumulation effect of emissions causes the concentration of pollutants to reach a certain critical value, the fan will be started; on the contrary, when the concentration is lower than the threshold, the fan is turned off.

[0020] b2, fan flow regulation: fan flow regulation depends on the concentration of pollutants in the tunnel, vehicle speed and vehicle flow factors, and the fan flow is accurately adjusted through a variable frequency drive device, that is, when the concentration of pollutants is too high, the flow of the fan is increased, thereby quickly discharging the pollutants, and when the vehicle speed is low, the accumulation effect of pollutants is significant, and the flow of the fan is increased to speed up the emission of pollutants, and the formula of the fan flow regulation is:

[0021] ,

[0022] wherein, and are adjustment coefficients related to the efficiency of the fan and the volume of the tunnel; for example, assuming: pollutant concentration = 150 mg / m³, vehicle flow = 1000 vehicles / hour, = 2, = 0.1, then the fan flow is: In this way, the flow adjustment of the fan can be dynamically adjusted according to the real-time data changes in the tunnel, ensuring air quality and saving energy.

[0023] b3, regional ventilation control: by setting local fans at different positions in the tunnel, adjusting the air volume in specific areas according to real-time pollutant concentration and vehicle speed data, the specific control method includes: entrance area: when the vehicle flow is greater than the set threshold and the pollutant concentration is higher than the set high threshold, increase the air volume, otherwise, maintain the air volume or make moderate adjustments.

[0024] Middle section area: when the vehicle flow is less than the set threshold and the pollutant concentration is lower than the set low threshold, reduce the air volume, otherwise, adjust the air volume according to the vehicle flow and pollutant concentration.

[0025] End area: dynamically adjust the air volume to avoid accumulation or excessive emission of pollutants, that is, flexibly control according to the instability of vehicle flow and pollutant concentration.

[0026] b4, dynamic adjustment and adaptive control: in order to cope with the continuous changes of the tunnel environment, the control module adopts an adaptive control algorithm, that is, a PID control algorithm is used to continuously monitor the vehicle speed, flow, and pollutant concentration parameters in the tunnel, and adjust the working state of the fan in real time according to environmental changes. The calculation formula of the PID control algorithm is:

[0027] where, is the control parameter of the fan, is the error, that is, the difference between the set pollutant concentration and the current pollutant concentration, is the proportional gain, is the integral gain, is the differential gain, is the error function, indicates the integral of the error function from time 0 to time t, which calculates the cumulative total of the error in this period of time.

[0028] When using the PID control algorithm to adjust the air volume of the fan, the input is:

[0029] set pollutant concentration: that is, the set safe pollutant concentration.

[0030] Actual pollutant concentration: That is, the actual measured pollutant concentration in the tunnel.

[0031] Error calculation: .

[0032] PID controller output: calculate the control parameters of the fan according to the error and PID formula , so as to control the air volume or start / stop state of the fan. Specifically, the error is large, indicating that the pollutant concentration deviates far from the target, and the PID controller will output a larger air volume or a strong fan start signal, The error is small, and the air volume or state of the fan will tend to be stable.

[0033] Through the above technical solution, through the PID control algorithm, the automatic adjustment of the tunnel fan can be realized, and the pollutant concentration is always ensured to be in the safe range, thereby improving the efficiency and stability of the tunnel ventilation system.

[0034] Preferably, the feedback and optimization module comprises:

[0035] c1, optimization scheduling module: the main goal is to schedule the fan through ant colony algorithm, so as to determine the best working mode, running time and intensity of the fan, that is, to simulate the process of ants finding food, calculate the scheduling path of the fan, and optimize the time period of fan start, stop and operation. In the running process, the ant colony algorithm shares optimization experience through pheromone transmission, so as to accelerate the search for the optimal solution. By setting the pheromone evaporation rate, premature falling into local optimal solution is avoided. The ant colony algorithm can optimize multiple targets at the same time, such as reducing energy consumption, reducing fan failure rate, improving ventilation effect, etc., to ensure the overall optimization of the system efficiency.

[0036] c2, digital model module: the core task is to simulate and analyze the ventilation system by using computational fluid dynamics, i.e. CFD technology, so as to optimize the design of the ventilation system.

[0037] Through the above technical solution, the digital model module optimizes the design and operation of the ventilation system through CFD analysis model, so as to ensure that the system can work efficiently under various environmental conditions. The optimized scheduling strategy and design scheme are implemented and continuously optimized through the feedback mechanism.

[0038] Preferably, the environment simulation and prediction module comprises:

[0039] d1, data collection and fusion: collect real-time environmental data in the tunnel, including air quality, traffic flow, temperature and humidity, integrate past operation data, including historical air quality and traffic flow data, provide training samples for deep learning model, clean, denoise and normalize the data to ensure accuracy and consistency, suitable for deep learning model input.

[0040] d2, deep learning model: use LSTM network to model time series data to capture long-term dependencies and predict future air quality and traffic flow, convert and process input data appropriately to extract useful features, ensure that the model learns the time series regularity of the data, train the LSTM model with historical data, continuously optimize network parameters to improve prediction accuracy.

[0041] d3, simulation and prediction results: predict air quality in the tunnel for a certain period of time, predict changes in traffic flow in the tunnel, accurately predict traffic and air quality requirements at different time periods.

[0042] d4, prediction analysis and optimization suggestions: based on air quality prediction results, provide optimization suggestions for ventilation system, predict periods that require enhanced ventilation, provide suggestions for traffic scheduling based on traffic flow prediction, help traffic management personnel achieve efficient diversion.

[0043] The above technical solutions provide optimization basis for ventilation and traffic management strategies, ensuring efficient operation of the tunnel while maintaining good air quality, improving safety and comfort of tunnel operation.

[0044] Preferably, the fan is a jet fan, which is installed in the entrance area, middle area and end area of the tunnel respectively, and the outer surface of the shell of the jet fan is provided with an angle adjusting mechanism, and the inside of the jet fan is provided with a linkage mechanism for controlling the angle deflection of the fan blades.

[0045] Preferably, the angle adjusting mechanism controls the angle adjustment of the whole jet fan, wherein the angle adjusting mechanism comprises a mounting bracket wrapped around the outer surface of the shell of the jet fan, the outer surface of the mounting bracket is symmetrically provided with movable joints, one end surface of the movable joint is fixedly connected with a rotating rod, the rotating rod is composed of an upper support rod and a lower support rod, and a connecting pipe rotatingly sleeved on the outer surfaces of the two support rods, the other movable joint on the free end of the rotating rod is fixedly installed on the inner wall of the tunnel through a mounting plate.

[0046] Preferably, the angle adjusting mechanism further comprises a support seat fixedly connected to the upper surface of the jet fan shell, the middle upper surface of the support seat is fixedly connected with a half gear plate, a rotating shaft is arranged above the support seat, the lower surface of the rotating shaft is fixedly connected with a U-shaped support beam, the inner top surface of the support beam is fixedly connected with a mounting block with a movable slot, and the inside of the mounting block is provided with a rack rod which is engaged with the half gear plate.

[0047] Preferably, the angle adjusting mechanism further comprises a threaded sleeve fixedly connected to the upper surface of the rack rod, the inner surface of the mounting block is rotatably connected with an adjusting screw through a bearing, the threaded sleeve is threadedly sleeved on the outer surface of the adjusting screw, the outer surface of the output shaft of a driving motor is fixedly connected with the outer surface of the adjusting screw through a shaft coupling, and the outer surface of the output shaft of the driving motor is fixedly connected with the outer surface of the adjusting screw through a shaft coupling.

[0048] Preferably, the linkage mechanism comprises a driving seat for rotatably mounting the fan blades, and the inside of the driving seat is provided with a cavity, one side surface of one of the driving seats is fixedly connected with a driving head, the cavity of the driving seat is rotatably connected with a main bevel gear through a bearing, the output shaft of the driving head is arranged to pass through two driving seats and is fixedly connected with the surface of the main bevel gear, the outer surface of the main bevel gear is annularly arranged and engaged with a slave bevel gear, and the surface of the slave bevel gear is fixedly connected with the mounting shaft corresponding to the fan blade.

[0049] The beneficial effects in the application are:

[0050] 1. By integrating data collection, analysis and processing, decision control and other modules, the system can dynamically adjust according to real-time air quality, traffic volume, vehicle speed and other data in the tunnel, optimize ventilation flow and fan operation state, and this intelligent adjustment can ensure that the air quality in the tunnel is always within a safe range, avoiding energy waste caused by excessive ventilation, improving air quality and ensuring personnel safety and comfort.

[0051] 2. By using a model based on vehicle speed, pollutant emission and accumulation effect, the required ventilation volume can be accurately calculated, and a self-adaptive control algorithm such as PID control algorithm is used to dynamically adjust the fan operation, which not only ensures the maximization of ventilation efficiency, but also optimizes the ventilation strategy for different time periods and traffic volume changes through self-adaptive control, improving the overall operation performance and stability of the system.

[0052] 3、Through the feedback and optimization module, such as using the ant colony algorithm to schedule the fan, using the CFD technology to optimize the design, etc., the system can continuously adjust the operation strategy according to real-time data and environmental changes. The introduction of the feedback mechanism enables the system to self-optimize in long-term operation, ensures the minimization of energy consumption, reduces the fan failure rate, and at the same time, keeps the ventilation effect efficient and stable.

[0053] 4、By setting the angle adjusting mechanism and the linkage mechanism, the ventilation effect of the jet fan can be adjusted. During the adjustment process, the rack rod moves horizontally in the movable slot of the mounting block, thereby driving the support seat to rotate, and driving the jet fan to make overall angle adjustment under the hoisting connection of the rotating rod, so as to accurately ventilate the target point. At the same time, the motor in the drive head drives the main bevel gears in the two drive seats to rotate, thereby making the plurality of driven bevel gears rotate, changing the deflection angle of the fan blades, adapting to the airflow in the tunnel, and making the fan blades rotate at a uniform speed to quickly adjust the ventilation in the tunnel. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 A schematic diagram of a high-performance ventilation system based on digital model design is proposed for the present application;

[0055] Figure 2 A data acquisition module flowchart of a high-performance ventilation system based on digital model design is proposed for the present application;

[0056] Figure 3 A regional ventilation control flowchart of a high-performance ventilation system based on digital model design is proposed for the present application;

[0057] Figure 4 A ventilation volume adjustment logic diagram of a high-performance ventilation system based on digital model design is proposed for the present application;

[0058] Figure 5 An ant colony algorithm diagram of a high-performance ventilation system based on digital model design is proposed for the present application;

[0059] Figure 6 A tunnel layout diagram of a high-performance ventilation system based on digital model design is proposed for the present application;

[0060] Figure 7 A jet fan structure perspective view of a high-performance ventilation system based on digital model design is proposed for the present application;

[0061] Figure 8 A rotating rod structure perspective view of a high-performance ventilation system based on digital model design is proposed for the present application;

[0062] Figure 9A perspective view of a support beam structure of a high-performance ventilation system based on digital model design according to the present application;

[0063] Figure 10 A perspective view of an adjusting screw structure of a high-performance ventilation system based on digital model design according to the present application;

[0064] Figure 11 A perspective view of a half gear plate structure of a high-performance ventilation system based on digital model design according to the present application;

[0065] Figure 12 A perspective view of a fan blade structure of a high-performance ventilation system based on digital model design according to the present application;

[0066] Figure 13 A perspective view of a main bevel gear structure of a high-performance ventilation system based on digital model design according to the present application.

[0067] In the figure: 1, jet fan; 11, fan blade; 2, angle adjusting mechanism; 21, mounting bracket; 22, movable knot; 23, rotating rod; 24, support seat; 25, half gear plate; 26, rotating shaft; 27, support beam; 28, mounting block; 29, rack rod; 30, threaded sleeve; 31, adjusting screw; 32, driving motor; 4, linkage mechanism; 41, driving seat; 42, driving head; 43, main bevel gear; 44, driven bevel gear. DETAILED DESCRIPTION

[0068] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all embodiments.

[0069] Referring to Figure 1 The ventilation system based on digital model design according to the present application comprises,

[0070] Data acquisition module: responsible for collecting vehicle data, air quality data, and flow number information from the tunnel.

[0071] Data analysis and processing module: using digital model to process and analyze data in real time, and predicting ventilation demand.

[0072] Decision and control module: according to the analysis result, controlling the start and stop of the fan, air volume, and ensuring air quality, that is, according to the control signal provided by the data analysis and processing module, adjusting the working state of the ventilation equipment through the control algorithm, including fan speed, air volume distribution, etc.

[0073] Feedback and optimization module: feedback and optimization according to actual situation and maintenance data.

[0074] Environmental simulation and prediction module: based on digital models and historical data, simulate and predict the air quality in the tunnel and the traffic flow in the future period of time, provide optimization suggestions for the decision-making module.

[0075] By integrating data collection, analysis and processing, decision control and other modules, the system can dynamically adjust according to real-time air quality, traffic flow, vehicle speed and other data in the tunnel, optimize ventilation flow and fan operation state. This intelligent adjustment can ensure that the air quality in the tunnel is always within a safe range, avoiding energy waste caused by excessive ventilation, and improving air quality, ensuring the safety and comfort of personnel.

[0076] Referring to Figure 2 , the data collection module includes,

[0077] Vehicle type identification: combined with laser radar and high-definition camera installed at the entrance and middle of the tunnel, computer vision technology is used to classify and identify vehicles in the tunnel, and radar technology is used to measure vehicle speed, while sensors equipped on each vehicle are used to obtain speed data through the tunnel lane monitoring system.

[0078] Flowing people identification: through high-definition cameras and infrared sensors, the number of vehicles passing through the tunnel is identified, and the number of flowing people is estimated.

[0079] Air quality identification: through , , Gas concentration sensors monitor the air quality in the tunnel in real time and give pollutant concentration data, and then through temperature and humidity sensors, the temperature and humidity in the tunnel are monitored in real time, and the data is used to evaluate the influence of air flow and exhaust demand.

[0080] Vehicle types are mainly divided into light vehicles, heavy vehicles and other special vehicles, and the identified vehicle type information will be used to analyze ventilation demand, especially when there are more heavy vehicles such as trucks, stronger ventilation capacity may be needed to exhaust exhaust gas, and vehicle speed information is crucial to the ventilation system, because different speeds will affect pollutant emissions, and flowing people identification will help evaluate the load of the ventilation system, especially during high traffic periods.

[0081] The data analysis and processing module includes, a1, pollutant emission and vehicle speed relationship model: according to the combination of different types of vehicles and vehicle speed, a pollutant emission model is constructed to control the ventilation flow to discharge these gases, the pollutant emission model is: .

[0082] Where, represents the amount of pollutants emitted by a single vehicle, and is a coefficient related to the vehicle type, to adjust the impact of vehicle speed on pollutant emissions, represents the baseline pollutant emissions specific to the vehicle type, for example, if we consider two vehicle types, light vehicles and heavy vehicles , the formula can be expressed as: , ;

[0083] where, and are the speed coefficients for each vehicle type, and are the baseline emissions for each vehicle type;

[0084] a2, the accumulation effect of emissions: the emissions of a vehicle are not only related to the speed, but also closely related to the accumulation effect of emissions, the factors of the accumulation effect include vehicle flow: the larger the vehicle flow, the higher the total amount of pollutant emissions, vehicle density: when the vehicle density is high, the amount of pollutant emissions per unit of time increases, airflow effect: the airflow in the tunnel can affect the diffusion and accumulation of pollutants, the airflow is weak at low speed, and the pollutants are not easy to be discharged quickly, the accumulated pollutants in the tunnel increase the concentration of pollutants when driving at low speed, the formula of the accumulation effect is: ,

[0085] where, is the concentration of pollutants per unit volume, is the number of vehicles passing per unit of time, is the volume of the tunnel, this formula reflects the influence of factors such as vehicle speed, vehicle flow, and tunnel volume on pollutant concentration, the larger the vehicle flow and the smaller the tunnel volume, the higher the concentration of pollutants.

[0086] a3, calculate the ventilation volume: the calculation of ventilation volume is based on the concentration of emissions, the formula is:

[0087] where, is the required ventilation volume, is a coefficient related to the efficiency of ventilation equipment and the structure of the tunnel.

[0088] The data analysis and processing module considers multiple factors such as pollutant emissions, vehicle speed, traffic density, and personnel flow, uses machine learning and pollutant emission models to predict the ventilation demand of the tunnel, this module can provide real-time ventilation volume prediction, provide scientific basis for the tunnel ventilation system, and optimize the air quality in the tunnel through automatic adjustment to ensure personnel safety.

[0089] The decision and control module includes, referring to Figure 4b1, fan start-stop control: it is mainly based on the real-time changes of vehicle speed, pollutant concentration, and vehicle flow to adjust, and the specific control logic is: when the pollutant concentration exceeds the set threshold, that is, when the accumulation effect of emissions causes the pollutant concentration to reach a certain critical value, the fan will be started; on the contrary, when the concentration is lower than the threshold, the fan is turned off.

[0090] b2, fan flow regulation: fan flow regulation depends on the pollutant concentration, vehicle speed, and vehicle flow factors in the tunnel, and the fan flow is accurately adjusted by the frequency drive device, that is, when the pollutant concentration is too high, the fan flow is increased to quickly discharge pollutants, and when the vehicle speed is low, the pollutant accumulation effect is significant, and the fan flow is increased to speed up the discharge of pollutants. The fan flow regulation formula is:

[0091] ,

[0092] wherein, and are adjustment coefficients related to fan efficiency and tunnel volume; for example, assuming that the pollutant concentration = 150 mg / m³, the vehicle flow = 1000 vehicles / hour, = 2, = 0.1, then the fan flow is:

[0093] In this way, the flow regulation of the fan can be dynamically adjusted according to the real-time data changes in the tunnel, which can ensure air quality and save energy.

[0094] Referring to Figure 3 b3, regional ventilation control: by setting local fans at different positions in the tunnel, the air volume of a specific area is adjusted according to real-time pollutant concentration and vehicle speed data. The specific control method includes: in the entrance area, when the vehicle flow is greater than the set threshold and the pollutant concentration is higher than the set high threshold, the air volume is increased, otherwise, the air volume is maintained or adjusted moderately.

[0095] In the middle section area, when the vehicle flow is less than the set threshold and the pollutant concentration is lower than the set low threshold, the air volume is reduced, otherwise, the air volume is adjusted according to the vehicle flow and the pollutant concentration.

[0096] In the terminal area, the air volume is dynamically adjusted to avoid pollutant accumulation or excessive discharge, that is, flexible control is performed according to the instability of vehicle flow and pollutant concentration.

[0097] b4. Dynamic adjustment and adaptive control: In order to cope with the changing tunnel environment, the control module adopts an adaptive control algorithm, i.e. a PID control algorithm that continuously monitors the vehicle speed, flow, and pollutant concentration parameters in the tunnel, and adjusts the working state of the fan in real time according to environmental changes. The calculation formula of the PID control algorithm is:

[0098] wherein, is the control parameter of the fan, is the error, i.e. the difference between the set pollutant concentration and the current pollutant concentration, is the proportional gain, is the integral gain, is the derivative gain, is the error function, represents the integral of the error function from time 0 to time t, which calculates the cumulative sum of the error in this period of time.

[0099] When using the PID control algorithm to adjust the air volume of the fan, the input is:

[0100] Set pollutant concentration: i.e. the set safe pollutant concentration.

[0101] Actual pollutant concentration: i.e. the actual measured pollutant concentration in the tunnel.

[0102] Error calculation: .

[0103] PID controller output: Calculate the control parameter of the fan according to the error and the PID formula , which is used to control the air volume or start / stop state of the fan. Specifically, the error is large, indicating that the pollutant concentration deviates far from the target, and the PID controller will output a large air volume or a strong fan start signal, the error is small, and the air volume or state of the fan will tend to be stable.

[0104] The control system also needs to consider the real-time vehicle speed and vehicle flow. The lower the vehicle speed, the more significant the accumulation effect, and the system should start and stop the fan more frequently to ensure ventilation effect. The fan start-stop frequency is higher at low vehicle speed or high flow. At high vehicle speed, the pollutant emission is less, and the fan start-stop frequency can be appropriately reduced. At the same time, through the PID control algorithm, automatic adjustment of the tunnel fan can be realized, ensuring that the pollutant concentration is always within the safe range, thereby improving the efficiency and stability of the tunnel ventilation system.

[0105] By using a model based on vehicle speed, pollutant emission and accumulation effect, the required ventilation volume is accurately calculated, and at the same time, an adaptive control algorithm such as PID control algorithm is used to dynamically adjust the fan operation, which not only ensures the maximization of ventilation efficiency, but also optimizes the ventilation strategy for different time periods and traffic changes through adaptive control, improving the overall operation performance and stability of the system.

[0106] The feedback and optimization module includes: reference Figure 5 As shown, c1, the optimization scheduling module: the main goal is to schedule the fan through the ant colony algorithm, to determine the best working mode, running time and intensity of the fan, that is, to simulate the process of ants finding food, to calculate the scheduling path of the fan, to optimize the time period of fan start, stop and operation, in the running process, the ant colony algorithm shares optimization experience through pheromone transmission, so as to accelerate the search for the optimal solution, by setting the evaporation rate of pheromone, to avoid falling into local optimal solution too early, the ant colony algorithm can optimize multiple goals at the same time, such as reducing energy consumption, reducing fan failure rate, improving ventilation effect, etc., to ensure the overall efficiency optimization of the system.

[0107] c2, digital model module: the core task is to simulate and analyze the ventilation system by using computational fluid dynamics, that is, CFD technology, so as to optimize the design of the ventilation system.

[0108] The system continuously collects data through real-time sensors, which are fed back to the optimization scheduling module and the digital model module. The optimization scheduling module uses intelligent algorithms to process real-time data and adjusts fan operation strategies according to current environmental and demand conditions to ensure the lowest energy consumption and the highest ventilation efficiency. The digital model module optimizes the design and operation of the ventilation system through CFD analysis model to ensure that the system can work efficiently under various environmental conditions. The optimized scheduling strategy and design scheme are implemented and continuously optimized through feedback mechanism. For example, if the pollutant concentration in a certain area is high, the system will automatically adjust the working mode of the fan to better meet the ventilation requirements.

[0109] The environmental simulation and prediction module includes: d1, data collection and fusion: collect real-time environmental data in the tunnel, including air quality, traffic flow, temperature and humidity, integrate past operation data, including historical air quality and traffic flow data, provide training samples for deep learning model, clean, denoise and normalize the data to ensure the accuracy and consistency of the data, suitable for deep learning model input.

[0110] d2, Deep learning model: LSTM network is used to model time series data to capture long-term dependencies and predict future air quality and traffic volume. The input data is properly converted and processed to extract useful features, ensuring that the model learns the time series regularity of the data. The LSTM model is trained on historical data to continuously optimize network parameters and improve prediction accuracy.

[0111] d3, Simulation and prediction results: Predict the air quality in the tunnel for a certain period of time and predict the changes in traffic volume in the tunnel. According to the traffic and air quality requirements of different time periods, accurate predictions are made.

[0112] d4, Prediction analysis and optimization suggestions: Based on the air quality prediction results, provide optimization suggestions for the ventilation system, predict the time period when ventilation needs to be strengthened, and provide suggestions for traffic scheduling based on traffic volume prediction to help traffic management personnel achieve efficient diversion.

[0113] The environmental simulation and prediction module uses deep learning algorithms, especially LSTM models, to accurately predict changes in air quality and traffic volume in the tunnel by combining real-time and historical data. These prediction results not only help to understand the environmental conditions in the tunnel in a timely manner, but also provide optimization basis for ventilation and traffic management strategies, ensuring that the tunnel operates efficiently while maintaining good air quality, improving the safety and comfort of tunnel operation.

[0114] Referring to Figures 7-13 To control regional fans to quickly meet their ventilation needs, the fan is a jet fan 1 installed at the entrance, middle, and end regions of the tunnel. The outer surface of the shell of the jet fan 1 is provided with an angle adjustment mechanism 2, and the inside of the jet fan 1 is provided with a linkage mechanism 4 for controlling the angle deflection of the fan blades 11. According to the adjustment signal, the angle adjustment mechanism 2 adjusts the installation angle of the jet fan 1 to adapt to the current tunnel conditions, and at the same time, to adjust the air volume of the jet fan 1, the linkage mechanism 4 adjusts the deflection angle of the fan blades 11 to quickly output the required air volume when rotating at high speed.

[0115] In order to adjust the angle of the jet fan 1, the angle adjusting mechanism 2 controls the whole jet fan 1 to adjust the angle, wherein the angle adjusting mechanism 2 comprises a mounting frame 21 fixedly wrapped on the outer surface of the shell of the jet fan 1, the outer surface of the mounting frame 21 is symmetrically provided with movable knots 22, one end surface of the movable knot 22 is fixedly connected with a rotating rod 23, the rotating rod 23 is composed of two sections of supporting rods and a connecting pipe rotatably sleeved on the outer surface of the two sections of supporting rods, the other movable knot 22 on the free end of the rotating rod 23 is fixedly installed on the inner wall of the tunnel through a mounting plate, the rotating rod 23 hoists the jet fan 1 with the mounting frame 21, and the connecting points are connected through the movable knots 22, that is, the connecting angles of the two ends of the rotating rod 23 can be arbitrarily adjusted, and the rotating rod 23 adopts a multi-section structure, so that the rotating rod 23 can rotate to satisfy the angle adjusting mechanism 2.

[0116] In order to drive the jet fan 1 to adjust the inclination angle and accurately ventilate towards the target point, the angle adjusting mechanism 2 further comprises a supporting seat 24 fixedly connected to the upper surface of the shell of the jet fan 1, a half gear plate 25 fixedly connected to the upper surface of the middle portion of the supporting seat 24, a rotating shaft 26 arranged above the supporting seat 24, a U-shaped supporting beam 27 fixedly connected to the lower surface of the rotating shaft 26, an installation block 28 with a movable slot fixedly connected to the inner top surface of the supporting beam 27, a rack rod 29 arranged in the installation block 28, the rack rod 29 engaged with the half gear plate 25, the half gear plate 25 driven to rotate the supporting seat 24 through the horizontal movement of the rack rod 29 in the movable slot of the installation block 28, so as to drive the whole jet fan 1 to adjust the angle under the hoisting connection of the rotating rod 23, and the driving component is installed on the inner top wall of the tunnel to control the rotation of the rotating shaft 26, so that the jet fan 1 can be driven to rotate in a limited range to adjust the ventilation direction angle, thereby meeting the ventilation demand in the tunnel.

[0117] In order to control the jet fan 1 to accurately adjust the angle, the angle adjusting mechanism 2 further comprises a threaded pipe sleeve 30 fixedly connected to the upper surface of the rack rod 29, an adjusting screw 31 rotatably connected to the inner surface of the installation block 28 through a bearing, the threaded pipe sleeve 30 threadedly sleeved on the outer surface of the adjusting screw 31, a driving motor 32 fixedly installed on the outer surface of the installation block 28, and the output shaft of the driving motor 32 fixedly connected with the outer surface of the adjusting screw 31 through a shaft coupling, so that the driving motor 32 is controlled by the decision and control module to drive the adjusting screw 31 to rotate, and then drive the threaded pipe sleeve 30 to horizontally move in the movable slot of the installation block 28, thereby driving the rack rod 29 to move to realize the deflection of the half gear plate 25.

[0118] In order to adjust the air volume of the fan blade of the jet flow fan 1, the linkage mechanism 4 comprises a driving seat 41 for rotatingly mounting the fan blade 11, and the inside of the driving seat 41 is provided with a cavity, and one side surface of one driving seat 41 is fixedly connected with a driving head 42, a main bevel gear 43 is rotatingly connected in the cavity of the driving seat 41 through a bearing, the output shaft outer surface of the driving head 42 penetrates through two driving seats 41 and is fixedly connected with the surface of the main bevel gear 43, the outer surface of the main bevel gear 43 is in mesh with a plurality of from bevel gears 44 which are arranged in an annular array, the surface of the from bevel gear 44 is fixedly connected with the mounting shaft of the corresponding fan blade 11, and the motor in the driving head 42 drives the main bevel gear 43 in the two driving seats 41 to rotate, thereby making the plurality of from bevel gears 44 rotate, changing the deflection angle of the fan blade 11, adapting to the airflow in the tunnel, and making the fan blade 11 rotate at a uniform speed to quickly adjust the ventilation in the tunnel.

[0119] By arranging the angle adjusting mechanism 2 and the linkage mechanism 4, the ventilation effect of the jet flow fan 1 can be adjusted, in the adjusting process, the rack bar 29 moves horizontally in the movable groove of the mounting block 28, thereby making the meshed half gear plate 25 drive the support seat 24 to rotate, thereby driving the jet flow fan 1 to make overall angle adjustment under the hoisting connection of the rotating rod 23, making the jet flow fan 1 accurately ventilate towards the target point, and the motor in the driving head 42 drives the main bevel gear 43 in the two driving seats 41 to rotate, thereby making the plurality of from bevel gears 44 rotate, changing the deflection angle of the fan blade 11, adapting to the airflow in the tunnel, and making the fan blade 11 rotate at a uniform speed to quickly adjust the ventilation in the tunnel.

[0120] Working principle: in the specific embodiment of the jet flow fan 1 in the application, the jet flow fan 1 with the mounting frame 21 is hoisted through the rotating rod 23, when the ventilation system needs to adjust the installation angle of the jet flow fan 1 to adapt to the ventilation demand in the tunnel, the decision and control module controls the driving motor 32, makes the adjusting screw rod 31 move horizontally in the movable groove of the mounting block 28 after the driving motor 32 drives the adjusting screw rod 31 to rotate, thereby driving the rack bar 29 to move, achieving the deflection of the half gear plate 25, thereby driving the jet flow fan 1 to make overall angle adjustment under the hoisting connection of the rotating rod 23, and the driving part is installed on the top wall in the tunnel, thereby controlling the rotating shaft 26 to rotate, thereby driving the jet flow fan 1 to rotate in a circumferential direction in a limited range, adjusting the ventilation direction angle, and meeting the ventilation demand in the tunnel.

[0121] In order to adjust the air volume of the fan blade of the jet flow fan 1, the motor in the driving head 42 drives the main bevel gear 43 in the two driving seats 41 to rotate, thereby making the plurality of from bevel gears 44 rotate, changing the deflection angle of the fan blade 11, adapting to the airflow in the tunnel, and making the fan blade 11 rotate at a uniform speed to quickly adjust the ventilation in the tunnel.

[0122] The above merely describes preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art, according to the technical solution and inventive concept of the present application, makes equivalent replacement or change within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A high performance ventilation system based on digital model design, characterized by: The ventilation system includes, Data acquisition module: responsible for collecting vehicle data, air quality data, and pedestrian traffic information from the tunnel; Data Analysis and Processing Module: Utilizes digital models to process and analyze data in real time to predict ventilation demand; Decision and control module: Based on the analysis results, control the start and stop of the fan and the air volume to ensure air quality; Feedback and Optimization Module: Provides feedback and optimization based on actual conditions and maintenance data; Environmental Simulation and Prediction Module: Based on digital models and historical data, it simulates and predicts air quality and traffic flow in the tunnel over a future period, providing optimization suggestions for the decision-making module.

2. A high performance ventilation system based on digital model design as claimed in claim 1, wherein: The data acquisition module includes, Vehicle type recognition: Combining LiDAR and high-definition cameras installed at the tunnel entrance and middle, computer vision technology is used to classify and identify vehicles in the tunnel, and radar technology is used to measure vehicle speed. At the same time, the sensors equipped on each vehicle obtain vehicle speed data through the tunnel's lane monitoring system. Traffic flow identification: Using high-definition cameras and infrared sensors, the number of vehicles passing through the tunnel is identified, and the number of people in the flow is estimated; Air quality identification: by , , The gas concentration sensor monitors the air quality in the tunnel in real time and gives the pollutant concentration data, and then the temperature and humidity sensor monitors the temperature and humidity in the tunnel in real time, and the data is used to evaluate the influence of air flow and exhaust demand.

3. A high-performance ventilation system based on digital model design according to claim 2, characterized in that: The data analysis and processing module includes, a1. Pollutant Emission and Vehicle Speed ​​Relationship Model: Based on the combination of different vehicle types and vehicle speeds, a pollutant emission model is constructed to control ventilation flow to emit these gases. The pollutant emission model is as follows: ， in, This indicates the amount of pollutants emitted by a single vehicle. and It is a coefficient related to vehicle type. Used to adjust the impact of vehicle speed on pollutant emissions. This indicates the baseline pollutant emissions specific to the vehicle type; a2. Accumulation Effect of Emissions: Vehicle emissions are not only related to vehicle speed, but also closely related to the accumulation effect of emissions. Factors contributing to the accumulation effect include vehicle flow, vehicle density, and airflow effects. The formula for the accumulation effect is: ， in, It refers to the concentration of pollutants per unit volume. It represents the number of vehicles passing through per unit of time. It is the volume of the tunnel; a3. Calculate ventilation volume: The ventilation volume is calculated based on the concentration of emissions, using the following formula: ， in, That is the required ventilation volume. It is a coefficient related to the efficiency of ventilation equipment and tunnel structure.

4. A high-performance ventilation system based on digital model design according to claim 3, characterized in that: The decision-making and control module includes, b1. Fan start / stop control: It is mainly based on the real-time changes in vehicle speed, pollutant concentration and vehicle flow. The specific control logic is: when the pollutant concentration exceeds the set threshold, that is, when the accumulation effect of emissions causes the pollutant concentration to reach a certain critical value, the fan will be started; conversely, when the concentration is lower than the threshold, the fan will be shut down. b2. Fan Flow Regulation: Fan flow regulation depends on pollutant concentration, vehicle speed, and traffic volume within the tunnel. The fan volume is precisely adjusted via a variable frequency drive. When pollutant concentration is too high, the fan flow increases to quickly expel pollutants. When vehicle speed is low, pollutant accumulation is significant, and the fan flow increases to accelerate pollutant discharge. The fan flow regulation formula is as follows: ， in, and It is an adjustment coefficient related to the efficiency of the fan and the volume of the tunnel; b3. Regional ventilation control: By installing local fans at different locations in the tunnel, the air volume in specific areas is adjusted based on real-time pollutant concentration and vehicle speed data. Specific control methods include... Entrance area: When the traffic flow exceeds the set threshold and the pollutant concentration is higher than the set high threshold, increase the air volume; otherwise, maintain the air volume or make appropriate adjustments. Mid-range zone: When the traffic flow is less than the set threshold and the pollutant concentration is lower than the set low threshold, reduce the air volume; otherwise, adjust the air volume according to the traffic flow and pollutant concentration. Terminal area: Dynamically adjust air volume to avoid pollutant accumulation or excessive emissions, i.e., flexibly control according to the instability of traffic flow and pollutant concentration; b4. Dynamic Adjustment and Adaptive Control: To cope with the constantly changing tunnel environment, the control module adopts an adaptive control algorithm, namely, using a PID control algorithm to continuously monitor vehicle speed, flow rate, and pollutant concentration parameters within the tunnel, and adjust the fan's operating status in real time according to environmental changes. The calculation formula for the PID control algorithm is: ,in, These are the control parameters of the fan. It is the error, that is, the difference between the set pollutant concentration and the current pollutant concentration. It is proportional gain. It is integral gain. It is the differential gain. It is an error function. Represents the error function Integrating from time 0 to time t calculates the cumulative sum of errors during this time period. When using a PID control algorithm to adjust the airflow of a fan, the input is: Set pollutant concentration: That is, the set safe concentration of pollutants; Actual pollutant concentration: That is, the actual concentration of pollutants measured inside the tunnel; Error calculation: ; PID controller output: The control parameters of the fan are calculated based on the error and the PID formula. This allows it to control the airflow or start / stop status of the fan; specifically, the error... A large value indicates that the pollutant concentration deviates significantly from the target, and the PID controller will output a larger air volume or a strong fan start signal. With a smaller error, the air volume or status of the fan will tend to stabilize.

5. A high-performance ventilation system based on digital model design according to claim 4, characterized in that: The feedback and optimization module includes: c1. Optimization and scheduling module: The main goal is to schedule wind turbines using the ant colony algorithm to determine the optimal working mode, running time and intensity of the wind turbines. That is, to simulate the process of ants looking for food, calculate the scheduling path of the wind turbines, and optimize the start-up, shutdown and running time cycle of the wind turbines. c2. Digital Model Module: The core task is to use computational fluid dynamics, or CFD technology, to simulate and analyze the ventilation system, thereby optimizing the design of the ventilation system.

6. A high-performance ventilation system based on digital model design according to claim 5, characterized in that: The environmental simulation and prediction module includes: d1. Data Acquisition and Fusion: Collect real-time environmental data inside the tunnel, including air quality, traffic flow, temperature and humidity; integrate past operational data, including historical air quality and traffic flow data; provide training samples for deep learning models; clean, denoise and normalize the data to ensure accuracy and consistency, making it suitable for input to deep learning models. d2. Deep learning model: The LSTM network is used to model time series data to capture long-term dependencies and predict air quality and traffic flow in the future. The input data is appropriately transformed and processed to extract useful features, ensuring that the model learns the temporal patterns of the data. The LSTM model is trained with historical data to continuously optimize the network parameters and improve the prediction accuracy. d3. Simulation and Prediction Results: Predict the air quality in the tunnel over a future period, predict changes in traffic flow within the tunnel, and make accurate predictions based on traffic and air quality requirements at different time periods. d4. Predictive Analysis and Optimization Suggestions: Based on air quality forecasts, provide optimization suggestions for the ventilation system, predict the periods when ventilation needs to be strengthened, and provide traffic flow scheduling suggestions based on traffic flow forecasts to help traffic management personnel achieve efficient traffic control.

7. A high-performance ventilation system based on digital model design according to claim 6, characterized in that: The fan is a jet fan (1), which is installed in the entrance area, middle section area and end area of ​​the tunnel respectively. An angle adjustment mechanism (2) is provided on the outer surface of the casing of the jet fan (1). A linkage mechanism (4) for controlling the angle deflection of the fan blades (11) is provided inside the jet fan (1). The angle adjustment mechanism (2) controls the overall angle adjustment of the jet fan (1). The angle adjustment mechanism (2) includes a mounting frame (21) fixedly wrapped around the outer surface of the jet fan (1) housing. Movable joints (22) are symmetrically distributed on the outer surface of the mounting frame (21). A rotating rod (23) is fixedly connected to one end of the movable joint (22). The rotating rod (23) consists of two upper and lower support rods and a connecting pipe rotatably sleeved on the outer surface of the two support rods. The other movable joint (22) on the free end of the rotating rod (23) is fixedly installed on the inner wall of the tunnel by a mounting plate.

8. A high-performance ventilation system based on digital model design according to claim 7, characterized in that: The angle adjustment mechanism (2) further includes a support base (24) fixedly connected to the upper surface of the casing of the jet fan (1). A half gear plate (25) is fixedly connected to the upper surface of the middle part of the support base (24). A rotating shaft (26) is provided directly above the support base (24). A U-shaped support beam (27) is fixedly connected to the lower surface of the rotating shaft (26). An installation block (28) with a movable groove is fixedly connected to the inner top surface of the support beam (27). A rack rod (29) is provided inside the installation block (28). The rack rod (29) meshes with the half gear plate (25).

9. A high-performance ventilation system based on digital model design according to claim 8, characterized in that: The angle adjustment mechanism (2) further includes a threaded sleeve (30) fixedly connected to the upper surface of the rack (29), an adjusting screw (31) rotatably connected to the inner surface of the mounting block (28) via a bearing, the threaded sleeve (30) being threaded onto the outer surface of the adjusting screw (31), a drive motor (32) being fixedly mounted on the outer surface of the mounting block (28), and the outer surface of the output shaft of the drive motor (32) being fixedly connected to the outer surface of the adjusting screw (31) via a coupling.

10. A high-performance ventilation system based on digital model design according to claim 9, characterized in that: The linkage mechanism (4) includes a drive seat (41) for rotating the fan blade (11), and the interior of the drive seat (41) is hollow. A drive head (42) is fixedly connected to one side surface of one of the drive seats (41). A main bevel gear (43) is rotatably connected to the cavity of the drive seat (41) through a bearing. The outer surface of the output shaft of the drive head (42) passes through the two drive seats (41) and is fixedly connected to the surface of the main bevel gear (43). The outer surface of the main bevel gear (43) is arranged in a ring array with driven bevel gears (44) meshing with it. The surface of the driven bevel gear (44) is fixedly connected to the mounting shaft of the corresponding fan blade (11).

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