Intelligent evaluation system for urban tunnel hot and humid environment and air pollutants

Abstract

The invention relates to the technical field of urban tunnel hot and humid environment and air pollution, in particular to an urban tunnel hot and humid environment and air pollutant intelligent evaluation system which comprises a monitoring area division module, a monitoring data acquisition module, a vehicle information analysis module, a pollutant concentration determination module and a predicted value evaluation module. The predicted value evaluation module is used for evaluating prediction results after predicting spatial and temporal change values of wind speed, temperature and pollutant concentration of each section so as to control start and stop of a fan of the section of the tunnel; according to the method, after the space-time change values of the wind speed, the temperature and the pollutant concentration of each section are predicted, the prediction result is evaluated, and therefore the draught fan of the section of the tunnel is controlled to be started and stopped.

Description

Technical Field

[0001] The invention relates to the technical field of urban tunnel thermal and humid environment and air pollution, and in particular to an intelligent evaluation system for urban tunnel thermal and humid environment and air pollutants. Background Art

[0002] As of June 2021, the number of motor vehicles in China reached 384 million, including 292 million cars; in the first half of 2021, 18.71 million new motor vehicles were registered nationwide, a record high for the same period. The accelerated increase in the number of motor vehicles has also brought about a series of problems. With the sharp increase in road traffic pressure, road traffic congestion has become a major problem facing major cities in my country. In the process of alleviating urban land shortages and traffic congestion from urban centers to main roads, the status of urban traffic tunnels has become increasingly important and has become an indispensable part of traffic planning.

[0003] In order to ensure the normal operation and emergency response of urban traffic tunnels, it is necessary to establish a reasonable ventilation system in the tunnel. In order to ensure that the number of fans in the tunnel can meet the air quality requirements in the tunnel and reduce energy consumption, the thermal and humid environment and pollutant concentrations in each section of the tunnel need to be as accurate as possible. Current research focuses more on the daily and hourly changes in tunnel environmental parameters. Urban road tunnels have obvious tidal characteristics and there are morning and evening peaks. Obviously, the daily average and hourly average cannot reflect the real-time change law of the parameters in the tunnel. It is more meaningful to have data with a smaller time step. In addition, previous studies usually use one parameter instead of the whole The research team has developed an intelligent prediction system for environmental parameters and pollutant concentrations, which can predict the temporal and spatial changes of each small section in the tunnel, and can evaluate the value of each section based on the prediction results and compare it with the existing standards to determine the number of fans to be turned on in the section, thereby realizing intelligent monitoring and control of the tunnel environment. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent evaluation system for the thermal and humid environment and air pollutants in urban tunnels to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] An intelligent evaluation system for thermal and humid environment and air pollutants in urban tunnels, characterized in that the system comprises the following modules: a monitoring area division module, a monitoring data acquisition module, a vehicle information analysis module, a pollutant concentration determination module and a prediction value evaluation module;

[0007] The monitoring area division module is used to divide the tunnel into N sections and mark them according to the camera positions;

[0008] The monitoring data acquisition module uses the video frame difference technology to determine the vehicle information passing through the section;

[0009] The vehicle information analysis module is used to analyze the relationship between the exhaust heat of the engines of different types of vehicles and the temperature value of the tunnel in the section;

[0010] The pollutant concentration determination module is used to analyze the relationship between the pollutant concentration and time in each section;

[0011] The predicted value evaluation module is used to predict the spatiotemporal variation values ​​of wind speed, temperature and pollutant concentration in each section, and then evaluate the predicted results to control the start and stop of the fan in the tunnel of the section.

[0012] Furthermore, the monitoring area division module includes a tunnel division unit and a node marking unit:

[0013] The tunnel division unit is used to divide the tunnel into N sections, and each section is provided with a camera device;

[0014] The node marking unit is used to obtain the camera device set in each section, and mark them in sequence according to the position of the camera device, which are recorded as nodes 0#, 1#, 2#..., i#..., n#, where 0# is the tunnel entrance position, n# is the tunnel exit position, and there are 4 sensors in the tunnel. One sensor is configured at 0#, recorded as sensor 1, and the other 3 sensors are evenly arranged along the length of the tunnel, recorded as sensor 2, sensor 3, and sensor 4 respectively. Sensor 1 needs to measure wind speed, temperature, and pollutant concentration to provide boundary conditions for calculation, and the remaining sensors only need to measure wind speed changes over time.

[0015] Furthermore, the method for calculating the temperature value and pollutant concentration of the corresponding section in the tunnel under the average wind speed value includes the following steps:

[0016] SA1, according to the formula u i =u(x i ,t) to obtain the functional relationship between wind speed and time from the tunnel entrance to node i#, where u i represents wind speed, x i Indicates the distance between the current node and the tunnel entrance;

[0017] SA2, according to formula T f，i =T f (x i ,t) to obtain the functional relationship between the temperature and time from the tunnel entrance to the node i#, where Tf，i represents the temperature, x i Indicates the distance between the current node and the tunnel entrance;

[0018] SA3, according to formula C i =C(x i ,t) to obtain the functional relationship between the pollutant concentration and time from the tunnel entrance to the node i#, where C i represents the pollutant concentration, x i Indicates the distance between the current node and the tunnel entrance.

[0019] Furthermore, the method of determining the vehicle information passing through the section by using the video frame difference technology comprises the following steps:

[0020] SB1. Obtain surveillance videos of each section;

[0021] SB2, using video frame difference technology to determine the traffic volume, speed and vehicle type ratio passing through the section;

[0022] SB3. Obtain tunnel dimension parameter values, fan parameter values ​​and initial wind speed conditions;

[0023] SB4. Analyze the piston force caused by vehicle movement, the friction between the tunnel wall, equipment and vehicles, the pressure difference at the tunnel entrance caused by the ambient wind speed, and the lift pressure applied by the jet fan to obtain the effective value of the average wind speed in each section.

[0024] Furthermore, the method of analyzing the piston force F1 caused by vehicle movement, the friction force F2 of the tunnel wall, equipment and vehicle, the cave entrance pressure difference F3 caused by the ambient wind speed, and the lift pressure F4 applied by the jet fan to obtain the effective value of the average wind speed in each section includes the following steps:

[0025] SC1, according to the formula Get the average wind speed u in section I I The governing equation is t represents the cross-sectional area of ​​the tunnel, ρ I represents the ambient air density in the tunnel of section I, F1 represents the piston force caused by vehicle movement, F2 represents the friction between the tunnel wall, equipment and vehicle, F3 represents the pressure difference at the tunnel entrance caused by the ambient wind speed, F4 represents the lift pressure applied by the jet fan, Δx I represents the length of the tunnel in section I, u I =u I (t) represents the hourly average wind speed in the tunnel of Section I;

[0026] SC2, according to the formula The piston force caused by the vehicle movement in section I is obtained, where A j represents the frontal area of ​​the jth type of vehicle, Cd jrepresents the drag coefficient of the j-th vehicle, V I,j represents the speed of the jth type of vehicle in the tunnel of section I, N I,j represents the number of vehicles of type j in the tunnel of section I, n cat Indicates the total number of types of vehicles in the tunnel;

[0027] SC3, according to the formula The friction force of tunnel wall, equipment and vehicle in section I is obtained, where f ve represents the equivalent friction coefficient of the vehicle, f w Denotes the equivalent friction coefficient of the tunnel wall and equipment, D e represents the hydraulic diameter of the tunnel;

[0028] SC4, according to the formula The pressure difference at the tunnel entrance caused by the ambient wind speed in the tunnel section I is obtained, where ρ0 represents the ambient air density in the tunnel, u0 represents the ambient wind speed, ξ en represents the tunnel entrance loss coefficient, ξ ex represents the tunnel exit loss coefficient, and L represents the tunnel length;

[0029] SC5, according to the formula F4 = ρ i K F A F ·n F，I (v F -u I )|v F |Get the lift pressure applied by the jet fan in section I, where K F Indicates the pressure rise coefficient of the jet fan, A F represents the jet fan outlet area, n F，I represents the number of jet fans running in the tunnel of section I, v F Indicates the exhaust speed of the jet fan;

[0030] SC6, according to the formula The wind speed control equation of the tunnel in section I is obtained, where F5 = -ρ1ξ en A t u1|u1|, represents the flow resistance at the tunnel entrance, ρ1 represents the ambient air density of the first section of the tunnel, and u1 represents the average wind speed in the first section; F6 = -ρ N ξ ex A t u N |u N |, represents the flow resistance at the tunnel exit, ρ N represents the ambient air density of the tunnel in section N, u N represents the average wind speed in the Nth section;

[0031] SC7. Input the initial conditions i-1 node initial wind speed u(0, t1), u(x1, t1), ..., u(x i , t i ), ..., u(x n , t n ), the average wind speed of the first section is obtained by calculation;

[0032] SC8. Assuming that the wind speed in each section of the tunnel conforms to the polynomial variation law, calculate the hourly wind speed prediction value u at sensor 2 during the test period (t1, t2) 2# (t), hourly wind speed prediction value u at sensor 3 3# (t), hourly wind speed prediction value u at sensor 4 4# (t); and the wind speed value u measured by sensor 2 2#e , Sensor 3 measured wind speed value u 3#e , Sensor 4 measured wind speed value u 4#e By comparison, the hourly wind speed prediction error Δ of sensor 2, sensor 3, and sensor 4 is calculated. 2# , Δ 3# , Δ 4# , further calculation to obtain the hourly wind speed average prediction error Δ=(Δ 2# +Δ 3# +Δ 4# ) / 3, where t3 represents the end time of the test;

[0033] SC9. When the hourly average wind speed prediction error Δ exceeds the target threshold, adjust the aerodynamic parameters in each section of the tunnel, including the vehicle drag coefficient Cd j Value, vehicle equivalent friction coefficient f ve Value, jet fan boost coefficient K F value, repeat SC1-SC7 calculation;

[0034] SC10, when the hourly wind speed average prediction error Δ is less than or equal to the target threshold, the average wind speed in each section of the tunnel calculated at this time is determined to be the effective value of the hourly wind speed in each section within the time range (t1, t2) u1(t),...,u I (t),...,u N (t), where t represents the calculation time, u I (t) represents the hourly average wind speed in the tunnel of Section I;

[0035] SC11. Assuming that the wind speed in each section of the tunnel conforms to the polynomial variation law, calculate the hourly wind speed u(0,t),...,u(x i ,t)...,u(x n ,t), where x irepresents the distance between node i# and the tunnel entrance, t represents the calculation time, u(x i ,t) represents the hourly wind speed of node i#.

[0036] The present invention is based on the formula Get the average wind speed u in the i-th section i The control equation is obtained by inputting the initial conditions i-1 node initial wind speed u(0, t1), u(x1, t1), ..., u(x i , t i ), ..., u(x n , t n ), the average wind speed of section I is obtained by calculation, which provides data reference for the subsequent evaluation of the prediction results and thus controls the start and stop of the fan in this section of the tunnel.

[0037] Furthermore, the method for the vehicle information analysis module to analyze the relationship between the exhaust heat of the engines of different vehicle models and the temperature value of the tunnel section includes the following steps:

[0038] SD1. Obtain vehicle information based on surveillance video;

[0039] SD2. Query the China Vehicle Environmental Management Annual Report and PIARC 2019 Ventilation Report Database to obtain the vehicle weight m of different models j and the corresponding vehicle engine efficiency η e，j ;

[0040] SD3. Obtain the slope s of the tunnel section I I ;

[0041] SD4. Get the actual power P of the jet fan F And the corresponding jet fan efficiency η F ;

[0042] SD5. According to the energy balance principle, the energy conservation equation in the tunnel of Section I is established, and the expression is: Among them C p represents the specific heat capacity of air at constant pressure, T f,i =T f (x i ,t) represents the hourly air temperature at node i#, Q1 represents the heat dissipation of the automobile engine in the first section of the tunnel, Q2 represents the heat dissipation of the jet fan in the first section of the tunnel, Q3 represents the heat dissipation of the lighting in the first section of the tunnel, and Q4 represents the heat transferred from the air to the tunnel wall;

[0043] SD6, according to the formula Get the heat of the car engine in the tunnel section I, where m j represents the mass of the j-type vehicle, V I,jrepresents the speed of the jth type of vehicle in the tunnel of section I, N I,j represents the number of vehicles of the jth type in the tunnel of the first section, η e,j represents the engine efficiency of the j-type vehicle;

[0044] SD7, according to the formula Q2 = η F,I P F (1-η F ) to obtain the heat dissipation of the jet fan in the first section of the tunnel, where η F,I represents the number of jet fans in the tunnel section I, P F Indicates the actual motor power value of the jet fan, η F represents the efficiency of the jet fan;

[0045] SD8, according to the formula Q3 = q L,I Δx i The heat dissipation of lighting in the tunnel section I is obtained, where q L,I It indicates the amount of heat dissipated by lighting for every kilometer of tunnel length;

[0046] SD9, according to the formula Q4 = -hA w,I (T f，I -T w，I ) to obtain the heat transferred from the air to the tunnel wall, where h represents the convection heat transfer coefficient between the air and the tunnel wall, A w,I represents the surface area of ​​the tunnel wall in section I, T w，I represents the tunnel wall temperature of section I, T f,I Indicates the air temperature in the tunnel of Section I;

[0047] SD10. Calculate the hourly air temperature T of each node within the time range (t1, t2) according to the energy conservation equation in the tunnel. f (x1,t),...,T f (x i ,t),...,T f (x n ,t), where x i represents the distance between node i# and the tunnel entrance, t represents the calculation time, T f (x i ,t) represents the hourly air temperature of node i#.

[0048] The present invention obtains the heat source inside the tunnel according to the energy balance principle, which is By entering the initial temperature condition T f (0, t), the temperature value at the i-1 node of section I, that is, the temperature value of section I, is obtained according to the energy balance in the tunnel, which provides data reference for the subsequent evaluation of the prediction results and thus controls the start and stop of the fan in this section of the tunnel.

[0049] Furthermore, the method for analyzing the relationship between the pollutant concentration in each section and time includes the following steps:

[0050] SE1. Obtain vehicle information based on surveillance video;

[0051] SE2. Combined with the China Vehicle Environmental Management Annual Report and the PIARC 2019 Ventilation Report Database, set the basic emission rates for different vehicle models. j ;

[0052] SE3. Obtain the time correction value f that affects the pollutant concentration t , vehicle mass correction value f m and altitude correction value f a ;

[0053] SE4, according to the formula Establish the pollutant concentration control equation, where ρ I represents the ambient air density of the tunnel in section I, u I represents the average wind speed in the tunnel of section I, A t represents the cross-sectional area of ​​the tunnel, S I represents the pollutant emission rate in the tunnel of Section I;

[0054] SE5, according to the formula Get the pollutant emission rate in the tunnel of section I, where n cat Indicates the total number of types of vehicles in the tunnel, g j represents the baseline emission rate of pollutants for the jth type of vehicle, g non-ex Represents the equivalent emission rate of pollutants emitted by non-motor vehicles, N I,j represents the number of vehicles of the jth type in the tunnel of section I; f t is the time correction value, f m is the vehicle mass correction value, f a is the altitude correction value;

[0055] SE6. Obtain the initial conditions of each pollutant in the PIARC 2019 database and bring the parameters that affect the pollutant concentration into the pollutant concentration control equation to calculate the hourly pollutant concentration C(x1,t),...,C(x i ,t),...,C(x n ,t), where x i represents the distance between node i# and the tunnel entrance, t represents the calculation time, C(x i ,t) represents the hourly pollutant concentration of node i#.

[0056] The present invention obtains the basic emission rate g of vehicles of different modelsj , according to the formula The pollutant emission rate in the I-th tunnel section is obtained, providing a data reference for subsequent evaluation of the prediction results and thus controlling the start and stop of the fans in this section of the tunnel.

[0057] Furthermore, the method for evaluating the prediction results and thus controlling the start and stop of the fans in this section of the tunnel includes the following steps:

[0058] SF1. According to the formula u(0, t), T f (0, t), C(0, t) t1 < t < t2, obtain the boundaries and initial conditions of the period t = (t1, t2);

[0059] SF2. According to the formula u(0, t1), u(x1, t1),..., u(x n , t1),..., u(x i , t1) t1 < t < t2, calculate the initial wind speed;

[0060] SF3. Couple the average wind speed control equation, the energy conservation equation, the pollutant concentration control equation, the boundary initial conditions of the period t = (t1, t2), and the initial wind speed to obtain the average wind speed in each section, the temperature and pollutant concentration at each calculation node;

[0061] SF4. Screen the time periods Δt when the predicted values of the wind speed at each node exceed the standard u and the tunnel section Δl u , where u(i, t) represents the hourly wind speed at the i-th node within the time range (t1, t2), and the predicted value of the wind speed at the node exceeding the standard means u(x i , t) ≤ wind speed threshold;

[0062] SF5. Screen the time periods Δt when the predicted values of the air temperature at each node exceed the standard T and the tunnel section Δl T , where T f (x i , t) represents the hourly air temperature at the i-th node within the time range (t1, t2), and the predicted value of the air temperature at the node exceeding the standard means T f (x i , t) ≥ temperature threshold;

[0063] SF6. For pollutant concentration evaluation, one or more of CO concentration, particulate matter mass concentration, and nitrogen oxide concentration can be selected as the evaluation objects, and screen the time periods Δt when the predicted values of each pollutant concentration exceed the standard c and the tunnel section Δl c , where, C(x i,t) represents the hourly pollutant concentration at the i#th node within the time range (t1,t2). The predicted pollutant concentration exceeding the standard means that C(x i ,t)≥corresponding pollutant concentration threshold;

[0064] SF7, calculate the node parameter exceeding the standard time period Δt = min (Δt u ,Δt T ,Δt c ), the tunnel section with excessive standard Δl=max(Δl u ,Δl T ,Δl c ), when Δt<target duration threshold, and Δl<target section length threshold, the tunnel environment meets the control requirements; when Δt≥target duration threshold, or Δl≥target section length threshold, the tunnel environment does not meet the control requirements, and the fan start-up plan is further adjusted;

[0065] SF8. Determine the alternative fan start-up schemes 1, 2, ..., m according to the fan setting in the tunnel;

[0066] SF9. For fan start-up schemes 1, 2, ..., m, repeat SC1-SC11, SD1-SD10, SE1-SE7 operations in sequence, calculate the hourly wind speed, air temperature, and pollutant concentration of each node within the time range (t1, t2) under different fan start-up schemes, and select the time period and tunnel section where the node wind speed, air temperature, and pollutant concentration exceed the standard, where Δt m Indicates the time period when the node parameter exceeds the standard under the fan start-up scheme m, Δl m Indicates the tunnel section where the node parameters exceed the standard under the fan start-up scheme m;

[0067] SF10. Based on the principle of the shortest time period in which node parameters exceed the standard or the smallest tunnel section in which the parameters exceed the standard, select the best fan start-up plan from the alternative fan start-up plans 1, 2, ..., m, and feed back the results to the tunnel operation management personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a vehicle information determination flow chart of an intelligent evaluation system for thermal and humid environment and air pollutants in urban tunnels of the present invention;

[0069] Figure 2 It is a flow chart of tunnel parameter prediction of an intelligent evaluation system for thermal and humid environment and air pollutants in urban tunnels of the present invention;

[0070] Figure 3 It is a flow chart of tunnel parameter evaluation of an intelligent evaluation system for thermal and humid environment and air pollutants in urban tunnels of the present invention;

[0071] Figure 4It is a wind speed variation diagram of four nodes 1#, 2#, 3# and 4# of an intelligent evaluation system for thermal and humid environment and air pollutants in an urban tunnel of the present invention;

[0072] Figure 5 It is a temperature variation diagram of four nodes 1#, 2#, 3# and 4# of an intelligent evaluation system for thermal and humid environment and air pollutants in an urban tunnel of the present invention;

[0073] Figure 6 It is a schematic diagram of system modules of an intelligent evaluation system for thermal and humid environment and air pollutants in urban tunnels according to the present invention. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] See also Figure 1-Figure 6 , the present invention provides a technical solution:

[0076] An intelligent evaluation system for thermal and humid environment and air pollutants in urban tunnels, characterized in that the system comprises the following modules: a monitoring area division module, a monitoring data acquisition module, a vehicle information analysis module, a pollutant concentration determination module and a prediction value evaluation module;

[0077] The monitoring area division module is used to divide the tunnel into N sections and mark them according to the camera positions;

[0078] The monitoring data acquisition module uses the video frame difference technology to determine the vehicle information passing through the section;

[0079] The vehicle information analysis module is used to analyze the relationship between the exhaust heat of the engines of different types of vehicles and the temperature value of the tunnel section;

[0080] The pollutant concentration determination module is used to analyze the relationship between the pollutant concentration and time in each section;

[0081] The predicted value evaluation module is used to predict the spatiotemporal variation values ​​of wind speed, temperature and pollutant concentration in each section, and then evaluate the predicted results to control the start and stop of the fan in the tunnel of the section.

[0082] The monitoring area division module includes a tunnel division unit and a node marking unit:

[0083] The tunnel division unit is used to divide the tunnel into N sections, and set a camera device in each section;

[0084] The computing node marking unit is used to mark in sequence according to the position of the camera, which is recorded as 0#, 1#, 2#..., i#..., n#, where 0# is the tunnel entrance position and n# is the tunnel exit position. The tunnel is equipped with 4 sensors, 1 sensor is configured at 0#, recorded as sensor 1, and the other 3 sensors are evenly arranged along the length of the tunnel, recorded as sensor 2, sensor 3, and sensor 4 respectively. Sensor 1 needs to measure wind speed, temperature, and pollutant concentration to provide boundary conditions for calculation, and the remaining sensors need to measure wind speed changes over time.

[0085] The method for calculating the temperature value and pollutant concentration of the corresponding section in the tunnel under the average wind speed value comprises the following steps:

[0086] SA1, according to the formula u i =u(x i ,t) to obtain the functional relationship between wind speed and time from the tunnel entrance to node i#, where u i represents wind speed, x i Indicates the distance between the current node and the tunnel entrance;

[0087] SA2, according to formula T f，i =T f (x i ,t) to obtain the functional relationship between the temperature and time from the tunnel entrance to the node i#, where T f，i represents the temperature, x i Indicates the distance between the current node and the tunnel entrance;

[0088] SA3, according to formula C i =C(x i ,t) to obtain the functional relationship between the pollutant concentration and time from the tunnel entrance to the node i#, where C i represents the pollutant concentration, x i Indicates the distance between the current node and the tunnel entrance.

[0089] The method for determining the vehicle information passing through the section by using the video frame difference technology comprises the following steps:

[0090] SB1. Obtain surveillance videos of each section;

[0091] SB2, using video frame difference technology to determine the traffic volume, speed and vehicle type ratio passing through the section;

[0092] SB3. Obtain tunnel dimension parameter values, fan parameter values ​​and initial wind speed conditions;

[0093] SB4. Analyze the piston force caused by vehicle movement, the friction force caused by the wall and the vehicle, the force caused by the inlet and outlet pressure difference and the lift pressure applied by the jet fan to obtain the effective value of the average wind speed in each section.

[0094] The method for analyzing the piston force F1 caused by vehicle movement, the friction force F2 of the tunnel wall, equipment and vehicle, the cave entrance pressure difference F3 caused by the ambient wind speed, and the lift pressure F4 applied by the jet fan to obtain the effective value of the average wind speed in each section includes the following steps:

[0095] SC1, according to the formula Get the average wind speed u in section I I The governing equation is t represents the cross-sectional area of ​​the tunnel, ρ I represents the ambient air density in the tunnel of section I, F1 represents the piston force caused by vehicle movement, F2 represents the friction between the tunnel wall, equipment and vehicle, F3 represents the pressure difference at the tunnel entrance caused by the ambient wind speed, F4 represents the lift pressure applied by the jet fan, Δx I represents the length of the tunnel in section I, u I =u I (t) represents the hourly average wind speed in the tunnel of Section I;

[0096] SC2, according to the formula The piston force caused by the vehicle movement in section I is obtained, where A j represents the frontal area of ​​the jth type of vehicle, Cd j represents the drag coefficient of the j-th vehicle, V I,j represents the speed of the jth type of vehicle in the tunnel of section I, N I,j represents the number of vehicles of type j in the tunnel of section I, n cat Indicates the total number of types of vehicles in the tunnel;

[0097] SC3, according to the formula The friction force of tunnel wall, equipment and vehicle in section I is obtained, where f ve represents the equivalent friction coefficient of the vehicle, f w Denotes the equivalent friction coefficient of the tunnel wall and equipment, D e represents the hydraulic diameter of the tunnel;

[0098] SC4, according to the formula The pressure difference at the tunnel entrance caused by the ambient wind speed in the tunnel section I is obtained, where ρ0 represents the ambient air density in the tunnel, u0 represents the ambient wind speed, ξ en represents the tunnel entrance loss coefficient, ξ ex represents the tunnel exit loss coefficient, and L represents the tunnel length;

[0099] SC5, according to the formula F4 = ρi K F A F ·n F，I (v F -u I )|v F |Get the lift pressure applied by the jet fan in section I, where K F Indicates the pressure rise coefficient of the jet fan, A F represents the jet fan outlet area, n F，I represents the number of jet fans running in the tunnel of section I, v F Indicates the exhaust speed of the jet fan;

[0100] SC6, according to the formula The wind speed control equation of the tunnel in section I is obtained, where F5 = -ρ1ξ en A t u1|u1|, represents the flow resistance at the tunnel entrance, ρ1 represents the ambient air density of the first section of the tunnel, and u1 represents the average wind speed in the first section; F6 = -ρ N ξ ex A t u N |u N |, represents the flow resistance at the tunnel exit, ρ N represents the ambient air density of the tunnel in section N, u N represents the average wind speed in the Nth section;

[0101] SC7. Input the initial conditions i-1 node initial wind speed u(0, t1), u(x1, t1), ..., u(x i , t i ), ..., u(x n , t n ), and the average wind speed of the first section is obtained by calculation.

[0102] SC8. Assuming that the wind speed in each section of the tunnel conforms to the polynomial variation law, calculate the hourly wind speed prediction value u at sensor 2 during the test period (t1, t2) 2# (t), hourly wind speed prediction value u at sensor 3 3# (t), hourly wind speed prediction value u at sensor 4 4# (t); and the wind speed value u measured by sensor 2 2#e , Sensor 3 measured wind speed value u 3#e , Sensor 4 measured wind speed value u 4#e By comparison, the hourly wind speed prediction error Δ of sensor 2, sensor 3, and sensor 4 is calculated. 2# , Δ 3# , Δ 4# , further calculation to obtain the hourly wind speed average prediction error Δ=(Δ2# +Δ 3# +Δ 4# ) / 3, where t3 represents the end time of the test;

[0103] SC9. When the hourly average wind speed prediction error Δ exceeds the target threshold, adjust the aerodynamic parameters in each section of the tunnel, including the vehicle drag coefficient Cd j Value, vehicle equivalent friction coefficient f ve Value, jet fan boost coefficient K F value, repeat SC1-SC7 calculation;

[0104] SC10, when the hourly wind speed average prediction error Δ is less than or equal to the target threshold, the average wind speed in each section of the tunnel calculated at this time is determined to be the effective value of the hourly wind speed in each section within the time range (t1, t2) u1(t),...,u I (t),...,u N (t), where t represents the calculation time, u I (t) represents the hourly average wind speed in the tunnel of Section I;

[0105] SC11. Assuming that the wind speed in each section of the tunnel conforms to the polynomial variation law, calculate the hourly wind speed u(0,t),...,u(x i ,t)...,u(x n ,t), where x i represents the distance between node i# and the tunnel entrance, t represents the calculation time, u(x i ,t) represents the hourly wind speed of node i#.

[0106] The method for analyzing the relationship between the exhaust heat of the engines of different types of vehicles and the temperature value of the tunnel section by the vehicle information analysis module comprises the following steps:

[0107] SD1. Obtain vehicle information based on surveillance video;

[0108] SD2. Query the China Vehicle Environmental Management Annual Report and PIARC 2019 Ventilation Report Database to obtain the vehicle weight m of different models j and the corresponding vehicle engine efficiency η e，j ;

[0109] SD3. Obtain the slope s of the tunnel section I I ;

[0110] SD4. Get the actual power P of the jet fan F And the corresponding jet fan efficiency η F ;

[0111] SD5. According to the energy balance principle, the energy conservation equation in the tunnel of Section I is established, and the expression is: Among them C p represents the specific heat capacity of air at constant pressure, T f,i =T f (x i ,t) represents the hourly air temperature at node i#, Q1 represents the heat dissipation of the automobile engine in the first section of the tunnel, Q2 represents the heat dissipation of the jet fan in the first section of the tunnel, Q3 represents the heat dissipation of the lighting in the first section of the tunnel, and Q4 represents the heat transferred from the air to the tunnel wall;

[0112] SD6, according to the formula Get the heat of the car engine in the tunnel section I, where m j represents the mass of the j-type vehicle, V I,j represents the speed of the jth type of vehicle in the tunnel of section I, N I,j represents the number of vehicles of the jth type in the tunnel of the first section, η e,j represents the engine efficiency of the j-type vehicle;

[0113] SD7, according to the formula Q2 = η F,I P F (1-η F ) to obtain the heat dissipation of the jet fan in the first section of the tunnel, where η F,I represents the number of jet fans in the tunnel section I, P F Indicates the actual motor power value of the jet fan, η F represents the efficiency of the jet fan;

[0114] SD8, according to the formula Q3 = q L,I Δx i The heat dissipation of lighting in the tunnel section I is obtained, where q L,I It indicates the amount of heat dissipated by lighting for every kilometer of tunnel length;

[0115] SD9, according to the formula Q4 = -hA w,I (T f，I -T w，I ) to obtain the heat transferred from the air to the tunnel wall, where h represents the convection heat transfer coefficient between the air and the tunnel wall, A w,I represents the surface area of ​​the tunnel wall in section I, T w，I represents the tunnel wall temperature of section I, T f,I Indicates the air temperature in the tunnel of Section I;

[0116] SD10. Calculate the hourly air temperature T of each node within the time range (t1, t2) according to the energy conservation equation in the tunnel. f (x1,t),...,T f (x i,t),...,T f (x n ,t), where x i represents the distance between node i# and the tunnel entrance, t represents the calculation time, T f (x i ,t) represents the hourly air temperature of node i#.

[0117] The method for analyzing the relationship between the pollutant concentration in each section and time comprises the following steps:

[0118] SE1. Obtain vehicle information based on surveillance video;

[0119] SE2. Combined with the China Vehicle Environmental Management Annual Report and the PIARC 2019 Ventilation Report Database, set the basic emission rates for different vehicle models. j ;

[0120] SE3. Obtain the time correction value f that affects the pollutant concentration t , vehicle mass correction value f m and altitude correction value f a ;

[0121] SE4. Input the effective hourly wind speed u1(t),...,u in each section of the tunnel within the time range (t1, t2). I (t),...,u N (t), input the hourly pollutant concentration C(0,t) of node 0 within the time range (t1,t2);

[0122] SE5, according to the formula Establish the pollutant concentration control equation, where ρ I represents the ambient air density of the tunnel in section I, u I represents the average wind speed in the tunnel of section I, A t represents the cross-sectional area of ​​the tunnel, S I represents the pollutant emission rate in the tunnel of Section I;

[0123] SE6, according to the formula Get the pollutant emission rate in the tunnel of section I, where n cat Indicates the total number of types of vehicles in the tunnel, g j represents the baseline emission rate of pollutants for the jth type of vehicle, g non-ex Represents the equivalent emission rate of pollutants emitted by non-motor vehicles, N I,j represents the number of vehicles of the jth type in the tunnel of section I; f t is the time correction value, f m is the vehicle mass correction value, f a is the altitude correction value;

[0124] SE7. Obtain the initial conditions of each pollutant in the PIARC 2019 database, and substitute the parameters affecting the pollutant concentration into the pollutant concentration control equation, then the hourly pollutant concentrations C(x1, t),..., C(x i , t),..., C(x n , t) at each node within the time range (t1, t2) can be calculated, where x i represents the distance of node i# from the tunnel entrance, t represents the calculation time, and C(x i , t) represents the hourly pollutant concentration of node i#.

[0125] The method for evaluating the prediction results and thus controlling the start and stop of the fan in this section of the tunnel includes the following steps:

[0126] SF1. According to the formulas u(0, t), T f (0, t), C(0, t) for t1 < t < t2, obtain the boundaries and initial conditions of the period t = (t1, t2);

[0127] SF2. According to the formulas u(0, t1), u(x1, t1),..., u(x n , t1),..., u(x i , t1) for t1 < t < t2, calculate the initial wind speed;

[0129] SF3. Couple the average wind speed control equation, the energy conservation equation, the pollutant concentration control equation, the boundary initial conditions of the period t = (t1, t2), and the initial wind speed to obtain the average wind speed in each section, the temperature and pollutant concentration at each calculation node;

[0130] SF4. Screen the time periods Δt when the predicted wind speed values at each node exceed the standard u and the tunnel section Δl u , where u(i, t) represents the hourly wind speed at the i# node within the time range (t1, t2), and the predicted wind speed value at the node exceeding the standard means u(x i , t) ≤ wind speed threshold;

[0131] SF5. Screen the time periods Δt when the predicted air temperature values at each node exceed the standard T and the tunnel section Δl T , where T f (x i , t) represents the hourly air temperature at the i# node within the time range (t1, t2), and the predicted air temperature value at the node exceeding the standard means T f (x i , t) ≥ temperature threshold;

[0132] SF6, pollutant concentration evaluation can select one or more of CO concentration, particulate matter mass concentration, and nitrogen oxide concentration as evaluation objects, and screen the time period Δt when the predicted value of each pollutant concentration exceeds the standard c and tunnel section Δl c , where C(x i ,t) represents the hourly pollutant concentration at the i#th node within the time range (t1,t2). The predicted pollutant concentration exceeding the standard means that C(x i ,t)≥corresponding pollutant concentration threshold;

[0133] SF7, calculate the node parameter exceeding the standard time period Δt = min (Δt u ,Δt T ,Δt c ), the tunnel section with excessive standard Δl=max(Δl u ,Δl T ,Δl c ), when Δt<target duration threshold, and Δl<target section length threshold, the tunnel environment meets the control requirements; when Δt≥target duration threshold, or Δl≥target section length threshold, the tunnel environment does not meet the control requirements, and the fan start-up plan is further adjusted;

[0134] SF8. Determine the alternative fan start-up schemes 1, 2, ..., m according to the fan setting in the tunnel;

[0135] SF9. For fan start-up schemes 1, 2, ..., m, repeat SC1-SC11, SD1-SD10, SE1-SE7 operations in sequence, calculate the hourly wind speed, air temperature, and pollutant concentration of each node within the time range (t1, t2) under different fan start-up schemes, and select the time period and tunnel section where the node wind speed, air temperature, and pollutant concentration exceed the standard, where Δt m Indicates the time period when the node parameter exceeds the standard under the fan start-up scheme m, Δl m Indicates the tunnel section where the node parameters exceed the standard under the fan start-up scheme m;

[0136] SF10. Based on the principle of the shortest time period of node parameter exceeding the standard or the smallest tunnel section exceeding the standard, select the best fan start-up plan from the alternative fan start-up plans 1, 2, ..., m, and feed back the results to the tunnel operation management personnel.

[0137] In this embodiment, the research team conducted on-site measurements of wind speed, temperature, and pollutant concentration during the evening peak hours in Yuhan Tunnel, Jinan City from January 4, 2022 to January 8, 2022. A total of four measuring points were arranged for the test, namely 1# (210m), 2# (1290m), 3# (1820m), and 4# (2540m). During the test, the tunnel was in a natural ventilation state without fans turned on.

[0138] (1) Wind speed

[0139] Taking the wind speed on January 6 (Thursday) as an example, it can be seen that the wind speed at each measuring point began to decrease after 17:40 during the evening peak period. During the period from 18:40 to 19:05, the wind speed at measuring points 2# and 3# was less than 1.5m / s, which is lower than the minimum wind speed requirement for longitudinal ventilation (such as Figure 4 shown).

[0140] (2) Temperature

[0141] During the working day, the temperature gradients of the 1#-2# measuring points were all greater than 3°C / km. According to the distance of each measuring point from the tunnel entrance, it can be seen that the temperature rise gradient of the tunnel section before the 2# measuring point was greater than 3°C / km, that is, more than 43% of the entire tunnel section exceeded the standard limit (such as Figure 5 shown).

[0142] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0143] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0144] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent evaluation system for thermal and humid environment and air pollutants in urban tunnels, characterized in that: The system includes the following modules; a monitoring area division module, a monitoring data acquisition module, a vehicle information analysis module, a pollutant concentration determination module, and a predicted value evaluation module; The monitoring area division module is used to divide the tunnel into N sections and mark them according to the camera positions; The monitoring data acquisition module uses the video frame difference technology to determine the vehicle information passing through the section; The vehicle information analysis module is used to analyze the relationship between the heat emitted by the engines of different vehicle models and the temperature value of this section of the tunnel; The pollutant concentration determination module is used to analyze the relationship between the pollutant concentration in each section and time; The predicted value evaluation module is used to predict the spatio-temporal change values of the wind speed, temperature, and pollutant concentration in each section, and then evaluate the prediction results, so as to control the start and stop of the fans in this section of the tunnel.

2. According to claim 1, the intelligent evaluation system for urban tunnel thermal and humid environment and air pollutants is characterized in that: The monitoring area division module includes a tunnel division unit and a node marking unit: The tunnel division unit is used to divide the tunnel into N sections and set a camera device in each section; The calculation node marking unit is used to mark them in sequence according to the positions of the cameras, denoted as 0#, 1#, 2#,..., i#,..., n#, where 0# is the tunnel entrance position and n# is the tunnel exit position. There are 4 sensors in the tunnel. One sensor is configured at 0#, denoted as sensor 1, and the other 3 sensors are evenly arranged along the tunnel length, denoted as sensor 2, sensor 3, and sensor 4 respectively. Among them, sensor 1 needs to measure the wind speed, temperature, and pollutant concentration to provide the boundary conditions for calculation, and the remaining sensors need to measure the change of wind speed with time.

3. The intelligent evaluation system for thermal and humid environment and air pollutants in urban tunnels according to claim 2 is characterized in that: The method for calculating the temperature value and pollutant concentration corresponding to the section in the tunnel under the calculated average wind speed includes the following steps: SA1, according to the formula u i =u(x i ,t) to obtain the functional relationship between wind speed and time from the tunnel entrance to node i#, where u i represents wind speed, x i Indicates the distance between the current node and the tunnel entrance; SA2, according to formula T f，i =T f (x i ,t) to obtain the functional relationship between the temperature and time from the tunnel entrance to the node i#, where T f，i represents the temperature, x i Indicates the distance between the current node and the tunnel entrance; SA3, according to formula C i =C(x i ,t) to obtain the functional relationship between the pollutant concentration and time from the tunnel entrance to the node i#, where C i represents the pollutant concentration, x i Indicates the distance between the current node and the tunnel entrance.

4. The intelligent evaluation system for thermal and humid environment and air pollutants in urban tunnels according to claim 3 is characterized in that: The method for using the video frame difference technology to determine the vehicle information passing through the section includes the following steps: SB1. Obtain the monitoring videos of each section; SB2. Use the video frame difference technology to determine the traffic flow, vehicle speed, and vehicle type ratio passing through the section; SB3. Obtain the tunnel size parameter values, fan parameter values, and initial wind speed conditions; SB4. Analyze the piston force caused by vehicle movement, the friction force of the tunnel wall, equipment, and vehicles, the orifice pressure difference caused by the environmental wind speed, and the lifting force exerted by the jet fan to obtain the effective value of the average wind speed in each section.

5. The intelligent evaluation system for thermal and humid environment and air pollutants in urban tunnels according to claim 4 is characterized in that: The method for analyzing the piston force F1 caused by vehicle movement, the friction force F2 of the tunnel wall, equipment, and vehicles, the orifice pressure difference F3 caused by the environmental wind speed, and the lifting force F4 exerted by the jet fan to obtain the effective value of the average wind speed in each section includes the following steps: SC1, according to the formula Get the average wind speed u in section I I The governing equation is t represents the cross-sectional area of ​​the tunnel, ρ I represents the ambient air density in the tunnel of section I, F1 represents the piston force caused by vehicle movement, F2 represents the friction between the tunnel wall, equipment and vehicle, F3 represents the pressure difference at the tunnel entrance caused by the ambient wind speed, F4 represents the lift pressure applied by the jet fan, Δx I represents the length of the tunnel in section I, u I =u I (t) represents the hourly average wind speed in the tunnel of Section I; SC2, according to the formula The piston force caused by the vehicle movement in section I is obtained, where A j represents the frontal area of ​​the jth type of vehicle, Cd j represents the drag coefficient of the j-th vehicle, V I,j represents the speed of the jth type of vehicle in the tunnel of section I, N I,j represents the number of vehicles of type j in the tunnel of section I, n cat Indicates the total number of types of vehicles in the tunnel; SC3, according to the formula The friction force of tunnel wall, equipment and vehicle in section I is obtained, where f ve represents the equivalent friction coefficient of the vehicle, f w Denotes the equivalent friction coefficient of the tunnel wall and equipment, D e represents the hydraulic diameter of the tunnel; SC4, according to the formula The pressure difference at the tunnel entrance caused by the ambient wind speed in the tunnel section I is obtained, where ρ0 represents the ambient air density in the tunnel, u0 represents the ambient wind speed, ξ en represents the tunnel entrance loss coefficient, ξ ex represents the tunnel exit loss coefficient, and L represents the tunnel length; SC5, according to the formula F4 = ρ i K F A F ·n F，I (v F -u I )|v F |Get the lift pressure applied by the jet fan in section I, where K F Indicates the pressure rise coefficient of the jet fan, A F represents the jet fan outlet area, n F，I represents the number of jet fans running in the tunnel of section I, v F Indicates the exhaust speed of the jet fan; SC6, according to the formula The wind speed control equation of the tunnel in section I is obtained, where F5 = -ρ1ξ en A t u1|u1|, represents the flow resistance at the tunnel entrance, ρ1 represents the ambient air density of the first section of the tunnel, and u1 represents the average wind speed in the first section; F6 = -ρ N ξ ex A t u N |u N |, represents the flow resistance at the tunnel exit, ρ N represents the ambient air density of the tunnel in section N, u N represents the average wind speed in the Nth section; SC7. Input the initial conditions i-1 node initial wind speed u(0, t1), u(x1, t1), ..., u(x i , t i ), ..., u(x n , t n ), the average wind speed of the first section is obtained by calculation; SC8. Assuming that the wind speed in each section of the tunnel conforms to the polynomial variation law, calculate the hourly wind speed prediction value u at sensor 2 during the test period (t1, t2) 2# (t), hourly wind speed prediction value u at sensor 3 3# (t), hourly wind speed prediction value u at sensor 4 4# (t); and the wind speed value u measured by sensor 2 2#e , Sensor 3 measured wind speed value u 3#e , Sensor 4 measured wind speed value u 4#e By comparison, the hourly wind speed prediction error Δ of sensor 2, sensor 3, and sensor 4 is calculated. 2# , Δ 3# , Δ 4# , further calculation to obtain the hourly wind speed average prediction error Δ=(Δ 2# +Δ 3# +Δ 4# ) / 3, where t3 represents the end time of the test; SC9. When the hourly average wind speed prediction error Δ exceeds the target threshold, adjust the aerodynamic parameters in each section of the tunnel, including the vehicle drag coefficient Cd j Value, vehicle equivalent friction coefficient f ve Value, jet fan boost coefficient K F value, repeat SC1-SC7 calculation; SC10, when the hourly wind speed average prediction error Δ is less than or equal to the target threshold, the average wind speed in each section of the tunnel calculated at this time is determined to be the effective value of the hourly wind speed in each section within the time range (t1, t2) u1(t),...,u I (t),...,u N (t), where t represents the calculation time, u I (t) represents the hourly average wind speed in the tunnel of Section I; SC11. Assuming that the wind speed in each section of the tunnel conforms to the polynomial variation law, calculate the hourly wind speed u(0,t),...,u(x i ,t)...,u(x n ,t), where x i represents the distance between node i# and the tunnel entrance, t represents the calculation time, u(x i ,t) represents the hourly wind speed of node i#.

6. The intelligent evaluation system for thermal and humid environment and air pollutants in urban tunnels according to claim 5 is characterized in that: The method for the vehicle information analysis module to analyze the relationship between the heat emitted by the engines of different vehicle models and the temperature value of this section of the tunnel includes the following steps: SD1. Obtain vehicle information according to the monitoring video; SD2. Query the China Vehicle Environmental Management Annual Report and PIARC 2019 Ventilation Report Database to obtain the vehicle weight m of different models j and the corresponding vehicle engine efficiency η e,j ; SD3. Obtain the slope s of the tunnel section I I ; SD4. Get the actual power P of the jet fan F And the corresponding jet fan efficiency η F ; SD5. According to the energy balance principle, the energy conservation equation in the tunnel of Section I is established, and the expression is: Among them C p represents the specific heat capacity of air at constant pressure, T f,i =T f (x i ,t) represents the hourly air temperature at node i#, Q1 represents the heat dissipation of the automobile engine in the first section of the tunnel, Q2 represents the heat dissipation of the jet fan in the first section of the tunnel, Q3 represents the heat dissipation of the lighting in the first section of the tunnel, and Q4 represents the heat transferred from the air to the tunnel wall; SD6, according to the formula Get the heat of the car engine in the tunnel section I, where m j represents the mass of the j-type vehicle, V I,j represents the speed of the jth type of vehicle in the tunnel of section I, N I,j represents the number of vehicles of the jth type in the tunnel of the first section, η e,j represents the engine efficiency of the j-type vehicle; SD7, according to the formula Q2 = η F,I P F (1-η F ) to obtain the heat dissipation of the jet fan in the first section of the tunnel, where η F,I represents the number of jet fans in the tunnel section I, P F Indicates the actual motor power value of the jet fan, η F represents the efficiency of the jet fan; SD8, according to the formula Q3 = q L,I Δx i The heat dissipation of lighting in the tunnel section I is obtained, where q L,I It indicates the amount of heat dissipated by lighting for every kilometer of tunnel length; SD9, according to the formula Q4 = -hA w,I (T f，I -T w，I ) to obtain the heat transferred from the air to the tunnel wall, where h represents the convection heat transfer coefficient between the air and the tunnel wall, A w,I represents the surface area of ​​the tunnel wall in section I, T w，I represents the tunnel wall temperature of section I, T f,I Indicates the air temperature in the tunnel of Section I; SD10. Calculate the hourly air temperature T of each node within the time range (t1, t2) according to the energy conservation equation in the tunnel. f (x1,t),...,T f (x i ,t),...,T f (x n ,t), where x i represents the distance between node i# and the tunnel entrance, t represents the calculation time, T f (x i ,t) represents the hourly air temperature of node i#.

7. The intelligent evaluation system for thermal and humid environment and air pollutants in urban tunnels according to claim 6 is characterized in that: The method for analyzing the relationship between the pollutant concentration in each section and time includes the following steps: SE1. Obtain vehicle information according to the monitoring video; SE2. Combined with the China Vehicle Environmental Management Annual Report and the PIARC 2019 Ventilation Report Database, set the basic emission rates for different vehicle models. j ; SE3. Obtain the time correction value f that affects the pollutant concentration t , vehicle mass correction value f m and altitude correction value f a ; SE4. Input the effective hourly wind speed u1(t),...,u in each section of the tunnel within the time range (t1, t2). I (t),...,u N (t), input the hourly pollutant concentration C(0,t) of node 0 within the time range (t1,t2); SE5, according to the formula Establish the pollutant concentration control equation, where ρ I represents the ambient air density of the tunnel in section I, u I represents the average wind speed in the tunnel of section I, A t represents the cross-sectional area of ​​the tunnel, S I represents the pollutant emission rate in the tunnel of Section I; SE6, according to the formula Get the pollutant emission rate in the tunnel of section I, where n cat Indicates the total number of types of vehicles in the tunnel, g j represents the baseline emission rate of pollutants for the jth type of vehicle, g non-ex Represents the equivalent emission rate of pollutants emitted by non-motor vehicles, N I,j represents the number of vehicles of the jth type in the tunnel of section I; f t is the time correction value, f m is the vehicle mass correction value, f a is the altitude correction value; SE7. Obtain the initial conditions of each pollutant in the PIARC 2019 database, and bring the parameters that affect the pollutant concentration into the pollutant concentration control equation to calculate the hourly pollutant concentration C(x1,t),...,C(x i ,t),...,C(x n ,t), where x i represents the distance between node i# and the tunnel entrance, t represents the calculation time, C(x i ,t) represents the hourly pollutant concentration of node i#.

8. The intelligent evaluation system for thermal and humid environment and air pollutants in urban tunnels according to claim 1 is characterized in that: The method for evaluating the prediction results and then controlling the start and stop of the fans in this section of the tunnel includes the following steps: SF1. According to the formula u(0, t), T f (0, t), C(0, t) where t1 < t < t2 to obtain the boundary and initial conditions of the period t = t1, t2); SF2, according to the formula u(0, t10, u(x1, t10, ..., u(x n ,t10,...,u(x i , t1)t1< Calculate the initial wind speed when t < t2; SF3, coupling the average wind speed control equation, energy conservation equation, pollutant concentration control equation, boundary initial conditions of period t = (t1, t2) and initial wind speed to obtain the average wind speed of each section, the temperature of each calculation node and the pollutant concentration; SF4, filter the time period Δt where the wind speed forecast value of each node exceeds the standard u and tunnel section Δl u , where u(i,t) represents the hourly wind speed at the i#th node within the time range (t1,t2). The predicted wind speed value of a node exceeds the standard when u(x i ,t)≤wind speed threshold; SF5, filter the time period Δt when the predicted value of air temperature at each node exceeds the standard T and tunnel section Δl T , where T f (x i ,t) represents the hourly air temperature of the i#th node within the time range (t1,t2). The predicted value of the node air temperature exceeds the standard if T f (x i ,t)≥temperature threshold; SF6, pollutant concentration evaluation can select one or more of CO concentration, particulate matter mass concentration, and nitrogen oxide concentration as evaluation objects, and screen the time period Δt when the predicted value of each pollutant concentration exceeds the standard c and tunnel section Δl c , where C(x i ,t) represents the hourly pollutant concentration at the i#th node within the time range (t1,t2). The predicted pollutant concentration exceeding the standard means that C(x i ,t)≥corresponding pollutant concentration threshold; SF7, calculate the node parameter exceeding the standard time period Δt = min (Δt u ,Δt T ,Δt c ), the tunnel section with excessive standard Δl=max(Δl u ,Δl T ,Δl c ), when Δt<target duration threshold, and Δl<target section length threshold, the tunnel environment meets the control requirements; when Δt≥target duration threshold, or Δl≥target section length threshold, the tunnel environment does not meet the control requirements, and the fan start-up plan is further adjusted; SF8. Determine the alternative fan start-up schemes 1, 2, ..., m according to the fan setting in the tunnel; SF9. For fan start-up schemes 1, 2, ..., m, repeat SC1-SC11, SD1-SD10, SE1-SE7 operations in sequence, calculate the hourly wind speed, air temperature, and pollutant concentration of each node within the time range (t1, t2) under different fan start-up schemes, and select the time period and tunnel section where the node wind speed, air temperature, and pollutant concentration exceed the standard, where Δt m Indicates the time period when the node parameter exceeds the standard under the fan start-up scheme m, Δl m Indicates the tunnel section where the node parameters exceed the standard under the fan start-up scheme m; SF10. Based on the principle of the shortest time period in which node parameters exceed the standard or the smallest tunnel section in which the parameters exceed the standard, select the best fan start-up plan from the alternative fan start-up plans 1, 2, ..., m, and feed back the results to the tunnel operation management personnel.

Images