CN111005890B - Traffic tunnel ventilation control method - Google Patents

Traffic tunnel ventilation control method Download PDF

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Publication number
CN111005890B
CN111005890B CN201911100769.XA CN201911100769A CN111005890B CN 111005890 B CN111005890 B CN 111005890B CN 201911100769 A CN201911100769 A CN 201911100769A CN 111005890 B CN111005890 B CN 111005890B
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tunnel
traffic
pollutants
traffic volume
fans
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CN111005890A (en
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韩直
徐冲聪
周广振
朱湧
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China Merchants Chongqing Communications Research and Design Institute Co Ltd
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China Merchants Chongqing Communications Research and Design Institute Co Ltd
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
    • F04D—NON-POSITIVE-DISPLACEMENT PUMPS
    • F04D27/00—Control, e.g. regulation, of pumps, pumping installations or pumping systems specially adapted for elastic fluids
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
    • E21F1/00—Ventilation of mines or tunnels; Distribution of ventilating currents
    • E21F1/08—Ventilation arrangements in connection with air ducts, e.g. arrangements for mounting ventilators
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
    • E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
    • E21F17/18—Special adaptations of signalling or alarm devices

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Abstract

The invention provides a traffic tunnel ventilation control method, which comprises the steps of judging whether the concentration of pollutants in a tunnel exceeds the standard or not if the tunnel is a single tunnel, and entering the step S3 if the concentration of pollutants in the tunnel exceeds the standard; if the tunnel group is the tunnel group, determining the pollutant suction amount of the human body in the tunnel according to the concentration of the pollutants, and judging whether the pollutant suction amount is larger than a set threshold value, if not, keeping the working state of a conventional ventilation fan in the tunnel, and if so, entering the step S3; s3, predicting the traffic volume in the tunnel at the next time period according to the historical traffic volume data in the tunnel, and determining the number of fans in working states required under different pollutants at the next time period; s4, the number of the fans in the working state, which are required under different pollutants in the next period, is sequenced, the maximum value of the number of the fans in the working state is selected as the number of the fans required by the ventilation of the tunnel in the next period, and the required number of the fans can be accurately determined according to the pollutants in the tunnel and the traffic flow state.

Description

Traffic tunnel ventilation control method
Technical Field
The invention relates to a ventilation control method, in particular to a traffic tunnel ventilation control method.
Background
The distance between two places is effectively shortened in the highway tunnel, provides very big convenience for modern vehicle is current, and when the vehicle passed through the highway tunnel, because the relative closure in tunnel, the pollutant that the vehicle discharged was difficult to in time discharge the tunnel, mainly includes smoke and dust, carbon dioxide and nitrogen dioxide, and when driver and crew inhaled and contain excessive above-mentioned pollutant, can cause great harm to human health, consequently, the ventilation in tunnel is just crucial.
In the prior art, ventilation in a tunnel is mainly realized by using fans, the working states of the fans in the tunnel are generally set, for example, set according to seasons, for example, set according to peak traffic volume in the morning and evening, more than all fans are in a constant mode, that is, all fans of the fans are always in working states, and the number of the fans working in the modes is always kept constant in a set time, so that the above-mentioned technology has the following defects: if in invariable work fan count, if the pollutant content in the tunnel is low, then will cause the ventilation demand to be less than the air volume that the fan provided to cause the waste of resource, and on the other hand, if the ventilation demand is greater than the air volume that the fan provided, that is to say the fan quantity that is in operating condition can not satisfy the ventilation demand, will cause the pollutant content of the car emission in the tunnel to pile up this moment, thereby bring serious potential safety hazard for driver and crew.
Therefore, in order to solve the above technical problems, a new technical means is continuously proposed to solve the above problems.
Disclosure of Invention
In view of the above, an object of the present invention is to provide a method for controlling ventilation of a traffic tunnel, which can accurately determine the number of fans required according to pollutants in the tunnel and a traffic flow state, and can also accurately predict the number of fans required in a next time period, so as to ensure that the fans can control the content of pollutants in the tunnel within a safe range, and effectively avoid unnecessary power loads for providing additional fans for operation, thereby achieving a good energy saving effect.
The invention provides a traffic tunnel ventilation control method, which comprises the following steps:
s1, detecting the concentration and traffic information of pollutants in a tunnel, wherein the pollutants comprise smoke dust, carbon monoxide and nitrogen dioxide; the traffic information comprises traffic volume and vehicle speed;
s2, judging whether the current tunnel is a single tunnel or a tunnel group, if so, judging whether the concentration of pollutants in the tunnel exceeds the standard, and if so, entering the step S3; if not, keeping the working state of the conventional ventilation fan in the tunnel, if the tunnel group is adopted, determining the pollutant suction amount of the human body in the tunnel according to the concentration of pollutants, including the smoke suction amount, the carbon monoxide suction amount and the nitrogen dioxide suction amount, judging whether the pollutant suction amount is larger than a set threshold value, if not, keeping the working state of the conventional ventilation fan in the tunnel, and if so, entering the step S3;
s3, predicting the traffic volume in the tunnel at the next time period according to the historical traffic volume data in the tunnel, and determining the number of fans in working states required under different pollutants at the next time period;
s4, the number of the fans in the working state, which are required under different pollutants in the next time period, is sequenced, the maximum value of the number of the fans in the working state is selected as the number of the fans required by the tunnel ventilation in the next time period, and the fans are controlled to work in the next time period according to the required number of the fans.
Further, in step S3, the number of fans in operation required under different pollutants is determined according to the following method:
segmenting the detection time of the pollutant discharge amount in the tunnel, and constructing a detection time set T:
T＝{t1,t2,…,tj,…,tn}，tjdenotes the j-th detection period, j ═ 1,2, …, n denotes the total number of detection periods; the detection time is divided according to the minimum start-stop time interval of the fan;
constructing different types of traffic sets Q and average vehicle speed sets V in the tunnel:
Figure BDA0002269797230000021
Figure BDA0002269797230000031
Figure BDA0002269797230000032
representing the traffic volumes of different types of vehicles in the tunnel in the jth detection time period;
Figure BDA0002269797230000033
representing the average vehicle speed of the vehicles in the tunnel in the jth detection time period;
constructing a set P of pollutant emission quantitiesX：
Figure BDA0002269797230000034
Wherein the content of the first and second substances,
Figure BDA0002269797230000035
the emission amount of pollutants X in the jth detection time period is shown, and the pollutants X are respectively smoke dust, carbon monoxide and nitrogen dioxide;
constructing a pollutant discharge amount model:
Figure BDA0002269797230000036
wherein the content of the first and second substances,
Figure BDA0002269797230000037
indicating the number of fans which are required to be in a working state for discharging the pollutants X in the tunnel at the jth detection moment; beta is a1,β2,β3,β4Are all the fitting coefficients of the two-dimensional image,
Figure BDA0002269797230000038
calculating a residual error; calculating a fitting coefficient according to the pollutant discharge amount model; then the next time interval t of the current time interval is solved through a calculation formulak+1Number of required fans
Figure BDA0002269797230000039
Figure BDA00022697972300000310
Wherein the content of the first and second substances,
Figure BDA00022697972300000311
is tk+1The amount of the discharge of the pollutants X in the period,
Figure BDA00022697972300000312
is tk+1The average vehicle speed over the time period,
Figure BDA00022697972300000313
is tk+1The amount of traffic in the time slot.
Further, in step S2, the inhaled amount of pollutants in the tunnel by the human body is determined according to the following method:
determining the running time of the vehicle between the detection points:
determining the distance S (i, i-1) between the ith pollutant detection point and the (i-1) th detection point in the tunnel and determining the distance S (i, i +1) between the ith pollutant detection point and the (i +1) th detection point in the tunnel;
calculating the travel time t of a vehiclei：
Figure BDA0002269797230000041
Figure BDA0002269797230000042
Is the average vehicle speed of the vehicle;
calculating the pollutant suction quantity PT of the human body in the tunnel according to the following formulaX：
PTX＝∑X×tiWherein, the pollutants represented by X are smoke dust, carbon monoxide and nitrogen dioxide respectively.
Further, in step S3, the traffic volume in the tunnel and the traffic volumes of different types of vehicles in the tunnel in the next time period are predicted from the traffic volume in the current tunnel and the historical data according to the following method:
s31, selecting a time period t in the current monitoring timekThe previous r periods of traffic, and the time period tkTraffic of qkForming a traffic data group I, wherein the traffic data group I is qk-r,qk-r+1,…,qk-1,qk；
S32, screening r traffic volumes which are the same as the time node type of the traffic data group I in historical monitoring time to form a traffic volume data group II, wherein the traffic volume data group II is
Figure BDA0002269797230000043
S33, calculating correlation coefficients of the traffic volume data group I and the traffic volume data group II
Figure BDA0002269797230000044
Figure BDA0002269797230000045
Wherein, E (qq)s)、E(q)、E(qs) Mathematical expectations for traffic volume data set I and traffic volume data set II, D (q) and D (q)s) The variances of the traffic volume data group I and the traffic volume data group II are respectively;
s34, sorting the correlation coefficients of the traffic data II and the traffic volume data group I in each historical monitoring time, finding out the traffic volume data group II with the maximum correlation number in the historical monitoring time, and determining the maximum correlation coefficient alphamaxT of the monitoring timek+1、tk+2、…，tk+rTraffic volume of time interval
Figure BDA0002269797230000046
As interpolation to the current monitoring time and the traffic volume
Figure BDA0002269797230000047
As t in the current monitoring timek+1Traffic volume of time interval
Figure BDA0002269797230000051
Further, the method also comprises the step of comparing the traffic volume
Figure BDA0002269797230000052
Step S35 of performing correction:
construction of a modified model QF：
Figure BDA0002269797230000053
Wherein:
Figure BDA0002269797230000054
will calculate QFIs taken as the final current detection time tk+1Predicted traffic volume for the time slot.
The invention has the beneficial effects that: according to the invention, the required number of the fans can be accurately determined according to pollutants in the tunnel and traffic flow states, and the required number of the fans in the next period can be accurately predicted, so that the content of the pollutants in the tunnel can be controlled within a safe range by the fans, unnecessary power load for providing additional fans for operation can be effectively avoided, and a good energy-saving effect is achieved.
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The invention is further described below with reference to the following figures and examples:
FIG. 1 is a flow chart of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings, in which:
the invention provides a traffic tunnel ventilation control method, which comprises the following steps:
s1, detecting the concentration and traffic information of pollutants in a tunnel, wherein the pollutants comprise smoke dust, carbon monoxide, nitrogen dioxide and the like; the traffic information comprises traffic volume and vehicle speed; it should be noted that, in this embodiment, only three kinds of pollutants are listed, and in fact, there are far more than three kinds of pollutants in the tunnel, and other pollutants may be adopted for processing according to the actual tunnel situation, and are all applicable to the algorithm of the present invention;
s2, judging whether the current tunnel is a single tunnel or a tunnel group, if so, judging whether the concentration of pollutants in the tunnel exceeds the standard, and if so, entering the step S3; if not, keeping the working state of the conventional ventilation fan in the tunnel, if the tunnel group is adopted, determining the pollutant suction amount of the human body in the tunnel according to the concentration of pollutants, including the smoke suction amount, the carbon monoxide suction amount and the nitrogen dioxide suction amount, judging whether the pollutant suction amount is larger than a set threshold value, if not, keeping the working state of the conventional ventilation fan in the tunnel, and if so, entering the step S3; wherein, the tunnel group is composed of a plurality of continuous and adjacent single tunnels;
s3, predicting the traffic volume in the tunnel at the next time period according to the historical traffic volume data in the tunnel, and determining the number of fans in working states required under different pollutants at the next time period;
s4, the number of the fans in the working state, which are required under different pollutants in the next time period, is sequenced, the maximum value of the number of the fans in the working state is selected as the number of the fans required by the tunnel ventilation in the next time period, and the fans are controlled to work in the next time period according to the required number of the fans. The method comprises the steps that time intervals are divided according to the minimum start-stop time interval of fans in detection time, namely the minimum working time from start to stop in all fans in a tunnel, because the fans in the tunnel are not always in work but have working time limits, each fan has different continuous working time according to the specification, namely from start to stop, when the fan in the next time interval of the current time interval is controlled to work, the fan working in the current time interval does not work in the next time interval in the fans in the tunnel, the fan stopped in the current time interval works, and in the fans not working in the current time interval, the stop time of each fan is arranged in a descending order from large to small, and the fan with the largest stop time is started preferentially; therefore, the fan can be effectively prevented from continuously working or entering into work when the rest time is not reached; by the method, the number of the required fans can be accurately determined according to pollutants in the tunnel and traffic flow states, and the number of the required fans in the next period can be accurately predicted, so that the condition that the content of the pollutants in the tunnel can be controlled within a safe range by the fans can be ensured, unnecessary power loads for running of additional fans can be effectively avoided, and a good energy-saving effect is achieved.
In this embodiment, in step S3, the number of fans in the working state required under different pollutants is determined according to the following method:
segmenting the detection time of the pollutant discharge amount in the tunnel, and constructing a detection time set T:
T＝{t1,t2,…,tj,…,tn}，tjdenotes the j-th detection period, j ═ 1,2, …, n denotes the total number of detection periods; the detection time is divided according to the minimum start-stop time interval of the fan;
constructing different types of traffic sets Q and average vehicle speed sets V in the tunnel:
Figure BDA0002269797230000071
Figure BDA0002269797230000072
Figure BDA0002269797230000073
representing the traffic volumes of different types of vehicles in the tunnel in the jth detection time period;
Figure BDA0002269797230000074
representing the average vehicle speed of the vehicles in the tunnel in the jth detection time period;
constructing a set P of pollutant emission quantitiesX：
Figure BDA0002269797230000075
Wherein the content of the first and second substances,
Figure BDA0002269797230000076
the emission amount of pollutants X in the jth detection time period is shown, and the pollutants X are respectively smoke dust, carbon monoxide and nitrogen dioxide; for example, if the pollutant is carbon monoxide (chemical formula is CO), the emission amount of the pollutant is set to PXIn position PCOAnd set PCOThe subscripts in (1) will all be changed to CO, similarly if the contaminant is nitrogen dioxide (formula NO)2) Then the pollutant discharge amount is integrated into
Figure BDA0002269797230000077
If the pollutant is smoke, the letter VI is used for replacing the pollutant, and the pollutant emission amount is PVIIt should be noted that: the pollutant discharge amount does not mean the discharge amount of a vehicle, but the discharge amount of pollutants discharged from the tunnel by the fan is less than a safety threshold range;
constructing a pollutant discharge amount model:
Figure BDA0002269797230000078
wherein the content of the first and second substances,
Figure BDA0002269797230000079
indicating the number of fans which are required to be in a working state for discharging the pollutants X in the tunnel at the jth detection moment; beta is a1,β2,β3,β4Are all the fitting coefficients of the two-dimensional image,
Figure BDA0002269797230000081
calculating a residual error; calculating a fitting coefficient according to the pollutant discharge amount model; then the next time interval t of the current time interval is solved through a calculation formulak+1Number of required fans
Figure BDA0002269797230000082
Figure BDA0002269797230000083
Wherein the content of the first and second substances,
Figure BDA0002269797230000084
is tk+1The amount of the discharge of the pollutants X in the period,
Figure BDA0002269797230000085
is tk+1The average vehicle speed over the time period,
Figure BDA0002269797230000086
is tk+1The amount of traffic in the time slot.
By the method, the number of the fans required under different pollutant conditions can be accurately predicted, so that the ventilation of the tunnel in the next period can be accurately controlled.
In this embodiment, in step S2, the pollutant inhalation amount of the human body in the tunnel is determined according to the following method:
determining the running time of the vehicle between the detection points:
determining the distance S (i, i-1) between the ith pollutant detection point and the (i-1) th detection point in the tunnel and determining the distance S (i, i +1) between the ith pollutant detection point and the (i +1) th detection point in the tunnel;
calculating the travel time t of a vehiclei：
Figure BDA0002269797230000087
Figure BDA0002269797230000088
Is the average vehicle speed of the vehicle;
calculating the pollutant suction quantity PT of the human body in the tunnel according to the following formulaX：
PTX＝∑X×tiIn the method, the time required by the vehicle to pass through the distance between two detection points is not adopted, but the detection points are determined according to the method, so that the problem that the detection data of which detection point is used as the standard cannot be defined when the distance between the two detection points is directly calculated is avoided, and the accuracy of the calculation result is effectively improved, wherein when i-1 is 0, the position i-1 is the entrance of the tunnel, and i +1 is greater than M, the position i +1 is the exit of the tunnel, and M is the total number of the detection point arrangement in the whole tunnel.
In this embodiment, in step S3, the traffic volume in the tunnel and the traffic volumes of different types of vehicles in the tunnel in the next time period are predicted from the traffic volume in the current tunnel and the historical data according to the following method:
s31, selecting a time period t in the current monitoring timekThe previous r periods of traffic, and the time period tkTraffic of qkForming a traffic data group I, wherein the traffic data group I is qk-r,qk-r+1,…,qk-1,qk；
S32, screening r traffic volumes which are the same as the time node type of the traffic data group I in historical monitoring time to form a traffic volume data group II, wherein the traffic volume data group II is
Figure BDA0002269797230000091
S33, calculating correlation coefficients of the traffic volume data group I and the traffic volume data group II
Figure BDA0002269797230000092
Figure BDA0002269797230000093
Wherein, E (qq)s)、E(q)、E(qs) Mathematical expectations for traffic volume data set I and traffic volume data set II, D (q) and D (q)s) The variances of the traffic volume data group I and the traffic volume data group II are respectively;
s34, sorting the correlation coefficients of the traffic data II and the traffic volume data group I in each historical monitoring time, finding out the traffic volume data group II with the maximum correlation number in the historical monitoring time, and determining the maximum correlation coefficient alphamaxT of the monitoring timek+1、tk+2、…，tk+rTraffic volume of time interval
Figure BDA0002269797230000094
As interpolation to the current monitoring time and the traffic volume
Figure BDA0002269797230000095
As t in the current monitoring timek+1Traffic volume of time interval
Figure BDA0002269797230000096
Generally, the monitoring time is generally limited to days, that is, all-weather monitoring, and 24 hours of a day are divided into a plurality of detection periods, for example, 1 hour is a period, then a day is divided into 24 periods, and the total number n of the detection periods or detection periods is 24; during the monitoring time of day, if k is 12, then t iskThe time interval is 12 to 13 points, tk+1A period of 13 to 14 points; if r is 5, k-5 is the time point of 7 am, the corresponding time interval is 7-8 am, for example, the number of 9/15 in 2019 is the current detection time, and the historical data selects the number of time intervals in the interval of 7-12 am in the monitoring time before 9/15 in 2019 to calculate the correlation coefficient; for example, the day with the largest correlation coefficient is selected from the days of 6/30-9/14 in 2019, the day with the largest correlation coefficient is calculated to be 31/8 in 2019, the traffic volume in the 5 detection periods of 13-18 in the day is used as the interpolation of the traffic volume in the 5 periods of 13 points-18 points in 15/15 in 2019, and the traffic volume in the period of 13-14 in 31/8 in 2019 is used as the predicted traffic volume at 13 points-14 points in 15/15 in 2019
Figure BDA0002269797230000101
And the average vehicle speed in the period of 31, 13-14 in 8 and 31 months in 2019 is taken as the prediction of 13-14 points in 15 days in 9 and 15 months in 2019
Figure BDA0002269797230000102
Also includes the traffic volume
Figure BDA0002269797230000103
Step S35 of performing correction:
construction of a modified model QF：
Figure BDA0002269797230000104
Wherein:
Figure BDA0002269797230000105
also take 31/8/2019 and 15/9/2019 as examples, average traffic volume
Figure BDA0002269797230000106
The average value of the traffic volume of 5 time periods from 7 o 'clock to 12 o' clock in 9/15/2019, and the average value of the traffic volume
Figure BDA0002269797230000107
The average value of the traffic volume of 5 periods from 7 o ' clock to 12 o ' clock on 31 o ' clock 8 m 2019. By the method, the traffic volume of the target time period can be accurately predicted.
Will calculate QFIs taken as the final current detection time tk+1Predicted traffic volume for the time slot.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.

Claims (5)

1. A traffic tunnel ventilation control method is characterized in that: the method comprises the following steps:
s1, detecting the concentration and traffic information of pollutants in a tunnel, wherein the pollutants comprise smoke dust, carbon monoxide and nitrogen dioxide; the traffic information comprises traffic volume and vehicle speed;
s2, judging whether the current tunnel is a single tunnel or a tunnel group, if so, judging whether the concentration of pollutants in the tunnel exceeds the standard, and if so, entering the step S3; if not, keeping the working state of the conventional ventilation fan in the tunnel, if the tunnel group is adopted, determining the pollutant suction amount of the human body in the tunnel according to the concentration of pollutants, including the smoke suction amount, the carbon monoxide suction amount and the nitrogen dioxide suction amount, judging whether the pollutant suction amount is larger than a set threshold value, if not, keeping the working state of the conventional ventilation fan in the tunnel, and if so, entering the step S3;
s3, predicting the traffic volume in the tunnel at the next time period according to the historical traffic volume data in the tunnel, and determining the number of fans in working states required under different pollutants at the next time period;
s4, the number of the fans in the working state, which are required under different pollutants in the next time period, is sequenced, the maximum value of the number of the fans in the working state is selected as the number of the fans required by the tunnel ventilation in the next time period, and the fans are controlled to work in the next time period according to the required number of the fans.
2. The traffic tunnel ventilation control method according to claim 1, characterized in that: in step S3, the number of fans in working condition required under different pollutants is determined according to the following method:
segmenting the detection time of the pollutant discharge amount in the tunnel, and constructing a detection time set T:
T＝{t1,t2,…，tj,…,tn}，tjdenotes the j-th detection period, j ═ 1,2, …, n denotes the total number of detection periods; the detection time is divided according to the minimum start-stop time interval of the fan;
constructing different types of traffic sets Q and average vehicle speed sets V in the tunnel:
Figure FDA0003099356230000011
Figure FDA0003099356230000012
Figure FDA0003099356230000013
representing the traffic volumes of different types of vehicles in the tunnel in the jth detection time period;
Figure FDA0003099356230000014
representing the average vehicle speed of the vehicles in the tunnel in the jth detection time period;
constructing a set P of pollutant emission quantitiesX：
Figure FDA0003099356230000021
Wherein the content of the first and second substances,
Figure FDA0003099356230000022
the emission amount of pollutants X in the jth detection time period is shown, and the pollutants X are respectively smoke dust, carbon monoxide and nitrogen dioxide;
constructing a pollutant discharge amount model:
Figure FDA0003099356230000023
wherein the content of the first and second substances,
Figure FDA0003099356230000024
indicating the number of fans which are required to be in a working state for discharging the pollutants X in the tunnel at the jth detection moment; beta is a1,β2,β3,β4Are all the fitting coefficients of the two-dimensional image,
Figure FDA0003099356230000025
calculating a residual error; calculating a fitting coefficient according to the pollutant discharge amount model; then the next time interval t of the current time interval is solved through a calculation formulak+1Number of required fans
Figure FDA0003099356230000026
Figure FDA0003099356230000027
Wherein the content of the first and second substances,
Figure FDA0003099356230000028
is tk+1The amount of the discharge of the pollutants X in the period,
Figure FDA0003099356230000029
is tk+1The average vehicle speed over the time period,
Figure FDA00030993562300000210
is tk+1The amount of traffic in the time slot.
3. The traffic tunnel ventilation control method according to claim 1, characterized in that: in step S2, the amount of inhaled pollutants in the tunnel by the human body is determined according to the following method:
determining the running time of the vehicle between the detection points:
determining the distance S (i, i-1) between the ith pollutant detection point and the (i-1) th detection point in the tunnel and determining the distance S (i, i +1) between the ith pollutant detection point and the (i +1) th detection point in the tunnel;
calculating the travel time t of a vehiclei：
Figure FDA0003099356230000031
Figure FDA0003099356230000032
Is the average vehicle speed of the vehicle;
calculating the pollutant suction quantity PT of the human body in the tunnel according to the following formulaX：
PTX＝∑X×tiWherein, the pollutants represented by X are smoke dust, carbon monoxide and nitrogen dioxide respectively.
4. The traffic tunnel ventilation control method according to claim 2, characterized in that: in step S3, the traffic volume in the tunnel and the traffic volumes of different types of vehicles in the tunnel in the next time period are predicted from the traffic volume in the current tunnel and the historical data according to the following method:
s31, selecting a time period t in the current monitoring timekThe previous r periods of traffic, and the time period tkTraffic of qkForming a traffic data group I, wherein the traffic data group I is qk-r,qk-r+1,…,qk-1,qk；
S32, screening r traffic volumes which are the same as the time node type of the traffic data group I in historical monitoring time to form a traffic volume data group II, wherein the traffic volume data group II is
Figure FDA0003099356230000033
S33, calculating correlation coefficients of the traffic volume data group I and the traffic volume data group II
Figure FDA0003099356230000034
Figure FDA0003099356230000035
Wherein, E (qq)s)、E(q)、E(qs) Mathematical expectations for traffic volume data set I and traffic volume data set II, D (q) and D (q)s) The variances of the traffic volume data group I and the traffic volume data group II are respectively;
s34, sorting the correlation coefficients of the traffic data group II and the traffic data group I in each historical monitoring time, finding out the traffic data group II with the maximum correlation number in the historical monitoring time, and determining the maximum correlation coefficient alphamaxT of the monitoring timek+1、tk+2、…，tk+rTraffic volume of time interval
Figure FDA0003099356230000036
As interpolation to the current monitoring time and the traffic volume
Figure FDA0003099356230000037
As t in the current monitoring timek+1Traffic volume of time interval
Figure FDA0003099356230000043
5. The traffic tunnel ventilation control method according to claim 4, characterized in that: also includes the traffic volume
Figure FDA0003099356230000044
Step S35 of performing correction:
construction of a modified model QF：
Figure FDA0003099356230000041
Wherein:
Figure FDA0003099356230000042
will calculate QFIs taken as the final current detection time tk+1Predicted traffic volume for the time slot.
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