Intelligent tunnel ventilation control method and system based on adaptive algorithm

Abstract

The invention discloses a smart tunnel ventilation control method and system based on an adaptive algorithm, and the method comprises the steps: collecting all air quality data in a tunnel within a set time, and generating an air quality index curve according to the collected air quality data; performing polynomial function fitting on a harmful gas concentration curve, a temperature curve and an air pressure curve in the generated air quality index curve to obtain a three-dimensional model of air quality distribution in the tunnel; calculating the ventilation demand according to the relationship between the harmful gas concentration, temperature and air pressure data in the tunnel and the ventilation flow; and according to the three-dimensional model of the air mass distribution in the tunnel and the ventilation demand quantity function expression, calculating the compensation ventilation flow in different time periods, and based on the compensation ventilation flow, regulating and controlling the ventilation flow of the tunnel. The method has the effects that the air quality in the tunnel can be monitored in real time, the air quality change can be accurately described, and the ventilation requirement can be scientifically calculated, so that the adaptive flow compensation control of the ventilation system can be better realized, the ventilation effect and efficiency in the tunnel can be better improved, and the air quality in the tunnel and the driving safety can be better guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent tunnel ventilation control, and particularly relates to a smart tunnel ventilation control method and system based on an adaptive algorithm. BACKGROUND

[0002] In the operation process of a tunnel, good air quality is a key factor to ensure driving safety and personnel health. As a relatively closed space, a vehicle driving in a tunnel will emit a large amount of exhaust gas, which contains a large amount of harmful gases such as carbon monoxide (CO) and nitrogen dioxide (NO2), etc. At the same time, heat will be generated during vehicle driving, and the pressure difference between the inside and outside of the tunnel will also have a significant impact on the tunnel environment.

[0003] Traditional tunnel ventilation systems mostly adopt a fixed ventilation mode, i.e. running according to the pre-set ventilation volume and ventilation time, etc. This way cannot adaptively adjust in real time according to the actual changes of the air quality in the tunnel.

[0004] In view of this, the present inventors, through deep thinking of the problems encountered in work, by referring to a large number of scientific research data and literature, and through retrieval novelty search, gradually conceived and designed the present application to solve the related technical problems. SUMMARY

[0005] The present application aims to at least solve one of the technical problems in the related art. To this end, the purpose of the present application is to propose a smart tunnel ventilation control method and system based on an adaptive algorithm.

[0006] To achieve one of the above purposes, the smart tunnel ventilation control method based on an adaptive algorithm according to an embodiment of the present application comprises the following steps:

[0007] S1, collecting various air quality data in the tunnel within a set time, and generating an air quality index curve according to the collected various air quality data;

[0008] S2, performing polynomial function fitting on the harmful gas concentration curve, temperature curve and air pressure curve in the generated air quality index curve, to obtain a three-dimensional air quality distribution model in the tunnel;

[0009] S3, calculating a ventilation demand according to the relationship between the harmful gas concentration, temperature, air pressure data and ventilation flow in the tunnel;

[0010] The ventilation demand function expression is: Q=k1(C-C0)+k2(T-T0)+k3(P-P0);

[0011] Wherein, Q is the ventilation demand, C is the initial harmful gas concentration, C0 is the harmful gas concentration in the tunnel, T is the ambient temperature, T0 is the temperature value in the tunnel, P is the air pressure in the tunnel that needs to be adjusted, P0 is the difference between the set standard air pressure and the actual air pressure in the tunnel, k1, k2, k3 are the ventilation demand function coefficients;

[0012] S4, according to the air quality distribution three-dimensional model in the tunnel and the ventilation demand function expression, the compensation ventilation flow in different time periods is calculated, and the tunnel is ventilated based on the compensation ventilation flow.

[0013] In addition, the intelligent tunnel ventilation control method and system based on the adaptive algorithm according to the above-mentioned embodiments of the application can also have the following additional technical features:

[0014] According to an embodiment of the application, the step S1 specifically comprises:

[0015] S11, setting the air quality data collection point, the air quality data collection index and the air quality data collection frequency;

[0016] S12, collecting each item of air quality data in the tunnel within a set time according to the set air quality data collection point, air quality data collection index and air quality data collection frequency;

[0017] S13, generating an air quality index curve according to the collected each item of air quality data; the each item of air quality data includes tunnel harmful gas concentration data, tunnel temperature data and tunnel air pressure data.

[0018] According to an embodiment of the application, the air quality data collection frequency is determined according to the tunnel length, the curve radius air pressure data; the air quality data collection point includes the tunnel entrance, the tunnel middle section and the tunnel exit; the air quality data collection index includes the tunnel harmful gas concentration, the tunnel temperature and the tunnel air pressure.

[0019] According to an embodiment of the application, the higher the tunnel length, the curve radius and / or the vehicle flow, the greater the air quality data collection frequency; the distance between every two adjacent air quality data collection points is not less than 100 meters and not more than 200 meters.

[0020] According to an embodiment of the application, the step S2 specifically comprises:

[0021] S21, using a quadratic polynomial function to preliminarily fit the harmful gas concentration curve, the temperature curve and the air pressure curve in the air quality index curve;

[0022] S22, the harmful gas concentration curve, the temperature curve and the air pressure curve in the air quality index curve are fitted by using a cubic polynomial function to obtain a harmful gas concentration function, a temperature function and an air pressure function;

[0023] S23, the harmful gas concentration function coefficient, the temperature function coefficient and the air pressure function coefficient are determined by using a least square method to obtain a three-dimensional air quality distribution model in the tunnel.

[0024] According to one embodiment of the present application, the step S4 is specifically:

[0025] If the actual ventilation flow is less than the calculated ventilation demand Q, the ventilation flow is increased so that the final ventilation flow is equal to the calculated ventilation demand Q;

[0026] If the actual ventilation flow is greater than the calculated ventilation demand Q, the ventilation flow is reduced so that the final ventilation flow is equal to the calculated ventilation demand Q.

[0027] To achieve the above-mentioned second purpose, the intelligent tunnel ventilation control system based on the adaptive algorithm according to the embodiment of the present application comprises:

[0028] The air quality data acquisition module is used to acquire various air quality data in the tunnel within a set time, and generate an air quality index curve according to the acquired various air quality data;

[0029] The three-dimensional air quality distribution model establishment module is used to perform polynomial function fitting on the harmful gas concentration curve, the temperature curve and the air pressure curve in the generated air quality index curve to obtain a three-dimensional air quality distribution model in the tunnel;

[0030] The ventilation demand calculation module is used to calculate the ventilation demand according to the relationship among the harmful gas concentration, the temperature, the air pressure data and the ventilation flow in the tunnel;

[0031] The ventilation demand function expression is: Q=k1(C-C0)+k2(T-T0)+k3(P-P0);

[0032] Wherein, Q is the ventilation demand, C is the initial harmful gas concentration, C0 is the harmful gas concentration in the tunnel, T is the ambient temperature, T0 is the temperature value in the tunnel, P is the air pressure in the tunnel that needs to be adjusted, P0 is the difference between the set standard air pressure and the actual air pressure in the tunnel, k1, k2 and k3 are the ventilation demand function coefficients;

[0033] The tunnel ventilation flow regulation module is used to calculate compensation ventilation flow in different time periods according to the three-dimensional model of air quality distribution in the tunnel and the ventilation demand function expression, and regulate the ventilation flow of the tunnel based on the compensation ventilation flow.

[0034] In addition, the intelligent tunnel ventilation control system based on the adaptive algorithm according to the above-mentioned embodiments of the application can also have the following additional technical features:

[0035] According to one embodiment of the application, the air quality data acquisition module comprises a tunnel harmful gas concentration data acquisition module, a tunnel temperature data acquisition module and a tunnel air pressure data acquisition module.

[0036] According to one embodiment of the application, the tunnel harmful gas concentration data acquisition module, the tunnel temperature data acquisition module and the tunnel air pressure data acquisition module are provided at the tunnel entrance, the middle section of the tunnel and the tunnel exit.

[0037] According to one embodiment of the application, the distance between each adjacent two harmful gas concentration data acquisition modules, the distance between each adjacent two tunnel temperature data acquisition modules and the distance between each adjacent two tunnel air pressure data acquisition modules are not less than 100 meters and not more than 200 meters.

[0038] The beneficial effects of the application are:

[0039] The application can monitor the air quality in the tunnel in real time, accurately describe the change of air quality, and scientifically calculate the ventilation demand, so that the adaptive flow compensation control of the ventilation system can be better realized, and the ventilation effect and efficiency in the tunnel can be better improved, so as to better guarantee the air quality and driving safety in the tunnel.

[0040] Additional aspects and advantages of the application will be given in part in the following description, will become apparent from the following description, or will be learned by practicing the application. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only show some embodiments of the application, and for those skilled in the art, other drawings can also be obtained from the structures shown in the drawings without creative labor.

[0042] Figure 1 is a step flow chart of the intelligent tunnel ventilation control method based on the adaptive algorithm of the application;

[0043] Figure 2 is the step flow chart of the step S1 in the embodiment of the present application;

[0044] Figure 3 is the step flow chart of the step S2 in the embodiment of the present application;

[0045] Figure 4 is the overall block diagram of the intelligent tunnel ventilation control system based on adaptive algorithm of the present application;

[0046] Figure 5 is the display of the tunnel in which the tunnel harmful gas concentration data acquisition module, the tunnel temperature data acquisition module and the tunnel air pressure data acquisition module are distributed in the embodiment of the present application Figure 1 .

[0047] Figure 6 is the display of the tunnel in which the tunnel harmful gas concentration data acquisition module, the tunnel temperature data acquisition module and the tunnel air pressure data acquisition module are distributed in the embodiment of the present application Figure 2 .

[0048] Figure 7 is the display of the tunnel in which the tunnel harmful gas concentration data acquisition module, the tunnel temperature data acquisition module and the tunnel air pressure data acquisition module are distributed in the embodiment of the present application Figure 3 .

[0049] Reference signs:

[0050] Air quality data acquisition module 1000 of the intelligent tunnel ventilation control system based on adaptive algorithm;

[0051] Air quality data acquisition module 10;

[0052] Tunnel harmful gas concentration data acquisition module 101;

[0053] Tunnel temperature data acquisition module 102;

[0054] Tunnel air pressure data acquisition module 103;

[0055] Air quality distribution three-dimensional model establishment module 20;

[0056] Ventilation demand quantity calculation module 30;

[0057] Tunnel ventilation flow regulation and control module 40;

[0058] Ventilation equipment 50;

[0059] The implementation, functional characteristics and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0060] The embodiments of the present application will be described in detail below with reference to the accompanying drawings, examples of which are shown in the drawings, wherein the same or similar components have the same reference numerals throughout the several views. The embodiments described below are exemplary and are intended to explain the present application, and are not to be understood as limiting the present application, and all other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative effort fall within the scope of the present application.

[0061] In the description of the present application, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "circumferential", "radial" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings of the specification, and are only for the purpose of facilitating the description of the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the device or element indicated must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application.

[0062] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly specified and limited.

[0063] In the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connecting", "fixing" and the like should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be the communication inside two elements. For those of ordinary skill in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0064] In the present application, unless otherwise explicitly specified and limited, the first feature is "on" or "under" the second feature can include that the first and second features are in direct contact, or can include that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, the first feature is "on", "above" and "over" the second feature includes that the first feature is directly above and obliquely above the second feature, or only indicates that the first feature is higher in horizontal height than the second feature. The first feature is "under", "below" and "underneath" the second feature includes that the first feature is directly below and obliquely below the second feature, or only indicates that the first feature is lower in horizontal height than the second feature.

[0065] The adaptive algorithm-based intelligent tunnel ventilation control method of the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0066] Embodiment one

[0067] Referring to Figure 1 , Figure 2 , Figure 3 , Figure 5 , Figure 6 and Figure 7 ;

[0068] The adaptive algorithm-based intelligent tunnel ventilation control method provided by the embodiments of the present application comprises the following steps:

[0069] S1, collecting various air quality data in the tunnel within a set time, and generating an air quality index curve according to the collected various air quality data;

[0070] Preferably, in the method step, the step S1 specifically comprises:

[0071] S11, setting an air quality data collection point, an air quality data collection index and an air quality data collection frequency;

[0072] That is, the position of the air quality data collection point needs to be set in advance, the factors that the air quality data collection index needs to include and how often the air quality data collection is performed need to be set.

[0073] S12, collecting various air quality data in the tunnel within a set time according to the set air quality data collection point, air quality data collection index and air quality data collection frequency;

[0074] Specifically, harmful gas concentration data in the tunnel within a set time, tunnel temperature data and tunnel air pressure data are collected.

[0075] S13, generating an air quality index curve according to the collected air quality data, wherein the air quality data comprises tunnel harmful gas concentration data, tunnel temperature data and tunnel air pressure data.

[0076] S2, performing polynomial function fitting on the harmful gas concentration curve, the temperature curve and the air pressure curve in the generated air quality index curve to obtain a tunnel air quality distribution three-dimensional model.

[0077] S3, calculating ventilation demand according to the relationship among tunnel harmful gas concentration, temperature, air pressure data and ventilation flow;

[0078] The ventilation demand function expression is Q=k1(C-C0)+k2(T-T0)+k3(P-P0).

[0079] Wherein, Q is the ventilation demand, C is the initial harmful gas concentration, C0 is the tunnel harmful gas concentration, T is the ambient temperature, T0 is the tunnel temperature value, P is the tunnel air pressure to be adjusted, P0 is the difference between the set standard air pressure and the actual tunnel air pressure, k1, k2 and k3 are ventilation demand function coefficients.

[0080] S4, calculating compensation ventilation flow in different time periods according to the tunnel air quality distribution three-dimensional model and the ventilation demand function expression, and controlling the ventilation flow of the tunnel based on the compensation ventilation flow.

[0081] According to one embodiment of the present application, the step S4 is specifically:

[0082] For example, in the set current time period, if the actual ventilation flow is less than the calculated ventilation demand Q, the ventilation flow is increased so that the final ventilation flow is equal to the calculated ventilation demand Q; if the actual ventilation flow is greater than the calculated ventilation demand Q, the ventilation flow is reduced so that the final ventilation flow is equal to the calculated ventilation demand Q.

[0083] Similarly, in the set next time period, if the actual ventilation flow is less than the calculated ventilation demand Q, the ventilation flow is also increased so that the final ventilation flow is equal to the calculated ventilation demand Q; if the actual ventilation flow is greater than the calculated ventilation demand Q, the ventilation flow is also reduced so that the final ventilation flow is equal to the calculated ventilation demand Q.

[0084] Based on the above, it can be clear that the present application mainly provides a smart tunnel ventilation control method based on self-adaptive algorithm when implemented.

[0085] Adopt the wisdom tunnel ventilation control method based on the adaptive algorithm provided in the application, sequentially take the steps S1 to S4, that is, collect various air quality data in the tunnel within a set time, and generate an air quality index curve according to the collected various air quality data; then perform polynomial function fitting on the harmful gas concentration curve, temperature curve and air pressure curve in the generated air quality index curve to obtain a three-dimensional air quality distribution model in the tunnel; and then calculate the ventilation demand according to the relationship between the harmful gas concentration, temperature, air pressure data and ventilation flow in the tunnel; the ventilation demand function expression is: Q=k1(C-C0)+k2(T-T0)+k3(P-P0); wherein Q is the ventilation demand, C is the initial harmful gas concentration, C0 is the harmful gas concentration in the tunnel, T is the ambient temperature, T0 is the temperature value in the tunnel, P is the air pressure in the tunnel that needs to be adjusted, P0 is the difference between the set standard air pressure and the actual air pressure in the tunnel, k1, k2 and k3 are ventilation demand function coefficients; finally, calculate the compensation ventilation flow in different time periods according to the three-dimensional air quality distribution model in the tunnel and the ventilation demand function expression, and control the ventilation flow of the tunnel based on the compensation ventilation flow.

[0086] By adopting the above-mentioned related method, it is obvious that the air quality in the tunnel can be monitored in real time, the air quality change can be accurately described, and the ventilation demand can be scientifically calculated, so that the adaptive flow compensation control of the ventilation system can be better realized, and the ventilation effect and efficiency in the tunnel can be better improved, so as to better guarantee the air quality and driving safety in the tunnel.

[0087] Further, the above-optimized design makes the whole application have strong practicability and good use effect.

[0088] Further, in specific implementation, according to an embodiment of the application, the air quality data collection frequency is determined according to the tunnel length, curve radius and air pressure data; the air quality data collection points include the tunnel entrance, the middle section of the tunnel and the tunnel exit; and the air quality data collection indexes include the harmful gas concentration in the tunnel, the temperature in the tunnel and the air pressure in the tunnel.

[0089] It should be noted here that for tunnels in different regions and different positions, even if their lengths and curve radii are the same, the vehicle flow is different. For tunnels with larger vehicle flow, the exhaust gas is also more, so that the harmful gas concentration in the tunnel will also be more in unit time. At this time, the air quality data collection frequency of the tunnel will be relatively large. In specific implementation, the related air quality data collection frequency is flexibly set according to actual needs.

[0090] And, for the air quality data collection points, the tunnel entrance and the tunnel exit must be set, and then, according to the tunnel length, a plurality of air quality data collection points are uniformly spaced in the section.

[0091] And, for the air quality data collection points, the tunnel entrance and the tunnel exit must be set, and then, according to the tunnel length, a plurality of air quality data collection points are uniformly spaced in the section.

[0092] Further, in specific implementation, according to an embodiment of the present application, the higher the tunnel length, the curve radius and / or the vehicle flow, the greater the air quality data collection frequency; the distance between every two adjacent air quality data collection points is preferably not less than 100 meters and not more than 200 meters.

[0093] If the tunnel is less than 100 meters, its length is short, so it does not need to set the air quality data collection module 10 and the associated ventilation equipment 50 applied in the present application, at this time, the present application is not considered; as shown in the contrast Figure 5 If the tunnel length is greater than or equal to 100 meters and less than or equal to 200 meters, an air quality data collection point is set at the entrance and the exit, respectively, so that the corresponding two air quality data collection points are provided with a harmful gas concentration data collection module, a tunnel temperature data collection module 102 and a tunnel air pressure data collection module 103; as shown in the contrast Figure 6 If the tunnel length is greater than or equal to 200 meters and less than or equal to 400 meters, an air quality data collection point is set at the entrance, the center position and the exit, respectively, so that the corresponding three air quality data collection points are provided with a harmful gas concentration data collection module, a tunnel temperature data collection module 102 and a tunnel air pressure data collection module 103; as shown in the contrast Figure 7 If the tunnel length is greater than 400 meters and less than or equal to 600 meters, an air quality data collection point is set at the entrance, two positions spaced 100 meters to 200 meters from the center and the exit, respectively, so that the corresponding four air quality data collection points are provided with a harmful gas concentration data collection module, a tunnel temperature data collection module 102 and a tunnel air pressure data collection module 103; and so on, as long as the distance between every two adjacent air quality data collection points is not less than 100 meters and not more than 200 meters.

[0094] Further, in specific implementation, according to an embodiment of the present application, the step S2 specifically includes:

[0095] S21, using a quadratic polynomial function to preliminarily fit the harmful gas concentration curve, the temperature curve and the air pressure curve in the air quality index curve;

[0096] S22, the harmful gas concentration curve, the temperature curve and the air pressure curve in the air quality index curve are fitted by using a cubic polynomial function to obtain a harmful gas concentration function, a temperature function and an air pressure function;

[0097] S23, the harmful gas concentration function coefficient, the temperature function coefficient and the air pressure function coefficient are determined by using a least square method to obtain a three-dimensional air quality distribution model in the tunnel.

[0098] For the above-mentioned steps, the harmful gas concentration curve, the temperature curve and the air pressure curve in the air quality index curve are fitted by using a quadratic polynomial function and a cubic polynomial function, and the harmful gas concentration function coefficient, the temperature function coefficient and the air pressure function coefficient are determined by using a least square method, which is a mature technology, so it is not necessary to be described in detail here.

[0099] Embodiment Two

[0100] Combined with Figure 4 , Figure 5 , Figure 6 and Figure 7 are shown.

[0101] The intelligent tunnel ventilation control system 1000 based on an adaptive algorithm according to the embodiment of the application comprises:

[0102] An air quality data acquisition module 10 is used to acquire various air quality data in the tunnel within a set time, and generate an air quality index curve according to the acquired various air quality data.

[0103] An air quality distribution three-dimensional model establishment module 20 is used to perform polynomial function fitting on the harmful gas concentration curve, the temperature curve and the air pressure curve in the generated air quality index curve to obtain a three-dimensional air quality distribution model in the tunnel.

[0104] A ventilation demand quantity calculation module 30 is used to calculate the ventilation demand quantity according to the relationship among the harmful gas concentration, the temperature, the air pressure data and the ventilation flow in the tunnel.

[0105] The ventilation demand quantity function expression is: Q=k1(C-C0)+k2(T-T0)+k3(P-P0).

[0106] Wherein, Q is the ventilation demand, C is the initial harmful gas concentration, C0 is the harmful gas concentration in the tunnel, T is the ambient temperature, T0 is the temperature value in the tunnel, P is the air pressure in the tunnel that needs to be adjusted, P0 is the difference between the set standard air pressure and the actual air pressure in the tunnel, k1, k2, k3 are the ventilation demand function coefficients;

[0107] The tunnel ventilation flow control module 40 is used to calculate the compensation ventilation flow in different time periods according to the air quality distribution three-dimensional model in the tunnel and the ventilation demand function expression, and control the ventilation flow of the tunnel based on the compensation ventilation flow.

[0108] In the specific implementation, if the actual ventilation flow is less than the calculated ventilation demand Q in the set current time period, the ventilation flow of the ventilation equipment 50 arranged in the tunnel is increased by the tunnel ventilation flow control module 40, so that the final ventilation flow is equal to the calculated ventilation demand Q; if the actual ventilation flow is greater than the calculated ventilation demand Q in the set current time period, the ventilation flow of the ventilation equipment 50 arranged in the tunnel is reduced by the tunnel ventilation flow control module 40, so that the final ventilation flow is equal to the calculated ventilation demand Q.

[0109] Similarly, if the actual ventilation flow is less than the calculated ventilation demand Q in the set next time period, the ventilation flow of the ventilation equipment 50 arranged in the tunnel is also increased by the tunnel ventilation flow control module 40, so that the final ventilation flow is equal to the calculated ventilation demand Q; if the actual ventilation flow is greater than the calculated ventilation demand Q in the set next time period, the ventilation flow of the ventilation equipment 50 arranged in the tunnel is also reduced by the tunnel ventilation flow control module 40, so that the final ventilation flow is equal to the calculated ventilation demand Q.

[0110] By adopting the above-mentioned related method, it is obvious that the air quality in the tunnel can be monitored in real time, the air quality change can be accurately described, and the ventilation demand can be scientifically calculated, so that the adaptive flow compensation control of the ventilation system can be better realized, the ventilation effect and the ventilation efficiency in the tunnel can be better improved, and the air quality and the driving safety in the tunnel can be better ensured.

[0111] Further, the above-optimized design makes the whole application have strong practicability and good use effect.

[0112] Preferably, in the technical solution, according to one embodiment of the application, the air quality data acquisition module 10 includes a harmful gas concentration data acquisition module 101 in the tunnel, a temperature data acquisition module 102 in the tunnel, and an air pressure data acquisition module 103 in the tunnel.

[0113] Moreover, in a specific implementation, according to one embodiment of the present invention, the tunnel entrance, the middle section of the tunnel and the tunnel exit are all provided with the tunnel harmful gas concentration data acquisition module 101, the tunnel temperature data acquisition module 102 and the tunnel air pressure data acquisition module 103.

[0114] As a preferred solution, according to one embodiment of the present invention, the distance between each two adjacent harmful gas concentration data acquisition modules, the distance between each two adjacent tunnel temperature data acquisition modules 102, and the distance between each two adjacent tunnel air pressure data acquisition modules 103 are all not less than 100 meters and not more than 200 meters.

[0115] If the tunnel is less than 100 meters, because of its short length, it is not necessary to set the air quality data acquisition module 10 and the associated ventilation equipment 50 applied to the present application inside. In this case, the present application will not consider it. Figure 5 As shown, if the tunnel length is greater than or equal to 100 meters and less than or equal to 200 meters, an air quality data collection point is set at its entrance and exit respectively, so that a harmful gas concentration data collection module, a tunnel temperature data collection module 102 and a tunnel air pressure data collection module 103 are respectively set at the corresponding two air quality data collection points; Figure 6 As shown, if the tunnel length is greater than or equal to 200 meters and less than or equal to 400 meters, an air quality data collection point is set at its entrance, center position and exit respectively, so that a harmful gas concentration data collection module, a tunnel temperature data collection module 102 and a tunnel air pressure data collection module 103 are respectively set at the corresponding three air quality data collection points; Figure 7 As shown, if the length of the tunnel is greater than 400 meters and less than or equal to 600 meters, an air quality data collection point is set at its entrance, two positions at intervals of 100 to 200 meters in the middle, and the exit, so that a harmful gas concentration data collection module, a tunnel temperature data collection module 102, and a tunnel air pressure data collection module 103 are respectively set at the corresponding four air quality data collection points; and so on, as long as the distance between each adjacent two air quality data collection points is not less than 100 meters and not more than 200 meters.

[0116] It should be noted that, for the ventilation equipment 50 installed in the tunnel, it is preferred to set up multiple ones, and evenly distribute them between the multiple air quality data collection points. The exhaust direction is consistent with the driving direction of the lane, and they work in a linked manner.

[0117] Other embodiments and the like are not described here as examples.

[0118] In summary, the intelligent tunnel ventilation control method and system based on the adaptive algorithm provided in the application can realize real-time monitoring of air quality in the tunnel, accurate description of air quality change, and scientific calculation of ventilation demand in specific implementation, so that adaptive flow compensation control of the ventilation system can be better realized, and the ventilation effect and efficiency in the tunnel can be better improved, so as to better guarantee the air quality and driving safety in the tunnel.

[0119] Furthermore, the intelligent tunnel ventilation control method and system based on the adaptive algorithm provided in the application is extremely practical and has excellent use effect, so that the application will certainly have good market promotion value and will certainly be very popular and effectively popularized.

[0120] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the application. In the specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine the different embodiments or examples described in the specification and the features of the different embodiments or examples without contradiction.

[0121] The above is only the preferred embodiment of the application, and does not limit the patent scope of the application, and any equivalent structural transformation made by using the content of the specification and drawings, or direct / indirect application in other related technical fields within the inventive concept of the application is included in the patent protection scope of the application.

Claims

1. A smart tunnel ventilation control method based on an adaptive algorithm, characterized in that: The following steps are involved: S1. Collect various air quality data within a set time in the tunnel, and generate an air quality index curve based on the collected air quality data; S2. Performing polynomial function fitting on the harmful gas concentration curve, temperature curve, and air pressure curve in the generated air quality index curve to obtain a three-dimensional model of air quality distribution in the tunnel; S3. Calculate ventilation demand based on the relationship between harmful gas concentration, temperature, air pressure data and ventilation flow in the tunnel; The ventilation demand function expression is: Q = k1 (C-C0) + k2 (T-T0) + k3 (P-P0); Where Q is the ventilation demand, C is the initial harmful gas concentration, C0 is the harmful gas concentration in the tunnel, T is the external ambient temperature, T0 is the temperature in the tunnel, P is the air pressure that needs to be adjusted in the tunnel, P0 is the difference between the set standard air pressure and the actual air pressure in the tunnel, k1, k2, and k3 are the ventilation demand function coefficients; S4. Calculate the compensation ventilation flow in different time periods according to the three-dimensional model of air quality distribution in the tunnel and the ventilation demand function expression, and regulate the ventilation flow in the tunnel based on the compensation ventilation flow.

2. The intelligent tunnel ventilation control method based on adaptive algorithm according to claim 1 is characterized in that: The step S1 specifically includes: S11. Setting air quality data collection points, air quality data collection indicators and air quality data collection frequency; S12, collecting various air quality data within a set time in the tunnel according to the set air quality data collection points, air quality data collection indicators and air quality data collection frequency; S13. Generate an air quality index curve based on the collected various air quality data; the various air quality data include harmful gas concentration data in the tunnel, temperature data in the tunnel, and air pressure data in the tunnel.

3. The intelligent tunnel ventilation control method based on adaptive algorithm according to claim 2 is characterized in that: The air quality data collection frequency is determined based on the tunnel length, curve radius and air pressure data; the air quality data collection points include the tunnel entrance, the middle section of the tunnel and the tunnel exit; the air quality data collection indicators include the concentration of harmful gases in the tunnel, the temperature in the tunnel and the air pressure in the tunnel.

4. The intelligent tunnel ventilation control method based on adaptive algorithm according to claim 3 is characterized in that: The higher the tunnel length, curve radius and / or vehicle flow, the greater the frequency of air quality data collection; the distance between each two adjacent air quality data collection points is not less than 100 meters and not more than 200 meters.

5. The intelligent tunnel ventilation control method based on adaptive algorithm according to claim 1 is characterized in that: The step S2 specifically includes: S21. Performing primary fitting on the harmful gas concentration curve, temperature curve, and air pressure curve in the air quality index curve using a quadratic polynomial function; S22. Performing a secondary fit on the harmful gas concentration curve, the temperature curve, and the air pressure curve in the air quality index curve using a cubic polynomial function to obtain a harmful gas concentration function, a temperature function, and an air pressure function; S23. Use the least squares method to determine the harmful gas concentration function coefficient, temperature function coefficient, and air pressure function coefficient to obtain a three-dimensional model of air quality distribution in the tunnel.

6. The intelligent tunnel ventilation control method based on adaptive algorithm according to claim 5 is characterized in that: The step S4 is specifically as follows: If the actual ventilation flow rate is less than the calculated ventilation demand Q, the ventilation flow rate is increased so that the final ventilation flow rate is equal to the calculated ventilation demand Q; If the actual ventilation flow rate is greater than the calculated ventilation demand Q, the ventilation flow rate is reduced so that the final ventilation flow rate is equal to the calculated ventilation demand Q.

7. An intelligent tunnel ventilation control system based on an adaptive algorithm, characterized in that: include: The air quality data collection module is used to collect various air quality data within a set time in the tunnel and generate an air quality index curve based on the collected air quality data; The air quality distribution three-dimensional model building module is used to perform polynomial function fitting on the harmful gas concentration curve, temperature curve and air pressure curve in the generated air quality index curve to obtain the three-dimensional model of air quality distribution in the tunnel; The ventilation demand calculation module is used to calculate the ventilation demand based on the relationship between the harmful gas concentration, temperature, air pressure data and ventilation flow in the tunnel; The ventilation demand function expression is: Q = k1 (C-C0) + k2 (T-T0) + k3 (P-P0); Where Q is the ventilation demand, C is the initial harmful gas concentration, C0 is the harmful gas concentration in the tunnel, T is the external ambient temperature, T0 is the temperature in the tunnel, P is the air pressure that needs to be adjusted in the tunnel, P0 is the difference between the set standard air pressure and the actual air pressure in the tunnel, k1, k2, and k3 are the ventilation demand function coefficients; The tunnel ventilation flow control module is used to calculate the compensation ventilation flow in different time periods according to the three-dimensional model of air quality distribution in the tunnel and the ventilation demand function expression, and to control the ventilation flow in the tunnel based on the compensation ventilation flow.

8. The intelligent tunnel ventilation control system based on adaptive algorithm according to claim 7 is characterized in that: The air quality data acquisition module includes a harmful gas concentration data acquisition module in the tunnel, a temperature data acquisition module in the tunnel, and an air pressure data acquisition module in the tunnel.

9. The intelligent tunnel ventilation control system based on adaptive algorithm according to claim 8 is characterized in that: The tunnel entrance, the middle section of the tunnel and the tunnel exit are all provided with the tunnel harmful gas concentration data acquisition module, the tunnel temperature data acquisition module and the tunnel air pressure data acquisition module.

10. The intelligent tunnel ventilation control system based on adaptive algorithm according to claim 9 is characterized in that: The distance between each two adjacent harmful gas concentration data acquisition modules, the distance between each two adjacent tunnel temperature data acquisition modules, and the distance between each two adjacent tunnel air pressure data acquisition modules are all not less than 100 meters and not more than 200 meters.

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