CN112432230A - Intelligent ventilation monitoring system and control method based on pollution source position information - Google Patents

Intelligent ventilation monitoring system and control method based on pollution source position information Download PDF

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CN112432230A
CN112432230A CN202011281097.XA CN202011281097A CN112432230A CN 112432230 A CN112432230 A CN 112432230A CN 202011281097 A CN202011281097 A CN 202011281097A CN 112432230 A CN112432230 A CN 112432230A
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ventilation
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曹世杰
任宸
冯壮波
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Southeast University
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F1/00—Room units for air-conditioning, e.g. separate or self-contained units or units receiving primary air from a central station
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F24F11/64—Electronic processing using pre-stored data
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/70—Control systems characterised by their outputs; Constructional details thereof
    • F24F11/72—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure
    • F24F11/74—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/89—Arrangement or mounting of control or safety devices
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F13/00—Details common to, or for air-conditioning, air-humidification, ventilation or use of air currents for screening
    • F24F13/02—Ducting arrangements
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F13/00—Details common to, or for air-conditioning, air-humidification, ventilation or use of air currents for screening
    • F24F13/02—Ducting arrangements
    • F24F13/06—Outlets for directing or distributing air into rooms or spaces, e.g. ceiling air diffuser
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/50—Air quality properties
    • F24F2110/64—Airborne particle content
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/50—Air quality properties
    • F24F2110/65—Concentration of specific substances or contaminants
    • F24F2110/70—Carbon dioxide

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Abstract

The invention discloses an intelligent ventilation monitoring system and a control method based on pollution source position information, the intelligent ventilation monitoring system comprises an air supply unit, an air conditioning unit and a return air unit, the air supply unit is arranged in the middle of walls on two sides of a building, the air supply unit comprises a spherical jet air inlet and an air supply pipeline arranged on one side of the air inlet, the air conditioning unit is arranged in the top area of the building, the return air unit is arranged at the bottom position of the wall, the return air unit comprises a strip seam type return air inlet and a return air pipeline arranged on one side of the return air inlet, and the air supply unit and the return air unit are connected with the air conditioning unit. The intelligent ventilation system can respond data in real time according to the concentration of pollutants, realize rapid prediction of the distribution result of the pollutants, finally realize intelligent control of the ventilation system, solve the problems that the traditional ventilation control system cannot realize accurate and intelligent regulation and control based on sensor monitoring data and the like, and build a healthier, more comfortable and energy-saving public building indoor environment.

Description

Intelligent ventilation monitoring system and control method based on pollution source position information
Technical Field
The invention relates to the field of ventilation monitoring systems, in particular to an intelligent ventilation monitoring system based on pollution source position information.
Background
The world health organization reports show that as the content of indoor air pollutants increases, the indoor air quality gradually decreases and has a serious impact on the health and productivity of people. For the indoor air pollution problem, formaldehyde and PM are included2.5And CO2Most pollutants in the air can be used as important indexes for evaluating the indoor air sanitation quality, when the content of the pollutants reaches a certain content, the pollutants can pose serious threat to human health, and no purifier can fully and effectively filter the air pollutants. Therefore, how to effectively remove the common indoor pollutants becomes an urgent necessity to solve the indoor air sanitation problem, especially for public space buildings. The ventilation system can be used as an effective means for reducing the concentration of indoor pollutants, however, most public buildings still have the problems of incomplete ventilation system, high energy consumption, difficult management and the like, and the phenomenon of opening doors and windows to open air conditioners is common. Therefore, in order to improve the ventilation efficiency on the premise of ensuring the indoor air quality, the intelligent ventilation system is also paid more and more attention by people.
The application of the current intelligent ventilation monitoring system mainly comprises the following steps: the intelligent monitoring system comprises an office multi-ventilation system (combination of mechanical ventilation and natural ventilation) based on monitoring data of an air outlet sensor, an air supply and exhaust intelligent monitoring system (suitable for industrial buildings) combined with data of a working area sensor and the like. Most of the systems realize intelligent control of the ventilation system according to a difference value between monitoring data and a control target on the basis of known indoor environment variables (such as pollutant concentration) and monitoring information. However, the sensor monitoring data is often single-point data, and cannot directly represent the distribution condition of regional or global environmental parameters, which may finally cause that the ventilation system cannot be accurately regulated and controlled, or even directly fails.
Disclosure of Invention
In order to solve the defects mentioned in the background art, the invention aims to provide an intelligent ventilation monitoring system and a control method based on the position information of a pollution source, which can realize the rapid prediction of a pollutant distribution result according to the real-time response data of the pollutant concentration, finally realize the intelligent control of a ventilation system, solve the problems that the traditional ventilation control system cannot realize accurate and intelligent regulation and control based on sensor monitoring data, and create a healthier, more comfortable and more energy-saving public building indoor environment.
The purpose of the invention can be realized by the following technical scheme:
an intelligent ventilation monitoring system based on pollution source location information comprises an air supply unit, an air conditioning unit and a return air unit, wherein the air supply unit is arranged in the middle of walls on two sides of a building, the air supply unit comprises a spherical jet air port and an air supply pipeline arranged on one side of the air port, the air conditioning unit is arranged in the top area of the building, the return air unit is arranged on the bottom of the wall, the return air unit comprises a strip seam type return air port and a return air pipeline arranged on one side of the return air port, and the air supply unit and the return air unit are connected with the air conditioning unit;
the intelligent ventilation monitoring system also comprises a monitoring unit arranged in a target area, and the monitoring unit can pass through CO2And PM2.5The pollutant sensor monitors pollutant concentration value, the monitoring unit is in signal connection with the control unit, and the control unit comprises a flow controller and the like.
The air supply pipeline is S-shaped, the air return pipeline is L-shaped, and the pipe orifices of the air supply pipeline and the air return pipeline are rectangular.
The air supply unit is arranged in the middle area of the wall on two sides of the building, the air supply pipeline is connected with the spherical jet air inlet, and the air return pipeline is connected with the strip seam type air return inlet.
The air conditioning unit is arranged at the top of the building, the air conditioner is respectively connected with the air return pipeline and the air supply pipeline, and the air conditioning unit feeds the indoor air introduced by the air return pipeline into the air supply unit again after filtration, cooling or heating treatment.
The air conditioning unit is provided with a fan assembly, and the fan assembly is provided with a flow controller for controlling the air supply speed.
A control method of an intelligent ventilation monitoring system based on pollution source position information comprises the following steps:
(1) constructing a database: the method comprises the following steps of completing simulation calculation of all ventilation cases by using numerical simulation open source software, obtaining numerical results of pollutant distribution and using the numerical results to construct a basic database, and designing the ventilation cases by considering parameters such as the existing ventilation mode, ventilation times, the position of a pollution source and the like;
(2) and (3) expanding the database: according to the linear superposition principle, when a plurality of pollution sources exist, the pollutant distribution result generated by the pollution sources is equal to the superposition of the pollutant distribution results generated when the pollution sources act independently, namely, the pollutant distribution result generated by any number of pollution sources can be rapidly obtained by using the principle, and the effective expansion of a database is completed;
(3) and (3) reconstructing the database: the method for reducing the grid data size by using the dimension reduction discretization method comprises the following three processes: (i) dividing a grid with a volume of omega into a plurality of volumes of omegaiWherein i is 1 to N, and (x, y, z) coordinates corresponding to the microcube lattice and lattice node data corresponding to the (x, y, z) coordinates are sequentially divided into an array aiPerforming the following steps; (ii) based on each array AiCalculating the volume average value of the data in each small cube by using the grid node data and the coordinate values; (iii) sequentially replacing grid node data of corresponding small cubes with volume average data results to realize discretization processing of high-precision data, namely realizing reconstruction of a basic database;
(4) training and prediction of the database: deep learning and training are carried out on the reconstructed database by adopting a machine learning method, a target variable is rapidly obtained, the expanded and reconstructed basic database is rapidly trained and tested by adopting a common radial basis function RBF in an ANN, when an output variable meets a convergence criterion, the RBF is automatically stopped, and finally effective prediction of the target variable is realized;
(5) the online monitoring of the intelligent ventilation system is realized by combining a prediction database: in order to reasonably evaluate the weight relationship between the number of ventilation and the concentration of pollutants, two variables can be weighted into an evaluation index EvThe expression is as follows:
Figure BDA0002780756110000041
wherein, w1And w2The weight coefficients of the ventilation times ACH and the average pollutant concentration Cmean of the respiratory area are respectively;
will EvAnd the minimum value is used as an optimal ventilation strategy evaluation standard, optimal ventilation strategies corresponding to different pollution source position information are determined according to the monitoring data of the pollution source position in the target area, and optimal ventilation frequency data corresponding to the optimal ventilation strategies are transmitted to a flow controller of the fan assembly, so that the online control of the intelligent ventilation system is realized.
The invention has the beneficial effects that:
1. the numerical simulation software OpenFOAM code open source adopted by the invention can automatically customize a numerical solving method, so that the calculation result is closer to the actual ventilation working condition.
2. The dimension reduction linear method (namely linear superposition and discretization method) adopted by the invention can effectively reduce the data volume required to be stored in the database, save the data storage cost and reduce the data calculation time.
3. The artificial neural network method adopted by the invention can accurately realize the rapid prediction of the target variable (such as pollutant concentration), and saves the time and cost of numerical simulation calculation.
4. The invention has the advantages that the quantity of the adopted sensors is less, but the information of the target area or the global environment parameter can be effectively obtained, the optimal ventilation strategy is determined, and the accurate control of the intelligent ventilation system is realized.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a schematic view of the overall layout of the ventilation monitoring system of the present invention;
FIG. 2 is a front view of a spherical jet tuyere of the ventilation monitoring system of the present invention;
FIG. 3 is a top view of a spherical jet tuyere of the ventilation monitoring system of the present invention;
FIG. 4 is a flow chart of the steps of the control method of the present invention;
FIG. 5 is a schematic diagram of a control method database reconstruction according to the present invention;
FIG. 6 is a database training and prediction schematic of the control method of the present invention;
FIG. 7 is a graph comparing the linear superposition result and the actual simulation result under the combined action of the pollution sources A and B;
FIG. 8 is a graph comparing discrete processing results with actual simulation results under certain ventilation modes and the effect of a pollution source A according to an embodiment of the present invention;
FIG. 9 is a comparison graph of the neural network prediction result and the actual simulation result under the action of a certain ventilation mode and a single pollution source A in the embodiment of the invention.
In the figure:
1-an air conditioning unit; 2-an air supply pipeline; 3-a return air duct; 4-a fan assembly; 5-a spherical jet tuyere; 6-air return unit; 7-target area; 8-contaminant sensor.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "opening," "upper," "lower," "thickness," "top," "middle," "length," "inner," "peripheral," and the like are used in an orientation or positional relationship that is merely for convenience in describing and simplifying the description, and do not indicate or imply that the referenced component or element must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be considered as limiting the present invention.
As shown in fig. 1, the design of an intelligent ventilation monitoring system for a certain building based on the location information of a pollution source: the geometric dimension of the building related to the embodiment is 10m (length) × 10m (width) × 5m (height), and the conversion of different ventilation modes can be realized by adjusting the closing modes of the air supply and exhaust outlets. Five air exchange times are set for each ventilation mode, namely 4, 6, 8, 10 and 12, four pollution source positions are set, and coordinates of the four air exchange times are A (2.875,2.55 and 1.7) m, B (7.625,2.55 and 1.7) m, C (2.875,7.85 and 1.7) m and D (7.625,7.85 and 1.7) m.
An intelligent ventilation monitoring system based on pollution source position information comprises an air supply unit, an air conditioning unit 1 and a return air unit 6, wherein the air supply unit is arranged in the middle of walls on two sides of a building, the air supply unit comprises a spherical jet air port 5 and an air supply pipeline 2 arranged on one side of the air port, the air conditioning unit 1 is arranged in the top area of the building, the return air unit 6 is arranged on the bottom of the wall, the return air unit 5 comprises a strip seam type return air port and a return air pipeline 3 arranged on one side of the return air port, and the air supply unit and the return air unit 6 are connected with the air conditioning unit 1;
the intelligent ventilation monitoring system based on the position information of the pollution source further comprises a monitoring unit arranged in the target area 7, and the monitoring unit can pass through CO2And PM2.5The pollutant sensor 8 monitors pollutant concentration value, the monitoring unit is in signal connection with the control unit, and the control unit comprises a flow controller and the like.
The air supply pipeline 2 is S-shaped, the air return pipeline 3 is L-shaped, and the pipe openings of the air supply pipeline 2 and the air return pipeline 3 are rectangular.
The air supply unit is arranged in the middle area of the wall on two sides of the building, the air supply pipeline 2 is connected to the spherical jet air inlet 5 of the air supply unit, and the air return pipeline 3 is connected to the 6 seam type air return inlets of the air return unit.
Air conditioning unit 1 sets up in building top position, and air conditioning unit 1 connects return air duct 3 and supply air duct 2 respectively, and air conditioning unit 1 sends into the air supply unit again after filtering, cooling or thermal treatment with the leading-in indoor air of return air duct.
The air conditioning unit 1 is provided with a fan assembly 4, and the fan assembly 4 is provided with a flow controller for controlling the air supply speed.
The control method of the intelligent ventilation monitoring system based on the pollution source position information specifically comprises the following steps:
(1) constructing a database: numerical simulation software OpenFOAM is adopted to complete numerical simulation calculation of ventilation cases, the embodiment relates to four ventilation modes, five ventilation times and four pollution source positions, and therefore the basic database comprises 4 × 5 × 4 ═ 80 ventilation cases in total;
(2) and (3) expanding the database: the basic database is effectively expanded by using a linear superposition principle, fig. 7 shows that the larger the numerical value is, the higher the pollutant concentration is, the larger the comparison result is, the linear superposition method can effectively realize the rapid expansion of the database in consideration of the comparison condition (z is a 1.1m plane) between the linear superposition result and the actual simulation result (the pollutant mole fraction) under the combined action of the pollution sources a and B;
(3) and (3) reconstructing the database: in this case, the grid is uniformly divided into 3 × 3 × 3 ═ 27 small cubes to realize the reconstruction of the database, fig. 8 is a comparison between a certain ventilation mode and a discrete processing result under the action of the pollution source a and an actual simulation result (the mole fraction of pollutants), and it can be known from the comparison result that the dimension reduction method based on 27 equal divisions can effectively realize the reconstruction of the database;
(4) training and prediction of the database: the radial basis function is used to realize efficient prediction of pollutant concentration. The RBF neural network can be realized by a newrb function, and the function expression of the RBF neural network is as follows:
net＝newrb(P,T,goal,spread,MN,DF)
wherein, P is an input variable, T is an output variable, coarse is a target of mean square error, and spread is an expansion speed of a radial basis. MN is the maximum number of neurons, namely the network training can be stopped immediately after the number of neurons reaches MN, and DF is a network parameter added each time and is only used during output. Fig. 9 is a comparison between the neural network prediction result under the action of a certain ventilation mode and a single pollution source a and an actual simulation result (mean pollutant mole fraction in a respiratory region, Cmean), and it can be known from the comparison result that the neural network method can well achieve the training of a database and the rapid prediction of a target variable (such as pollutant concentration).
(5) The online monitoring of the intelligent ventilation system is realized by combining a prediction database, evaluation indexes and a flow controller: the ventilation evaluation index Ev is utilized to further realize the online evaluation of the intelligent ventilation system, the weight coefficients of the ventilation times ACH and the average pollutant concentration Cmean in the respiratory area are determined to be 0.45 and 0.55, the average pollutant concentration in the respiratory area and the energy consumption of the ventilation system are greatly reduced by the intelligent ventilation monitoring system and can reach 30 percent and 50 percent, and the high efficiency of the intelligent monitoring system is verified.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.

Claims (6)

1. The intelligent ventilation monitoring system based on the pollution source location information is characterized by comprising an air supply unit, an air conditioning unit (1) and a return air unit (6), wherein the air supply unit is arranged in the middle of walls on two sides of a building, the air supply unit comprises a spherical jet air port (5) and an air supply pipeline (2) arranged on one side of the air port, the air conditioning unit (1) is arranged in the top area of the building, the return air unit (6) is arranged at the bottom of the wall, the return air unit comprises a strip seam type return air port and a return air pipeline (3) arranged on one side of the return air port, and the air supply unit and the return air unit (6) are connected with the air conditioning unit (1);
the intelligent ventilation monitoring system also comprises a monitoring unit arranged in a target area (7), and the monitoring unit can pass through CO2And PM2.5Wait pollutant sensor (8) monitoring pollutant concentration numerical value, monitoring unit and the control unit signal connection, the control unit includes flow controller etc..
2. The intelligent ventilation monitoring system based on the pollution source location information as claimed in claim 1, wherein the air supply duct is S-shaped, the air return duct is L-shaped, and the openings of the air supply duct (2) and the air return duct (3) are rectangular.
3. The intelligent ventilation monitoring system based on the pollution source location information is characterized in that the air supply units are arranged in the middle areas of two side walls of a building, the air supply pipelines (2) are connected with spherical jet air openings (5), and the air return pipelines (3) are connected with strip seam type air return openings.
4. The intelligent ventilation monitoring system based on the pollution source location information as recited in claim 1, wherein the air conditioning unit (1) is arranged at the top of the building, the air conditioning unit (1) is respectively connected with the return air duct (3) and the supply air duct (2), and the air conditioning unit (1) re-sends the indoor air introduced by the return air duct to the supply air unit after being filtered, cooled or heated.
5. The intelligent pollution source location information-based ventilation monitoring system according to claim 1, wherein the air conditioning unit is provided with a fan assembly (4) provided with a flow controller for controlling the air supply speed.
6. A control method of an intelligent ventilation monitoring system based on pollution source position information is characterized by comprising the following steps:
(1) constructing a database: the method comprises the following steps of completing simulation calculation of all ventilation cases by using numerical simulation open source software, obtaining numerical results of pollutant distribution and using the numerical results to construct a basic database, and designing the ventilation cases by considering parameters such as the existing ventilation mode, ventilation times, the position of a pollution source and the like;
(2) and (3) expanding the database: according to the linear superposition principle, when a plurality of pollution sources exist, the pollutant distribution result generated by the pollution sources is equal to the superposition of the pollutant distribution results generated when the pollution sources act independently, namely, the pollutant distribution result generated by any number of pollution sources can be rapidly obtained by using the principle, and the effective expansion of a database is completed;
(3) and (3) reconstructing the database: the method for reducing the grid data size by using the dimension reduction discretization method comprises the following three processes: (i) dividing a grid with a volume of omega into a plurality of volumes of omegaiWherein i is 1 to N, and (x, y, z) coordinates corresponding to the microcube lattice and lattice node data corresponding to the (x, y, z) coordinates are sequentially divided into an array aiPerforming the following steps; (ii) based on each array AiCalculating the volume average value of the data in each small cube by using the grid node data and the coordinate values; (iii) sequentially replacing grid node data of corresponding small cubes with volume average data results to realize discretization processing of high-precision data, namely realizing reconstruction of a basic database;
(4) training and prediction of the database: deep learning and training are carried out on the reconstructed database by adopting a machine learning method, a target variable is rapidly obtained, the expanded and reconstructed basic database is rapidly trained and tested by adopting a common radial basis function RBF in an ANN, when an output variable meets a convergence criterion, the RBF is automatically stopped, and finally effective prediction of the target variable is realized;
(5) the online monitoring of the intelligent ventilation system is realized by combining a prediction database: to reasonably estimate the weighted relationship between ventilation frequency and pollutant concentration, two variables may be weightedBecomes an evaluation index EvThe expression is as follows:
Figure FDA0002780756100000031
wherein, w1And w2The weight coefficients of the ventilation times ACH and the average pollutant concentration Cmean of the respiratory area are respectively;
will EvAnd the minimum value is used as an optimal ventilation strategy evaluation standard, optimal ventilation strategies corresponding to different pollution source position information are determined according to the monitoring data of the pollution source position in the target area, and optimal ventilation frequency data corresponding to the optimal ventilation strategies are transmitted to a flow controller of the fan assembly, so that the online control of the intelligent ventilation system is realized.
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刘京: "《建筑环境计算流体力学及其应用》", 30 November 2017 *
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CN113175722A (en) * 2021-04-27 2021-07-27 广东天濠建设工程有限公司 Building ventilation energy-saving regulation and control system and method
CN113280490A (en) * 2021-06-26 2021-08-20 座头鲸通信技术(武汉)有限公司 New wind system control method and system based on Internet of things and artificial intelligence and storage medium
CN113280490B (en) * 2021-06-26 2022-07-01 浙江正理生能科技有限公司 Control method, control system and storage medium of fresh air system based on Internet of things and artificial intelligence
CN113883695A (en) * 2021-11-01 2022-01-04 东南大学 High-speed railway station bathroom self-adaptation ventilation system based on dynamic response

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