CN112432317A - Sensor optimal arrangement method for classroom and ventilation monitoring system thereof - Google Patents

Sensor optimal arrangement method for classroom and ventilation monitoring system thereof Download PDF

Info

Publication number
CN112432317A
CN112432317A CN202011281038.2A CN202011281038A CN112432317A CN 112432317 A CN112432317 A CN 112432317A CN 202011281038 A CN202011281038 A CN 202011281038A CN 112432317 A CN112432317 A CN 112432317A
Authority
CN
China
Prior art keywords
classroom
ventilation
database
air
public
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202011281038.2A
Other languages
Chinese (zh)
Inventor
曹世杰
席畅
冯壮波
任宸
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Southeast University
Original Assignee
Southeast University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Southeast University filed Critical Southeast University
Priority to CN202011281038.2A priority Critical patent/CN112432317A/en
Publication of CN112432317A publication Critical patent/CN112432317A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • 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
    • 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
    • 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
    • F24F11/89—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
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Air Conditioning Control Device (AREA)
  • Ventilation (AREA)

Abstract

The invention discloses an optimal sensor arrangement method for a classroom and a ventilation monitoring system thereof, wherein the arrangement method comprises the following steps: s1, constructing a basic database; s2, expanding a database and performing low-dimensional discrete processing; s3, clustering analysis of the database; s4, making an optimal sensor arrangement strategy; and S5, constructing an intelligent ventilation monitoring system. The ventilation monitoring system comprises an air conditioning unit, a fan assembly, an air supply pipeline, a return air pipeline, a strip seam type air supply outlet, a strip seam type return air inlet and a fresh air inletWind unit, sensor and desk area. The invention is based on the low-dimensional linear data processing method, and can effectively reduce the storage cost and time cost of numerical calculation; based on unsupervised machine learning clustering algorithm, CO in public classroom can be realized2Concentration clustering is carried out, and a reasonable monitoring point deployment scheme is determined; for CO in public classroom2Concentration and position can realize the on-line quick control of an indoor ventilation system and can quickly reduce CO in a public classroom2And (4) concentration.

Description

Sensor optimal arrangement method for classroom and ventilation monitoring system thereof
Technical Field
The invention belongs to the field of room ventilation, and particularly relates to an optimal arrangement method of sensors for a classroom and a ventilation monitoring system thereof.
Background
The indoor air quality of public classrooms of primary and middle schools and colleges directly influences physical and mental health and learning efficiency of students, and people pay more attention to formaldehyde and PM in the research of the indoor air quality2.5And pollutants such as VOCs, and CO2As an important index for evaluating the indoor air sanitation quality, the method is often overlooked by people. When indoor CO2When the concentration of (C) is 1% (1000ppm), people feel bored, begin to have no concentration and have palpitation. Public classrooms of primary and middle schools and colleges have more people and are centralized, and long-term class in the classrooms can cause indoor CO2The concentration increased rapidly. Therefore, how to reduce CO efficiently in the public classroom2Concentration is a difficult problem which is urgently needed to be solved at present.
The current ventilation modes for public classrooms are mainly two modes of independent air conditioners and mixed ventilation, wherein the independent air conditioners can provide cooling and heating functions for the public classrooms independently, but when high-concentration CO exists in the rooms2The purification effect is small. When the mixed ventilation is used for removing indoor pollutants, the air supply amount is often higher than the actually required air amount, so that the problem of high energy consumption of the ventilation system is caused. Along with the rapid development of technologies such as internet of things monitoring and low-cost sensors, the intelligent ventilation system is gradually started, and the intelligent ventilation system is used for the CO of public classrooms2On-line concentration control still faces difficulties.
In view of the above-mentioned problems, an optimal sensor arrangement method for a classroom and a ventilation monitoring system thereof are designed.
Disclosure of Invention
Aim at the presentThe invention aims to provide an optimal sensor arrangement method for a classroom and a ventilation monitoring system thereof, which solve the problem that an independent air conditioner in the prior art has high-concentration CO indoors2The purification effect is small.
The purpose of the invention can be realized by the following technical scheme:
a method for optimal placement of sensors for a classroom, comprising the steps of:
s1, constructing a basic database, namely, completing the simulation calculation of all basic ventilation cases by using numerical simulation software to obtain CO2The distributed numerical results are used for constructing a basic database;
s2, expansion of database and low-dimensional discrete processing-according to the principle of linear superposition, when there are multiple COs2CO produced by pollution sources2Distribution results equal to these COs2CO produced when pollution sources act alone2Superposition of distribution results, i.e. by using the principle, any number of CO more than or equal to 2 can be quickly obtained2CO produced by pollution sources2Distributing the situation and finishing the effective expansion of the database;
s3, performing database clustering analysis, namely performing clustering analysis on the basic database and the expanded database by adopting a fuzzy clustering algorithm, so as to quickly obtain the pollution clustering information of the ventilation environment;
s4, making an optimal sensor arrangement strategy, namely, monitoring indoor pollution, mainly aiming at obtaining indoor pollution distribution information as comprehensive as possible so as to provide reference information for decisions such as evaluation and control of a ventilation system, and determining the number and the arrangement positions of sensors according to the set clustering number in a clustering algorithm and by combining the actual indoor volume and layout;
s5, constructing an intelligent ventilation monitoring system, and setting a specific ventilation system to realize CO in a public classroom2Effective discharge of the water.
Further, the S2 numerical simulation calculation is premised on the determination of a large number of grid nodes, and a discretization method is used to reduce the grid data volume, which is divided into the following three processes:
s2.1, dividing the numerical simulation grid with the volume of omega into a plurality of small cubic grids with the volume of omega i, wherein i is 1-N, N < < the number of grid nodes, and coordinates (x, y, z) corresponding to the small cubic grids are also sequentially divided into an array Ai;
s2.2, according to the arrays Ai, sequentially dividing the grid node data corresponding to the (x, y, z) coordinates into corresponding arrays, and calculating the volume average value of all data in each small cube based on the grid data in each array Ai;
and S2.3, expressing all grid data results of the corresponding minicubes by using the volume average data result, and realizing discrete processing of high-precision grid data, namely completing the reconstruction of the basic database.
Further, in S3, feature construction is performed on the database after the expansion and low-dimensional discretization processing, the database is used as an input of an algorithm, the data with the ventilation frequency i is used as a feature for cluster analysis, and the data with other ventilation frequencies is used for verification of the measurement point deployment.
Further, the ventilation monitoring system for the optimal arrangement method of the sensors in the classroom, which is provided by the step S5, includes an air conditioning unit disposed at the upper end of the outside of the public classroom, a fresh air unit for air circulation is disposed at the upper end of the air conditioning unit, and the fresh air unit is communicated with the air conditioning unit.
A ventilation monitoring system for an optimal sensor arrangement method of a classroom is characterized in that one end of an air conditioning unit is provided with an air supply pipeline, the other end of the air conditioning unit is provided with an air return pipeline, one end of the air supply pipeline is communicated with the air conditioning unit, and the other end of the air supply pipeline extends into a public classroom from the upper end outside the public classroom and is provided with a slit type air supply outlet.
Furthermore, one end of the air return pipeline is communicated with the air conditioning unit, the other end of the air return pipeline extends into the public classroom from the outer side end of the public classroom and is provided with a strip slit type air return opening, a fan assembly is arranged at the communication position of the air conditioning unit and the air supply pipeline, a desk area is arranged in the public classroom, and a sensor electrically connected with the public classroom is arranged in the desk area.
The invention has the beneficial effects that:
1. the optimal sensor arrangement method for the classroom and the ventilation monitoring system thereof are based on a low-dimensional linear data processing method, so that the storage cost and the time cost of numerical calculation can be effectively reduced;
2. the optimal sensor arrangement method for the classroom and the ventilation monitoring system thereof are based on the unsupervised machine learning clustering algorithm, and can realize the CO in the public classroom2Concentration clustering is carried out, and a reasonable monitoring point deployment scheme is determined;
3. the invention provides an optimal sensor arrangement method for a classroom and a ventilation monitoring system thereof, which aim at CO in a public classroom2Concentration and position can realize the on-line quick control of an indoor ventilation system and can quickly reduce CO in a public classroom2And (4) concentration.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a public classroom oriented CO of the present invention2A flow chart of a clustering control method;
FIG. 2 is a schematic diagram of the database expansion and low-dimensional discretization processing principles of the present invention;
FIG. 3 is a schematic diagram of database cluster analysis of the present invention;
FIG. 4 is a schematic view of the intelligent ventilation monitoring system of the present invention;
FIG. 5 is a schematic diagram of the contamination source location and cluster partition of the present invention;
fig. 6 is a schematic diagram of a sensor arrangement of the present invention.
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, a method for optimal arrangement of sensors for a classroom includes the steps of:
s1, constructing a basic database, namely, completing the simulation calculation of all basic ventilation cases by using numerical simulation software to obtain CO2The distributed numerical results are used for constructing a basic database;
s2, expansion of database and low-dimensional discrete processing-according to the principle of linear superposition, when there are multiple COs2CO produced by pollution sources (sites)2Distribution results equal to these COs2CO produced when pollution sources act alone2Superposition of distribution results, namely, by utilizing the principle, any amount (more than or equal to 2) of CO can be quickly obtained2CO produced by pollution sources2Distributing the situation and completing the effective expansion of the database, as shown in FIG. 2;
s3, performing database clustering analysis, namely performing clustering analysis on a basic database and an expanded database by adopting a Fuzzy clustering algorithm, and quickly obtaining pollution clustering information of the ventilation environment, wherein the common Fuzzy algorithm mainly comprises K-means (K-means clustering algorithm) and Fuzzy C-means (Fuzzy C-means clustering algorithm), and is shown in FIG. 3;
s4, making an optimal sensor arrangement strategy, namely, monitoring indoor pollution, mainly aiming at obtaining indoor pollution distribution information as comprehensive as possible so as to provide reference information for decisions such as evaluation and control of a ventilation system, and determining the number and the arrangement positions of sensors according to the set clustering number in a clustering algorithm and by combining the actual indoor volume and layout;
s5, constructing an intelligent ventilation monitoring system, and setting a specific ventilation system to realize CO in a public classroom2As shown in fig. 4.
All the S1 cases are fixed by considering the ventilation mode, including the number of times of ventilation (air supply) and single CO2And arranging and combining the positions of the pollution sources.
The S2 numerical simulation calculation is premised on the determination of a large number of mesh nodes, and thus a discretization method is used to reduce the amount of mesh data, which is divided into the following three processes:
s2.1, dividing the numerical simulation grid with the volume of omega into a plurality of small cubic grids with the volume of omega i, wherein i is 1-N, N < < the number of grid nodes, and coordinates (x, y, z) corresponding to the small cubic grids are also sequentially divided into an array Ai;
s2.2-grid node data (e.g., CO) corresponding to (x, y, z) coordinates according to the array Ai2Content) are also sequentially divided into corresponding arrays, and then the volume average value of all data in each small cube is calculated based on the grid data in each array Ai;
and S2.3, expressing all grid data results of the corresponding minicubes by using the volume average data result, and realizing the discrete (low-dimensional) processing of high-precision grid data, namely completing the reconstruction of the basic database.
And S3, performing feature construction on the database after the expansion and low-dimensional discretization processing, taking the database as an input of an algorithm, performing cluster analysis on data with the ventilation frequency i (i is 1-N, and N is the number of cubic blocks after the low-dimensional discretization) as features, and using the data with other ventilation frequencies for verification of the measurement point deployment.
The S4 can obtain more information about indoor pollution distribution as the number of stations increases. However, as the number of monitoring points increases to a certain number, on the one hand, the cost of the sensor increases; on the other hand, data information monitored by the measuring points has redundancy, and the load of the system in processing the information is increased. Therefore, the first task to construct a reliable and efficient indoor sensor network is to determine the number of sensors (i.e. the set clustering number in the fuzzy clustering algorithm).
The ventilation monitoring system for the optimal arrangement method of the sensors in the classroom, which is provided by the S5, comprises an air conditioning unit 1 arranged at the outer upper end of the public classroom, wherein a fresh air unit 7 used for air circulation is arranged at the upper end of the air conditioning unit 1, and the fresh air unit 7 is communicated with the air conditioning unit 1. And one end of the air conditioning unit 1 is provided with an air supply pipeline 3, and the other end is provided with an air return pipeline 4. One end of the air supply pipeline 3 is communicated with the air conditioning unit 1, and the other end of the air supply pipeline extends into the public classroom from the upper end outside the public classroom and is provided with a strip slit type air supply outlet 5. One end of the air return pipeline 4 is communicated with the air conditioning unit 1, and the other end of the air return pipeline extends into the public classroom from the outer side end of the public classroom and is provided with a strip seam type air return opening 6. And a fan assembly 2 is arranged at the communication position of the air conditioning unit 1 and the air supply pipeline 3. A desk area 9 is arranged in the public classroom, and a sensor 8 electrically connected with the public classroom is arranged in the desk area 9.
During actual ventilation, the sensor 8 monitors CO in the desk area 9 in real time2The air conditioning unit 1 and the fan assembly 2 are started, and outdoor fresh air enters the public classroom from the fresh air unit 7 through the air conditioning unit 1 and the air supply pipeline 3; turbid air in the public classroom is discharged through the air conditioning unit 1 and the fresh air unit 7 by the return air pipeline 4. The purpose of gas exchange in a public classroom is achieved.
The invention is further explained below by way of an example.
Example 1
The geometry size of the public classroom is 10m (length) x 6m (width) x 5m (height), and 3 CO at different positions are arranged in the room in consideration of 6 air exchange times2The source of the contamination, and therefore the base database, comprises a total of 6 x 3 ═ 18 ventilation cases.
Obtaining CO of all ventilation cases by adopting numerical simulation open source software FLUENT2The simulation results are distributed and used for basic database construction. Then, the linear superposition principle is applied to expand the database, namely, the existence of more databasesThe pollutant distribution result generated by each pollution source (position) is equal to the superposition of the pollutant distribution results generated by the pollution sources when the pollution sources act independently. Then, a low-dimensional discretization method is used for data preprocessing. The grid of the ventilation structure is evenly divided into 27 small cubes, 3 x 3, to achieve the reconstruction of the database, as shown in fig. 5, and is guaranteed to be within acceptable engineering tolerances (< 10%).
Based on the new database obtained by low-dimensional processing, deep machine learning can be performed according to specific ventilation case variables, that is, functional relationships among variables or data in the microcubes Ω i are explored, for example, according to a specific pollution source position and ventilation times, the number of sensors is determined by the set clustering number in the fuzzy clustering algorithm, as shown in fig. 6, and then the method is based on the CO in the public classroom2Location and concentration, quick response to the control scheme of the intelligent ventilation system.
In the above embodiment, the upper end of the public classroom is provided with an air supply outlet 10, and the side end is provided with an air return inlet 12. Wherein A, B, C is CO2The contamination source location, D, E, F, is the sensor placement location.
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. A method for optimal placement of sensors for a classroom, comprising the steps of:
s1, constructing a basic database, namely, completing the simulation calculation of all basic ventilation cases by using numerical simulation software to obtain CO2The distributed numerical results are used for constructing a basic database;
s2, expansion of database and low-dimensional discrete processing-according to the principle of linear superposition, when there are multiple COs2CO produced by pollution sources2Distribution results equal to these COs2CO produced when pollution sources act alone2The superposition of distribution results can be utilized to quickly obtain CO with the quantity more than or equal to 2 at will2CO produced by pollution sources2Distributing the situation and finishing the effective expansion of the database;
s3, performing database clustering analysis, namely performing clustering analysis on the basic database and the expanded database by adopting a fuzzy clustering algorithm, so that pollution clustering information in the ventilation environment can be quickly obtained;
s4, making an optimal sensor arrangement strategy, namely, monitoring indoor pollution, mainly aiming at obtaining indoor pollution distribution information as comprehensive as possible so as to provide reference information for decisions such as evaluation and control of a ventilation system, and determining the number and the arrangement positions of sensors according to the set clustering number in a clustering algorithm and by combining the volume and the arrangement of an actual room;
s5, constructing an intelligent ventilation monitoring system, and setting a specific ventilation system to realize CO in a public classroom2Effective discharge of the water.
2. The method as claimed in claim 1, wherein the S2 numerical simulation calculation is based on the determination of a large number of grid nodes, and a discretization method is used to reduce the grid data volume, which is divided into the following three processes:
s2.1, dividing the numerical simulation grid with the volume of omega into a plurality of small cubic grids with the volume of omega i, wherein i is 1-N, N < < the number of grid nodes, and coordinates (x, y, z) corresponding to the small cubic grids are also sequentially divided into an array Ai;
s2.2, according to the arrays Ai, sequentially dividing the grid node data corresponding to the (x, y, z) coordinates into corresponding arrays, and calculating the volume average value of all data in each small cube based on the grid data in each array Ai;
and S2.3, expressing all grid data results of the corresponding minicubes by using the volume average data result, and realizing dimension reduction and dispersion processing of high-precision grid data, namely completing the reconstruction of the basic database.
3. The method as claimed in claim 1, wherein the S3 is used to perform feature construction on the database after the expansion and dimension reduction discretization processing and to use the database as an input of the algorithm, perform cluster analysis on the data with ventilation frequency i as a feature, and use the data with other ventilation frequency for verification of the survey point deployment.
4. The method as claimed in claim 1, wherein the ventilation monitoring system of the optimal sensor arrangement method for the classroom, which is proposed by S5, comprises an air conditioning unit (1) disposed at the upper end of the exterior of the public classroom, wherein the upper end of the air conditioning unit (1) is provided with a fresh air unit (7) for ventilation, and the fresh air unit (7) is communicated with the air conditioning unit (1).
5. The ventilation monitoring system for the optimal arrangement method of the sensors in the classroom according to claim 4, characterized in that one end of the air conditioning unit (1) is provided with an air supply duct (3), the other end is provided with an air return duct (4), one end of the air supply duct (3) is communicated with the air conditioning unit (1), the other end of the air supply duct extends from the upper outer end of the public classroom into the public classroom and is provided with a slotted air supply outlet (5).
6. The ventilation monitoring system for the optimal arrangement method of the sensors in the classroom as claimed in claim 5, wherein one end of the return air duct (4) is connected with the air conditioning unit (1), the other end of the return air duct extends from the outer side end of the public classroom into the public classroom and is provided with a slit-shaped return air inlet (6), the connection position of the air conditioning unit (1) and the supply air duct (3) is provided with the fan assembly (2), a desk area (9) is arranged in the public classroom, and the sensor (8) electrically connected with the public classroom is arranged in the desk area (9).
CN202011281038.2A 2020-11-16 2020-11-16 Sensor optimal arrangement method for classroom and ventilation monitoring system thereof Pending CN112432317A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011281038.2A CN112432317A (en) 2020-11-16 2020-11-16 Sensor optimal arrangement method for classroom and ventilation monitoring system thereof

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011281038.2A CN112432317A (en) 2020-11-16 2020-11-16 Sensor optimal arrangement method for classroom and ventilation monitoring system thereof

Publications (1)

Publication Number Publication Date
CN112432317A true CN112432317A (en) 2021-03-02

Family

ID=74700092

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011281038.2A Pending CN112432317A (en) 2020-11-16 2020-11-16 Sensor optimal arrangement method for classroom and ventilation monitoring system thereof

Country Status (1)

Country Link
CN (1) CN112432317A (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090309744A1 (en) * 2008-06-13 2009-12-17 National Taiwan University System and method of detecting air pollution, route-planning method applied to said detection system, and warning method of air pollution
CN102967689A (en) * 2012-11-22 2013-03-13 天津大学 Pollution source identification method based on correlation coefficient and monitoring stationing method
CN104595995A (en) * 2014-12-31 2015-05-06 重庆川仪自动化股份有限公司 Purification, constant-temperature and constant-humidity system for regulating microenvironment of collection cultural relics
CN107607342A (en) * 2017-09-22 2018-01-19 沈阳工业大学 The healthy efficiency detection method of Air Conditioning Facilities device cluster
CN108446443A (en) * 2018-02-12 2018-08-24 北京工业大学 A kind of dynamic measurement method of ventilation of room ventilation volume
US20180312400A1 (en) * 2017-04-28 2018-11-01 University Of Southern California Biogas upgrading technology for power generation
CN108733893A (en) * 2018-04-25 2018-11-02 同济大学 The public building burst pollution of coupling depth learning method is traced to the source
CN111928407A (en) * 2020-08-10 2020-11-13 东南大学 Intelligent flow guiding and ventilating device and control method

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090309744A1 (en) * 2008-06-13 2009-12-17 National Taiwan University System and method of detecting air pollution, route-planning method applied to said detection system, and warning method of air pollution
CN102967689A (en) * 2012-11-22 2013-03-13 天津大学 Pollution source identification method based on correlation coefficient and monitoring stationing method
CN104595995A (en) * 2014-12-31 2015-05-06 重庆川仪自动化股份有限公司 Purification, constant-temperature and constant-humidity system for regulating microenvironment of collection cultural relics
US20180312400A1 (en) * 2017-04-28 2018-11-01 University Of Southern California Biogas upgrading technology for power generation
CN107607342A (en) * 2017-09-22 2018-01-19 沈阳工业大学 The healthy efficiency detection method of Air Conditioning Facilities device cluster
CN108446443A (en) * 2018-02-12 2018-08-24 北京工业大学 A kind of dynamic measurement method of ventilation of room ventilation volume
CN108733893A (en) * 2018-04-25 2018-11-02 同济大学 The public building burst pollution of coupling depth learning method is traced to the source
CN111928407A (en) * 2020-08-10 2020-11-13 东南大学 Intelligent flow guiding and ventilating device and control method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
SHI-JIE CAOA, CHEN REN: "Ventilation control strategy using low-dimensional linear ventilation models and artificial neural network", 《BUILDING AND ENVIRONMENT》 *

Similar Documents

Publication Publication Date Title
Yang et al. The coupled effects of personalized occupancy profile based HVAC schedules and room reassignment on building energy use
Ng et al. Indoor air quality analyses of commercial reference buildings
CN108800426B (en) Analysis method and evaluation method of variable air volume air conditioning system
Adekunle et al. Winter performance, occupants’ comfort and cold stress in prefabricated timber buildings
Wu et al. A top-down strategy with temporal and spatial partition for fault detection and diagnosis of building HVAC systems
CN112432230A (en) Intelligent ventilation monitoring system and control method based on pollution source position information
Brahme et al. Complex building performance analysis in the early stages of design
CN112432231B (en) Intelligent ventilation monitoring system and control method based on limited sensor
Belias et al. Outdoor PM2. 5 air filtration: optimising indoor air quality and energy
CN112432317A (en) Sensor optimal arrangement method for classroom and ventilation monitoring system thereof
CN112417553A (en) Limited sensor arrangement strategy and intelligent ventilation system of industrial building
CN112432229B (en) HVAC (heating ventilation and ventilation air conditioning) online monitoring system based on indoor humidity source information and control method
Utami et al. Optimal thermal sensors placement based on indoor thermal environment characterization by using CFD model
Kuo et al. The effect of diffuser's location on thermal comfort analysis with different air distribution strategies
Bai et al. Multi-criteria performance dynamic optimization for heating operation of stratum ventilation considering different metabolic rates
Fikry et al. Improving the indoor air quality (IAQ) in naturally ventilated lecture hall with a single facade by solar chimneys
Mysen et al. Robustness and true performance of demand controlled ventilation in educational buildings–review and needs for future development
Sadeghi et al. Effect of U-Value Factor in External Walls on Total Energy Consumption in an Office Building
Yau et al. Numerical simulation of a novel VRF-SV hybrid system performance in a large retail facility in the tropics
Talib et al. Modeling of chilled water VAV systems using a machine learning approach
Lee et al. Optimal Economizer Control of VAV System using Machine Learning
Nugrahanti et al. Building Simulation Software for Indoor Air Quality Research: A Review
Hong Assessment of energy savings potential from the use of demand control ventilation systems in general office spaces in California
Busato et al. Ventilation and energy efficiency in Air Systems for future buildings: a four dimensions approach
Emmerich et al. Impact of Natural Ventilation Strategies and Design Issues for California Applications, Including Input to ASHRAE Standard 62 and California Title 24–Report on Task 4.4. 3a and 4.4. 3b of CEC-EEB

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20210302