CN108302735A - Device and method for controlling air cleaning system - Google Patents
Device and method for controlling air cleaning system Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000004140 cleaning Methods 0.000 title abstract 6
- 238000004887 air purification Methods 0.000 claims abstract description 82
- 238000005457 optimization Methods 0.000 claims abstract description 34
- 238000005265 energy consumption Methods 0.000 claims abstract description 11
- 230000000694 effects Effects 0.000 claims description 45
- 238000012544 monitoring process Methods 0.000 claims description 21
- 239000003344 environmental pollutant Substances 0.000 claims description 17
- 231100000719 pollutant Toxicity 0.000 claims description 17
- 230000008859 change Effects 0.000 claims description 13
- 238000011217 control strategy Methods 0.000 claims description 13
- 238000003905 indoor air pollution Methods 0.000 claims description 12
- 230000002068 genetic effect Effects 0.000 claims description 9
- 241001269238 Data Species 0.000 abstract 1
- 238000013481 data capture Methods 0.000 abstract 1
- 238000000746 purification Methods 0.000 description 6
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 4
- MWUXSHHQAYIFBG-UHFFFAOYSA-N Nitric oxide Chemical compound O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 description 4
- XYFCBTPGUUZFHI-UHFFFAOYSA-N Phosphine Chemical compound P XYFCBTPGUUZFHI-UHFFFAOYSA-N 0.000 description 4
- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 description 4
- 238000004590 computer program Methods 0.000 description 4
- 238000013178 mathematical model Methods 0.000 description 4
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 3
- 230000007613 environmental effect Effects 0.000 description 3
- 230000036541 health Effects 0.000 description 3
- VEXZGXHMUGYJMC-UHFFFAOYSA-N Hydrochloric acid Chemical compound Cl VEXZGXHMUGYJMC-UHFFFAOYSA-N 0.000 description 2
- 239000000809 air pollutant Substances 0.000 description 2
- 231100001243 air pollutant Toxicity 0.000 description 2
- 238000003915 air pollution Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 229910002092 carbon dioxide Inorganic materials 0.000 description 2
- 239000001569 carbon dioxide Substances 0.000 description 2
- 229910002091 carbon monoxide Inorganic materials 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 229910000041 hydrogen chloride Inorganic materials 0.000 description 2
- IXCSERBJSXMMFS-UHFFFAOYSA-N hydrogen chloride Substances Cl.Cl IXCSERBJSXMMFS-UHFFFAOYSA-N 0.000 description 2
- 230000035772 mutation Effects 0.000 description 2
- 229910000073 phosphorus hydride Inorganic materials 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 description 1
- CBENFWSGALASAD-UHFFFAOYSA-N Ozone Chemical compound [O-][O+]=O CBENFWSGALASAD-UHFFFAOYSA-N 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 229910002056 binary alloy Inorganic materials 0.000 description 1
- 239000000356 contaminant Substances 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 238000005202 decontamination Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 239000003546 flue gas Substances 0.000 description 1
- -1 formaldehyde, benzene series Chemical class 0.000 description 1
- 239000003517 fume Substances 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 238000010353 genetic engineering Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 244000005700 microbiome Species 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000013618 particulate matter Substances 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
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- 238000006467 substitution reaction Methods 0.000 description 1
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B30/00—Energy efficient heating, ventilation or air conditioning [HVAC]
- Y02B30/70—Efficient control or regulation technologies, e.g. for control of refrigerant flow, motor or heating
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- Air Conditioning Control Device (AREA)
Abstract
This application provides a kind of equipment for controlling air cleaning system, including：Data capture unit, is configured as obtaining indoor and outdoors air quality datas, indoor air purification historical data and the estimated value of time for opening air cleaning system in advance；Modeling unit is configured as establishing Model for Multi-Objective Optimization based on acquired data and according to multiple targets；And control unit, it is configured as controlling the operation of air cleaning system by solving the Model for Multi-Objective Optimization.Present invention also provides a kind of methods for controlling air cleaning system.The application is ensureing comfortable living and working environment simultaneously, reduces the energy consumption of air cleaning system as much as possible.
Description
Technical Field
The present application relates to the field of data analysis, and in particular, to an apparatus and method for controlling an air purification system.
Background
With the advance of industrial development and modernization process, global air pollution is increasingly serious, and indoor air quality of people living and working is gradually deteriorated. According to statistics, people stay in various indoor environments for 70% -80%, and urban residents stay indoors for even 90%. The concentration of indoor pollutants such as formaldehyde, benzene series, ammonia gas, ozone and the like is far higher than that of the pollutants outdoors. In reality, the PM2.5 standard in the room is not inferior to that in the outdoor. The main sources of indoor PM2.5 include flue gas, microorganisms, kitchen fumes, air conditioners and the like, and are also affected by outdoor PM 2.5.
In terms of the influence of environmental pollution on human health, as people spend most of the day indoors, the quality of indoor environment directly influences the work and life of people and even possibly threatens the physical health of people, and one main aspect of the indoor environmental problem is the improvement of air quality. To ensure health and a good living and working environment, indoor air needs to be purified. Most of the existing air purifiers adopt suction of indoor air, and the air is filtered and discharged through a purification module and is purified in a repeated circulation mode. Meanwhile, a fresh air system can be installed indoors, and circulation of outdoor air is guaranteed. Therefore, each device needs to be coordinated and controlled, so that the air purifier and the fresh air machine can efficiently purify indoor air and quickly reach the comfortable and safe indoor environmental standard.
Reference 1 (chinese patent application CN201510561871.5) proposes to purify indoor air in a targeted manner by different control modes and adjust according to the difference between indoor and outdoor air temperatures. Specifically, the scheme firstly collects air pollutants and temperature values, and accordingly judges the pollution degree of indoor and outdoor air and the outdoor temperature difference. And then, calculating the outdoor Air Quality (AQI) and the indoor AQI to judge the pollution degree of the indoor and outdoor air, thereby selectively opening and closing the air inlet or the air return inlet. And finally, selecting a corresponding purification mode according to the analysis result.
Disclosure of Invention
However, reference 1 purifies indoor air in real time only according to current indoor and outdoor environmental information, but does not consider the activity of people. The purification of the indoor space without personnel or long-time personnel activities causes great energy consumption. In addition, because there is some delay in the air purification effect, it is desirable to open the purification system in advance to purify the indoor air before the person enters the room, so that there is a comfortable environment when the person enters the room.
The application provides that according to indoor purification historical data, a dissipation function of pollutants (such as PM2.5, carbon monoxide, sulfur dioxide, nitric oxide, phosphine, hydrogen chloride, VOC and the like) in the air in the purification process is fitted, preliminary estimation of time required by air purification and indoor and outdoor air quality prediction information are obtained, and therefore a multi-objective optimization mathematical model is built, wherein the multi-objective optimization mathematical model comprises the steps of reducing the concentration of the pollutants in the air, reducing the difference value between indoor temperature and target temperature and humidity, reducing energy consumption of a system and the like. And then, solving the multi-objective optimization mathematical model to obtain an accurate control strategy of the air purification system. Therefore, before people enter the room, the indoor air quality can be good.
Specifically, according to one aspect of the present application, there is provided an apparatus for controlling an air purification system, including: a data acquisition unit configured to acquire indoor and outdoor air quality data, indoor air purification history data, and an estimated value of a time for turning on an air purification system in advance; a modeling unit configured to establish a multi-objective optimization model based on the acquired data and in accordance with a plurality of objectives; and a control unit configured to control an operation of the air purification system by solving the multi-objective optimization model.
In one embodiment, the indoor and outdoor air quality data may be historical data or predictive data. The indoor air purification history data may include one or more of: the time and gear of various devices within the air purification system, the change in concentration of pollutants in the air, the change in temperature or humidity of the air.
In one embodiment, an estimate of the time to turn on the air purification system in advance may be estimated from a human activity schedule.
In one embodiment, the plurality of objectives may include two or more of: the concentration of pollutants in the indoor air is reduced, the difference between the indoor temperature or humidity and the indoor target temperature or humidity is reduced, and the energy consumption of the air purification system is reduced.
In one embodiment, the control unit is configured to: solving the multi-objective optimization model through any one algorithm of linear programming, convex optimization and genetic algorithm to obtain a control strategy of the air purification system; and controlling operation of the air purification system based on the obtained control strategy.
Furthermore, there may be unpredictable indoor human activities due to certain uncertainty and randomness of human activities. To this end, in one embodiment, the apparatus further comprises a monitoring unit that monitors indoor human activity and indoor and outdoor air quality. And if the personnel activity is monitored, storing the personnel activity data into a database. Accordingly, the control unit is configured to: if the monitoring unit monitors the activities of people, determining the indoor air pollution degree according to the monitoring data of indoor and outdoor air quality; and immediately starting the air purification system if the indoor air pollution degree meets a specific condition.
According to another aspect of the present application, there is provided a method for controlling an air purification system, including: acquiring indoor and outdoor air quality data, indoor air purification historical data and estimated values of time for starting an air purification system in advance; establishing a multi-objective optimization model based on the acquired data and according to a plurality of objectives; and controlling operation of the air purification system by solving the multi-objective optimization model.
In one embodiment, the indoor and outdoor air quality data may be historical data or predictive data. The indoor air purification history data may include one or more of: the time and gear of various devices within the air purification system, the change in concentration of pollutants in the air, the change in temperature or humidity of the air.
In one embodiment, an estimate of the time to turn on the air purification system in advance may be estimated from a human activity schedule.
In one embodiment, the plurality of objectives may include two or more of: the concentration of pollutants in the indoor air is reduced, the difference between the indoor temperature or humidity and the indoor target temperature or humidity is reduced, and the energy consumption of the air purification system is reduced.
In one embodiment, the multi-objective optimization model is solved through any one algorithm of linear programming, convex optimization and genetic algorithm to obtain a control strategy of the air purification system; and controlling operation of the air purification system based on the obtained control strategy.
In one embodiment, the method further comprises monitoring indoor human activity and indoor and outdoor air quality. And if the personnel activity is monitored, storing the personnel activity data into a database. Accordingly, if the human activities are monitored, the indoor air pollution level is determined based on the monitored data of the indoor and outdoor air quality. If the indoor air pollution degree meets a specific condition, the air purification system is immediately started.
According to the method and the system, the air purification system is started in advance according to the activity condition of personnel and the indoor and outdoor air quality information, and multiple indoor environment influence factors are coordinated through a multi-objective optimization method, so that the comfortable living and working environment is ensured, and the energy consumption of the air purification system is reduced as much as possible.
Drawings
The above and other features of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings, in which:
fig. 1 is a block diagram illustrating an apparatus for controlling an air purification system according to an embodiment of the present application.
Fig. 2 is a flowchart illustrating a method for controlling an air purification system according to an embodiment of the present application.
Detailed Description
The principles and implementations of the present application will become apparent from the following description of specific embodiments thereof, taken in conjunction with the accompanying drawings. It should be noted that the present application should not be limited to the specific embodiments described below. In addition, a detailed description of known technologies not related to the present application is omitted for the sake of brevity.
Fig. 1 is a block diagram illustrating an apparatus 10 for controlling an air purification system according to one embodiment of the present application. As shown in fig. 1, the apparatus 10 may include a data acquisition unit 110, a modeling unit 120, and a control unit 130. Optionally, the device 10 may further comprise a monitoring unit 140 (shown in dashed lines in fig. 1).
The data acquisition unit 110 is configured to acquire indoor and outdoor air quality data, indoor air purification history data, and an estimated value of a time at which the air purification system is turned on in advance. For example, the data acquisition unit 110 may acquire known indoor and outdoor air quality data. Alternatively, the data obtaining unit 110 may construct an air quality prediction model by using a data mining method such as a support vector machine, a neural network, a decision tree, and the like based on indoor and outdoor air quality historical data, geographical location information, and the like, so as to predict the air quality for a period of time in the future.
The historical data of indoor air purification is the data collected in the indoor air purification process, and includes the opening time and the gear of each equipment (such as a purifier, a fresh air blower, a humidifier, an air conditioner and the like) in the air purification system, the concentration of pollutants in the air, the temperature and humidity change and the like. From the historical data, a dissipation function of the contaminant during the decontamination process can be fitted.
Here, the indoor air purification history data may include turn-on times and gear positions of respective devices within the air purification system, a change in concentration of pollutants in the air, or a change in temperature or humidity of the air.
Further, from the human activity schedule that is known or obtained by prediction, it is possible to estimate the time at which the air purification system needs to be turned on in advance before a person arrives indoors (i.e., the estimated value of the time at which the air purification system is turned on in advance).
In the following, a specific example of solving the dissipation function is given. The following table shows PM2.5 dissipation data at various time points.
| Time of day | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
| Concentration of | 0.348 | 0.284 | 0.234 | 0.198 | 0.169 | 0.142 | 0.119 | 0.101 | 0.086 | 0.073 | 0.062 |
| Time of day | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 |
| Concentration of | 0.052 | 0.045 | 0.037 | 0.032 | 0.027 | 0.022 | 0.02 | 0.017 | 0.015 | 0.014 | 0.011 |
| Time of day | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 |
| Concentration of | 0.01 | 0.008 | 0.007 | 0.006 | 0.005 | 0.004 | 0.003 | 0.002 | 0.001 | 0.001 | 0.001 |
| Time of day | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 |
| Concentration of | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 | 0.001 |
| Time of day | 45 | 46 | |||||||||
| Concentration of | 0.001 | 0.001 |
TABLE 1 PM2.5 Dispersion data
In the present example, exponential function curve fitting is performed by the least squares method. The exponential function can be deformed as:
y＝aebx→lny＝lna+bx→y′＝a′+bx
the system of equations at this time is:
∴
∴a1＝-1.253，b＝-0.146
∴a＝0.285，b＝-0.146
∴y＝0.285c0.146xPM2.5 dissipation function for the above data.
The modeling unit 120 is configured to build a multi-objective optimization model based on the acquired data and in accordance with a plurality of objectives. Here, the plurality of targets may include, for example, two or more of the following: the concentration of pollutants in the indoor air is reduced, the difference between the indoor temperature or humidity and the indoor target temperature or humidity is reduced, and the energy consumption of the air purification system is reduced.
For example, the multi-objective optimization model may be:
wherein,
w () represents the energy consumption,
c () represents a particulate matter concentration function,
at () represents a set temperature and actual temperature difference function,
p represents the power of the power source,
f () represents a concentration variation function (dissipation function),
x, t represent control variables (gear and time).
The control unit 130 is configured to control the operation of the air purification system by solving the multi-objective optimization model. For example, the control unit 130 may be configured to solve the multi-objective optimization model through any one of linear programming, convex optimization, and genetic algorithm to obtain the control strategy of the air purification system.
In the following, a specific example of solving the multi-objective optimization model is given.
Suppose that a purifier, a fresh air machine and an air conditioner are installed indoors. These devices each have only one gear. Assuming that the power of the purifier, the fresh air fan and the air conditioner is 20W, 30W and 20W respectively and the dissipation function is known, a multi-objective optimization mathematical model is established as follows:
s.t.t1is a non-negative integer of 60 or less
t2Is a non-negative integer of 30 or less
t3Is a non-negative integer of 30 or less
p1＝0.02，p2＝0.03，p3＝0.02
In this example, the above optimization model is solved using a genetic algorithm as follows:
1) and converting the control variable t into a binary system for encoding. The total length of the individual codes is 16 bits, and the first 6 bits are t1Then 5 is t2And the last 5 th position is t3. The fitness function is the same as the objective function. 5 individuals were set for each generation.
2) Randomly generating a first generation population:
X01＝(110010 00111 01101)
X02＝(000101 10010 10110)
X03＝(000001 00011 10011)
X04＝(001000 00010 00100)
X05＝(101101 10001 01000)
these 5 individuals were evaluated, and 5 individual fitness values were calculated, respectively, to obtain:
fitness01＝1.778
fitness02＝1.611
fitness03＝1.923
fitness04＝1.706
fitnesso5＝1.773
3) and selecting the individuals with better fitness to perform genetic operations, wherein the genetic operations comprise crossover and mutation operations. The crossover operation is: two better individuals are selected, their codes are partially exchanged, and newly generated individuals are in the next generation of individual loci.
X02＝(000101 10010 10110)→(001001 00010 10100)→X11
X04＝(001000 00010 00100)→(000100 10010 00110)→X12
And the mutation operation means: then some bits in the good individual code are changed.
X02＝(000101 10010 10110)→(001101 00010 00110)→X13
X04＝(001000 00010 00100)→(001000 01010 01100)→X14
At the same time, the best individuals in the previous generation are also retained to X in the next generation02→X15
4) At this point 5 new individuals of the first generation population were obtained: x11，X12，X13，X14，X15. Calculating their fitness
fitness11＝1.724
fitness12＝1.601
fitness13＝1.461
fitness14＝1.230
fitness1s＝1.611
5) Further as shown in step 3, genetic manipulation was performed up to the nth generation
….
To obtain
Xn1＝(001111 01100 01000)fitnessn1＝1.201
Xn2＝(010011 01110 01011)fitnessn2＝1.237
Xn3＝(001110 01001 01011)fitnessn3＝1.162
Xn4＝(001111 01010 01100)fitnessn4＝1.167
Xn5＝(010001 01000 00111)fitnessn5＝1.224
Selection of Xn3For the final result, i.e. t1＝14，t2＝9，t3＝11
Namely, before the personnel arrive at the room, the purifier is started 14 minutes in advance, the fresh air fan is started 9 minutes in advance, and the air conditioner is started 11 minutes in advance.
The control unit 130 may control the operation of the air purification system based on the obtained control strategy. In this way, the control unit 130 can control the devices (such as a purifier, a fresh air blower, a humidifier, an air conditioner, etc.) to be turned on and the turn-on/turn-off time of the air purification system accordingly.
Furthermore, there may be unpredictable indoor human activities due to certain uncertainty and randomness of human activities. To better monitor the activity of the persons, the device 10 may further comprise a monitoring unit 140, the monitoring unit 140 monitoring the indoor activity of the persons and the indoor and outdoor air quality. For example, indoor personnel activity, including number of personnel, time in the room, etc., may be monitored by sensors. In the event of human activity, further analysis of the indoor environment may be performed.
Monitoring of indoor and outdoor air quality may include collecting air pollutants (e.g., PM2.5, carbon monoxide, sulfur dioxide, nitrogen monoxide, phosphine, hydrogen chloride, VOC concentrations, etc.) and temperature and humidity values through gas sensors, from which indoor and outdoor air pollution levels and temperature differences are determined.
Alternatively, if the monitoring unit 140 monitors the human activity, the human activity data is stored in a database. The stored data can be used as historical data, so that more accurate prediction and judgment can be provided when the data is used in the future.
Accordingly, the control unit 130 is configured to determine the indoor air pollution level according to the monitoring data of the indoor and outdoor air quality in the case where the monitoring unit 140 monitors the human activities. If the indoor air pollution level satisfies a certain condition, the control unit 130 immediately turns on the air purification system.
The control unit 130 may perform a hierarchical control of the air purification system. For example, under the condition that the indoor air quality does not reach the standard, if the outdoor air quality is in the first level, the new fan is started; if the outdoor air quality is not in the first level and the carbon dioxide concentration is not high, the air purifier is started; if the outdoor air quality is higher and the indoor carbon dioxide concentration is higher, the air purifier and the fresh air fan are started simultaneously.
By monitoring the activities of the persons in real time by the monitoring unit 140, it is possible to compensate for an emergency or an inaccurate prediction, and to ensure a good indoor environment in the case of activities of the persons.
Fig. 2 is a flowchart illustrating a method for controlling an air purification system according to an embodiment of the present application. As shown in fig. 2, the method 20 begins at step S210.
In step S220, indoor and outdoor air quality data, indoor air purification history data, and an estimated value of time for which the air purification system is turned on in advance are acquired. As described above, the indoor and outdoor air quality data may be historical data or predictive data. The indoor air purification history data may include one or more of: the time and gear of various devices within the air purification system, the change in concentration of pollutants in the air, the change in temperature or humidity of the air. In addition, an estimate of the time to turn on the air purification system in advance may be estimated from the staff activity schedule.
In step S230, a multi-objective optimization model is built based on the acquired data and according to a plurality of objectives. The plurality of goals may include two or more of: the concentration of pollutants in the indoor air is reduced, the difference between the indoor temperature or humidity and the indoor target temperature or humidity is reduced, and the energy consumption of the air purification system is reduced.
In step S240, the operation of the air purification system is controlled by solving the multi-objective optimization model. Preferably, the multi-objective optimization model can be solved through any one of linear programming, convex optimization and genetic algorithm to obtain the control strategy of the air purification system. In addition, the operation of the air purification system may be controlled based on the obtained control strategy.
Alternatively, the method 20 may also include (not shown in fig. 2) monitoring indoor human activity and indoor and outdoor air quality, and storing human activity data in a database when human activity is monitored. Accordingly, if the activity of the person is monitored, the indoor air pollution level is determined according to the monitoring data of the indoor and outdoor air quality; and immediately starting the air purification system if the indoor air pollution degree meets a specific condition. Therefore, the personnel activities can be monitored in real time, the emergency or the inaccurate prediction condition can be compensated, and the indoor environment is ensured to be good under the condition of personnel activities.
Finally, the method 20 ends at step S250.
It should be understood that the above-described embodiments of the present application may be implemented by software, hardware, or a combination of both software and hardware. For example, various components within the systems in the above embodiments may be implemented by a variety of devices, including but not limited to: analog circuits, digital circuits, general purpose processors, Digital Signal Processing (DSP) circuits, programmable processors, Application Specific Integrated Circuits (ASIC), Field Programmable Gate Arrays (FPGA), programmable logic devices (CPLD), and the like.
In addition, those skilled in the art will appreciate that the parameters and/or data described in the embodiments of the present application may be stored in a local database, a distributed database, or a remote database.
Furthermore, embodiments of the present application disclosed herein may be implemented on a computer program product. More specifically, the computer program product is one of the following: there is a computer readable medium having computer program logic encoded thereon that, when executed on a computing device, provides related operations to implement the above-described aspects of the present application. When executed on at least one processor of a computing system, the computer program logic causes the processor to perform the operations (methods) described in embodiments of the present application. Such arrangements of the present application are typically provided as downloadable software images, shared databases, etc. arranged or encoded on a computer readable medium such as an optical medium (e.g., CD-ROM), floppy or hard disk, or other media such as firmware or microcode on one or more ROM or RAM or PROM chips, or in one or more modules, code and/or other data structures. The software or firmware or such configurations may be installed on a computing device to cause one or more processors in the computing device to perform the techniques described in embodiments of the present application.
Although the present application has been shown and described with respect to preferred embodiments thereof, those skilled in the art will appreciate that various modifications, substitutions and alterations can be made thereto without departing from the spirit and scope of the present application. Accordingly, the present application should not be limited by the above-described embodiments, but should be defined by the following claims and their equivalents.
Claims (16)
1. An apparatus for controlling an air purification system, comprising:
a data acquisition unit configured to acquire indoor and outdoor air quality data, indoor air purification history data, and an estimated value of a time for turning on an air purification system in advance;
a modeling unit configured to establish a multi-objective optimization model based on the acquired data and in accordance with a plurality of objectives; and
a control unit configured to control operation of the air purification system by solving the multi-objective optimization model.
2. The apparatus of claim 1, wherein the indoor and outdoor air quality data is historical data or predictive data.
3. The apparatus of claim 1, wherein the indoor air purification history data comprises one or more of: the time and gear of various devices within the air purification system, the change in concentration of pollutants in the air, the change in temperature or humidity of the air.
4. The apparatus of claim 1, wherein the estimate of the time to turn on the air purification system in advance is estimated from a human activity schedule.
5. The apparatus of claim 1, wherein the plurality of targets comprises two or more of: the concentration of pollutants in the indoor air is reduced, the difference between the indoor temperature or humidity and the indoor target temperature or humidity is reduced, and the energy consumption of the air purification system is reduced.
6. The device of claim 1, wherein the control unit is configured to:
solving the multi-objective optimization model through any one algorithm of linear programming, convex optimization and genetic algorithm to obtain a control strategy of the air purification system; and
controlling operation of the air purification system based on the obtained control strategy.
7. The apparatus of claim 1, further comprising:
a monitoring unit configured to monitor indoor human activity and indoor and outdoor air quality; and if the personnel activity is monitored, storing the personnel activity data into a database.
8. The device of claim 7, wherein the control unit is configured to:
if the monitoring unit monitors the activities of people, determining the indoor air pollution degree according to the monitoring data of indoor and outdoor air quality; and
if the indoor air pollution degree meets a specific condition, the air purification system is immediately started.
9. A method for controlling an air purification system, comprising:
acquiring indoor and outdoor air quality data, indoor air purification historical data and estimated values of time for starting an air purification system in advance;
establishing a multi-objective optimization model based on the acquired data and according to a plurality of objectives; and
controlling operation of the air purification system by solving the multi-objective optimization model.
10. The method of claim 9, wherein the indoor and outdoor air quality data is historical data or predictive data.
11. The method of claim 9, wherein the indoor air purification history data includes one or more of: the time and gear of various devices within the air purification system, the change in concentration of pollutants in the air, the change in temperature or humidity of the air.
12. The method of claim 9, wherein the estimate of the time to turn on the air purification system in advance is estimated from a human activity schedule.
13. The method of claim 9, wherein the plurality of objectives comprises two or more of: the concentration of pollutants in the indoor air is reduced, the difference between the indoor temperature or humidity and the indoor target temperature or humidity is reduced, and the energy consumption of the air purification system is reduced.
14. The method of claim 9, wherein the multi-objective optimization model is solved by any one of linear programming, convex optimization, genetic algorithm to obtain a control strategy of the air purification system; and controlling operation of the air purification system based on the obtained control strategy.
15. The method of claim 9, further comprising:
monitoring indoor personnel activity and indoor and outdoor air quality; and
and if the personnel activity is monitored, storing the personnel activity data into a database.
16. The method of claim 15, wherein,
if the activity of the personnel is monitored, determining the indoor air pollution degree according to the monitoring data of the indoor and outdoor air quality; and
if the indoor air pollution degree meets a specific condition, the air purification system is immediately started.
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