CN114383275B - Indoor air conditioning method and system of Internet of things - Google Patents
Indoor air conditioning method and system of Internet of things Download PDFInfo
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- CN114383275B CN114383275B CN202111507699.7A CN202111507699A CN114383275B CN 114383275 B CN114383275 B CN 114383275B CN 202111507699 A CN202111507699 A CN 202111507699A CN 114383275 B CN114383275 B CN 114383275B
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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
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/54—Control or safety arrangements characterised by user interfaces or communication using one central controller connected to several sub-controllers
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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
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/56—Remote control
- F24F11/58—Remote control using Internet communication
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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
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/61—Control or safety arrangements characterised by user interfaces or communication using timers
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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
- 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
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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
- F24F2110/00—Control inputs relating to air properties
- F24F2110/50—Air quality properties
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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
Abstract
The invention discloses an indoor air conditioning method and system of the Internet of things, comprising the following steps: s1, predicting indoor air data in a future time period according to indoor air data acquired in a current time period, and drawing a curve form of the indoor air data in the future time period to serve as an air prediction curve; s2, receiving an air expected curve set by a user in a future time period, and constructing a power intensity optimizing model of the air conditioning equipment based on the air expected curve and an air prediction curve; and step S3, the air conditioning equipment adjusts indoor air according to the power intensity so that the air quality of the future time period in the room reaches the expectations of users. According to the invention, the power intensity optimizing model of the air conditioning equipment is constructed based on the air expected curve and the air prediction curve, so that a power intensity adjusting scheme of the air conditioning equipment with win-win adjusting cost and adjusting efficiency can be constructed, and the interactive experience sense is improved while the user expectation is met.
Description
Technical Field
The invention relates to the technical field of air conditioning, in particular to an indoor air conditioning method and system of the Internet of things.
Background
Atmospheric dust haze contamination leading to respirable particulates and the resultant hazards has been demonstrated. Indoor air should be exchanged with outdoor air on average once per hour, as required by civil building design codes. Simulation tests show that under the condition of closing doors and windows, the concentration of indoor inhalable particles of civil buildings is about 0.6-08 times that of outdoor, for example: when the concentration of the outdoor inhalable particles is 300ug/m 3 When the indoor area is about 200ug/m 3 Far exceeding the specified average daily value of 35ug/m 3 Is a basic standard of (2). In addition, volatile organic compounds such as styrene, propylene glycol, gan Wan, phenol, toluene, ethylbenzene, xylene, formaldehyde, etc. have a great influence on human health. When the concentration of the volatile organic compounds in the living room reaches a certain level, people feel headache, nausea, vomiting, hypodynamia and the like in a short time, and when serious, the people can suffer from convulsion and coma, and can hurt the liver, the kidney, the brain and the nervous system of the people, so that the serious consequences such as hypomnesis and the like are caused.
The prior art discloses a terminal and an indoor air conditioning method of CN202010193409.5, comprising: generating control instructions corresponding to various types of air conditioning equipment according to the numerical combination of the control parameters; transmitting the control instruction to the communication unit; and the communication unit is used for respectively sending the control instructions to the corresponding air conditioning equipment after receiving the control instructions so that the corresponding air conditioning equipment can condition indoor air according to the corresponding control instructions. The embodiment of the invention can uniformly control various types of air conditioning equipment through the adjustment of a user, thereby simplifying the operation process.
The above prior art, although capable of conditioning indoor air, still suffers from certain drawbacks such as: the regulating power of the regulating equipment is constant, so that the regulating capacity is limited, air conditioning cannot be performed according to the user demands, and the user experience is poor.
Disclosure of Invention
The invention aims to provide an indoor air conditioning method and system of the Internet of things, which are used for solving the technical problems that in the prior art, the conditioning capacity is limited due to constant conditioning power of conditioning equipment, and meanwhile, air conditioning cannot be performed according to user requirements, so that the user experience is poor.
In order to solve the technical problems, the invention specifically provides the following technical scheme:
an indoor air conditioning method of the internet of things comprises the following steps:
s1, predicting indoor air data in a future time period according to the indoor air data acquired in the current time period to realize predictability of the indoor air data in the future time period, and drawing a curve form of the indoor air data in the future time period to serve as an air prediction curve, wherein the air data are a data set for representing air quality;
s2, receiving an air expected curve set by a user in a future time period, and constructing a power intensity optimizing model of the air conditioning equipment based on the air expected curve and an air prediction curve, wherein the power intensity optimizing model is used for obtaining the power intensity for adjusting indoor air data from the air prediction curve to the air expected curve so that the air conditioning equipment performs adjustment operation on the power intensity output by the power intensity optimizing model to achieve win-win in adjustment cost and adjustment efficiency, and the air expected curve is characterized by an expected value of the user on the indoor air data;
and S3, adjusting the indoor air by the air-conditioning equipment according to the power intensity so as to enable the air quality of the indoor future time period to reach the expectations of users and improve the prospective of indoor air quality regulation.
As a preferred aspect of the present invention, the indoor air data collected during the current time period predicts the indoor air data of the future time period, including:
-comparing said current time period { t } 1 ,t 2 ,…,t n Indoor air data collected
Input to LSTM artificial neural network for outputting future time period { t ] n+1 ,t n+2 ,…,t n+m Indoor air data of }
Wherein (1)>
Characterised by the current time t n Indoor air data of the room->
Characterised by a future time t n+m Indoor air data at n is the total number of current times in the current time period and m is the total number of future times in the future time period.
As a preferred embodiment of the present invention, the plotting the indoor air data of the future time period into a curve form as an air prediction curve includes:
establishing a two-dimensional coordinate system, taking time as an abscissa axis of the two-dimensional coordinate system, and taking a data value as an ordinate axis of the two-dimensional coordinate system;
data of indoor air
Conversion into a set of coordinate points
Drawing all coordinate points into a two-dimensional coordinate system according to horizontal coordinate values and vertical coordinate values, sequentially connecting all coordinate points in the two-dimensional coordinate system to fit to obtain a first curve, and obtaining a function expression of the first curve based on a group of coordinate point fitting;
and taking the first curve as an air prediction curve, taking a functional expression of the second curve as an expression X=g (t) of the air prediction curve, wherein X is a functional expression of indoor air data, t is a functional expression of future time, and g is characterized by a relation function of the indoor air data and the future time.
As a preferred aspect of the present invention, the user sets an air desired curve in a future period of time, including:
manually setting by a user in an interactive panel air data desired values at each of the future times in a future time period results in a set of indoor air data desired values
Expected value of air data
Conversion into a set of coordinate points
Drawing all coordinate points into a two-dimensional coordinate system according to horizontal coordinate values and vertical coordinate values, sequentially connecting all coordinate points in the two-dimensional coordinate system to fit to obtain a second curve, and obtaining a function expression of the second curve based on a group of coordinate point fitting;
and taking the second curve as an air expected curve, taking a functional expression of the second curve as an expression Y=f (t) of the air expected curve, wherein Y is a functional expression of an expected value of indoor air data, t is a functional expression of future time, and f is characterized as a relation function body of the expected value of the indoor air data and the future time.
As a preferred embodiment of the present invention, the constructing a power intensity optimizing model of an air conditioning apparatus based on an air desired curve and an air prediction curve includes:
the method comprises the steps of constructing a time length for an air conditioning device to adjust an air prediction curve to an air expected curve as an efficiency index, wherein a function expression of the efficiency index is as follows:
where T (T) is characterized as the duration of the air data on the air prediction curve at the future time T being adjusted to the air data on the air desired curve, g (T) is characterized as the air data on the air desired curve at the future time T, f (T) is characterized as the air data on the air prediction curve at the future time T, and p (T) is characterized as the power intensity of the air conditioning device at the future time T;
the cost of the air conditioning equipment for adjusting the air prediction curve to the air expectation curve is used for constructing an economic index, and the functional expression of the economic index is as follows:
K(t)＝A*p(t)；
where K (t) is characterized by the cost of air data on the air prediction curve to air data on the air desired curve at a future time t, A is characterized by the unit price of regulating lost power, and p (t) is characterized by the power intensity of the air conditioning device at the future time t;
combining the economic index and the efficiency index to obtain a function expression of a power intensity optimizing model, wherein the function expression of the power intensity optimizing model is as follows:
wherein S is characterized as a optimizing value of a power intensity optimizing model, and min is characterized as a minimizing function body.
As a preferred embodiment of the present invention, the solving constraint condition of the power intensity optimizing model includes:
power strength constraint:
p min ≤p(t)≤p max ；
duration constraint:
T min ≤T(t)≤T max ；
wherein p is min 、p max Respectively characterized by a lower power intensity limit and an upper power intensity limit of the air conditioning equipment, T min 、T max Characterized by the lower and upper duration limits, respectively.
As a preferred scheme of the invention, the power intensity optimizing model adopts an intelligent searching algorithm to solve in constraint conditions to obtain the power intensity p (t) of the air conditioning equipment at each future time in the future time period.
As a preferred embodiment of the present invention, the air conditioning apparatus for conditioning indoor air according to power intensity includes:
quantizing the power intensity p (t) from a continuous form to a discrete point form to obtain a set of coordinate points
A group of coordinate points are formed by combining two adjacent points
Orderly matching to obtain a group of adjusting point pairs
Determining a direction of adjustment of the power intensity according to each adjustment point pair, wherein +_>
If it is
When it is determined that the air conditioning apparatus is at the rate +.>
To increase power strength;
if it is
When it is determined that the air conditioning apparatus is at the rate +.>
Reducing the power intensity;
if it is
When the power intensity of the air conditioning equipment is maintained;
wherein i is a measurement constant and has no substantial meaning.
As a preferred embodiment of the present invention, the present invention provides an air conditioning system according to the indoor air conditioning method of the internet of things, which is characterized by comprising:
the prediction curve making unit is used for predicting indoor air data in a future time period according to the indoor air data acquired in the current time period, and drawing a curve form of the indoor air data in the future time period to be used as an air prediction curve, wherein the air data is a data set for representing air quality;
the optimizing model making unit is used for receiving an air expected curve set by a user in a future time period, and constructing a power intensity optimizing model of the air conditioning equipment based on the air expected curve and the air prediction curve, wherein the power intensity optimizing model is used for obtaining the power intensity of the air prediction curve adjusted to the air expected curve so as to win the adjustment cost and the adjustment efficiency, and the air expected curve is characterized by an expected value of the user on indoor air data;
and the adjusting application unit is used for adjusting the indoor air by the air conditioning equipment according to the power intensity so that the air quality of the indoor future time period reaches the expectations of users.
As a preferable scheme of the invention, the optimizing model making unit is also integrated with an interactive panel for setting the expected value of the air data by a user, the prediction curve making unit is also integrated with data acquisition equipment, and the adjusting application unit is also integrated with air conditioning equipment.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, an air prediction curve is constructed on air data of a future time period, then an air expected curve set by a user in the future time period is received, and a power intensity optimizing model of the air conditioning equipment is constructed based on the air expected curve and the air prediction curve, so that a power intensity adjusting scheme of the air conditioning equipment with win-win adjusting cost and adjusting efficiency can be constructed, and the interactive experience sense is improved while the user expectation is met.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It will be apparent to those of ordinary skill in the art that the drawings in the following description are exemplary only and that other implementations can be obtained from the extensions of the drawings provided without inventive effort.
Fig. 1 is a flowchart of an indoor air conditioning method according to an embodiment of the present invention;
FIG. 2 is a block diagram of an air conditioning system according to an embodiment of the present invention;
FIG. 3 is a graph of air prediction curves and air expectations provided by an embodiment of the present invention.
Reference numerals in the drawings are respectively as follows:
1-a prediction curve making unit; 2-optimizing model making unit; 3-an adjustment application unit; 4-air prediction curve; 5-air expectancy curve.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the invention provides an indoor air conditioning method of the internet of things, which comprises the following steps:
s1, predicting indoor air data in a future time period according to the indoor air data acquired in the current time period to realize predictability of the indoor air data in the future time period, and drawing a curve form of the indoor air data in the future time period to serve as an air prediction curve, wherein the air data are a data set for representing air quality;
indoor air data collected in a current time period predicts indoor air data in a future time period, comprising:
will be the current time period { t } 1 ,t 2 ,…,t n Indoor air data collected
Input to LSTM artificial neural network for outputting future time period { t ] n+1 ,t n+2 ,…,t n+m Indoor air data of }
Wherein (1)>
Characterised by the current time t n Indoor air data of the room->
Characterised by a future time t n+m Indoor air data at n is the total number of current times in the current time period, m is the total number of future times in the future time periodOrder (1).
The air data may be a single type of data value, such as: temperature, bacteria content, particulate matter concentration, oxygen content, etc., may also be a fusion value of various types of data, such as: the fusion values of temperature, bacteria content, particle concentration and oxygen content adopt a weight fusion mode at the fusion moment,
respectively representing future time t n+1 Temperature, bacteria content, particulate matter concentration and oxygen content, w 1 、w 2 、w 3 、w 4 The research importance levels respectively characterized by temperature, bacteria content, particulate matter concentration and oxygen content can be adjusted according to the research requirement, and the higher the value is, the higher the importance level is.
The steps can predict indoor air data in a future time period, so that the development trend of the indoor air data is mastered in advance, and data preparation is carried out for the power intensity adjustment process of the air conditioning equipment.
As shown in fig. 3, plotting indoor air data for a future time period in a curve form as an air prediction curve includes:
establishing a two-dimensional coordinate system, taking time as an abscissa axis of the two-dimensional coordinate system, and taking a data value as an ordinate axis of the two-dimensional coordinate system;
data of indoor air
Conversion into a set of coordinate points
Drawing all coordinate points into a two-dimensional coordinate system according to horizontal coordinate values and vertical coordinate values, sequentially connecting all coordinate points in the two-dimensional coordinate system to fit to obtain a first curve, and obtaining a function expression of the first curve based on a group of coordinate point fitting;
taking the first curve as an air prediction curve, and taking a functional expression of the second curve as an expression X=g (t) of the air prediction curve, wherein X is a functional expression of indoor air data, t is a functional expression of future time, and g is characterized by a relation function of the indoor air data and the future time.
S2, receiving an air expected curve set by a user in a future time period, and constructing a power intensity optimizing model of the air conditioning equipment based on the air expected curve and an air prediction curve, wherein the power intensity optimizing model is used for obtaining the power intensity for adjusting indoor air data from the air prediction curve to the air expected curve so that the air conditioning equipment performs adjustment operation on the power intensity output by the power intensity optimizing model to achieve win-win in adjustment cost and adjustment efficiency, and the air expected curve is characterized by an expected value of the user on the indoor air data;
the user sets an air expectancy profile at a future time period, comprising:
manually setting by a user in an interactive panel air data desired values at each of the future times in a future time period results in a set of indoor air data desired values
Expected value of air data
Conversion into a set of coordinate points
Drawing all coordinate points into a two-dimensional coordinate system according to horizontal coordinate values and vertical coordinate values, sequentially connecting all coordinate points in the two-dimensional coordinate system to fit to obtain a second curve, and obtaining a function expression of the second curve based on a group of coordinate point fitting;
and taking the second curve as an air expected curve, and taking a functional expression of the second curve as an expression Y=f (t) of the air expected curve, wherein Y is a functional expression of an expected value of indoor air data, t is a functional expression of future time, and f is characterized as a relation function body of the expected value of the indoor air data and the future time.
The user can set the expected value of the air data by himself, and can set the required air environment more flexibly, for example: at future time t n+2 The temperature expected by the user is 26 ℃ and the bacterial content is 100/m 3 The concentration of the particles is 60ug/m 3 And an oxygen content of 22%, and a predicted temperature of 33℃and a bacterial content of 600/m 3 The concentration of the particles is 200ug/m 3 And oxygen content of 21%, temperature control equipment (air conditioner), bacteria control equipment (disinfection spray gun), dust sweeping equipment (humidifier) and oxygenation equipment in the regulating equipment are required to be controlled to be 33 ℃ and the bacteria content is 600/m 3 The concentration of the particles is 200ug/m 3 And oxygen content of 21% for cooling, sterilizing, dust removing and oxygenating to improve indoor air data to 26 deg.C and 100/m 3 The concentration of the particles is 60ug/m 3 And an oxygen content of 22%.
Constructing a power intensity optimizing model of the air conditioning equipment based on the air expected curve and the air prediction curve, comprising:
the method comprises the steps of constructing a time length for an air conditioning device to adjust an air prediction curve to an air expected curve as an efficiency index, wherein a function expression of the efficiency index is as follows:
where T (T) is characterized as the duration of the air data on the air prediction curve at the future time T being adjusted to the air data on the air desired curve, g (T) is characterized as the air data on the air desired curve at the future time T, f (T) is characterized as the air data on the air prediction curve at the future time T, p (T) is characterized as the power intensity of the air conditioning apparatus at the future time T, the higher the power intensity is, the stronger the conditioning ability of the air conditioning apparatus is, the shorter the duration of the adjustment is, and therefore the difference between the air data on the air prediction curve and the air data on the air desired curve can be directly used as the adjustment duration divided by the power intensity;
when the air data is of a single type, the above formula is directly used for operation, for example: the air data is temperature, and the air conditioning device is a temperature control device, so only the time length of the temperature control device from the air data on the air prediction curve to the air data on the air expectation curve needs to be calculated, and the air data is a fusion value of various types of data, such as: the fusion value of the power intensity p (t) is the fusion value p (t) =w of the power intensity of the temperature control equipment (air conditioner), the bacteria control equipment (disinfection spray gun), the dust sweeping equipment (humidifier) and the oxygenation equipment during operation 1 *p 1 (t)+w 2 *p 2 (t)+w 3 *p 3 (t)+w 4 *p 4 (t)，w 1 、w 2 、w 3 、w 4 The degree of study importance, i.e., p (t) and the composition of the air data, respectively, characterized by temperature, bacterial content, particulate matter concentration, and oxygen content, was the same.
The cost of the air conditioning equipment for adjusting the air prediction curve to the air expectation curve is used for constructing an economic index, and the functional expression of the economic index is as follows:
K(t)＝A*p(t)；
where K (t) is characterized as the cost of air data on the air prediction curve at the future time t to air data on the air expectancy curve, a is characterized as the unit price of the adjustment loss power, p (t) is characterized as the power intensity of the air conditioning device at the future time t, the higher the power intensity is, the higher the power consumption per unit time is, and therefore the unit price of the adjustment loss power multiplied by the power intensity is directly used as the adjustment cost;
combining the economic index and the efficiency index to obtain a function expression of the power intensity optimizing model, wherein the function expression of the power intensity optimizing model is as follows:
since K (T) and T (T) are continuous functions, integral solution is needed to obtain a total optimal searching value in a future time period, and the minimum optimal searching value is guaranteed, so that the minimum adjustment cost and minimum adjustment time length in the future time period can be realized, and the power intensity in the future time period can be planned.
Wherein S is characterized as a optimizing value of a power intensity optimizing model, and min is characterized as a minimizing function body.
The solving constraint conditions of the power intensity optimizing model comprise:
power strength constraint:
p min ≤p(t)≤p max ；
where p is min 、p max With the same composition format of p (t), p min ＝w 1 *p 1 min+w 2 *p 2 min+w 3 *p 3 min+w 4 *p 4 min，p max ＝w 1 *p 1 max+w 2 *p 2 max+w 3 *p 3 max+w 4 *p 4 max，p 1 min、p 2 min、p 3 min、p 4 min is respectively characterized by the lowest value of power intensity of temperature control equipment (air conditioner), bacteria control equipment (disinfection spray gun), dust sweeping equipment (humidifier) and oxygenation equipment, and p 1 max、p 2 max、p 3 max、p 4 max are respectively characterized by the highest power intensity values of temperature control equipment (air conditioner), bacteria control equipment (disinfection spray gun), dust sweeping equipment (humidifier) and oxygenation equipment.
Duration constraint:
T min ≤T(t)≤T max ；
wherein p is min 、p max Respectively characterized by a lower power intensity limit and an upper power intensity limit of the air conditioning equipment, T min 、T max Respectively characterized by a duration lower limit and a duration upper limit, T min 、T max The user can customize the device.
The power intensity optimizing model is solved in a constraint condition by adopting an intelligent searching algorithm to obtain the power intensity p (t) of the air conditioning equipment at each future time in the future time period.
The intelligent search algorithm includes ant colony algorithm, genetic algorithm, etc., and such search algorithm belongs to a method known in the art, and this embodiment is not described herein.
And S3, adjusting the indoor air by the air-conditioning equipment according to the power intensity so as to enable the air quality of the indoor future time period to reach the expectations of users and improve the prospective of indoor air quality regulation.
The air conditioning device conditions indoor air by power intensity, comprising:
quantizing the power intensity p (t) from a continuous form to a discrete point form to obtain a set of coordinate points
A group of coordinate points are formed by combining two adjacent points
Orderly matching to obtain a group of adjusting point pairs
Determining an adjustment direction of the power intensity according to each adjustment point pair, wherein,
if it is
When it is determined that the air conditioning apparatus is at the rate +.>
To increase power strength;
if it is
When it is determined that the air conditioning apparatus is at the rate +.>
Reducing the power intensity;
if it is
When the power intensity of the air conditioning equipment is maintained;
wherein i is a measurement constant and has no substantial meaning.
The steps can realize the operation of continuously adjusting the power of the air conditioning equipment and avoid the phenomenon of clamping the conditioning equipment.
As shown in fig. 2, the present invention provides an indoor air conditioning method based on the above internet of things, which includes:
the prediction curve making unit 1 is used for predicting indoor air data in a future time period according to the indoor air data acquired in the current time period, and drawing a curve form of the indoor air data in the future time period to be used as an air prediction curve, wherein the air data is a data set for representing air quality;
a optimizing model making unit 2 for receiving an air expected curve set by a user in a future time period and constructing a power intensity optimizing model of the air conditioning equipment based on the air expected curve and an air prediction curve;
and a conditioning application unit 3 for conditioning the indoor air by the air conditioning apparatus according to the power intensity so that the air quality of the indoor future time period reaches the user's desire.
The optimizing model making unit is also integrated with an interactive panel for a user to set an expected value of air data, the prediction curve making unit is also integrated with data acquisition equipment, the adjusting application unit is also integrated with air conditioning equipment, the air data of the current indoor time period acquired by the data acquisition equipment is fed back to a data processor of the prediction curve making unit for curve drawing, the expected value of the air data is set in the interactive panel by the user, the interactive panel is fed back to the data processor of the optimizing model making unit for curve drawing and model construction, and the adjusting application unit adjusts the indoor air according to the power intensity air conditioning equipment.
According to the invention, an air prediction curve is constructed on air data of a future time period, then an air expected curve set by a user in the future time period is received, and a power intensity optimizing model of the air conditioning equipment is constructed based on the air expected curve and the air prediction curve, so that a power intensity adjusting scheme of the air conditioning equipment with win-win adjusting cost and adjusting efficiency can be constructed, and the interactive experience sense is improved while the user expectation is met.
The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application, the scope of which is defined by the claims. Various modifications and equivalent arrangements may be made to the present application by those skilled in the art, which modifications and equivalents are also considered to be within the scope of the present application.
Claims (6)
1. An indoor air conditioning method of the internet of things is characterized by comprising the following steps:
s1, predicting indoor air data in a future time period according to the indoor air data acquired in the current time period, and drawing a curve form of the indoor air data in the future time period to serve as an air prediction curve, wherein the air data are data sets representing air quality;
s2, receiving an air expected curve set by a user in a future time period, and constructing a power intensity optimizing model of the air conditioning equipment based on the air expected curve and the air prediction curve, wherein the power intensity optimizing model is used for obtaining the win-win of adjusting the air prediction curve to the power intensity of the air expected curve so as to enable the adjustment cost and the adjustment efficiency, and the air expected curve is characterized by an expected value of the user on indoor air data;
step S3, the air conditioning equipment adjusts indoor air according to the power intensity so that the air quality of an indoor future time period reaches the expectations of users;
the indoor air data collected in the current time period predicts the indoor air data in the future time period, and the indoor air data comprises:
-comparing said current time period { t } 1 ,t 2 ,…,t n Indoor air data collected
Input to LSTM artificial neural network for outputting future time period { t ] n+1 ,t n+2 ,…,t n+m Indoor air data of }
Wherein (1)>
Characterised by the current time t n Indoor air data of the room->
Characterised by a future time t n+m Indoor air data at n is the total number of current times in the current time period and m is the total number of future times in the future time period;
the plotting the indoor air data of the future time period into a curve form as an air prediction curve comprises:
establishing a two-dimensional coordinate system, taking time as an abscissa axis of the two-dimensional coordinate system, and taking a data value as an ordinate axis of the two-dimensional coordinate system;
data of indoor air
Conversion into a set of coordinate points
Drawing all coordinate points into a two-dimensional coordinate system according to horizontal coordinate values and vertical coordinate values, sequentially connecting all coordinate points in the two-dimensional coordinate system to fit to obtain a first curve, and obtaining a function expression of the first curve based on a group of coordinate point fitting;
taking the first curve as an air prediction curve, taking a functional expression of a second curve as an expression X=g (t) of the air prediction curve, wherein X is a functional expression of indoor air data, t is a functional expression of future time, and g is characterized by a relation function of the indoor air data and the future time;
the user sets an air expectancy profile at a future time period, comprising:
manually setting by a user in an interactive panel air data desired values at each of the future times in a future time period results in a set of indoor air data desired values
Expected value of air data
Conversion into a set of coordinate points
Drawing all coordinate points into a two-dimensional coordinate system according to horizontal coordinate values and vertical coordinate values, sequentially connecting all coordinate points in the two-dimensional coordinate system to fit to obtain a second curve, and obtaining a function expression of the second curve based on a group of coordinate point fitting;
taking the second curve as an air expected curve, taking a functional expression of the second curve as an expression Y=f (t) of the air expected curve, wherein Y is a functional expression of an expected value of indoor air data, t is a functional expression of future time, and f is characterized as a relation function body of the expected value of the indoor air data and the future time;
the construction of the power intensity optimizing model of the air conditioning equipment based on the air expected curve and the air prediction curve comprises the following steps:
the method comprises the steps of constructing a time length for an air conditioning device to adjust an air prediction curve to an air expected curve as an efficiency index, wherein a function expression of the efficiency index is as follows:
where T (T) is characterized as the duration of the air data on the air prediction curve at the future time T being adjusted to the air data on the air desired curve, g (T) is characterized as the air data on the air desired curve at the future time T, f (T) is characterized as the air data on the air prediction curve at the future time T, and p (T) is characterized as the power intensity of the air conditioning device at the future time T;
the cost of the air conditioning equipment for adjusting the air prediction curve to the air expectation curve is used for constructing an economic index, and the functional expression of the economic index is as follows:
K(t)＝A*p(t)；
where K (t) is characterized by the cost of air data on the air prediction curve to air data on the air desired curve at a future time t, A is characterized by the unit price of regulating lost power, and p (t) is characterized by the power intensity of the air conditioning device at the future time t;
combining the economic index and the efficiency index to obtain a function expression of a power intensity optimizing model, wherein the function expression of the power intensity optimizing model is as follows:
wherein S is characterized as a optimizing value of a power intensity optimizing model, and min is characterized as a minimizing function body.
2. The indoor air conditioning method of the internet of things according to claim 1, wherein: the solving constraint conditions of the power intensity optimizing model comprise:
power strength constraint:
p min ≤p(t)≤p max ；
duration constraint:
T min ≤T(t)≤T max ；
wherein p is min 、p max Characterised by the air-conditioning apparatus respectivelyLower power intensity limit and upper power intensity limit, T min 、T max Characterized by the lower and upper duration limits, respectively.
3. The indoor air conditioning method of the internet of things according to claim 2, wherein the power intensity optimizing model is solved in a constraint condition by adopting an intelligent search algorithm to obtain the power intensity p (t) of the air conditioning equipment at each future time in a future time period.
4. The indoor air conditioning method of the internet of things according to claim 3, wherein the conditioning the indoor air by the air conditioning device according to the power intensity comprises:
quantizing the power intensity p (t) from a continuous form to a discrete point form to obtain a set of coordinate points
A group of coordinate points are formed by combining two adjacent points
Orderly matching to obtain a group of adjusting point pairs +.>
Determining an adjustment direction of the power intensity according to each adjustment point pair, wherein,
if it is
When it is determined that the air conditioning apparatus is at the rate +.>
To increase power strength;
if it is
When it is determined that the air conditioning apparatus is at the rate +.>
Reducing the power intensity;
if it is
When the power intensity of the air conditioning equipment is maintained;
wherein i is a measurement constant and has no substantial meaning.
5. An air conditioning system of an indoor air conditioning method of the internet of things according to any one of claims 1 to 4, comprising:
the prediction curve making unit is used for predicting indoor air data in a future time period according to the indoor air data acquired in the current time period, and drawing a curve form of the indoor air data in the future time period to be used as an air prediction curve, wherein the air data is a data set for representing air quality;
the optimizing model making unit is used for receiving an air expected curve set by a user in a future time period, and constructing a power intensity optimizing model of the air conditioning equipment based on the air expected curve and the air prediction curve, wherein the power intensity optimizing model is used for obtaining the power intensity of the air prediction curve adjusted to the air expected curve so as to win the adjustment cost and the adjustment efficiency, and the air expected curve is characterized by an expected value of the user on indoor air data;
and the adjusting application unit is used for adjusting the indoor air by the air conditioning equipment according to the power intensity so that the air quality of the indoor future time period reaches the expectations of users.
6. An air conditioning system according to claim 5, wherein the optimizing model making unit is further integrated with an interactive panel for setting an air data expected value by a user, the prediction curve making unit is further integrated with a data acquisition device, and the conditioning application unit is further integrated with an air conditioning device.
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