CN114963459B - Thermal comfort degree adjusting method and equipment thereof - Google Patents
Thermal comfort degree adjusting method and equipment thereof Download PDFInfo
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- CN114963459B CN114963459B CN202110194434.XA CN202110194434A CN114963459B CN 114963459 B CN114963459 B CN 114963459B CN 202110194434 A CN202110194434 A CN 202110194434A CN 114963459 B CN114963459 B CN 114963459B
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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
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or 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
- F24F2120/00—Control inputs relating to users or occupants
- F24F2120/10—Occupancy
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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
- F24F2120/00—Control inputs relating to users or occupants
- F24F2120/20—Feedback from users
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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
- F24F2130/00—Control inputs relating to environmental factors not covered by group F24F2110/00
- F24F2130/10—Weather information or forecasts
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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
The invention discloses a thermal comfort level adjusting method and equipment thereof, which are used for providing personalized thermal comfort parameter recommendation and adjusting intelligent air equipment aiming at the thermal comfort feeling level of different users. The method comprises the following steps: determining the current regional environment and thermal comfort preference data of a user; screening out a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from a preset thermal comfort set, wherein the thermal comfort set comprises all thermal comfort levels and a plurality of thermal comfort parameter subsets corresponding to each thermal comfort level, and each thermal comfort parameter subset is used for representing attribute information of the same sample user and thermal comfort information of the environment; recommending the thermal comfort parameters contained in the screened thermal comfort parameter subset to the user, responding to a setting instruction sent by the user based on the recommended thermal comfort parameters, and adjusting the intelligent air equipment according to the thermal comfort parameters corresponding to the setting instruction.
Description
Technical Field
The invention relates to the technical field of intelligent home, in particular to a thermal comfort degree adjusting method and equipment thereof.
Background
At present, an important means for ensuring indoor thermal comfort is to regulate and control air equipment such as an air conditioner, a fresh air machine, a purifier, a humidifier and the like, and then evaluate the environment by using a human thermal comfort evaluation method. Existing standards such as ANSI (American National Standards Institute, american national institute of standards)/ASHRAE 55-2017 (American Society of Heating, refrigerating and Air-Conditioning Engineers, american society of heating, cooling and air conditioning engineers), ISO 7730-2005 (International Standard Organization, international organization for standardization), GB/T18049-2017 (mandatory national standard/recommended national standard), etc. classify human thermal comfort into 7 classes (including cold, cool, slightly cool, comfortable, slightly warm, hot), control air equipment in linkage by adjusting the 7 classes of thermal comfort to achieve desired indoor temperature, indoor humidity, indoor air flow rate.
However, the thermal comfort of the human body is affected by factors such as regions, seasons, indoor temperature, indoor humidity, indoor air flow rate, dressing index, metabolic rate, outdoor temperature and the like, and no scheme for providing personalized regulation and control for the requirements of different users on the thermal comfort exists at present.
Disclosure of Invention
The invention provides a thermal comfort level adjusting method and equipment thereof, which are used for providing personalized thermal comfort parameter recommendation and adjusting intelligent air equipment aiming at the thermal comfort feeling level of different users.
In a first aspect, a thermal comfort adjustment method provided by an embodiment of the present invention includes:
determining the current regional environment and thermal comfort preference data of a user, wherein the thermal comfort preference data is used for representing the thermal comfort feeling degree of the user on the indoor environment;
screening out a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from a preset thermal comfort set, wherein the thermal comfort set comprises all thermal comfort levels and a plurality of thermal comfort parameter subsets corresponding to each thermal comfort level, and each thermal comfort parameter subset is used for representing attribute information of the same sample user and thermal comfort information of the environment;
recommending the thermal comfort parameters contained in the screened thermal comfort parameter subset to the user, responding to a setting instruction sent by the user based on the recommended thermal comfort parameters, and adjusting the intelligent air equipment according to the thermal comfort parameters corresponding to the setting instruction.
According to the embodiment of the invention, personalized thermal comfort parameters can be recommended for different users according to the thermal comfort feeling degree of the different users, and the personalized thermal comfort parameters can be utilized to regulate intelligent air equipment, so that a personalized regulation scheme is provided for the users, and the use experience of the users is effectively improved.
As an optional implementation manner, the screening the subset of thermal comfort parameters matched with the geographical environment and the thermal comfort preference data from the preset thermal comfort set includes:
if a thermal comfort level setting instruction of the user is received, a plurality of thermal comfort parameter subsets corresponding to the thermal comfort level setting instruction are selected from a preset thermal comfort set;
and screening out a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from the thermal comfort parameter subsets.
As an optional implementation manner, before the step of screening out the thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from the preset thermal comfort set, the method further includes:
acquiring historical thermal comfort data of the user;
according to the current regional environment of the user, historical thermal comfort data matched with the regional environment are screened out from the historical thermal comfort data;
And if the accumulated number of days of the storage date corresponding to the screened historical thermal comfort data is smaller than a threshold value, executing the step of screening a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from a preset thermal comfort set.
As an optional implementation manner, if the storage date corresponding to the historical thermal comfort data is not less than a threshold value, the method further includes:
averaging each thermal comfort parameter in the matched historical thermal comfort data;
recommending the averaged thermal comfort parameters to the user;
and responding to a setting instruction sent by the user based on the recommended thermal comfort parameter, and adjusting the intelligent air equipment according to the thermal comfort parameter corresponding to the setting instruction.
As an alternative embodiment, the thermal comfort set is determined by:
inputting a thermal comfort parameter subset of all sample users into a random forest model to obtain thermal comfort levels corresponding to the thermal comfort parameter subset, wherein the thermal comfort parameter subset is obtained by splitting a thermal comfort parameter set corresponding to the thermal comfort level subjectively evaluated by the sample users by using a clustering method;
And determining all obtained thermal comfort levels and a thermal comfort parameter subset of a sample user corresponding to each thermal comfort level as the thermal comfort set.
In a second aspect, an embodiment of the present invention provides a thermal comfort adjustment device, including a processor and a memory, where the memory is configured to store a program executable by the processor, and the processor is configured to read the program in the memory and execute the following steps:
determining the current regional environment and thermal comfort preference data of a user, wherein the thermal comfort preference data is used for representing the thermal comfort feeling degree of the user on the indoor environment;
screening out a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from a preset thermal comfort set, wherein the thermal comfort set comprises all thermal comfort levels and a plurality of thermal comfort parameter subsets corresponding to each thermal comfort level, and each thermal comfort parameter subset is used for representing attribute information of the same sample user and thermal comfort information of the environment;
recommending the thermal comfort parameters contained in the screened thermal comfort parameter subset to the user, responding to a setting instruction sent by the user based on the recommended thermal comfort parameters, and adjusting the intelligent air equipment according to the thermal comfort parameters corresponding to the setting instruction.
As an alternative embodiment, the processor is specifically configured to perform:
if a thermal comfort level setting instruction of the user is received, a plurality of thermal comfort parameter subsets corresponding to the thermal comfort level setting instruction are selected from a preset thermal comfort set;
and screening out a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from the thermal comfort parameter subsets.
As an alternative embodiment, before the screening out the subset of thermal comfort parameters matching the geographical environment and the thermal comfort preference data from the preset thermal comfort set, the processor is specifically further configured to perform:
acquiring historical thermal comfort data of the user;
according to the current regional environment of the user, historical thermal comfort data matched with the regional environment are screened out from the historical thermal comfort data;
and if the accumulated number of days of the storage date corresponding to the screened historical thermal comfort data is smaller than a threshold value, executing the step of screening a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from a preset thermal comfort set.
As an alternative embodiment, if the storage date corresponding to the historical thermal comfort data is not less than a threshold, the processor is specifically further configured to perform:
averaging each thermal comfort parameter in the matched historical thermal comfort data;
recommending the averaged thermal comfort parameters to the user;
and responding to a setting instruction sent by the user based on the recommended thermal comfort parameter, and adjusting the intelligent air equipment according to the thermal comfort parameter corresponding to the setting instruction.
As an alternative embodiment, the processor is specifically configured to determine the thermal comfort set by:
inputting a thermal comfort parameter subset of all sample users into a random forest model to obtain thermal comfort levels corresponding to the thermal comfort parameter subset, wherein the thermal comfort parameter subset is obtained by splitting a thermal comfort parameter set corresponding to the thermal comfort level subjectively evaluated by the sample users by using a clustering method;
and determining all obtained thermal comfort levels and a thermal comfort parameter subset of a sample user corresponding to each thermal comfort level as the thermal comfort set.
In a third aspect, an embodiment of the present invention further provides a thermal comfort adjustment device, including:
The method comprises the steps of determining a user data unit, wherein the user data unit is used for determining the current regional environment of a user and thermal comfort preference data, and the thermal comfort preference data are used for representing the thermal comfort degree of the user on the indoor environment;
a screening unit, configured to screen out a thermal comfort parameter subset matching the regional environment and the thermal comfort preference data from a preset thermal comfort set, where the thermal comfort set includes all thermal comfort levels, and a plurality of thermal comfort parameter subsets corresponding to each thermal comfort level, and each thermal comfort parameter subset is used to characterize attribute information of a same sample user and thermal comfort information of an environment to which the thermal comfort parameter subset belongs;
and the recommendation adjusting unit is used for recommending the thermal comfort parameters contained in the screened thermal comfort parameter subset to the user, responding to a setting instruction sent by the user based on the recommended thermal comfort parameters, and adjusting the intelligent air equipment according to the thermal comfort parameters corresponding to the setting instruction.
As an alternative embodiment, the screening unit is specifically configured to:
if a thermal comfort level setting instruction of the user is received, a plurality of thermal comfort parameter subsets corresponding to the thermal comfort level setting instruction are selected from a preset thermal comfort set;
And screening out a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from the thermal comfort parameter subsets.
As an optional implementation manner, before the screening the subset of thermal comfort parameters matching the geographical environment and the thermal comfort preference data from the preset thermal comfort set, the screening unit is specifically further configured to:
acquiring historical thermal comfort data of the user;
according to the current regional environment of the user, historical thermal comfort data matched with the regional environment are screened out from the historical thermal comfort data;
and if the accumulated number of days of the storage date corresponding to the screened historical thermal comfort data is smaller than a threshold value, executing the step of screening a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from a preset thermal comfort set.
As an optional implementation manner, if the storage date corresponding to the historical thermal comfort data is not less than a threshold, the screening unit is specifically further configured to:
averaging each thermal comfort parameter in the matched historical thermal comfort data;
recommending the averaged thermal comfort parameters to the user;
And responding to a setting instruction sent by the user based on the recommended thermal comfort parameter, and adjusting the intelligent air equipment according to the thermal comfort parameter corresponding to the setting instruction.
As an alternative embodiment, the screening unit is specifically configured to determine the thermal comfort set by:
inputting a thermal comfort parameter subset of all sample users into a random forest model to obtain thermal comfort levels corresponding to the thermal comfort parameter subset, wherein the thermal comfort parameter subset is obtained by splitting a thermal comfort parameter set corresponding to the thermal comfort level subjectively evaluated by the sample users by using a clustering method;
and determining all obtained thermal comfort levels and a thermal comfort parameter subset of a sample user corresponding to each thermal comfort level as the thermal comfort set.
In a fourth aspect, embodiments of the present application also provide a computer storage medium having stored thereon a computer program for carrying out the steps of the method of the first aspect described above when executed by a processor.
These and other aspects of the application will be more readily apparent from the following description of the embodiments.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it will be apparent that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of an embodiment of a thermal comfort adjustment method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a first interactive interface according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a second interactive interface according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a third interactive interface according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a user display interface according to an embodiment of the present invention;
FIG. 6 is a flowchart illustrating an implementation of a method for overall thermal comfort adjustment according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a thermal comfort adjustment device according to an embodiment of the present invention;
fig. 8 is a schematic diagram of a thermal comfort adjustment device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, 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.
In the embodiment of the invention, the term "and/or" describes the association relation of the association objects, which means that three relations can exist, for example, a and/or B can be expressed as follows: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
The application scenario described in the embodiment of the present invention is for more clearly describing the technical solution of the embodiment of the present invention, and does not constitute a limitation on the technical solution provided by the embodiment of the present invention, and as a person of ordinary skill in the art can know that the technical solution provided by the embodiment of the present invention is applicable to similar technical problems as the new application scenario appears. In the description of the present invention, unless otherwise indicated, the meaning of "a plurality" is two or more.
In embodiment 1, although there is a control method based on human thermal comfort, there is no personalized control method, which can provide different thermal comfort control demands for users according to the preference of different users for thermal comfort as a control factor. In order to solve the problems, the embodiment of the invention provides a thermal comfort degree adjusting method which can provide personalized thermal comfort parameter recommendation and regulate intelligent air equipment according to the thermal comfort degree of different users.
As shown in fig. 1, a specific implementation flow of a thermal comfort level adjusting method provided by the embodiment of the invention is as follows:
step 100, determining the current regional environment of a user and thermal comfort preference data, wherein the thermal comfort preference data is used for representing the thermal comfort degree of the user on the indoor environment;
in practice, the geographic environment includes, but is not limited to: regional, seasonal, outdoor weather conditions, etc. The thermal comfort preference data includes, but is not limited to, the user's perception of temperature, wind speed, humidity.
As an alternative embodiment, the thermal comfort preference data of the user is determined by:
and determining thermal comfort preference data of the user according to user information and historical operation data, wherein the user information is used for representing attribute information of the user and environment data of a region where the user is located, and the historical operation data is used for representing operation data of intelligent air equipment operated by the user.
In the implementation, the user space portrait is constructed from the angles of people to which the user belongs, regional climate, temperature preference, humidity preference, wind speed preference, whether sensitive people and the like by dynamically sensing the change and the demand of environmental factors, climate factors, human body feeling and physical health factors in the space scene of the user, so that the thermal comfort preference of the user is better described. Wherein, construct the source of portrait data:
(1) Receiving portraits set by a user through an APP and other interactive interfaces, wherein the portraits comprise crowd, occupation, regional climate and the like;
(2) Drawing temperature, humidity and wind speed labels according to historical operation data of users in a period of time (for example, 15 days) and combining with thermal neutral temperatures of different regions; the specific rules are as follows: counting the average value of temperature, humidity and wind speed settings, marking by referring to a weather area thermal neutral temperature and comfortable temperature interval table, if the current user-set temperature is less than the thermal neutral temperature-X, the preference of the user for thermal comfort is cool, if the temperature is more than the thermal neutral temperature +X, heat preference is given, otherwise, the temperature is thermal neutral, and the preference of the user for thermal comfort of humidity and wind speed can be determined as well; wherein X is a number greater than zero.
As shown in fig. 2, 3 and 4, the present embodiment provides an interactive interface capable of receiving user preference or demand for thermal comfort parameters such as temperature or wind speed.
Step 101, screening out a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from a preset thermal comfort set, wherein the thermal comfort set comprises all thermal comfort levels and a plurality of thermal comfort parameter subsets corresponding to each thermal comfort level, and each thermal comfort parameter subset is used for representing attribute information of the same sample user and thermal comfort information of the environment;
According to the embodiment, the matched thermal comfort parameter subsets can be directly selected from the preset thermal comfort sets according to the current regional environment and thermal comfort preference data of the user, and different thermal comfort parameter subsets are provided for different users. Because the thermal comfort set in this embodiment includes all thermal comfort levels and a plurality of thermal comfort parameter subsets corresponding to each thermal comfort level, and each thermal comfort parameter subset is used for characterizing attribute information of the same sample user and thermal comfort information of an environment to which the sample user belongs, when the thermal comfort information of the sample user matching with the regional environment and thermal comfort preference data corresponding to the user can be screened out based on the attribute information of the same sample user and the thermal comfort information of the environment during screening, so that personalized thermal comfort information is determined for different users.
Step 102, recommending the thermal comfort parameters contained in the screened thermal comfort parameter subset to the user, responding to a setting instruction sent by the user based on the recommended thermal comfort parameters, and adjusting the intelligent air equipment according to the thermal comfort parameters corresponding to the setting instruction.
As shown in fig. 5, the present embodiment provides a user display interface, through which the thermal comfort parameters included in the screened thermal comfort parameter subset are recommended to the user.
Optionally, recommending the thermal comfort parameters for representing the indoor environment information, such as temperature, humidity, wind speed and the like, contained in the screened thermal comfort parameter subset to the user; and responding to a setting instruction sent by a user, and controlling the intelligent air equipment to operate according to the temperature, the humidity and the wind speed.
In implementation, the thermal comfort level corresponding to the screened thermal comfort parameter subset may be recommended to the user, and the intelligent air device may be adjusted according to the thermal comfort parameter contained in the thermal comfort parameter subset in response to a setting instruction sent by the user based on the recommended thermal comfort level.
The selected thermal comfort parameters contained in the thermal comfort parameter subset and the thermal comfort level corresponding to the thermal comfort parameter subset can be recommended to the user, a setting instruction sent by the user based on the recommended thermal comfort parameters is responded, and the intelligent air equipment is adjusted according to the thermal comfort parameters corresponding to the setting instruction; or, responding to a setting instruction sent by the user based on the recommended thermal comfort level, and adjusting the intelligent air equipment according to the thermal comfort parameters contained in the thermal comfort parameter subset.
As an optional implementation manner, the embodiment may further provide, according to a received user instruction for setting the thermal comfort level, a personalized regulation requirement for the user, which is specifically as follows:
If a thermal comfort level setting instruction of the user is received, a plurality of thermal comfort parameter subsets corresponding to the thermal comfort level setting instruction are selected from a preset thermal comfort set;
and screening out a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from the thermal comfort parameter subsets.
As an optional implementation manner, the embodiment of the present invention may further provide a method for providing personalized regulation and control requirements for a user based on the historical thermal comfort data of the user, where the specific implementation steps are as follows:
step 1) before a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data is screened out from a preset thermal comfort set, historical thermal comfort data of the user is obtained;
for example, thermal comfort data may be acquired once every ts seconds and stored as historical thermal comfort data thereafter, the acquired thermal comfort data including, but not limited to, time, indoor temperature, indoor humidity, air conditioning status, air conditioning mode, air conditioning set temperature, air conditioning set wind speed.
Step 2) screening historical thermal comfort data matched with the regional environment from the historical thermal comfort data according to the current regional environment of the user;
In implementation, historical thermal comfort data corresponding to the same region, the same season, the same or similar outdoor weather can be screened from the historical thermal comfort data.
And 3) if the accumulated number of days of the storage date corresponding to the screened historical thermal comfort data is smaller than a threshold value, a step of screening a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from a preset thermal comfort set is executed.
Step 4) if the storage date corresponding to the historical thermal comfort data is not smaller than a threshold value, averaging each thermal comfort parameter in the matched historical thermal comfort data;
step 5) recommending the averaged thermal comfort parameters to the user;
and 6) responding to a setting instruction sent by the user based on the recommended thermal comfort parameter, and adjusting the intelligent air equipment according to the thermal comfort parameter corresponding to the setting instruction.
The 7-level thermal comfort is a relatively rough control mode because the thermal comfort of the human body is affected by factors such as regions, seasons, indoor temperature, indoor humidity, indoor air flow rate, dressing index, metabolic rate, outdoor temperature and the like. By analyzing ASHRAE RP-884 disclosed thermal comfort data set, the predictive thermal sensation average voting PMV evaluation method adopted in the existing standard has the accuracy rate of calculating human thermal sensation value which is only more than 30% compared with the true thermal sensation value of a subject. Therefore, based on the existing 7-level thermal comfort level regulation granularity, the accuracy is lower, the control effect of the indoor air environment cannot be improved, and higher user satisfaction cannot be achieved.
The embodiment provides a finer thermal comfort level regulation scheme, wherein a preset thermal comfort set in the embodiment comprises smaller thermal comfort level regulation granularity, because the thermal comfort set in the embodiment is obtained by inputting thermal comfort parameter subsets of all sample users into a random forest model, and the input thermal comfort parameter subsets of the sample users are obtained by splitting thermal comfort parameter sets corresponding to thermal comfort levels subjectively evaluated by the users by using a clustering method, namely, a preset number of thermal comfort parameter subsets are obtained after the sample thermal comfort parameter sets are split, and because each sample thermal comfort parameter set corresponds to one thermal comfort level, each thermal comfort parameter subset obtained after the splitting corresponds to one thermal comfort level, namely, each thermal comfort level is also split into a preset number of thermal comfort levels; the present embodiment can split the current 5-level thermal comfort into smaller-granularity thermal comfort levels.
As an alternative embodiment, the thermal comfort set is determined by:
1) Inputting the thermal comfort parameter subsets of all sample users into a random forest model to obtain thermal comfort levels corresponding to the thermal comfort parameter subsets; the thermal comfort parameter subset is obtained by splitting a thermal comfort parameter set corresponding to a thermal comfort level subjectively evaluated by the sample user by using a clustering method;
In practice, the thermal comfort level of the subjective evaluation by the user includes: the method comprises the steps of heating, slightly heating, moderately cooling and cooling 5 grades, wherein a thermal comfort parameter set corresponding to each thermal comfort grade comprises regions, seasons, indoor temperatures, indoor humidity, air conditioning equipment states, air conditioning equipment modes, air conditioning equipment set temperatures, air conditioning set wind speeds, outdoor temperatures, outdoor humidity, personnel dressing indexes, personnel activity levels and the like, and firstly, the thermal comfort parameter set corresponding to the thermal comfort grade subjectively evaluated by a sample user is subjected to symbolization processing as follows:
X＝[x (1) ，x (2) ，…，x (m) ]representing the thermal comfort parameter set for all sample users, where m represents the number of thermal comfort parameter sets, x (i) Representing an ith set of thermal comfort parameters;
the i-th set of thermal comfort parameters is represented as comprising n number of thermal comfort parameters. Y= [ Y ] (1) ，y (2) ，…，y (m) ]Representing the thermal comfort level corresponding to all sets of thermal comfort parameters, where m represents the number of sample users, y (i) The thermal comfort level corresponding to the ith thermal comfort parameter set is indicated, and the thermal comfort level can be warm, slightly warm, moderate, slightly cool and cool, and the corresponding value is 1-5.
Sample set d= [ (x) for all sample users (1) ，y (1) )，(x (2) ，y (2) )，…，(x (m) ，y (m) )]Wherein (x) (i) ，y (i) ) The i-th sample among the 1 st to m-th samples is represented.
In implementation, the data in the sample set of all sample users may be preprocessed, which specifically includes the following contents:
(1) Missing value processing: filling the median in the thermal comfort parameter set as the missing thermal comfort parameter for the missing thermal comfort parameter appearing in the thermal comfort parameter set;
(2) Thermal comfort parameter coding: because the state of the air conditioning equipment, the running mode of the air conditioner, the wind speed set value, the personnel activity level and the like are the enumerated thermal comfort parameters, the label coding is carried out, for example, the personnel activity level has values of sitting, standing, reading, slight exercise, walking, running and the like, and after the label coding is carried out, the personnel activity level takes the value of 1-N;
(3) Thermal comfort parameter normalization: for each thermal comfort parameter, the dispersion min-max is adopted for standardization, and the formula is thatWherein x in sample set D (1) Is a vector with n columns, i.e., n features; x is x i A number representing a row at the time of normalizing a certain column, a numerator representing the data minus the minimum value of the column, a denominator representing the maximum value minus the minimum value of the column, and yi representing the data after normalization.
After normalization, the thermal comfort parameters can be mapped to [0,1 ] ]Within the interval, normalized sequence y 1 、y 2 、...、y n At [0,1]Within the interval and without dimension;
(4) Training set and test set partitioning: sample set D was randomly split, 80% training and 20% test set. Wherein the training set is used to train a random forest model and the test set is used to verify the effect of the model.
In practice, the corresponding set of thermal comfort parameters Si for each thermal comfort level Gi (1.ltoreq.i.ltoreq.5) includes, but is not limited to: regional, seasonal, indoor temperature, humidity, air flow rate, personnel activity level, personnel dressing index, outdoor temperature, outdoor humidity;
specifically, the thermal comfort parameter set Si corresponding to each thermal comfort level Gi may be split into 3 thermal comfort levels (Gil, gi2, gi 3) by using a clustering method, i.e. the thermal comfort parameter sets corresponding to each thermal comfort level Gi are clustered into 3 thermal comfort parameter subsets, and each thermal comfort parameter set contains thermal comfort parameter subsets of Si1, si2, si3, where si1= [ (x) (1) ，y (1) )，(x (2) ，y (2) )，…，(x (si) ，y (si) )]，x (1) ，x (2) ，...x (si) Attribute information representing a user or thermal comfort information of an environment to which the user belongs. y is (1) ，y (2) ，y (si) ) Indicating the level of thermal comfort, i.e. y in Si1 (1) ，y (2) ，y (si) ) For indicating the same thermal comfort level.
The average value of the indoor temperature in each subset of thermal comfort parameters is calculated and then sorted in ascending order, for example, T (Si 1) < T (Si 2) < T (Si 3), then the subsets corresponding to thermal comfort levels Gi1, gi2, gi3 are Si1, si2, si3, respectively. According to the method, the current 5-level thermal comfort level and 15-level thermal comfort level are corresponding to the following conditions, namely, cool (1, 2, 3), slightly cool (4, 5, 6), comfortable (7, 8, 9), slightly warm (10, 11, 12) and warm (13, 14, 15).
The clustering method is to take the thermal comfort parameter subsets as a whole and calculate the similarity between the thermal comfort parameter subsets.
2) And determining all obtained thermal comfort levels and a thermal comfort parameter subset of a sample user corresponding to each thermal comfort level as the thermal comfort set.
The random forest model in this embodiment is obtained by training the sample set D, that is, the thermal comfort parameter subset of the sample user and the thermal comfort level corresponding to the thermal comfort parameter subset of the sample user. The specific training process is as follows: the subset of input thermal comfort parameters is s= [ Si1, si2, si3], where 1.ltoreq.i.ltoreq.5, the number of random forest sub-trees is T, for each sub-tree T e (1, 2., T).
(1) D training samples are randomly extracted from the D and taken as a training set Dt of t;
(2) Randomly selecting k thermal comfort parameter subsets from Dt, training a t-th subtree, and taking each thermal comfort parameter subset as a feature subset;
the process of training the t-th subtree is as follows: starting from the root node of the tree, calculating the coefficient of the key of each feature subset by using CART (Classification and Regression Tree) algorithm, selecting the feature with the smallest coefficient of key as the root node, splitting according to the value rule of the feature subset, and generating new child nodes for each split child node in the same way until the coefficient of key is smaller than the threshold value or exceeds the set tree depth d.
(3) Each tree t grows to a specified depth d.
For each training sample, the voting method is used for selecting the thermal comfort level with the maximum number of votes from the T trees, namely the final output thermal comfort level.
Evaluating the training random forest model by using F1 score in machine learning, wherein F1 is a harmonic mean of precision and recall;
wherein, the liquid crystal display device comprises a liquid crystal display device,precision represents the percentage of actual true in all training samples predicted to be true, and recovery represents the percentage of successful predictions to be true in all samples actually true.
After the random forest model is trained, the random forest model can be continuously and iteratively upgraded, when a new training sample is added, the random forest model is retrained to generate a new random forest model, if the F1 score of the random forest model obtained by the new training is larger than the F1 score of the original random forest model, the original random forest model is replaced by the new random forest model, the thermal comfort parameter subsets of all sample users are input into the new random forest model to obtain thermal comfort levels corresponding to the thermal comfort parameter subsets, and all the obtained thermal comfort levels and the thermal comfort parameter subsets of the sample users corresponding to each thermal comfort level are determined to be the thermal comfort sets.
According to the obtained random forest model, the metabolic level, dressing index, indoor temperature, indoor humidity, indoor wind speed, outdoor temperature and outdoor humidity are selected in a traversing way within a certain range and are input as a subset of thermal comfort parameters, the thermal comfort levels (15 levels in total) are obtained through calculation by using the random forest model, and then the combinations are preset and stored for use when the thermal comfort parameters are recommended. Specifically, the metabolic level range may be [ 0.8-sedentary, 1.0-sedentary, 1.2-write, 1.6-light physical activity, 2.0-homework ], the dressing index range may be [0.4,0.5,0.6,0.7,0.8,0.9,1.0,1.1,1.2], the indoor temperature range may be 16 ℃ to 32 ℃ (interval 1 ℃), the indoor humidity range may be 20 to 70 (interval 10), the indoor wind speed range may be 0 to 0.5 (interval 0.1), the outdoor temperature range may be 0 to 36 ℃, the outdoor humidity range may be 10 to 100, and the seasonal range may be spring, summer, autumn and winter. According to the characteristic input in a certain range, all the grades are calculated and obtained, and the characteristic parameters corresponding to each grade are stored. For example as [ feature 1, feature 2, feature 3, ]. Wherein the features are used to characterize the thermal comfort information of the same sample user and the thermal comfort information of the environment to which the thermal comfort parameter subset is comprised.
According to the embodiment, based on the collected regional environment where the user is currently located and thermal comfort preference data, such as parameters of temperature, humidity, wind speed, PM2.5, formaldehyde, CO2 and the like, a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data is screened out from a preset thermal comfort set through a thermal comfort parameter calculation method provided by a cloud, and thermal comfort parameters contained in the screened thermal comfort parameter subset are recommended to the user. The algorithm provided by the cloud end can also screen out a plurality of thermal comfort parameter subsets corresponding to the thermal comfort level setting instruction from a preset thermal comfort set after the thermal comfort level setting instruction of the user is received; screening a subset of thermal comfort parameters matching the geographic environment and the thermal comfort preference data from the plurality of subsets of thermal comfort parameters; recommending the thermal comfort parameters contained in the screened thermal comfort parameter subset to the user. For example: optimal temperature, humidity and wind speed parameters are recommended for a user, and the operation mode and the operation state of the air equipment combination such as an air conditioner, a fresh air fan and a purifier can be controlled by combining a multidimensional control strategy, so that a comfortable and healthy space environment is provided for the user.
As shown in fig. 6, the embodiment of the present invention further provides a comprehensive thermal comfort adjustment method, and a specific implementation flow of the method is as follows:
step 600, determining the current regional environment and thermal comfort preference data of the user;
wherein the thermal comfort preference data is used to characterize the user's level of perception of thermal comfort to the indoor environment in which it is located;
step 601, acquiring historical thermal comfort data of the user;
step 602, according to the current regional environment of the user, historical thermal comfort data matched with the regional environment is screened out from the historical thermal comfort data;
step 603, judging whether the accumulated number of days of the storage date corresponding to the screened historical thermal comfort data is smaller than a threshold value, if yes, executing step 604, otherwise, executing step 605;
step 604, judging whether a thermal comfort level setting instruction of the user is received, if yes, executing step 606, otherwise, executing step 608;
step 606, selecting a plurality of thermal comfort parameter subsets corresponding to the thermal comfort level setting instruction from a preset thermal comfort set;
the thermal comfort set is determined by:
inputting a thermal comfort parameter subset of all sample users into a random forest model to obtain thermal comfort levels corresponding to the thermal comfort parameter subset, wherein the thermal comfort parameter subset is obtained by splitting a thermal comfort parameter set corresponding to the thermal comfort level subjectively evaluated by the sample users by using a clustering method;
And determining all obtained thermal comfort levels and a thermal comfort parameter subset of a sample user corresponding to each thermal comfort level as the thermal comfort set.
Step 607, screening out a subset of thermal comfort parameters matching the geographical environment and the thermal comfort preference data from the plurality of subsets of thermal comfort parameters, and executing step 609;
step 608, screening out a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from a preset thermal comfort set;
the thermal comfort set is determined by:
inputting a thermal comfort parameter subset of all sample users into a random forest model to obtain thermal comfort levels corresponding to the thermal comfort parameter subset, wherein the thermal comfort parameter subset is obtained by splitting a thermal comfort parameter set corresponding to the thermal comfort level subjectively evaluated by the sample users by using a clustering method;
and determining all obtained thermal comfort levels and a thermal comfort parameter subset of a sample user corresponding to each thermal comfort level as the thermal comfort set.
Step 609, recommending the thermal comfort parameters contained in the screened thermal comfort parameter subset to the user;
Step 610, responding to a setting instruction sent by the user based on the recommended thermal comfort parameter, and adjusting the intelligent air equipment according to the thermal comfort parameter corresponding to the setting instruction.
Step 605, averaging each thermal comfort parameter in the matched historical thermal comfort data, and executing step 611;
step 611, recommending the averaged thermal comfort parameter to the user;
step 612, responding to a setting instruction sent by the user based on the recommended thermal comfort parameter, and adjusting the intelligent air equipment according to the thermal comfort parameter corresponding to the setting instruction.
Embodiment 2, based on the same inventive concept, the embodiment of the present invention further provides a thermal comfort adjusting device, and since the device is the device in the method in the embodiment of the present invention, and the principle of the device for solving the problem is similar to that of the method, the implementation of the device may refer to the implementation of the method, and the repetition is omitted.
As shown in fig. 7, the apparatus includes a processor 700 and a memory 701, the memory is used for storing a program executable by the processor, and the processor is used for reading the program in the memory and executing the following steps:
determining the current regional environment and thermal comfort preference data of a user, wherein the thermal comfort preference data is used for representing the thermal comfort feeling degree of the user on the indoor environment;
Screening out a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from a preset thermal comfort set, wherein the thermal comfort set comprises all thermal comfort levels and a plurality of thermal comfort parameter subsets corresponding to each thermal comfort level, and each thermal comfort parameter subset is used for representing attribute information of the same sample user and thermal comfort information of the environment;
recommending the thermal comfort parameters contained in the screened thermal comfort parameter subset to the user, responding to a setting instruction sent by the user based on the recommended thermal comfort parameters, and adjusting the intelligent air equipment according to the thermal comfort parameters corresponding to the setting instruction.
As an alternative embodiment, the processor is specifically configured to perform:
if a thermal comfort level setting instruction of the user is received, a plurality of thermal comfort parameter subsets corresponding to the thermal comfort level setting instruction are selected from a preset thermal comfort set;
and screening out a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from the thermal comfort parameter subsets.
As an alternative embodiment, before the screening out the subset of thermal comfort parameters matching the geographical environment and the thermal comfort preference data from the preset thermal comfort set, the processor is specifically further configured to perform:
Acquiring historical thermal comfort data of the user;
according to the current regional environment of the user, historical thermal comfort data matched with the regional environment are screened out from the historical thermal comfort data;
and if the accumulated number of days of the storage date corresponding to the screened historical thermal comfort data is smaller than a threshold value, executing the step of screening a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from a preset thermal comfort set.
As an alternative embodiment, if the storage date corresponding to the historical thermal comfort data is not less than a threshold, the processor is specifically further configured to perform:
averaging each thermal comfort parameter in the matched historical thermal comfort data;
recommending the averaged thermal comfort parameters to the user;
and responding to a setting instruction sent by the user based on the recommended thermal comfort parameter, and adjusting the intelligent air equipment according to the thermal comfort parameter corresponding to the setting instruction.
As an alternative embodiment, the processor is specifically configured to determine the thermal comfort set by:
inputting a thermal comfort parameter subset of all sample users into a random forest model to obtain thermal comfort levels corresponding to the thermal comfort parameter subset, wherein the thermal comfort parameter subset is obtained by splitting a thermal comfort parameter set corresponding to the thermal comfort level subjectively evaluated by the sample users by using a clustering method;
And determining all obtained thermal comfort levels and a thermal comfort parameter subset of a sample user corresponding to each thermal comfort level as the thermal comfort set.
Embodiment 3, based on the same inventive concept, the embodiment of the present invention further provides a thermal comfort adjusting device, and since the device is the device in the method of the embodiment of the present invention, and the principle of the device for solving the problem is similar to that of the method, the implementation of the device can refer to the implementation of the method, and the repetition is omitted.
As shown in fig. 8, the apparatus includes:
a user data unit 800 for determining a current geographical environment and thermal comfort preference data of a user, wherein the thermal comfort preference data is used for representing a thermal comfort feeling degree of the user on an indoor environment;
a screening unit 801, configured to screen out a thermal comfort parameter subset matching the regional environment and the thermal comfort preference data from a preset thermal comfort set, where the thermal comfort set includes all thermal comfort levels, and a plurality of thermal comfort parameter subsets corresponding to each thermal comfort level, and each thermal comfort parameter subset is used to characterize attribute information of a same sample user and thermal comfort information of an environment to which the thermal comfort parameter subset belongs;
And a recommendation adjustment unit 802, configured to recommend thermal comfort parameters included in the screened thermal comfort parameter subset to the user, respond to a setting instruction sent by the user based on the recommended thermal comfort parameters, and adjust the intelligent air device according to the thermal comfort parameters corresponding to the setting instruction.
As an alternative embodiment, the screening unit is specifically configured to:
if a thermal comfort level setting instruction of the user is received, a plurality of thermal comfort parameter subsets corresponding to the thermal comfort level setting instruction are selected from a preset thermal comfort set;
and screening out a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from the thermal comfort parameter subsets.
As an optional implementation manner, before the screening the subset of thermal comfort parameters matching the geographical environment and the thermal comfort preference data from the preset thermal comfort set, the screening unit is specifically further configured to:
acquiring historical thermal comfort data of the user;
according to the current regional environment of the user, historical thermal comfort data matched with the regional environment are screened out from the historical thermal comfort data;
And if the accumulated number of days of the storage date corresponding to the screened historical thermal comfort data is smaller than a threshold value, executing the step of screening a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from a preset thermal comfort set.
As an optional implementation manner, if the storage date corresponding to the historical thermal comfort data is not less than a threshold, the screening unit is specifically further configured to:
averaging each thermal comfort parameter in the matched historical thermal comfort data;
recommending the averaged thermal comfort parameters to the user;
and responding to a setting instruction sent by the user based on the recommended thermal comfort parameter, and adjusting the intelligent air equipment according to the thermal comfort parameter corresponding to the setting instruction.
As an alternative embodiment, the screening unit is specifically configured to determine the thermal comfort set by:
inputting a thermal comfort parameter subset of all sample users into a random forest model to obtain thermal comfort levels corresponding to the thermal comfort parameter subset, wherein the thermal comfort parameter subset is obtained by splitting a thermal comfort parameter set corresponding to the thermal comfort level subjectively evaluated by the sample users by using a clustering method;
And determining all obtained thermal comfort levels and a thermal comfort parameter subset of a sample user corresponding to each thermal comfort level as the thermal comfort set.
A computer storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
determining the current regional environment and thermal comfort preference data of a user, wherein the thermal comfort preference data is used for representing the thermal comfort feeling degree of the user on the indoor environment;
screening out a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from a preset thermal comfort set, wherein the thermal comfort set comprises all thermal comfort levels and a plurality of thermal comfort parameter subsets corresponding to each thermal comfort level, and each thermal comfort parameter subset is used for representing attribute information of the same sample user and thermal comfort information of the environment;
recommending the thermal comfort parameters contained in the screened thermal comfort parameter subset to the user, responding to a setting instruction sent by the user based on the recommended thermal comfort parameters, and adjusting the intelligent air equipment according to the thermal comfort parameters corresponding to the setting instruction.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. A method of thermal comfort adjustment, the method comprising:
determining the current regional environment and thermal comfort preference data of a user, wherein the thermal comfort preference data is used for representing the thermal comfort feeling degree of the user on the indoor environment;
screening out a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from a preset thermal comfort set, wherein the thermal comfort set comprises all thermal comfort levels and a plurality of thermal comfort parameter subsets corresponding to each thermal comfort level, and each thermal comfort parameter subset is used for representing attribute information of the same sample user and thermal comfort information of the environment; the thermal comfort set is determined by: inputting a thermal comfort parameter subset of all sample users into a random forest model to obtain thermal comfort levels corresponding to the thermal comfort parameter subset, wherein the thermal comfort parameter subset is obtained by splitting a thermal comfort parameter set corresponding to the thermal comfort level subjectively evaluated by the sample users by using a clustering method; determining all obtained thermal comfort levels and a subset of thermal comfort parameters of a sample user corresponding to each thermal comfort level as the thermal comfort set;
Recommending the thermal comfort parameters contained in the screened thermal comfort parameter subset to the user, responding to a setting instruction sent by the user based on the recommended thermal comfort parameters, and adjusting the intelligent air equipment according to the thermal comfort parameters corresponding to the setting instruction.
2. The method of claim 1, wherein the screening out a subset of thermal comfort parameters from a pre-set of thermal comfort parameters that match the geographic environment and the thermal comfort preference data comprises:
if a thermal comfort level setting instruction of the user is received, a plurality of thermal comfort parameter subsets corresponding to the thermal comfort level setting instruction are selected from a preset thermal comfort set;
and screening out a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from the thermal comfort parameter subsets.
3. The method of claim 1, further comprising, prior to screening out a subset of thermal comfort parameters from the pre-set thermal comfort set that match the geographic environment and the thermal comfort preference data:
acquiring historical thermal comfort data of the user;
according to the current regional environment of the user, historical thermal comfort data matched with the regional environment are screened out from the historical thermal comfort data;
And if the accumulated number of days of the storage date corresponding to the screened historical thermal comfort data is smaller than a threshold value, executing the step of screening a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from a preset thermal comfort set.
4. The method of claim 3, wherein if the stored date corresponding to the historical thermal comfort data is not less than a threshold, the method further comprises:
averaging each thermal comfort parameter in the matched historical thermal comfort data;
recommending the averaged thermal comfort parameters to the user;
and responding to a setting instruction sent by the user based on the recommended thermal comfort parameter, and adjusting the intelligent air equipment according to the thermal comfort parameter corresponding to the setting instruction.
5. A thermal comfort adjustment device, characterized in that it comprises a processor and a memory, said memory being adapted to store a program executable by said processor, said processor being adapted to read the program in said memory and to perform the steps of:
determining the current regional environment and thermal comfort preference data of a user, wherein the thermal comfort preference data is used for representing the thermal comfort feeling degree of the user on the indoor environment;
Screening out a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from a preset thermal comfort set, wherein the thermal comfort set comprises all thermal comfort levels and a plurality of thermal comfort parameter subsets corresponding to each thermal comfort level, and each thermal comfort parameter subset is used for representing attribute information of the same sample user and thermal comfort information of the environment; the thermal comfort set is determined by: inputting a thermal comfort parameter subset of all sample users into a random forest model to obtain thermal comfort levels corresponding to the thermal comfort parameter subset, wherein the thermal comfort parameter subset is obtained by splitting a thermal comfort parameter set corresponding to the thermal comfort level subjectively evaluated by the sample users by using a clustering method; determining all obtained thermal comfort levels and a subset of thermal comfort parameters of a sample user corresponding to each thermal comfort level as the thermal comfort set;
recommending the thermal comfort parameters contained in the screened thermal comfort parameter subset to the user, responding to a setting instruction sent by the user based on the recommended thermal comfort parameters, and adjusting the intelligent air equipment according to the thermal comfort parameters corresponding to the setting instruction.
6. The device of claim 5, wherein the processor is specifically configured to perform:
if a thermal comfort level setting instruction of the user is received, a plurality of thermal comfort parameter subsets corresponding to the thermal comfort level setting instruction are selected from a preset thermal comfort set;
and screening out a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from the thermal comfort parameter subsets.
7. The device according to claim 5, wherein before said screening out a subset of thermal comfort parameters matching said geographical environment and said thermal comfort preference data from a pre-set of thermal comfort parameters, said processor is in particular further configured to perform:
acquiring historical thermal comfort data of the user;
according to the current regional environment of the user, historical thermal comfort data matched with the regional environment are screened out from the historical thermal comfort data;
and if the accumulated number of days of the storage date corresponding to the screened historical thermal comfort data is smaller than a threshold value, executing the step of screening a thermal comfort parameter subset matched with the regional environment and the thermal comfort preference data from a preset thermal comfort set.
8. The apparatus of claim 7, wherein if the stored date corresponding to the historical thermal comfort data is not less than a threshold, the processor is further specifically configured to perform:
averaging each thermal comfort parameter in the matched historical thermal comfort data;
recommending the averaged thermal comfort parameters to the user;
and responding to a setting instruction sent by the user based on the recommended thermal comfort parameter, and adjusting the intelligent air equipment according to the thermal comfort parameter corresponding to the setting instruction.
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