CN111444943A - Device and method for adaptive personalized thermal comfort prediction based on human body similarity - Google Patents
Device and method for adaptive personalized thermal comfort prediction based on human body similarity Download PDFInfo
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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
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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Abstract
The invention discloses a device and a method for self-adaptive personalized thermal comfort prediction based on human body similarity, and relates to the field of human body thermal comfort detection. The device comprises a data acquisition unit, a data self-adaptive adjustment unit, an individualized thermal comfort prediction model construction unit and an environment control unit. The method comprises the following steps: collecting a large amount of user data to form a data set; calculating the thermal perception similarity between the target user and other users in the data set; resampling a basic data set according to the thermal perception similarity to form a new data set; establishing an individualized thermal comfort prediction model according to the new data set; collecting relevant data of a target user in real time, calculating thermal comfort data according to the model, and generating an air conditioner control signal; and sending out an air conditioner control signal to regulate and control the air conditioner. The method can effectively capture the preference of the target user to the thermal environment, so that the established thermal comfort model has personalized characteristics, improves the accuracy of prediction and has practical value.
Description
Technical Field
The invention relates to the field of human body thermal comfort detection, in particular to a device and a method for self-adaptive personalized thermal comfort prediction based on human body similarity.
Background
With the development of society and economic progress, the requirement of people on environmental comfort is gradually improved. Modern people usually spend more than 80% of the time indoors, so the indoor thermal environment can bring great influence to physical and mental health, comfort and working efficiency of people, and the maintenance of a good and comfortable indoor thermal environment has important significance for ensuring normal work and study life of people.
The evaluation and prediction of the thermal comfort state of the human body are always a quite important research content in the thermal comfort research, and whether the thermal comfort state of the human body can be accurately evaluated is the basis for further deeply researching the physiological mechanism of the thermal comfort of the human body and the basis for effectively controlling the thermal environment in practical application. Currently, international common thermal comfort prediction models can be divided into two types, one is an average model established based on groups, and the other is a personal comfort model established aiming at individuals.
Early averaging models included Fanger's PMV-PDD model and Gagge's two-node model. The model is mostly established according to a heat balance model of human body and environment, and is obtained based on physiological and psychological measurement data of groups, so that the model has certain universality. However, because the difference between people is not considered, the accuracy rate is often only 40% -60% in the actual application process, and the method cannot be well used in the specific individual-specific scene. More models are built by integrating physiological and psychological factors, physiological parameters are added into a human body thermal comfort model, but most formula data in the models are from research on a certain group, and the thermal comfort requirement difference caused by individual difference cannot be solved.
Considering the shortcomings of the average model in accuracy, there is an increasing interest in the establishment of personal thermal comfort models and more in the physiological response of individuals to the environment. Patent publication No. CN104490371B proposes a thermal comfort detection method based on human physiological parameters, which performs regression analysis by collecting human physiological parameters including skin temperature, electrocardiosignals, fingertip pulse, respiration, and skin impedance as input, establishes a corresponding relationship between the physiological parameters and comfort, and uses a neural network for discrimination. Although this method takes individual differences into account, the selected parameters are too complex to facilitate actual measurement, and a large amount of data needs to be collected during the modeling process. Patent publication No. CN109857175A proposes a non-invasive AI sensing method for human thermal comfort, which collects image data of human skin in a computer vision manner, and generates a network model through deep learning network and training. The establishment of the model requires long-term tracking data acquisition of the subject to obtain enough data, is difficult to be widely used, and has no universality and portability.
Therefore, those skilled in the art are dedicated to develop a simple and effective personalized thermal comfort prediction apparatus and method that does not require long-term tracking of the target user to collect a large amount of data for modeling, facilitates actual measurement, and has universality and portability.
Disclosure of Invention
In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is to provide a new personalized thermal comfort prediction apparatus and method, which can accurately capture the preference of a target user for a thermal environment through the personal characteristics of a plurality of target users without tracking the target user for a long time to acquire a large amount of data for establishing a model, and achieve the purposes of quickly establishing a personalized thermal comfort prediction model for any target user, accurately predicting a thermal comfort state, and effectively regulating and controlling the environment.
In order to achieve the above object, the present invention provides an adaptive personalized thermal comfort prediction apparatus and method based on human body similarity, wherein the method comprises the following steps:
step 1, collecting a large number of physiological parameters, environmental parameters, personal characteristics of a user and subjective thermal comfort data of the user to form a basic data set;
step 3, resampling a basic data set according to the thermal perception similarity, and enabling the probability that the data with high thermal perception similarity are sampled to be larger to form a new data set;
step 4, training a machine learning model by taking the physiological parameters and the environmental parameters of the new data set as input and the subjective thermal comfort data of the user in the new data set as output, and establishing the personalized thermal comfort prediction model;
step 5, acquiring the physiological parameters of the target user and the environmental parameters of the target user in real time, inputting the physiological parameters and the environmental parameters into the personalized thermal comfort prediction model, calculating to obtain the thermal comfort data of the target user, and generating an air conditioner control signal;
and 6, sending the air conditioner control signal to an air conditioner to regulate and control the air conditioner.
Further, the physiological parameters include: skin temperature, skin humidity, skin conductance, heart rate.
Further, the environmental parameters include: ambient temperature, ambient humidity, ambient wind speed.
Further, the personal characteristics include: gender, age, height, weight, clothing index, body fat percentage, skin surface area.
Further, the subjective thermal comfort data is a subjective evaluation of the user on the indoor thermal environment.
Further, the calculation method of the thermal perception similarity is that all the personal characteristics of the users in the data set are subjected to standardization operation with the mean value of 0 and the variance of 1, so that the influence caused by different dimensions is eliminated, the weights of different indexes are determined according to an entropy weight method, and calculation is carried out according to the weighted Euclidean distance.
Further, in step 3, the thermal perception similarity may be mapped and normalized by a gaussian function, so that individuals with closer thermal perception similarity have higher weights, thereby calculating weights of different data, and resampling accordingly to form the new data set.
A device for self-adaptive personalized thermal comfort prediction based on human body similarity is characterized by comprising a data acquisition unit, a data self-adaptive adjustment unit, a personalized thermal comfort prediction model construction unit and an environment control unit.
Further, the data acquisition unit acquires the physiological parameter through a wearable device.
Further, the data acquisition unit acquires the environmental parameters in real time through a sensor which is arranged indoors in advance and transmits the environmental parameters through a wireless communication module.
Due to the adoption of the technical scheme, the invention has the following beneficial effects:
1. the thermal comfort prediction model establishment scheme takes the difference between different individuals into consideration, and can quickly establish an accurate prediction model aiming at different individuals so as to meet the requirements of different people.
2. The establishment process of the thermal comfort prediction model does not need a large amount of data of the target user to establish the model, but fits the real distribution of the target user by adjusting the data distribution of the basic data set, omits a complicated data acquisition process aiming at the target user, and is beneficial to wide application.
3. The thermal comfort prediction model takes the physiological parameters of the target person and the corresponding environmental parameters as input, not only considers the direct influence of the environment on the thermal comfort, but also obtains the current state information of the person from the physiological parameters, and considers the dynamic change of the thermal comfort state of the person, so that the result is more accurate.
4. The wearable signal acquisition device integrates the environmental parameter acquisition module and the physiological parameter acquisition module, can accurately and automatically acquire the physiological parameters of a target person and the environmental information of the surrounding environment of the target person, and ensures the accuracy of input.
5. The control center integrates a thermal comfort prediction model and an air conditioner control unit, can calculate and analyze real-time physiological environment parameters and output control signals, and achieves the purposes of intellectualization and high efficiency of indoor environment control.
Drawings
Fig. 1 is a schematic structural diagram of an apparatus for adaptive personalized thermal comfort prediction based on human body similarity according to the present invention.
The system comprises a data acquisition unit (unit A), a 11-user personal characteristic acquisition module (A1), a 12-user physiological parameter acquisition module (A2), a 13-environmental parameter acquisition module (A3), a 14-user subjective thermal comfort data acquisition module (A4), a 2-data updating unit (unit B), a 21-user thermal perception similarity measurement algorithm (B1), a 22-data resampling algorithm (B2), a 23-training data set (B3), A3-personalized thermal comfort prediction model establishing unit (unit C), a 31-machine learning algorithm (C1), a 32-personalized thermal comfort state prediction model (C2), a 4-environmental control unit (unit D), a 41-thermal comfort state calculation module (D1) and a 42-air conditioner control calculation module (D2).
Detailed Description
The technical contents of the preferred embodiments of the present invention will be more clearly and easily understood by referring to the drawings attached to the specification. The present invention may be embodied in many different forms of embodiments and the scope of the invention is not limited to the embodiments set forth herein.
In the drawings, structurally identical elements are represented by like reference numerals, and structurally or functionally similar elements are represented by like reference numerals throughout the several views. The size and thickness of each component shown in the drawings are arbitrarily illustrated, and the present invention is not limited to the size and thickness of each component. The thickness of the components may be exaggerated where appropriate in the figures to improve clarity.
The embodiment comprises the following steps:
as shown in FIG. 1, the present invention firstly needs to establish a basic data set through a data acquisition unit (unit A), and later model establishment needs to depend on the data set. Specifically, the data set needs to include physiological parameters, environmental parameters, subjective feedback of thermal comfort status, and personal characteristics of the user for different individuals under different environmental conditions. The physiological parameters can comprise skin temperature, skin humidity, skin conductance and the like, and are acquired by a user physiological parameter acquisition module (A2) 12; the environmental parameters may include ambient temperature, ambient humidity, ambient wind speed, etc., through the environmental parameter collection module (a3) 13. These two types of parameters will be used as input data for the thermal comfort prediction model. The subjective feedback of thermal comfort state may be obtained by the user subjective thermal comfort data acquisition module (a4)14 in a timed questionnaire, and specifically, the thermal comfort state feedback may be acquired as a scale shown in table 1, and the thermal comfort state feedback is used as an output of the thermal comfort prediction model. In addition, personal characteristics including parameters such as height, weight, body fat rate, clothing index, sex, age, skin surface area and the like are recorded and collected by the user personal characteristic collecting module (A1)11, and the indexes are all related to the heat preference of one individual and can be used for judging the heat perception similarity among different individuals.
TABLE 1 thermal comfort staff
Further, for a target user needing to establish an individualized thermal comfort prediction model, personal characteristics including parameters such as height, weight, body fat rate, clothing index, gender, age, skin surface area and the like are firstly acquired, and the similarity of the target user and other users in the data set is calculated through a thermal perception similarity measurement algorithm (B1) 21. In particular, a data set is assumedM users are provided, the number of personal features of each user is n, and then the vector S formed by the personal features of each personiCan be expressed in the form of:
Si＝[Xi1,Xi2,…Xin],(i＝1,…,m)
wherein S isiPersonal feature vectors for user i; xijThe j-th personal characteristic value of the user i (j ═ 1, …, n);
firstly, all personal features are subjected to standardization operation with the mean value of 0 and the variance of 1, so that the influence caused by different dimensions is eliminated, and the standardized personal feature vector is obtained as follows:
si＝[xi1,xi2,…xin],(i＝1,…,m)
wherein s isiThe personal feature vector of the user i after standardization; x is the number ofijThe j-th personal characteristic value of the user i after normalization (j ═ 1, …, n);
then determining the weights a of different personal characteristics according to the entropy weight methodj：
Wherein HjEntropy value of j-th class feature; a isjIs the weight value of the j-th class feature.
And finally, calculating the thermal perception similarity distance between the target user and any user in the data set by taking the weighted Euclidean distance as a basis. The calculation method of the thermal perception similarity is as follows:
starget＝[x1,x2,…xn]；
A＝diag[a1,a2,…am]；
Dist(si,starget)＝(Si-Starget)TA(Si-Starget),(i＝1,…,m)，
wherein s istargetThe personal feature vector is standardized by the target user; a is a diagonal matrix composed of n personal feature weights; dist(s)i,starget) Is the similar distance between user i and the target user.
Further, after the thermal perception similarity distances of the target user to all individuals in the basic data set are calculated by the above formula, resampling is performed by the data resampling algorithm (B2) 22. Specifically, mapping and normalizing operation can be performed on all similarity degrees through a gaussian function, so that individuals with the similarity degrees closer to each other have higher weights, weights of different data are calculated, and the data set is resampled according to the weights to form a new data set:
wherein, wiA weight for user i; and a, b and c are hyper-parameters of the Gaussian function, and the mapping relation of the similarity distance and the weight is adjusted.
Further, after forming the new data set, the personalized thermal comfort prediction model building unit (C unit) 3 will be used to build the model. Specifically, the training data set 23(B3) is used to train the machine learning model (C1)31, which may include a neural network, a support vector machine, and the like, and can fit various complex non-linear mappings.
Further, the thermal comfort prediction model formed by the above steps needs to be integrated in the environment control unit (D unit) 4. In the embodiment of the present invention, the thermal comfort state calculation module (D1)41 calculates the thermal comfort state of the user in real time, and the air conditioner control calculation module (D2)42 outputs a control signal to perform autonomous control of the air conditioner.
Finally, it is noted that the above-mentioned preferred embodiments illustrate rather than limit the invention, and that, although the invention has been described in detail with reference to the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.
Claims (10)
1. A method for adaptive personalized thermal comfort prediction based on human body similarity, characterized in that the method comprises the steps of:
step 1, collecting a large number of physiological parameters, environmental parameters, personal characteristics of a user and subjective thermal comfort data of the user to form a basic data set;
step 2, comparing the personal characteristics of the target user with the personal characteristics of other users in the data set, and calculating the heat perception similarity;
step 3, resampling a basic data set according to the thermal perception similarity, enabling the probability of the data with high thermal perception similarity to be higher, and updating the data into a new data set;
step 4, training a machine learning model by taking the physiological parameters and the environmental parameters of the new data set as input and the subjective thermal comfort data of the user in the new data set as output, and establishing the personalized thermal comfort prediction model;
step 5, acquiring the physiological parameters of the target user and the environmental parameters of the target user in real time, inputting the physiological parameters and the environmental parameters into the personalized thermal comfort prediction model, calculating to obtain the thermal comfort data of the target user, and generating an air conditioner control signal;
and 6, sending the air conditioner control signal to an air conditioner to regulate and control the air conditioner.
2. The method for adaptive personalized thermal comfort prediction based on human similarity according to claim 1, wherein the physiological parameters comprise: skin temperature, skin humidity, skin conductance, heart rate.
3. The method for adaptive personalized thermal comfort prediction based on human similarity according to claim 1, wherein the environmental parameters comprise: ambient temperature, ambient humidity, ambient wind speed.
4. The method for adaptive personalized thermal comfort prediction based on human similarity according to claim 1, wherein the personal characteristics comprise: gender, age, height, weight, clothing index, body fat percentage, skin surface area.
5. The method for adaptive personalized thermal comfort prediction based on human similarity according to claim 1, wherein the subjective thermal comfort data is the user's subjective assessment of indoor thermal environment.
6. The method according to claim 1, wherein the calculation method of the thermal perception similarity is to perform normalization operation on all the personal characteristics of the users in the data set with a mean value of 0 and a variance of 1, eliminate the influence caused by different dimensions, determine weights of different indexes according to an entropy weight method, and calculate based on a weighted Euclidean distance.
7. The method for adaptive personalized thermal comfort prediction based on human body similarity according to claim 1, wherein the step 3 can map and normalize the thermal perception similarity through a gaussian function, so that individuals with closer thermal perception similarity have higher weight, thereby calculating the weight of different data, and resampling accordingly to form the new data set.
8. A device for self-adaptive personalized thermal comfort prediction based on human body similarity is characterized by comprising a data acquisition unit, a data self-adaptive adjustment unit, a personalized thermal comfort prediction model construction unit and an environment control unit.
9. The apparatus for adaptive personalized thermal comfort prediction based on human similarity according to claim 8, wherein the data acquisition unit acquires the physiological parameter through a wearable device.
10. The apparatus for adaptive personalized thermal comfort prediction based on human similarity according to claim 8, wherein the data acquisition unit acquires the environmental parameters in real time through a sensor pre-arranged indoors and transmits the environmental parameters through a wireless communication module.
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Application publication date: 20200724 |