CN114462467A - Recommendation method for air conditioner parameters and air conditioner - Google Patents
Recommendation method for air conditioner parameters and air conditioner Download PDFInfo
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Abstract
The invention discloses a recommendation method of air conditioner parameters and an air conditioner, wherein the method comprises the following steps: clustering the characteristic data of each air conditioner user according to a preset clustering algorithm to generate a plurality of user clustering centers; scoring a plurality of preset scoring items corresponding to the preset parameter items according to the air conditioner historical use data of each user clustering center, and generating scoring matrixes corresponding to the preset parameter items according to scoring results; processing the scoring matrix based on a preset matrix decomposition algorithm, and determining recommendation parameters corresponding to the clustering centers of the users; when a target user for starting the air conditioner is detected, pushing a target recommendation parameter corresponding to a target user clustering center to a user terminal of the target user; the preset scoring items are determined according to different value areas or different internal classifications of the preset parameter items, so that the accuracy of air conditioner parameter recommendation is further improved, and the user experience is improved.
Description
Technical Field
The application relates to the technical field of smart home, in particular to an air conditioner parameter recommendation method and an air conditioner.
Background
Along with the increasing living standard of people, intelligent household electrical appliances are more and more popular, and the market share of intelligent air conditioners is also rapidly promoted. At present, the intelligent air conditioner mostly carries out statistics according to the historical behaviors of a user, and parameters corresponding to the historical behaviors of the user are pushed to the user through scene fitting degree, so that the user can control the air conditioner according to recommended parameters, but the recommendation process is single in characteristic value and low in accuracy.
Therefore, how to further improve the accuracy of air conditioner parameter recommendation is a technical problem to be solved at present.
Disclosure of Invention
The invention provides a recommendation method of air conditioning parameters, which is used for solving the technical problem of low accuracy in recommendation of the air conditioning parameters in the prior art.
The method comprises the following steps:
clustering the characteristic data of each air conditioner user according to a preset clustering algorithm to generate a plurality of user clustering centers;
scoring a plurality of preset scoring items corresponding to preset parameter items according to the air conditioner historical use data of each user clustering center, and generating scoring matrixes corresponding to the preset parameter items according to scoring results;
processing the scoring matrix based on a preset matrix decomposition algorithm, and determining recommendation parameters corresponding to the clustering centers of the users;
when a target user for starting an air conditioner is detected, pushing a target recommendation parameter corresponding to a target user clustering center to a user terminal of the target user;
and each preset scoring item is determined according to different value areas or different internal classifications of the preset parameter items, and the target user clustering center is a user clustering center to which the target user belongs.
In some embodiments of the present application, a plurality of preset scoring items corresponding to preset parameter items are scored according to historical air conditioner usage data of each user clustering center, specifically:
determining the use times of the user for each preset scoring item and the total use times of the air conditioner according to the historical use data of the air conditioner;
and determining the grading result of each preset grading item according to the ratio of the use times of the user on each preset grading item to the total use times of the user on the air conditioner.
In some embodiments of the present application, determining recommendation parameters corresponding to each user clustering center after processing the scoring matrix based on a preset matrix decomposition algorithm specifically includes:
processing the scoring matrix based on a preset matrix decomposition algorithm and determining the prediction score of each preset scoring item;
and sequencing the prediction scores, determining a target scoring item with the highest prediction score, and determining the recommendation parameters according to the target scoring item.
In some embodiments of the present application, after pushing a target recommendation parameter corresponding to the target user clustering center to the user terminal of the target user, the method further includes:
and acquiring an operation result of the target user on the target recommendation parameter from the user terminal, and storing the air conditioner parameter corresponding to the operation result in a database.
In some embodiments of the present application, the operation result includes pressing a key of the target recommended parameter, or modifying the target recommended parameter, or maintaining a current control parameter of the air conditioner.
In some embodiments of the present application, the preset parameter item includes a set temperature of the air conditioner, or an operation mode of the air conditioner, or a wind speed of the air conditioner.
In some embodiments of the present application, the air conditioner historical usage data is a parameter set last time before a user turns off the air conditioner.
In some embodiments of the present application, the user's profile data includes environmental characteristics of the environment in which the user is located, personal representation characteristics of the user, and family representation characteristics of the user.
In some embodiments of the present application, the environmental characteristics include geography, air conditioner usage time period, weather, and air quality, the personal representation characteristics include age, gender, occupation, and physical characteristics, and the family representation characteristics include family membership and family annual income.
Correspondingly, the invention also provides an air conditioner, which comprises:
the communication module is used for communicating with the user terminal and the cloud server;
a controller configured to:
uploading log data of the air conditioner used by a user to the cloud server periodically;
when a control instruction sent by the user terminal is received, controlling an air conditioner according to the control instruction;
the control instruction is triggered after the user terminal receives an operation of a user on a target recommendation parameter, and the target recommendation parameter is pushed to the user terminal by the cloud server according to the method.
Clustering the characteristic data of each air conditioner user according to a preset clustering algorithm by applying the technical scheme and generating a plurality of user clustering centers; scoring a plurality of preset scoring items corresponding to the preset parameter items according to the air conditioner historical use data of each user clustering center, and generating scoring matrixes corresponding to the preset parameter items according to scoring results; processing the scoring matrix based on a preset matrix decomposition algorithm, and determining recommendation parameters corresponding to the clustering centers of the users; when a target user for starting the air conditioner is detected, pushing a target recommendation parameter corresponding to a target user clustering center to a user terminal of the target user; the preset scoring items are determined according to different value areas or different internal classifications of the preset parameter items, the target user clustering center is a user clustering center to which the target user belongs, air-conditioning parameters required to be set by the user in the current state are predicted, and the air-conditioning parameters are automatically pushed to the user, so that the accuracy of air-conditioning parameter recommendation is further improved, and the user experience is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart illustrating a method for recommending air conditioning parameters according to an embodiment of the present invention;
fig. 2 is a schematic flow chart illustrating a method for recommending air conditioning parameters according to another embodiment of the present invention;
fig. 3 is a schematic structural diagram of an air conditioner according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
The terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless otherwise specified.
The air conditioner performs a refrigeration cycle by using a compressor, a condenser, an expansion valve, and an evaporator in the present application. The refrigeration cycle includes a series of processes involving compression, condensation, expansion, and evaporation, and supplies refrigerant to the air that has been conditioned and heat-exchanged.
The compressor compresses a refrigerant gas in a high temperature and high pressure state and discharges the compressed refrigerant gas, the discharged refrigerant gas flows into a condenser, the condenser condenses the compressed refrigerant into a liquid phase, and heat is released to the surrounding environment through a condensation process.
The expansion valve expands the liquid-phase refrigerant in a high-temperature and high-pressure state condensed in the condenser into a low-pressure liquid-phase refrigerant. The evaporator evaporates the refrigerant expanded in the expansion valve and returns the refrigerant gas in a low-temperature and low-pressure state to the compressor. The evaporator can achieve a cooling effect by heat-exchanging with a material to be cooled using latent heat of evaporation of a refrigerant. The air conditioner can adjust the temperature of the indoor space throughout the cycle.
The outdoor unit of the air conditioner refers to a portion of a refrigeration cycle including a compressor and an outdoor heat exchanger, the indoor unit of the air conditioner includes an indoor heat exchanger, and an expansion valve may be provided in the indoor unit or the outdoor unit.
The indoor heat exchanger and the outdoor heat exchanger serve as a condenser or an evaporator. When the indoor heat exchanger is used as a condenser, the air conditioner is used as a heater in a heating mode, and when the indoor heat exchanger is used as an evaporator, the air conditioner is used as a cooler in a cooling mode.
The embodiment of the application provides a recommendation method of air conditioner parameters, which can be applied to a cloud server, wherein the cloud server is in communication connection with a plurality of air conditioners, each air conditioner uploads historical data of a user when the air conditioner is used by the user to the cloud server at regular intervals, and the air conditioner parameters are used for controlling the air conditioner, as shown in fig. 1, the method comprises the following steps:
and S101, clustering the characteristic data of each air conditioner user according to a preset clustering algorithm and generating a plurality of user clustering centers.
In this embodiment, the clustering algorithm divides the data set into a plurality of clusters by calculating the similarity between data objects, so that the same cluster object has a higher similarity, and the difference between different cluster objects is larger. The preset clustering algorithm may include, but is not limited to, hierarchical clustering, k-means algorithm, EM algorithm, DBSCAN algorithm, OPTICS algorithm, Mean Shift algorithm, spectral clustering algorithm, and may be flexibly selected by those skilled in the art as needed.
And clustering the characteristic data of each air conditioner user according to a preset clustering algorithm to generate a plurality of user clustering centers, wherein each user clustering center is a cluster.
In some embodiments of the present application, the user profile data includes an environmental profile of an environment in which the user is located, a personal profile of the user, and a family profile of the user.
In this embodiment, the personal portrait characteristics of the user are generated from information of the user in a plurality of dimensions, and the family portrait characteristics of the user are generated from information of the user's family in a plurality of dimensions. In addition, the user's feature data is acquired with permission from the user.
Other user characteristic data may be used by those skilled in the art according to actual needs, without affecting the scope of the present application.
For more accurate recommendation of air conditioning parameters, in some embodiments of the present application, the environmental characteristics include geographic area, air conditioner usage time period, weather and air quality, the personal profile characteristics include age, gender, occupation and physical characteristics, and the family profile characteristics include family member composition and family annual income.
Those skilled in the art may adopt other environmental features, personal representation features and family representation features according to actual needs, which does not affect the scope of protection of the present application.
And S102, scoring a plurality of preset scoring items corresponding to preset parameter items according to the historical air conditioner use data of each user clustering center, and generating scoring matrixes corresponding to the preset parameter items according to scoring results.
In this embodiment, each of the preset scoring items is determined according to different value sections or different internal classifications of the preset parameter item, for example, the preset parameter item is a set temperature of the air conditioner, all possible set temperatures of the air conditioner are divided into a plurality of temperature sections, such as [18-20 ℃,21-22 ℃,23-25 ℃,26-27 ℃,28-30 ℃,30-32 ℃,32-35 ℃, and more than 35 ℃), and each temperature section is each preset scoring item. The historical use data of the air conditioners can be obtained according to log data uploaded by each air conditioner in each user cluster.
And scoring the plurality of preset scoring items according to the air conditioner historical use data of each user clustering center to generate a scoring matrix corresponding to the preset parameter items, wherein the scoring matrix represents the scoring of each user clustering center on different preset scoring items.
In order to more accurately score each preset scoring item, in some embodiments of the present application, a plurality of preset scoring items corresponding to preset parameter items are scored according to air conditioner historical usage data of each user clustering center, specifically:
determining the use times of the user for each preset scoring item and the total use times of the air conditioner according to the historical use data of the air conditioner;
and determining the grading result of each preset grading item according to the ratio of the use times of the user on each preset grading item to the total use times of the user on the air conditioner.
In this embodiment, the number of times of use of each preset rating item and the total number of times of use of the air conditioner are determined according to historical use data of the air conditioner, then, the ratio of the number of times of use of each preset rating item by a user to the total number of times of use of the air conditioner by the user is determined, and the rating result of each preset rating item is determined according to each ratio.
For example, if each temperature interval of the set temperature of the air conditioner is scored, the set times of the users to each temperature interval in the user clustering center and the total use times of the users to the air conditioner are determined according to historical use data of the air conditioner, then the ratio of the set times of each temperature interval to the total use times of the air conditioner is respectively determined, and the score of each temperature interval can be determined according to each ratio.
It should be noted that the scheme of the above embodiment is only a specific implementation scheme proposed by the present application, and other manners of scoring a plurality of preset scoring items corresponding to preset parameter items according to historical air conditioner usage data of each user clustering center all belong to the protection scope of the present application.
In some embodiments of the present application, the preset parameter item includes a set temperature of the air conditioner, or an operation mode of the air conditioner, or a wind speed of the air conditioner, for more accurate recommendation of air conditioning parameters.
It can be understood that when the preset parameter item is the set temperature of the air conditioner or the wind speed of the air conditioner, each preset scoring item corresponds to a different value interval; and when the preset parameter items are the working modes of the air conditioner, each preset scoring item corresponds to different internal classifications.
The skilled person can also select other preset parameter items according to actual needs, which does not affect the scope of protection of the present application.
In some embodiments of the present application, the air conditioner historical usage data is a last set parameter before the air conditioner is turned off by a user.
In this embodiment, a user may perform parameter setting for multiple times when using the air conditioner to achieve comfortable experience, and the last parameter set by the user before turning off the air conditioner is generally a parameter meeting the user requirement, so that the last parameter set by the user before turning off the air conditioner is used as historical use data of the air conditioner.
And S103, processing the scoring matrix based on a preset matrix decomposition algorithm, and determining recommendation parameters corresponding to the clustering centers of the users.
In this embodiment, the recommended parameter is an air conditioner control parameter corresponding to the preset parameter item, for example, if the preset parameter item is a set temperature of the air conditioner, the recommended parameter may be a temperature value in a temperature zone or a temperature range.
The matrix decomposition algorithm maps the high-dimensional User-Item scoring matrix into two low-dimensional User and Item matrixes, and the problem of data sparsity is solved. The predetermined matrix decomposition algorithm may include, but is not limited to, SVD, Funk-SVD, Bias-SVD, SVD + +, TimeSVD + +, TSVD, NMF, and the like.
In order to improve the accuracy of the recommendation parameters, in some embodiments of the present application, the recommendation parameters corresponding to the user clustering centers are determined after the scoring matrix is processed based on a preset matrix decomposition algorithm, and specifically:
processing the scoring matrix based on a preset matrix decomposition algorithm and determining the prediction score of each preset scoring item;
and sequencing the prediction scores, determining a target scoring item with the highest prediction score, and determining the recommendation parameters according to the target scoring item.
In this embodiment, the scoring matrix is processed based on a preset matrix decomposition algorithm, the prediction scores of the preset scoring items are determined, the prediction scores are ranked, the preset scoring item with the highest prediction score is used as a target scoring item, and the recommendation parameter is determined according to the target scoring item.
It is understood that the scoring result in step S102 is a corresponding actual score, and may be trained through an objective function of a preset matrix decomposition algorithm to minimize a sum of squares of residuals between the predicted score and the actual score, and finally, a target score item may be output.
It should be noted that the scheme of the above embodiment is only a specific implementation scheme provided by the present application, and other ways of determining the recommended parameter after processing the scoring matrix based on the preset matrix decomposition algorithm all belong to the protection scope of the present application.
And step S104, when a target user for starting the air conditioner is detected, pushing a target recommendation parameter corresponding to a target user clustering center to a user terminal of the target user.
In this embodiment, the target user clustering center is a user clustering center to which the target user belongs, and the user terminal may be a mobile phone, a tablet, an intelligent wearable device, or an intelligent home device with a screen designated by the user, such as a refrigerator, a sound box, or the like. And when detecting that a target user for starting the air conditioner exists, pushing corresponding target recommendation parameters to a user terminal of the target user.
In order to recommend parameters more accurately, in some embodiments of the present application, after pushing a target recommendation parameter corresponding to the target user clustering center to the user terminal of the target user, the method further includes:
and acquiring an operation result of the target user on the target recommendation parameter from the user terminal, and storing the air conditioner parameter corresponding to the operation result in a database.
In the embodiment, the user terminal sends a prompt to the user after receiving the target recommendation parameter, the user performs corresponding operation when seeing that the target recommendation parameter exists from the user terminal, and the accuracy of prediction scoring can be improved by storing the air conditioner parameter corresponding to the operation result in the database, so that the parameter recommendation is performed more accurately.
In order to improve the user experience, in some embodiments of the present application, the operation result includes pressing one key to operate the target recommended parameter, or modifying the target recommended parameter, or maintaining a current control parameter of the air conditioner.
Optionally, the operation result of the current control parameter of the air conditioner is determined after the operation of the target recommended parameter by the user is not received within a preset time period, and the preset time period may be 2 minutes.
Those skilled in the art can set other operation options according to actual needs, which does not affect the protection scope of the present application.
Clustering the characteristic data of each air conditioner user according to a preset clustering algorithm by applying the technical scheme and generating a plurality of user clustering centers; scoring a plurality of preset scoring items corresponding to the preset parameter items according to the air conditioner historical use data of each user clustering center, and generating scoring matrixes corresponding to the preset parameter items according to scoring results; processing the scoring matrix based on a preset matrix decomposition algorithm, and determining recommendation parameters corresponding to the clustering centers of the users; when a target user for starting the air conditioner is detected, pushing a target recommendation parameter corresponding to a target user clustering center to a user terminal of the target user; the preset scoring items are determined according to different value areas or different internal classifications of the preset parameter items, the target user clustering center is a user clustering center to which the target user belongs, air-conditioning parameters required to be set by the user in the current state are predicted, and the air-conditioning parameters are automatically pushed to the user, so that the accuracy of air-conditioning parameter recommendation is further improved, and the user experience is improved.
In order to further illustrate the technical idea of the present invention, the technical solution of the present invention will now be described with reference to specific application scenarios.
The embodiment of the application provides a recommendation method of air conditioner parameters, which is characterized in that a set of prediction model is built by machine learning to predict proper air conditioner operation mode, temperature and wind speed, and more proper air conditioner parameters can be recommended to a user, so that the air conditioner can be conveniently and intelligently controlled, and the user experience is greatly improved.
The construction of the prediction model comprises the following steps:
1. collecting air conditioner characteristic value
Screening out the parameter values set by the user for the last time before the air conditioner is closed each time from the log data reported by the air conditioner: the set temperature of the air conditioner, the working mode of the air conditioner and the air speed of the air conditioner.
2. Collecting user characteristic data
The environmental characteristics of the user comprise the characteristics of region, time period, weather and air, the personal portrait characteristics of the user comprise the characteristics of age, sex, occupation, body and the like of the user, and the family portrait characteristics of the user comprise the characteristics of family member composition, family annual income and the like.
3. As shown in fig. 2, firstly, the users are clustered according to the feature data of the users, and the feature of each cluster center user and all the user sets belonging to each cluster class are obtained. And taking the user clustering center as a user in the scoring matrix, taking the characteristic value of the air conditioner as a scoring item in the scoring matrix, and then determining the scoring matrix of the user clustering center on the operation parameters according to the historical use data of the user. And finally, carrying out matrix decomposition on the scoring matrix so as to determine the predictive scoring of each user clustering center on the air conditioner operation parameters. And carrying out personalized equipment parameter setting on each user according to the equipment parameter prediction score of the user clustering center where the user is located.
The specific process is as follows:
(1) user is subjected to user clustering according to K-means clustering algorithmiCharacteristic g of the environmenti(including region, weather, air characteristics), user profile characteristics pi(including age, gender, occupation, body, etc.), family representation characteristics f of the useriEqual characteristic data (g)i,pi,fi…) to obtain K user clustering centers CKK), each user is assigned to a useriDivision into affiliated clustering centers CKObtaining a cluster center CKAll users of (2) set CK＝{userk1,userk2,...}。
(2) The control parameters of the air conditioner are divided into a plurality of scoring items, and all possible temperature settings of the air conditioner are divided into a plurality of temperature intervals, such as [18-20 ℃,21-22 ℃,23-25 ℃,26-27 ℃,28-30 ℃,30-32 ℃,32-35 ℃ and more than 35 ℃, by taking the set temperature of the air conditioner as an example ].
(3) Determining the grade of each temperature parameter item (namely the temperature grade item) by the user according to the historical use temperature data of the air conditioner of the user:
a) according to each useriThe air conditioner history uses the temperature data to determine the score of each air conditioner temperature parameter item, and the calculation mode is as follows
b) Obtaining a clustering center CKScore vector of (1)k＝(Scorek1，scorek2，...，scorek8)。
c) Constructing a scoring matrix R (Score) for clustering user center-air conditioner temperature parameter items1,Score2,...，ScoreK)T。
(4) Solving the scoring matrix R according to a matrix decomposition algorithm, and calculating the clustering center C of each userKFor each air conditioner temperature parameter item TjIs scored
(5) Clustering center C for each userKAll temperature parameter terms T ofjAccording to
Carry out sequencingAnd taking the highest temperature parameter item as the user clustering center CKThe recommended parameters of (1).
(6) And (5) calculating recommended parameters corresponding to parameters such as the working mode of the air conditioner, the wind speed and the like according to the same processes of (1) to (5).
4. When a target user for starting the air conditioner is detected, target recommendation parameters corresponding to the target user clustering center are pushed to a user terminal of the target user, and the user can select to press one key of the target recommendation parameters for operation, or modify the target recommendation parameters, or keep the current control parameters of the air conditioner.
5. For the unused target recommendation parameters, the changed data can be re-entered into the algorithm model, so that the accuracy of the model is continuously corrected.
An embodiment of the present application further provides an air conditioner, as shown in fig. 3, including:
the communication module 100 is used for communicating with a user terminal and a cloud server;
a controller 200 configured to:
uploading log data of the air conditioner used by a user to a cloud server periodically;
when a control instruction sent by a user terminal is received, controlling the air conditioner according to the control instruction;
the control instruction is triggered after the user terminal receives the operation of the user on the target recommendation parameter, and the target recommendation parameter is pushed to the user terminal by the cloud server according to the method.
In this embodiment, the communication module 100 is an equipment component having functions of communication and data processing, and may be a WiFi module, an 2/3/4/5G module, or an NB-IoT module. After the cloud server sends the target recommendation parameters to the user terminal, the user terminal displays the target recommendation parameters to the user, and if the user receives or modifies the target recommendation parameters, the user terminal sends corresponding control instructions to the controller 200 of the air conditioner, so that the controller 200 controls the air conditioner.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not necessarily depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A recommendation method for air conditioner parameters is characterized by comprising the following steps:
clustering the characteristic data of each air conditioner user according to a preset clustering algorithm to generate a plurality of user clustering centers;
scoring a plurality of preset scoring items corresponding to preset parameter items according to the air conditioner historical use data of each user clustering center, and generating scoring matrixes corresponding to the preset parameter items according to scoring results;
processing the scoring matrix based on a preset matrix decomposition algorithm, and determining recommendation parameters corresponding to the clustering centers of the users;
when a target user for starting an air conditioner is detected, pushing a target recommendation parameter corresponding to a target user clustering center to a user terminal of the target user;
and each preset scoring item is determined according to different value areas or different internal classifications of the preset parameter items, and the target user clustering center is a user clustering center to which the target user belongs.
2. The method of claim 1, wherein a plurality of preset scoring items corresponding to the preset parameter items are scored according to the air conditioner historical usage data of each user clustering center, specifically:
determining the use times of each preset scoring item and the total use times of the air conditioner by a user according to the historical use data of the air conditioner;
and determining the grading result of each preset grading item according to the ratio of the use times of the user on each preset grading item to the total use times of the user on the air conditioner.
3. The method of claim 1, wherein the recommendation parameters corresponding to the user cluster centers are determined after the scoring matrix is processed based on a preset matrix decomposition algorithm, and specifically:
processing the scoring matrix based on a preset matrix decomposition algorithm and determining the prediction score of each preset scoring item;
and sequencing the prediction scores, determining a target scoring item with the highest prediction score, and determining the recommendation parameters according to the target scoring item.
4. The method of claim 1, wherein after pushing the target recommendation parameter corresponding to the target user cluster center to the user terminal of the target user, the method further comprises:
and acquiring an operation result of the target user on the target recommendation parameter from the user terminal, and storing the air conditioner parameter corresponding to the operation result in a database.
5. The method of claim 4, wherein the operation result comprises one-key operation of the target recommended parameter, or modification of the target recommended parameter, or maintenance of a current control parameter of an air conditioner.
6. The method of claim 1, wherein the preset parameter item includes a set temperature of the air conditioner, or an operation mode of the air conditioner, or a wind speed of the air conditioner.
7. The method as set forth in claim 1, wherein the air conditioner historical usage data is a parameter set last time a user turned off the air conditioner.
8. The method of claim 1, wherein the user's profile data includes environmental profiles of the environment in which the user is located, personal representation profiles of the user, and family representation profiles of the user.
9. The method of claim 8, wherein the environmental characteristics include geography, air conditioner usage time period, weather, and air quality, the personal representation characteristics include age, gender, occupation, and physical characteristics, and the family representation characteristics include family member composition and family annual income.
10. An air conditioner, comprising:
the communication module is used for communicating with the user terminal and the cloud server;
a controller configured to:
uploading log data of the air conditioner used by a user to the cloud server periodically;
when a control instruction sent by the user terminal is received, controlling an air conditioner according to the control instruction;
the method according to any one of claims 1 to 9, wherein the control instruction is triggered by the user terminal after receiving an operation of a target recommendation parameter by a user, and the target recommendation parameter is pushed to the user terminal by the cloud server according to the method according to any one of claims 1 to 9.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115200178A (en) * | 2022-06-23 | 2022-10-18 | 深圳康佳电子科技有限公司 | Building terminal equipment control method and device, electronic equipment and storage medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115200178A (en) * | 2022-06-23 | 2022-10-18 | 深圳康佳电子科技有限公司 | Building terminal equipment control method and device, electronic equipment and storage medium |
| CN115200178B (en) * | 2022-06-23 | 2023-06-02 | 深圳康佳电子科技有限公司 | Building terminal equipment control method and device, electronic equipment and storage medium |
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Address after: No.8, Ronggang Road, Ronggui street, Shunde District, Foshan City, Guangdong Province Applicant after: HISENSE HOME APPLIANCE GROUP Co.,Ltd. Applicant after: Hisense Air Conditioning Co.,Ltd. Address before: No.8, Ronggang Road, Ronggui street, Shunde District, Foshan City, Guangdong Province Applicant before: HISENSE HOME APPLIANCE GROUP Co.,Ltd. Applicant before: HISENSE (SHANDONG) AIR-CONDITIONING Co.,Ltd. |
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