CN110513835A - Control method and device for improving comfort of air conditioner and air conditioner - Google Patents
Control method and device for improving comfort of air conditioner and air conditioner 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
- 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
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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
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
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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
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Abstract
The invention relates to a control method and equipment for improving the comfort of an air conditioner and the air conditioner, wherein the method comprises the following steps: acquiring user personalized data and environment data; obtaining a preliminary comfort parameter of each user according to a pre-stored comfort prediction model, user personalized data and environmental data; when the current indoor space is a multi-target user, determining priority information of each user according to a pre-stored user grading model and user personalized data, and correcting the primary comfort parameter according to the priority information to obtain a comfort parameter after primary correction; and determining a final comfort parameter according to the comfort parameter after the primary correction, and controlling the operation of the air conditioner according to the final comfort parameter. The technical scheme provided by the invention can balance the comfort requirements of different users under the condition that multiple target users exist indoors, and can ensure that the comfort requirements of the multiple target users are considered and balanced on the basis of user classification.
Description
Technical Field
The invention relates to the technical field of intelligent control of air conditioners, in particular to a control method and device for improving comfort of an air conditioner and the air conditioner.
Background
Air conditioners are being penetrated into every corner of life as an industrial product for improving the quality of life of people. Air conditioning is also known as air conditioning and is referred to as air conditioning for short. The technology for processing the temperature, humidity, cleanliness and airflow distribution of indoor air by an artificial method. It can make some places obtain air with a certain temp. and a certain humidity so as to meet the requirements of user and production process and improve labour hygiene and indoor climate condition.
The quality of the comfort of the air conditioner directly influences the quality of product evaluation of a user. Most of air conditioners in the market claim to have the function of improving comfort, but the effect is not good in the actual use process.
In the related art, in the aspect of improving the comfort experience of the air conditioner, a mode of detecting the indoor environment temperature in real time and setting a threshold value is generally adopted, the indoor environment temperature detected in real time is compared with the set threshold value, corresponding adjustment is carried out according to a comparison result, an adjustment scheme is generally set according to expert experience, or a comfort mode is fed back by a user.
However, when a plurality of users are present indoors, the above method cannot distinguish the use demands of multiple users due to differences among individual users, and is not ideal in the overall satisfaction of multiple users.
Disclosure of Invention
In order to overcome the problems in the related art at least to a certain extent, the invention provides a control method and device for improving the comfort of an air conditioner and the air conditioner.
According to a first aspect of the embodiments of the present invention, there is provided a control method for improving comfort of an air conditioner, including:
acquiring user personalized data and environment data;
obtaining a preliminary comfort parameter of each user according to a pre-stored comfort prediction model, the user personalized data and the environment data;
when the current indoor space is a multi-target user, determining priority information of each user according to a pre-stored user grading model and the user personalized data, and correcting the preliminary comfort parameter according to the priority information to obtain a comfort parameter after primary correction;
and determining a final comfort parameter according to the comfort parameter after the primary correction, and controlling the operation of the air conditioner according to the final comfort parameter.
Preferably, the determining a final comfort parameter according to the comfort parameter after the primary correction includes:
determining the comfort parameter after the primary correction as a final comfort parameter; or,
obtaining real-time feedback data of a user to the environment, obtaining comfort parameters after secondary correction according to the real-time feedback data and the comfort parameters after primary correction, and determining the comfort parameters after secondary correction as final comfort parameters.
Preferably, the modifying the preliminary comfort parameter according to the priority information to obtain a comfort parameter after one modification includes:
calculating a specific gravity value of the comfort of each user relative to the comfort of all users according to the priority information;
constructing an objective function according to the specific gravity value corresponding to the comfort of each user, the preliminary comfort parameter of each user and the comfort parameter to be calculated;
and carrying out optimization solution on the objective function, and determining the comfort parameter corresponding to the optimal objective function as the comfort parameter after one-time correction.
Preferably, the determining the priority information of each user according to the pre-stored user rating model and the user personalization data includes:
grouping users on each level according to a pre-stored user grading model and sequencing each group;
and determining the priority information of each user according to the sequencing result.
Preferably, the control method further includes:
and when the current indoor space is the single-target user, determining the preliminary comfort parameter of the single-target user as the comfort parameter after the first correction.
Preferably, the real-time feedback data is a comfort parameter fed back by a user, and the obtaining of a comfort parameter after secondary correction according to the real-time feedback data and the comfort parameter after primary correction includes:
calculating the difference between the recorded final comfort parameter adopted at the last moment and the real-time feedback data;
and carrying out weighted summation on the difference and the comfort parameter after the primary correction, and determining a weighted summation value as the comfort parameter after the secondary correction.
Preferably, the weight value used in the weighted summation is a preset fixed value, and the weight value corresponding to the difference is greater than the weight value corresponding to the comfort parameter after the first correction.
Preferably, the comfort parameter comprises at least one of:
the rotating speed of the compressor, the wind speed and the opening degree of the expansion valve.
According to a second aspect of the embodiments of the present invention, there is provided a control device for improving comfort of an air conditioner, including:
the acquisition module is used for acquiring user personalized data and environment data;
the comfort parameter preliminary acquisition module is used for obtaining a preliminary comfort parameter of each user according to a pre-stored comfort prediction model, the user personalized data and the environment data;
the comfort parameter correction module is used for determining the priority information of each user according to a pre-stored user grading model and the user personalized data when the current indoor is a multi-target user, and correcting the preliminary comfort parameter according to the priority information to obtain a comfort parameter after primary correction;
and the control module is used for determining a final comfort parameter according to the comfort parameter after the primary correction and controlling the operation of the air conditioner according to the final comfort parameter.
Preferably, the control module is specifically configured to:
determining the comfort parameter after the primary correction as a final comfort parameter; or,
obtaining real-time feedback data of a user to the environment, obtaining comfort parameters after secondary correction according to the real-time feedback data and the comfort parameters after primary correction, and determining the comfort parameters after secondary correction as final comfort parameters.
According to a third aspect of embodiments of the present invention, there is provided an air conditioner including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
acquiring user personalized data and environment data;
obtaining a preliminary comfort parameter of each user according to a pre-stored comfort prediction model, the user personalized data and the environment data;
when the current indoor space is a multi-target user, determining priority information of each user according to a pre-stored user grading model and the user personalized data, and correcting the preliminary comfort parameter according to the priority information to obtain a comfort parameter after primary correction;
and determining a final comfort parameter according to the comfort parameter after the primary correction, and controlling the operation of the air conditioner according to the final comfort parameter.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects:
according to the technical scheme provided by the invention, the indoor multi-target users are classified through the user classification model according to the acquired user personalized data, and the preliminary comfort parameters of the multi-target users are corrected according to the priority information obtained through classification, so that the comfort requirements of the multi-target users are considered in different proportions by taking the priority information as the basis, and the differentiation requirements of the indoor multi-target users in the process of using the air conditioner are considered. The invention considers the benefits of different users, and can balance and balance the comfort requirements of multiple targets of users on the basis of user classification.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow chart illustrating a control method for improving comfort of an air conditioner in accordance with an exemplary embodiment;
FIG. 2 is a flow chart illustrating a control method for improving comfort of an air conditioner in accordance with another exemplary embodiment;
FIG. 3 is a block diagram illustrating the flow steps of a control method for improving comfort of an air conditioner in accordance with another exemplary embodiment;
fig. 4 is a schematic block diagram illustrating a control apparatus for improving comfort of an air conditioner according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
Fig. 1 is a flowchart illustrating a control method for improving comfort of an air conditioner according to an exemplary embodiment, as shown in fig. 1, the method including the steps of:
step S11, obtaining user personalized data and environment data;
step S12, obtaining a preliminary comfort parameter of each user according to a pre-stored comfort prediction model, the user personalized data and the environment data;
step S13, when the current indoor is a multi-target user, determining the priority information of each user according to a pre-stored user grading model and the user personalized data, and correcting the preliminary comfort parameter according to the priority information to obtain a comfort parameter after primary correction;
and step S14, determining the comfort parameter after the primary correction as a final comfort parameter, and controlling the air conditioner operation according to the final comfort parameter.
Preferably, the comfort parameter comprises at least one of:
the rotating speed of the compressor, the wind speed and the opening degree of the expansion valve.
It should be noted that the environment data includes, but is not limited to: indoor environmental data and outdoor environmental data, such as indoor and outdoor temperature, humidity, wind speed, air quality, etc.; the environmental data can be acquired from devices such as a temperature and humidity sensor, an air speed sensor, an air quality detector and the like which are installed indoors and outdoors.
The user personalization data includes, but is not limited to, user attribute data and user real-time data, wherein the user attribute data includes, but is not limited to: the height, the weight, the sex, the age, the constitution (such as whether the pregnant woman is a cold-afraid constitution or not), the health condition, the occupation and the like of the user; user real-time data includes, but is not limited to: real-time motion amount, user comfort level, real-time feedback data, and the like.
The user personalized data can be obtained through an intelligent terminal, a user personal data API (application program interface), a public information platform, an intelligent wearable terminal or an intelligent home system and the like.
Taking an intelligent terminal as an example, a user inputs own user personalized data through the intelligent terminal, or the user authorizes the intelligent terminal, and the intelligent terminal acquires the user personalized data from social contact platforms or user information management platforms such as a WeChat system, a QQ system, a social security system and a household registration management system of a place to be dispatched, which are networked.
The intelligent terminal comprises but is not limited to a mobile phone, a tablet personal computer, a notebook computer, an intelligent remote controller of an air conditioner and an embedded intelligent control computer on the air conditioner.
Preferably, the data obtained in step S11 further includes internet assistance data, which includes but is not limited to: the user individual data acquired from the internet have a large influence on the comfort of the user, and comfort parameter recommendation data, comfort prediction model data and the like of different crowds are acquired from the internet. The internet auxiliary data is mainly used for helping a user to establish a comfort prediction model and a user grading model.
In step S12, the pre-stored comfort prediction model is pre-established. The comfort prediction model can be established and completed through training by a training module, and the training module can be deployed on a cloud server or a local high-performance computing intelligent terminal. The main methods of model training include, but are not limited to: BP (Back Propagation) Neural network, residual error network, RNN Neural network (Recurrent Neural network), reinforcement learning, and the like.
The comfort prediction module mainly inputs user personalized data and environment data and outputs comfort parameters, a large amount of user personalized data and environment data and corresponding comfort parameters can be collected in a training stage, and the comfort prediction model is obtained through training by the neural network and other training methods. When collecting data, the user personalized data, the environment data and the corresponding comfort parameters can be the existing experience data, or can be determined by the internet auxiliary data.
After the model training is stable, the relevant data of the model training can be backed up in different places and updated synchronously at regular intervals, so that the model prediction collapse caused by the abnormal model training is prevented.
It is understood that the specific training mode of the comfort prediction model can be referred to in the prior art, and is not described in detail herein.
After the model training is completed, the model can be applied to a prediction stage, and the prediction stage can be completed by a prediction module, namely, a comfort prediction model is stored in the prediction module, input data of the model prediction mainly comprise real-time user personalized data and environmental data, and the input data is output as comfort parameters, and the output comfort parameters can be called as preliminary comfort parameters of a user.
In the step S13, whether the indoor space is a multi-target user or a single-target user may be determined according to the prior art, for example, whether the indoor space is a single-target user or a multi-target user may be determined by installing an infrared human body detection device or an image acquisition device in the indoor space and combining a moving target tracking technology. Since the technology is the prior art and is not within the protection scope of the present invention, it is not described herein again.
In addition, since the user personalized data obtained in step S11 all have user attributes, it is also possible to determine whether the current room is a single-target user or a multi-target user according to the number of users to which the obtained user personalized data belongs.
In step S13, if the current room is a single-target user, the preliminary comfort parameter of the single-target user is directly determined as the comfort parameter after the first modification, without determining the priority information of the single-target user first, and then modifying the preliminary comfort parameter of the single-target user according to the priority information.
In step S13, if the current room is a multi-target user, the priority information of each user is determined according to the following steps:
determining priority information of each user according to a pre-stored user grading model and the user personalized data, wherein the priority information comprises the following steps:
grouping users on each level according to a pre-stored user grading model and sequencing each group;
and determining the priority information of each user according to the sequencing result.
For example, there are 6 users in the room, each represented by A, B, C, D, E, F, and the user personalization data of each user is shown in table 1:
TABLE 1
| User' s | User personalized data |
| A | Age 67, sex men, rheumatism, fear of cold, the refrigeration temperature of frequent setting is 27 ~ 29 ℃. |
| B | Age 60, sex female, aversion to cold, constant refrigeration temperature: … … at 26-29 ℃. |
| C | Age 38, sex male, fear of heat, constant refrigeration temperature: … … at 24-26 DEG C |
| D | Age 36, sex, pregnancy, aversion to heat, constant refrigeration temperature: … … at 26-27 DEG C |
| E | Age 15, sex female, aversion to heatThe constant refrigeration temperature is as follows: 22-25 DEG C |
| F | Age 12, sex men, fear of heat, constant refrigeration temperature: 23 to 27 DEG C |
TABLE 2
The user hierarchical model can define grouping basis and sequencing basis on each hierarchy, so that users can be grouped and sequenced according to the user hierarchical model.
For example, it is defined in the user classification model that grouping at the first level is based on parameters related to physical constitution and physical state, and specific parameters include three parameters of age, whether there is special physical constitution (e.g., cold feeling, air conditioning uses a cooling mode in most cases), whether there is a disease (e.g., rheumatism), and whether there is pregnancy (e.g., pregnancy) in table 1, and the older people who are cold feeling, sick and pregnant are divided into one group at the first level, and the rest of people are divided into another group, and then the user is divided into a special population and a general population at the first level, the special population includes A, B, D, and the general population includes C, E, F, as shown in table 2. In addition, the user ranking model can also define that special people are arranged in front of general people according to the priority from high to low, and the A, B, D group is ordered before the C, E, F group in the first hierarchy. It is to be understood that the persons within each grouping at each level are not ordered, such as A, B, D at the first level.
Similarly, assume that the grouping basis and the sorting basis at the second level corresponding to the user group of the special population are determined according to the user ranking model: dividing the sick and pregnant users into one group, the remaining people into another group, and the sick and pregnant users are ranked before the remaining people, then the users on the second level corresponding to the user group of the special population can be divided into A, D group, B group, and A, D ranked before B, see table 2.
And so on, if more than one user of a group, the grouping continues until there is only one user per group. For example, for the A, D grouping, assuming that the next level of the grouping according to the user rating model is to group the diseases into one group, the rest into one group, and the diseases are ranked before, then referring to table 2, the next level of the grouping corresponding to A, D is to group a, group D, and the a is ranked before D.
Similarly, assuming that the next level of C, E, F is a grouping of younger ages, the rest of the groups are ranked one after the other, and younger ages are ranked first, according to the user ranking model, corresponding to C, E, F, see table 2, the next level of C, E, F is a grouping of E, F, a grouping C, and a ranking of E, F precedes C.
Similarly, assuming that the next level of E, F is divided by gender and women are ranked before men according to the user ranking model, then the next level of E, F is E-group, F-group, and E is ranked before F, see table 2.
After the sorting, the priority information of each user may be determined according to the overall sorting result, for example, the priority of each user is determined according to the sorting result, then the sorting is performed according to the priority, and the serial number after the sorting is determined as the priority information of each user.
For example, through the above sorting, the overall sorting result is that, in order of priority from high to low: a > D > B > E > F > C, then the ascending order according to the priority is: C. f, E, B, D, A, the corresponding sequence numbers are: c is 1, F is 2, E is 3, B is 4, D is 5, a is 6, i.e., A, B, C, D, E, F the priority information of the 6 users are: 6. 4, 1, 5, 3 and 2.
In step S13, if the current room is a multi-target user, the comfort parameter of the multi-target user after once correction is obtained according to the following steps:
calculating a specific gravity value of the comfort of each user relative to the comfort of all users according to the priority information;
constructing an objective function according to the specific gravity value corresponding to the comfort of each user, the preliminary comfort parameter of each user and the comfort parameter to be calculated;
and carrying out optimization solution on the objective function, and determining the comfort parameter corresponding to the optimal objective function as the comfort parameter after one-time correction.
Calculating the specific gravity value of the comfort of each user relative to the comfort of all users according to the priority information, wherein the specific formula is as follows:
wherein, RankuThe sequence number, which is the priority ranking of user u, and m is the total number of users. For example, if the current room is also the aforementioned A, B, C, D, E, F target users, then m is 6.
According to the formulaConstructing an objective function R, where g (Data)u) Is a preliminary comfort parameter for the user u,comfort parameters to be calculated;
performing optimization solution on the objective function R, for example, performing minimization solution on R, that is, performing argminR operation, and optimizing the comfort parameter corresponding to the objective function RAnd determining the comfort parameter after one correction.
It should be noted that, the optimization solution of the objective function R may be performed according to a preset population intelligent heuristic algorithm, where the preset population intelligent heuristic algorithm includes but is not limited to: genetic algorithm, particle swarm algorithm, simulated annealing algorithm, frog leaping algorithm and the like. Since how to optimize the solution of the objective function R is the prior art and does not belong to the protection scope of the present invention, it is not described herein again.
FIG. 2 is a flowchart illustrating a control method for improving comfort of an air conditioner in accordance with another exemplary embodiment, and FIG. 3 is a block diagram illustrating steps of the control method illustrated in FIG. 2; as shown in fig. 2 and 3, the method includes the steps of:
step S21, obtaining user personalized data and environment data;
step S22, obtaining a preliminary comfort parameter of each user according to a pre-stored comfort prediction model, the user personalized data and the environment data;
step S23, judging whether the current indoor is a single-target user or a multi-target user, if so, executing step S24, otherwise, executing step S25;
step S24: taking the preliminary comfort parameter of the single target user as the comfort parameter of the single target user after the first correction, and then executing S26;
step S25, determining priority information of each user according to a pre-stored user grading model and the user personalized data, correcting the preliminary comfort parameter according to the priority information to obtain a comfort parameter after one-time correction, and then executing S26;
and step S26, acquiring real-time feedback data of the user to the environment, obtaining comfort parameters after secondary correction according to the real-time feedback data and the comfort parameters after primary correction, and determining the comfort parameters after secondary correction as final comfort parameters.
It can be understood that, compared with fig. 1, fig. 2 shows a control method for improving the comfort of the air conditioner, the main differences are that: the control method shown in fig. 2 adds a process of secondarily correcting the comfort parameter. Therefore, the control method shown in fig. 2 has the beneficial effects of the control method shown in fig. 1, and can also realize closed-loop adjustment of comfort parameters of the air conditioner, respond to user feedback in time, meet comfort requirements of multiple target users more accurately, and fit true experience of users.
It should be noted that the real-time feedback data of the user to the environment includes, but is not limited to: wind speed, temperature, humidity, wind speed orientation and the like; the manner of obtaining real-time feedback data of the user to the environment includes but is not limited to: the remote controller of the air conditioner, an embedded intelligent computer on the air conditioner, a comfort parameter adjusting button on the air conditioner, a computer and a mobile phone which are connected with the air conditioner in a network and the like are adopted.
After the real-time feedback data of the user to the environment is obtained, the real-time feedback data can be converted into data such as the rotating speed of the compressor, the wind speed and the opening degree adjusting value of the expansion valve, and the like, so that the comfort parameter after the first correction is subjected to the second correction, and the comfort parameter after the second correction is obtained.
For ease of understanding, the active formula is explained by way of example as follows:
assuming that the current time is t, the comfort parameter obtained at the time t after one correction isThe real-time feedback data of the user to the environment at the moment t isThe real-time feedback data of the user is the feedback data of the user to the environment in the last state due to the characteristic that the air conditioner has a delay lag in the operation process, and the time delay exists when the air conditioner is changed from one state to another state. Because the data in the last state has a short-term 'memory disturbance' to the current state, the real-time feedback data of the user is the feedback to the last state.
Assuming that the comfort parameter currently actually implemented isThe actually performed comfort parameter expression can be written in the form:
when the value of λ is 0,namely, when the air conditioner is started, no real-time data is fed back by the user at present, and the comfort parameter of the air conditioner is corrected once according to the previous stateRunning;
when the lambda is 1, the k-value is,the air conditioner performs output after a short time delay according to the last state, and then stops.
Preferably, the weighted values λ and 1- λ used in the weighted summation are preset fixed values, and the weighted value λ corresponding to the difference is greater than the weighted value 1- λ corresponding to the comfort parameter after the first correction.
In general, λ is a fixed parameter, and in order to avoid the occurrence of step-like jitter in the air conditioner output in a short time and to avoid discomfort to the human body, λ should be a large fixed value. It can be seen from the above formula that the formula can maintain a certain memory effect when λ takes a larger value.
When lambda is fixed to a small value 1, the air conditioner can keep certain characteristics of the operation of the previous state in a fixed mode in each state, but the fixed mode can be changed into an elastic variable mode to adapt to the specific current environment at any time. For example:
wherein rand (0,0.5) represents a number randomly generated in [0, 0.5 ]; 1-max (0.25, rand (0,0.5)) corresponds to λ in the above formula, and max (0.25, rand (0,0.5)) corresponds to 1- λ in the above formula. By means of random generation, λ is changed from a large fixed value to a number in the range of 0.5, 0.75.
Fig. 4 is a schematic block diagram illustrating a control apparatus 100 for improving comfort of an air conditioner according to an exemplary embodiment. Referring to fig. 4, the apparatus 100 includes:
an obtaining module 101, configured to obtain user personalized data and environment data;
a comfort parameter preliminary obtaining module 102, configured to obtain a preliminary comfort parameter of each user according to a pre-stored comfort prediction model, the user personalized data, and the environment data;
the comfort parameter correction module 103 is used for determining the priority information of each user according to a pre-stored user classification model and the user personalized data when the current indoor is a multi-target user, and correcting the preliminary comfort parameter according to the priority information to obtain a comfort parameter after primary correction;
and the control module 104 is configured to determine a final comfort parameter according to the comfort parameter after the primary correction, and control the operation of the air conditioner according to the final comfort parameter.
Preferably, the control module 104 is specifically configured to:
determining the comfort parameter after the primary correction as a final comfort parameter; or,
obtaining real-time feedback data of a user to the environment, obtaining comfort parameters after secondary correction according to the real-time feedback data and the comfort parameters after primary correction, and determining the comfort parameters after secondary correction as final comfort parameters.
It should be noted that, in the control device for improving comfort of an air conditioner provided in this embodiment, in terms of implementation, each module may refer to the foregoing specific description of each flow step in the control method for improving comfort of an air conditioner shown in fig. 1 or fig. 2, and is not described again here.
It can be understood that, according to the control device for improving comfort of the air conditioner provided by this embodiment, according to the obtained user personalized data, the indoor multi-target users are classified through the user classification model, and the preliminary comfort parameters of the multi-target users are corrected according to the priority information obtained through classification, so that the comfort requirements of the multi-target users are considered in different proportions according to the priority information, and the differentiation requirements of the indoor multi-target users in the process of using the air conditioner are considered. The invention considers the benefits of different users, and can balance and balance the comfort requirements of multiple targets of users on the basis of user classification.
In addition, an air conditioner according to an exemplary embodiment is shown, including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
acquiring user personalized data and environment data;
obtaining a preliminary comfort parameter of each user according to a pre-stored comfort prediction model, the user personalized data and the environment data;
when the current indoor space is a multi-target user, determining priority information of each user according to a pre-stored user grading model and the user personalized data, and correcting the preliminary comfort parameter according to the priority information to obtain a comfort parameter after primary correction;
and determining a final comfort parameter according to the comfort parameter after the primary correction, and controlling the operation of the air conditioner according to the final comfort parameter.
It can be understood that, the air conditioner provided by this embodiment can classify the indoor multi-target users through the user classification model according to the acquired user personalized data, and correct the preliminary comfort parameters of the multi-target users according to the priority information obtained through classification, so that the comfort requirements of the multi-target users are considered with different proportions according to the priority information, and the differentiation requirements of the indoor multi-target users in the air conditioner using process are considered. The invention considers the benefits of different users, and can balance and balance the comfort requirements of multiple targets of users on the basis of user classification.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that the terms "first," "second," and the like in the description of the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present invention, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A control method for improving the comfort of an air conditioner is characterized by comprising the following steps:
acquiring user personalized data and environment data;
obtaining a preliminary comfort parameter of each user according to a pre-stored comfort prediction model, the user personalized data and the environment data;
when the current indoor space is a multi-target user, determining priority information of each user according to a pre-stored user grading model and the user personalized data, and correcting the preliminary comfort parameter according to the priority information to obtain a comfort parameter after primary correction;
and determining a final comfort parameter according to the comfort parameter after the primary correction, and controlling the operation of the air conditioner according to the final comfort parameter.
2. The control method according to claim 1, wherein determining a final comfort parameter from the once-corrected comfort parameter comprises:
determining the comfort parameter after the primary correction as a final comfort parameter; or,
obtaining real-time feedback data of a user to the environment, obtaining comfort parameters after secondary correction according to the real-time feedback data and the comfort parameters after primary correction, and determining the comfort parameters after secondary correction as final comfort parameters.
3. The control method according to claim 1, wherein the modifying the preliminary comfort parameter according to the priority information to obtain a once-modified comfort parameter comprises:
calculating a specific gravity value of the comfort of each user relative to the comfort of all users according to the priority information;
constructing an objective function according to the specific gravity value corresponding to the comfort of each user, the preliminary comfort parameter of each user and the comfort parameter to be calculated;
and carrying out optimization solution on the objective function, and determining the comfort parameter corresponding to the optimal objective function as the comfort parameter after one-time correction.
4. The control method of claim 1, wherein the determining the priority information of each user according to the pre-stored user rating model and the user personalization data comprises:
grouping users on each level according to a pre-stored user grading model and sequencing each group;
and determining the priority information of each user according to the sequencing result.
5. The control method according to claim 1, characterized by further comprising:
and when the current indoor space is the single-target user, determining the preliminary comfort parameter of the single-target user as the comfort parameter after the first correction.
6. The control method according to claim 2, wherein the real-time feedback data is a comfort parameter fed back by a user, and the obtaining of the secondarily corrected comfort parameter according to the real-time feedback data and the primarily corrected comfort parameter comprises:
calculating the difference between the recorded final comfort parameter adopted at the last moment and the real-time feedback data;
and carrying out weighted summation on the difference and the comfort parameter after the primary correction, and determining a weighted summation value as the comfort parameter after the secondary correction.
7. The control method according to claim 6, wherein a weight value used in the weighted summation is a preset fixed value, and a weight value corresponding to the difference is greater than a weight value corresponding to the comfort parameter after the one-time correction.
8. The control method of claim 1, wherein the comfort parameter comprises at least one of:
the rotating speed of the compressor, the wind speed and the opening degree of the expansion valve.
9. A control device for improving comfort of an air conditioner, comprising:
the acquisition module is used for acquiring user personalized data and environment data;
the comfort parameter preliminary acquisition module is used for obtaining a preliminary comfort parameter of each user according to a pre-stored comfort prediction model, the user personalized data and the environment data;
the comfort parameter correction module is used for determining the priority information of each user according to a pre-stored user grading model and the user personalized data when the current indoor is a multi-target user, and correcting the preliminary comfort parameter according to the priority information to obtain a comfort parameter after primary correction;
and the control module is used for determining a final comfort parameter according to the comfort parameter after the primary correction and controlling the operation of the air conditioner according to the final comfort parameter.
10. An air conditioner, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
acquiring user personalized data and environment data;
obtaining a preliminary comfort parameter of each user according to a pre-stored comfort prediction model, the user personalized data and the environment data;
when the current indoor space is a multi-target user, determining priority information of each user according to a pre-stored user grading model and the user personalized data, and correcting the preliminary comfort parameter according to the priority information to obtain a comfort parameter after primary correction;
and determining a final comfort parameter according to the comfort parameter after the primary correction, and controlling the operation of the air conditioner according to the final comfort parameter.
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