CN111076379A

CN111076379A - Room temperature control method and air conditioner

Info

Publication number: CN111076379A
Application number: CN201911368090.9A
Authority: CN
Inventors: 卢保东; 陈坚波
Original assignee: Hisense Co Ltd
Current assignee: Hisense Group Co Ltd; Hisense Co Ltd
Priority date: 2019-12-26
Filing date: 2019-12-26
Publication date: 2020-04-28

Abstract

The application relates to the technical field of intelligent household appliance manufacturing, in particular to a room temperature control method and an air conditioner. The application provides a room temperature control method, which comprises the following steps: acquiring indoor temperature through a sensor arranged inside a room, and acquiring climate environment temperature through a sensor arranged outside the room; collecting the characteristic information of the room and the geographic information of the area where the room is located; calculating to obtain a comprehensive temperature through a neural network model based on the characteristic information, the geographic information and the climate environment temperature; calculating by a neural network model based on the indoor temperature, the comprehensive temperature and the user set temperature to obtain a comfortable control temperature; the air conditioner is based on its temperature setting of comfortable control temperature automatic control can solve the comfort level that the air conditioner refrigeration or the change of heating efficiency arouse to a certain extent and descend, the not intelligent problem of air conditioner temperature regulation enough.

Description

Room temperature control method and air conditioner

Technical Field

The application relates to the technical field of intelligent household appliance manufacturing, in particular to a room temperature control method and an air conditioner.

Background

The air conditioning system generally adjusts the air conditioner to the preset indoor temperature according to the temperature manually set by the user. Research shows that the functions of the thermal regulation system of the body are different for different groups of old people, children, patients and the like, so that the requirements of the old people on the thermal environment are greatly different, and the thermal preference among individuals is obviously different, for example, some old people prefer warmer thermal environment because of reduction of metabolic heat.

In the places where many people are distributed such as the existing offices and hospitals, the existing overall regulation and control mode of the central air conditioning system enables the large environment to be kept at a fixed temperature or controls the heat comfort requirement based on the crowd statistics. In order to meet different differentiated requirements, many air conditioning equipment manufacturers offer many functional options for users. For example, various functions such as mute, still sleep, afternoon nap, sleep, etc. automatically operate various air parameters such as preset temperature, humidity, wind speed, wind direction, etc. according to preset time periods.

However, in some places, especially in the inland areas, the atmospheric environment temperature and the ground surface temperature during the early morning at night are high, the refrigerating capacity of the air conditioner and the heat exchange capacity of the outdoor unit are relatively low, and meanwhile, the indoor room temperature is high due to the fact that the outdoor heat conducts and radiates to the indoor space. Users usually set the set temperature of the air conditioner to be low, and hope to quickly cool down and sleep comfortably. However, near the early morning, as the ground surface and atmospheric temperature decrease, the cooling capacity of the air conditioner and the heat exchange capacity of the outdoor unit gradually increase, and the indoor conduction radiation of the outdoor heat is weakened or disappeared, so that the cooling capacity of the air conditioner becomes stronger, and the indoor temperature gradually decreases to a lower set temperature, thereby causing people to catch a cold and get ill.

Disclosure of Invention

The application provides a room temperature control method and an air conditioner, which are characterized in that a plurality of indoor and outdoor sensor acquisition devices are arranged to acquire characteristic information, geographic information and climate environment temperature of a room, and a comfort control temperature is further calculated through a neural network model, so that the problems that the comfort level is reduced and the air conditioner temperature is not intelligent enough due to the change of the refrigeration or heating efficiency of the air conditioner can be solved to a certain extent.

The embodiment of the application is realized as follows:

a first aspect of an embodiment of the present application provides a room temperature control method, including:

the method comprises the steps that indoor temperature is obtained through an indoor temperature sensor arranged inside a room, and climate environment temperature is obtained through an outdoor temperature sensor arranged outside the room;

collecting the characteristic information of the room and the geographic information of the area where the room is located;

calculating to obtain a comprehensive temperature through a neural network model based on the characteristic information, the geographic information and the climate environment temperature;

calculating by a neural network model based on the characteristic information, the indoor temperature, the comprehensive temperature and the user set temperature to obtain a comfortable control temperature;

the air conditioner automatically controls its temperature setting based on the comfort control temperature.

A second aspect of embodiments of the present application provides an air conditioner, including:

the collecting component is arranged inside a room to obtain indoor temperature, and arranged outside the room to obtain climate environment temperature; the system is used for acquiring the characteristic information of the room and the geographic information of the area where the room is located;

the operation part calculates to obtain comprehensive temperature through a neural network model based on the characteristic information, the geographic information and the climate environment temperature; calculating by a neural network model based on the characteristic information, the indoor temperature, the comprehensive temperature and the user set temperature to obtain a comfortable control temperature;

the time component is used for timing or acquiring the current time of the area where the air conditioner is located;

the communication component is used for communication between an indoor unit and an outdoor unit of the air conditioner, communication between the air conditioner and the cloud server and communication between the air conditioner and the mobile phone app, and the air conditioner receives commands of a remote controller or a wire controller;

and the function control chip is used for carrying out function control and driving on each part of the air conditioner and automatically controlling the temperature setting of the air conditioner based on the comfortable control temperature.

The method has the advantages that the characteristic information, the geographic information and the climate environment temperature of the room are acquired by arranging the plurality of indoor and outdoor sensor acquisition devices, so that the set temperature of the air conditioner can be reasonably adjusted by referring to factors such as outdoor environment temperature, room structure and layout and the like; the comfortable control temperature reaching the comfortable temperature curve in the human sleeping room under the condition of a plurality of factors is further calculated through the optimized neural network, the temperature control precision of the air conditioner can be improved, and the problem of comfort reduction caused by the change of the refrigerating or heating efficiency of the air conditioner is solved.

Drawings

Specifically, in order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments are briefly described below, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without any creative effort.

FIG. 1 shows a schematic diagram of a room temperature control system 100 according to an embodiment of the present application;

FIG. 2 illustrates a schematic diagram of an exemplary computing device 200 in an embodiment of the present application;

FIG. 3 is a flow chart illustrating a room temperature control method according to an embodiment of the present application;

FIG. 4 is a schematic diagram illustrating functional blocks of an air conditioner according to an embodiment of the present invention;

FIG. 5 is a schematic diagram showing a temperature sensor arrangement according to an embodiment of the present application;

FIG. 6 is a schematic diagram illustrating a setup of an outdoor temperature sensor added in the room temperature control method according to the embodiment of the present application;

FIG. 7 is a schematic diagram illustrating a plurality of outdoor temperature sensors added in a room temperature control method according to an embodiment of the present application;

FIG. 8 is a schematic diagram showing a summer climate ambient temperature curve of a room temperature control method according to an embodiment of the present application;

FIG. 9 is a schematic diagram of a neural network model for integrating temperature calculations in a room temperature control method according to an embodiment of the present application;

FIG. 10 is a schematic diagram illustrating a comfortable temperature curve in a human sleeping room according to an embodiment of the room temperature control method;

fig. 11 shows a neural network model diagram for comfort control of room temperature according to an embodiment of the present invention.

Detailed Description

Certain exemplary embodiments will now be described to provide an overall understanding of the principles of the structure, function, manufacture, and use of the devices and methods disclosed herein. One or more examples of these embodiments are illustrated in the accompanying drawings. Those of ordinary skill in the art will understand that the devices and methods specifically described herein and illustrated in the accompanying drawings are non-limiting exemplary embodiments and that the scope of the various embodiments of the present application is defined solely by the claims. Features illustrated or described in connection with one exemplary embodiment may be combined with features of other embodiments. Such modifications and variations are intended to be included within the scope of the present application.

Reference throughout this specification to "embodiments," "some embodiments," "one embodiment," or "an embodiment," etc., means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases "in various embodiments," "in some embodiments," "in at least one other embodiment," or "in an embodiment," or the like, throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Thus, the particular features, structures, or characteristics shown or described in connection with one embodiment may be combined, in whole or in part, with the features, structures, or characteristics of one or more other embodiments, without limitation. Such modifications and variations are intended to be included within the scope of the present application.

Flow charts are used herein to illustrate operations performed by systems according to some embodiments of the present application. It should be expressly understood that the operations of the flow diagrams may be performed out of order, with precision. Rather, these operations may be performed in the reverse order or simultaneously. Also, one or more other operations may be added to the flowchart. One or more operations may be removed from the flowchart.

It should be noted that the "room" in the present application may be a room in a general sense, or may be a living room, or other room space, such as a study room, a building, and the like.

According to the room temperature control method and the air conditioner, the air conditioner can comprise a one-driving-one air conditioner, a multi-driving-one air conditioner, a mobile air conditioner, a central air conditioner and the like.

FIG. 1 is a schematic diagram of a room temperature control system 100 according to some embodiments of the present application. A room temperature control system 100 is a platform for obtaining comfortable temperature control for an air conditioner. A room temperature control system 100 may include a server 110, at least one storage device 120, at least one network 130, one or more acquisition components 150-1, 150-2 … … 150-N. The server 110 may include a processing engine 112.

In some embodiments, the server 110 may be a single server or a group of servers. The server farm can be centralized or distributed (e.g., server 110 can be a distributed system). In some embodiments, the server 110 may be local or remote. For example, server 110 may access data stored in storage device 120 via network 130. Server 110 may be directly connected to storage device 120 to access the stored data. In some embodiments, the server 110 may be implemented on a cloud platform. The cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, multiple clouds, the like, or any combination of the above. In some embodiments, server 110 may be implemented on a computing device as illustrated in FIG. 2 herein, including one or more components of computing device 200.

In some embodiments, the server 110 may include a processing engine 112. Processing engine 112 may process information and/or data related to the service request to perform one or more of the functions described herein. For example, the processing engine 112 may be based on information collected by the collecting component 150 for obtaining the location setting of the monitoring point, and the collecting component may be specifically set as a sensor; and transmitted over the network 130 to the storage device 120 for updating the data stored therein. In some embodiments, processing engine 112 may include one or more processors. The processing engine 112 may include one or more hardware processors, such as a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an application specific instruction set processor (ASIP), an image processor (GPU), a physical arithmetic processor (PPU), a Digital Signal Processor (DSP), a field-programmable gate array (FPGA), a Programmable Logic Device (PLD), a controller, a micro-controller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination of the above.

Storage device 120 may store data and/or instructions. In some embodiments, storage device 120 may store data obtained from acquisition component 150 positioned at the site of detection. In some embodiments, storage device 120 may store data and/or instructions for execution or use by server 110, which server 110 may execute or use to implement the embodiment methods described herein. In some embodiments, storage device 120 may include mass storage, removable storage, volatile read-write memory, read-only memory (ROM), the like, or any combination of the above. In some embodiments, storage device 120 may be implemented on a cloud platform. For example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, multiple clouds, the like, or any combination of the above.

In some embodiments, storage device 120 may be connected to network 130 to enable communication with one or more components in a room temperature control system 100. One or more components of a room temperature control system 100 may access data or instructions stored in a storage device 120 through a network 130. In some embodiments, the storage device 120 may be directly connected to or in communication with one or more components of a room temperature control system 100. In some embodiments, storage device 120 may be part of server 110.

The network 130 may facilitate the exchange of information and/or data. In some embodiments, one or more components of a room temperature control system 100 may send information and/or data to other components of a room temperature control system 100 via a network 130. For example, the server 110 can obtain/obtain requests from the acquisition component 150 via the network 130. In some embodiments, the network 130 may be any one of a wired network or a wireless network, or a combination thereof. In some embodiments, the network 130 may include one or more network access points. For example, the network 130 may include wired or wireless network access points, such as base stations and/or Internet switching points 130-1, 130-2, and so forth. Through the access point, one or more components of a room temperature control system 100 may be connected to a network 130 to exchange data and/or information.

The acquisition component 150 may include a temperature sensor, a wind speed sensor, a humidity sensor. In some embodiments, the acquisition component 150 can be used to perform data acquisition of the surrounding environment at the location. In some embodiments, the acquisition component 150 can transmit the various data information acquired to one or more devices in a room temperature control system 100. For example, the capturing component 150 may send the captured picture or image to the server 110 for processing or storage in the storage device 120. In some embodiments, a collection component may be disposed in the room for acquiring room information and indoor temperature and geographic information. The collecting component can be arranged outside the room in a certain range, and the collecting component can also be arranged inside the wall of the room or at other positions.

FIG. 2 is a schematic diagram of an exemplary computing device 200 shown in accordance with some embodiments of the present application. Server 110, storage 120, and acquisition component 150 may be implemented on computing device 200. For example, the processing engine 112 may be implemented on the computing device 200 and configured to implement the functionality disclosed herein.

Computing device 200 may include any components used to implement the systems described herein. For example, the processing engine 112 may be implemented on the computing device 200 by its hardware, software programs, firmware, or a combination thereof. For convenience, only one computer is depicted in the figures, but the computational functions described herein in connection with a room temperature control system 100 may be implemented in a distributed manner by a set of similar platforms to distribute the processing load of the system.

Computing device 200 may include a communication port 250 for connecting to a network for enabling data communication. Computing device 200 may include a processor 220 that may execute program instructions in the form of one or more processors. An exemplary computer platform may include an internal bus 210, various forms of program memory and data storage including, for example, a hard disk 270, and Read Only Memory (ROM)230 or Random Access Memory (RAM)240 for storing various data files that are processed and/or transmitted by the computer. An exemplary computing device may include program instructions stored in read-only memory 230, random access memory 240, and/or other types of non-transitory storage media that are executed by processor 220. The methods and/or processes of the present application may be embodied in the form of program instructions. Computing device 200 also includes input/output component 260 for supporting input/output between the computer and other components. Computing device 200 may also receive programs and data in the present disclosure via network communication.

For ease of understanding, only one processor is exemplarily depicted in fig. 2. However, it should be noted that the computing device 200 in the present application may include multiple processors, and thus the operations and/or methods described in the present application that are implemented by one processor may also be implemented by multiple processors, collectively or independently. For example, if in the present application a processor of computing device 200 performs steps 1 and 2, it should be understood that steps 1 and 2 may also be performed by two different processors of computing device 200, either collectively or independently.

Fig. 3 is a flowchart illustrating a room temperature control method according to an embodiment of the present application.

In step 301, an indoor temperature is obtained by an indoor temperature sensor disposed inside a room, and a climate ambient temperature is obtained by an outdoor temperature sensor disposed outside the room.

Fig. 4 is a schematic diagram illustrating functional modules of an air conditioner according to an embodiment of the present invention.

The air conditioner is provided with a collecting component, a communication component, an arithmetic component, a time component, a temperature control component, a function control chip and the like.

The collection component comprises a temperature collection component and an information collection component, the temperature collection component is used for collecting the indoor temperature and the outdoor temperature of a room where the air conditioner is located, and generally speaking, the temperature collection component is a temperature sensor. The indoor temperature sensor is used for collecting indoor temperature and is usually arranged in an indoor unit of the air conditioner; the outdoor temperature sensor is generally disposed outside the outdoor unit or a wall of the room on the side facing the atmosphere. It should be noted that, in the present embodiment, since precise control of the temperature is involved, the number of the indoor temperature sensor and the outdoor temperature sensor may be plural, and the installation positions thereof are not limited to the indoor unit and the outdoor unit, and the selection of the specific positions thereof will be described in detail below. The information acquisition component is used for acquiring characteristic information and geographic information of a room.

The operation component is used for executing an artificial intelligent neural network model algorithm and logic operation and processing the operation and temperature control of the air conditioner. The time component is used for timing or acquiring the current time of the area where the air conditioner is located. The communication component is used for communication between an air conditioner indoor unit and an outdoor unit, communication between the air conditioner and a cloud server and communication between the air conditioner and mobile phone apps, and the air conditioner receives commands of a remote controller or a wire controller and the like. The air conditioner performs function control and driving on each part of the air conditioner through the function control chip. The temperature control chip is used for controlling and adjusting the indoor temperature.

In some embodiments, the air conditioner may further include a user terminal for comfort adjustment.

The user terminal includes: and the registration login module is used for responding to a registration request of the user, receiving the registration information of the user, sending the registration information to the server and responding to the login request of the user. And the terminal establishes communication connection with the server, wherein the server is used for calculating a neural network model according to the user registration information, the current time information and the weather data information.

The user terminal comprises various common intelligent devices, including a smart phone, a tablet, a computer or other intelligent devices. The registration login module may be an application on the user terminal, such as: the mobile phone APP, the webpage web side, the WeChat applet and other application programs with user registration and login functions. The user registers through the registration login module, and the submitted registration information is sent to the server for registration and is stored in the database of the server. The user terminal and the server are connected through a wireless network to transmit and exchange data.

Particularly, the user terminal can also be various air conditioners with intelligent modules, and with the popularization of intelligent home furnishing, a plurality of current household appliances can also have a networking function, and the networking function can realize various intelligent functions.

According to the user terminal provided by the application, data support can be provided for calculating comfortable control temperature of the air conditioner finally according to personal conditions, family conditions and house conditions of a user and by combining current time and current weather conditions, so that air generated by the air conditioner is more suitable for the user, and the control of the air conditioning equipment is more intelligent.

The arrangement of the temperature sensor in the present embodiment will be explained below.

Fig. 5 shows a schematic diagram of a temperature sensor arrangement according to an embodiment of the present application.

In some embodiments, the outdoor temperature sensor may use a temperature sensor that is inherent to the outdoor unit of the air conditioner, as shown in fig. 5. It should be noted that, since the outer unit generates heat and a large amount of wind when operating, the measurement accuracy of the temperature sensor provided in the outer unit is relatively low. In some implementations, the position of the temperature sensor of the outdoor unit of the air conditioner may also be adjusted to be away from the outdoor unit, so that the outdoor unit of the air conditioner is not affected by the air flow of the outdoor unit of the air conditioner, and the acquisition accuracy is improved.

In some embodiments, the sensor is disposed in the user's air conditioner, on which a sensor for detecting ambient air parameters is disposed, including a temperature sensor, a humidity sensor, an air pressure sensor, a fresh air volume sensor, and the like. The sensors may be separate physical hardware devices or may be hardware modules integrated into various air conditioning devices.

After the sensor detects that the room is in, the sensor sends collected data to the cloud computing platform and/or the air conditioner through the communication device for storage. The communication device is used for communicating with the sensor, the air conditioner and an upper computer system of the air conditioner, and the communication can be wired communication or wireless communication.

Fig. 6 shows a schematic diagram of the setting of an outdoor temperature sensor added in the room temperature control method according to the embodiment of the present application.

In some embodiments, the air conditioner may further include an outdoor temperature sensor mounted on a side of an outer wall of the room facing the ambient environment, the outdoor temperature sensor is far away from the outlet airflow of the outdoor unit, so that the climate ambient temperature may be measured relatively accurately, the number of the sensors may be determined according to actual conditions, and the number of the outdoor temperature sensors is not limited in the present application.

Fig. 7 is a schematic diagram illustrating a plurality of outdoor temperature sensors added in the room temperature control method according to the embodiment of the present application.

In some embodiments, the air conditioner may be provided with a plurality of outdoor temperature sensors according to actual conditions, and for example, when the 4 walls of the room are in direct contact with the atmosphere, the measurement accuracy can be remarkably improved by increasing the number of the sensors. The added outdoor temperature sensors can be connected to an electric control system of an air conditioner indoor unit, and can also be selectively connected to an electric control system of an air conditioner outdoor unit.

It should be noted that, because the outdoor temperature sensor is disposed on the outer wall of the room, the accuracy of the ambient temperature acquisition is also reduced due to the influence of the temperature radiation inside the room. The measured climate temperature such as weather forecast, etc., the collected data of which is derived from the atmospheric temperature, is relatively accurate in overall measurement, but for the area where the room is located, an error is generated, that is, it can be considered that the local climate ambient temperature and the macroscopic climate temperature of the room are different. Therefore, the neural network model is used in the subsequent steps of the application to correct the climate environment temperature again.

In step 302, characteristic information of the room and geographic information of the area where the room is located are collected.

The characteristic information of the room comprises physical characteristics such as the size, the shape, the material of the wall body, the thickness of the wall body and the like of the room, and the characteristic information influences the air conditioning cooling or heating control and the comfortable temperature control due to different layouts. The geographic information comprises longitude and latitude, altitude, the characteristic that the ambient temperature changes along with the time of the morning and the evening, traditional solar term information and the like of the area where the room is located.

Fig. 8 is a schematic diagram illustrating a summer climate ambient temperature curve of a room temperature control method according to an embodiment of the present application.

As shown in the figure, from after sunset, that is, after about 18:00, since the earth, the building wall, etc. slowly release the heat accumulated in the daytime, it is found that the decrease of the ambient temperature is slow until the zero point; after late night, namely zero point, because the heat accumulated by the earth, building walls and the like in the daytime is basically and completely released, the speed of the reduction of the ambient temperature from zero point to before sunrise, namely about 6 points in the morning can be found to be high; the ambient temperature starts to rise after sunrise.

It should be noted that the heating efficiency of the air conditioner is different depending on the ambient temperature. Although the air conditioner in a room sets a preset temperature, the cooling or heating efficiency of the air conditioner is different along with the change of time and the ambient temperature, so that the actual temperature sensed by a human body is different, and the comfort is also reduced due to the untimely adjustment of the temperature setting.

For example, before the zero point in the morning, since the ambient temperature is high and the cooling efficiency of the air conditioner is low, the user is biased to set a low temperature to rapidly cool down, so that the user feels more comfortable. However, when the time shifts to before sunrise to morning zero, because ambient temperature is lower, air conditioner refrigeration efficiency uprises, and the temperature of predetermineeing this moment will hang down excessively, including the wind speed isoparametric of its setting, these overcooling settings can make human comfort reduce, can discover that the air conditioner sets up the temperature at night and leads to the people to catch a cold excessively easily, and the condition such as the emergence cold is unfavorable for human health.

In some embodiments, a location sensor may be located inside or outside of a room for collecting geographic information of the room, including latitude and longitude information, altitude information, etc. of the room. For example, the temperature drop of a room at night in the area of the room may vary depending on the latitude, altitude, geographical climate and environment of the area of the room.

In some embodiments, characteristic information of the room may also be collected, including orientation of the room, building structure, location of bed and air conditioner in the room, sensor installation location, and the like. The building structure comprises the thickness of the wall and the wall material of the room as already explained above, since different materials have different insulation effects and the thickness of the wall will also have an influence on the insulation properties of the room in case the same material constitutes the wall. In some embodiments, the orientation of the room determines the length of time the room receives sunlight; the orientation of the room windows, if facing the seasonal wind direction, also causes a drop in the average room temperature; the size and shape of the room can affect the heating effect of the refrigerant of the air conditioner; the relative position relation of the air conditioning bed in the room is different from the sleeping human body to the indoor temperature, and because the air conditioner has return air in the processes of refrigeration and heating, the indoor unit can also influence the skin temperature of the human body if the bed of a user is on the return air path of the air conditioner. The collection of information may be collected in some embodiments by the setting of sensors; in some embodiments, the room information may be collected by sending the room information through a key value through a function interface of the air conditioner and a remote controller.

In step 303, based on the room information and the climate ambient temperature, a comprehensive temperature is calculated by a neural network model.

In some embodiments, the neural network model is trained in advance, and the training set includes information such as outdoor temperatures collected by a plurality of outdoor temperature sensors, a climate environment temperature change curve of a room shown in fig. 8, latitude, longitude, altitude, orientation, house structure, room size, positions of beds and air conditioners in the room, sensor installation positions, output, and actual temperature of the bed where the user sleeps, so as to obtain better model parameters capable of calculating the comprehensive temperature.

Fig. 9 is a schematic diagram of a neural network model for integrating temperature calculation in a room temperature control method according to an embodiment of the present application.

As shown, T _ out _1, T _ out _2, and T _ out _. And calculating the outdoor temperature, the room information and the geographic information acquired by the plurality of sensors through the calculation of the neural network model to obtain the comprehensive temperature.

In some embodiments, the neural network model is disposed on a cloud computing platform, and the room information and the climate environment temperature are sent to an input end of the neural network model so as to calculate a comprehensive temperature.

In some embodiments, the neural network model is disposed in an arithmetic component inside an air conditioner, and the air conditioner sends the room information and the climate environment temperature to an input end of the neural network model so as to calculate a comprehensive temperature. It should be noted that, because the air conditioner has limited computing capability, in some implementation manners, the training and learning of the neural network model may be set to be set in the cloud computing platform so as to utilize abundant hardware and software computing resources, then the cloud computing platform transmits the optimized neural network model parameters to the air conditioner in the room, and the air conditioner separately performs the computation of the integrated temperature, thereby adapting to the limitation of insufficient computing capability of the air conditioner.

Fig. 10 shows a schematic diagram of a comfortable temperature curve in a human sleeping room according to an embodiment of the present application.

As shown, the curves show that the corresponding ambient temperatures during different sleep stages are comfortable for the human body. The curve varies depending on factors such as sex, age, constitution, and living environment of a person. As can be seen from the figure, at the early stage of the sleep process, i.e. the falling asleep stage, the linear rise of the ambient temperature keeps the comfort of people better, and the ambient temperature rises from 22 ℃ to 28 ℃; when the human body enters a deep sleep stage, the temperature needs to be kept at 26-28 ℃; when a human body is in a light sleep stage in the early morning, the ambient temperature is basically linearly reduced from 28 ℃ to 25 ℃; when a person wakes up from light sleep, the ambient temperature is 25 to 27 degrees celsius, which is comfortable. It was found that the temperature during sleep was low, the temperature during deep sleep was high and the temperature during wake-up decreased slightly.

Therefore, in consideration of human comfort, the ideal condition of the ambient temperature of the area where the bed is located is to achieve the curve change shown in fig. 10, and the training of the neural network model in the embodiment is also performed for this purpose.

It should be noted that the climate environmental temperature, latitude, altitude factor, etc. of the room all affect the curve of 10; on the other hand, factors such as air humidity, wind power and wind direction also influence the heat dissipation speed of the room, and further influence the change of the curve; the orientation of the room, the structure of the room, including the thickness of the wall and the type of material of the wall, affect the heat dissipation rate of the room, and in some embodiments, the location of the added sensor can be reasonably selected according to the orientation and structure of the room. For example, different materials have different insulating effects, and in the case of walls made of the same material, the thickness thereof also has an effect on the insulating properties of the room. The orientation of a room and the house structure can influence the flow direction of airflow blown out by the air conditioner, so that the indoor temperature distribution and the feeling of a human body are influenced; the bed and the air conditioner are in different positions in the room, the position of an airflow loop in which a person is positioned is different, and the feeling of the human body is different. The sensor has different influences on the indoor environment temperature, and has different coefficient factors in the neural network meter. For example, the sensor weighting factors near air conditioners, beds, windows may be large.

In some embodiments, the effect of the indoor air parameters on the overall temperature may also be considered. The indoor parameters include at least one or more of temperature, relative humidity, and air flow rate. And judging whether the air parameters are all in the range required by GB/T18883-2002.

In the GBT-18883-2002-used indoor air quality standard, the temperature, the relative humidity and the fresh air volume standard value in the indoor environment are set with the aim of protecting human health as the final target, and the ranges are 22-28 ℃ in spring and summer and 16-24 ℃ in autumn and winter respectively; 40 to 80 percent of spring and summer and 30 to 60 percent of autumn and winter; the fresh air volume standard is more than 30m 3/h.p. If the air parameters in the room of the user and the matched air comfort parameters are in the range required by GB/T18883-. If not, the air parameter is adjusted. By the method, on the premise of ensuring the comfort of a user, the adjusting times of the air conditioning equipment are reduced, on one hand, the green and energy-saving effect can be achieved, in addition, the service life of the air conditioning equipment can be prolonged to a certain extent, and the service life of components and parts of the air conditioning equipment is prevented from being reduced due to frequent control change.

In step 304, a comfort control temperature is calculated by a neural network model based on the characteristic information, the indoor temperature, the integrated temperature and the user set temperature.

And integrating the temperature in the steps, and inputting the indoor temperature of a sensor collector arranged in the room and the temperature set by the user into a neural network model to calculate to obtain the comfortable control temperature of the air conditioner. The middle, i.e. hidden, layer of the neural network model already contains characteristic information of the room. The user-set temperature may be considered to be approximately a comfortable temperature curve in the human sleeping room as shown in fig. 10, and it should be noted that the curve may be different according to factors such as sex, age, physique, living environment, and the like of a human. The indoor temperature is collected by sensors arranged in the room, and the number and the positions of the sensors can be determined according to actual conditions.

In some embodiments, the neural network model is disposed on a cloud computing platform, and the indoor temperature, the integrated temperature, and the user-set temperature are sent to an input end of the neural network model to calculate a comfort control temperature, wherein an intermediate layer, i.e., a hidden layer, of the neural network model already contains characteristic information of the room.

In some embodiments, the neural network model is disposed in an arithmetic unit inside an air conditioner, and the air conditioner sends the indoor temperature, the integrated temperature, and the user-set temperature to an input end of the neural network model so as to calculate the comfort control temperature, wherein an intermediate layer, i.e., a hidden layer, of the neural network model already contains characteristic information of the room. It should be noted that, because the air conditioner has limited computing capability, in some implementation manners, the training and learning of the neural network model may be set to be set in the cloud computing platform so as to utilize abundant hardware and software computing resources, then the cloud computing platform transmits the optimized neural network model parameters to the air conditioner in the room, and the air conditioner independently performs the calculation of comfortable control temperature, thereby adapting to the limitation of insufficient computing capability of the air conditioner.

In the neural network model, the factors of the ambient temperature curve, the room size, the room shape, the thickness of the wall, the material of the wall, the position of the bed and the air conditioner in the room, and the like of fig. 8 are included in the hidden layer calculation of the neural network model, and the comfortable temperature curve, the integrated temperature, and the indoor temperature in the human sleeping room shown in fig. 11 are transmitted to the neural network model as input variables.

As shown in the figure, T _ out is the integrated temperature obtained in step 303, T _ room is the ambient temperature of the room, i.e. the indoor temperature, and T _ set is the comfortable temperature curve in the human sleeping room shown in fig. 10. The neural network model can convert the outdoor environment temperature, the indoor temperature and the user set temperature into the comfortable air conditioner operation control temperature of the human body, and the comfortable control temperature of the air conditioner is adjusted at any time along with the change of indoor and outdoor factors, so that the human body can keep comfortable.

In some embodiments, the input variables of the neural network model may also include personal information of the user.

Matching the personal information of the air conditioner with the air conditioner may enable a particular user to be used to match the air comfort level to obtain a comfort control temperature appropriate for the particular user.

The personal information matching information comprises registered information of the registered user called from a user information database, current time information acquired through the Internet and weather data information.

The personal information includes sex, age, and whether it is a special person. The family information comprises the number of family members, the sex and the age of the family members and the condition of whether special persons exist. The house information comprises a house geographical position, a floor height, a direct lighting direction of a room, a room size and an air conditioning equipment condition; the family special personnel comprises infants, senior aged old people, pregnant women and no special personnel.

The time information includes time period information and throttle information. The time information is obtained through the internet. The time period information can be divided by 1 hour according to a 24-hour timing method, and can also be divided by a traditional time period division method based on sunrise and sunset: early morning (0 to 5), morning (5 to 8), morning (8 to 11), midday (11 to 13), afternoon (13 to 16), evening (16 to 19), evening (19 to 24). The embodiment of the application adopts a time period division method based on sunrise and sunset. The solar terms information is twenty-four solar terms, which refers to weather change at a certain stage in one year. In twenty-four solar terms, "festival" refers to a segment of a year, which is a representation of a period of time; and "gas" refers to the climate, which is a summary of weather changes. The twenty-four solar terms are specifically divided into: the formula comprises the following components of beginning to spring, rainwater, frightened hibernation, spring equinox, clear, grain rain, beginning to summer, plumule, mango seeds, summer solstice, sunstroke, big summer heat, beginning to autumn, sunstroke, white dew, autumn equinox, cold dew, frost and descent, beginning to winter, small snow, big snow, winter solstice, small cold and big cold.

The weather data information comprises temperature, relative humidity, wind direction, wind power, rain and snow amount and cloud amount. The weather data information is obtained through the Internet, is accurate to specific hours and is updated in real time.

The input information already contains most elements which possibly influence the user to adjust the indoor air parameters, so that the matching condition caused by only using individual matching elements is avoided, the matching sidedness and the inaccuracy of the matching result are avoided, and meanwhile, the user can have more comprehensive selection space to meet the specific individual matching requirement of the user.

In step 305, the air conditioner automatically adjusts its temperature setting over time based on the comfort control temperature.

In some embodiments, the neural network model is installed on a cloud computing platform, and the air conditioner receives comfort control temperature parameters from the cloud computing platform, so that real-time automatic temperature adjustment of the air conditioner is realized.

In some embodiments, the neural network model is disposed in an arithmetic unit inside the air conditioner, and the air conditioner can directly obtain the comfortable control temperature output by the neural network model to perform real-time temperature automatic adjustment.

It should be noted that, in the present embodiment, the problem that too low temperature set at night of the air conditioner is likely to cause people to catch a cold is taken as an example, but the room temperature control method according to the embodiment of the present application is not only suitable for cooling in summer, but also suitable for heating in winter.

The present application further provides an air conditioner, including: the device comprises a collecting component, an arithmetic component, a time component, a communication component and a function control chip. The acquisition component is used for acquiring indoor temperature by a sensor arranged inside a room, and acquiring climate environment temperature by a sensor arranged outside the room; and collecting the characteristic information of the room and the geographic information of the area where the room is located. The operation part calculates to obtain a comprehensive temperature through a neural network model based on the characteristic information, the geographic information and the climate environment temperature; and calculating by a neural network model based on the indoor temperature, the comprehensive temperature and the user set temperature to obtain the comfortable control temperature. The time component is used for timing or acquiring the current time of the area where the air conditioner is located. The communication component is used for communication between an air conditioner indoor unit and an outdoor unit, communication between the air conditioner and the cloud server and communication between the air conditioner and mobile phone app, and the air conditioner receives commands of a remote controller or a wire controller. The function control chip performs function control and driving on each part of the air conditioner, and automatically controls the temperature setting of the air conditioner based on the comfortable control temperature.

Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.

Claims

1. A room temperature control method, comprising the steps of:

2. A room temperature control method as set forth in claim 1, wherein said indoor temperature sensor is provided inside an air conditioner; the outdoor temperature sensor is arranged on the outer side of one side wall of the room facing the atmospheric environment.

3. A room temperature control method as claimed in claim 1, characterized in that the geographical information is in particular latitude and longitude, altitude, characteristics of the climate environment temperature as a function of time of the morning and evening.

4. The room temperature control method of claim 1, wherein the characteristic information is a size of the room, a shape of the room, a material of the wall, a thickness of the wall, a relative position of the bed and the air conditioner, and a sensor installation position.

5. The room temperature control method of claim 1, wherein the neural network model is specifically located on a cloud computing platform.

6. A room temperature control method as claimed in claim 5, wherein the air conditioner receives the comfort control temperature parameter from the cloud computing platform for real-time temperature adjustment of the air conditioner.

7. The room temperature control method of claim 1, wherein the neural network model is specifically provided inside an air conditioner.

8. The room temperature control method of claim 1, wherein the user-set temperature is set to a comfortable temperature profile in the human sleeping room.

9. An air conditioner, comprising:

10. The air conditioner of claim 9, further comprising a user terminal for comfort adjustment.