CN114484516A

CN114484516A - Kitchen range and temperature prediction method

Info

Publication number: CN114484516A
Application number: CN202210255546.6A
Authority: CN
Inventors: 梁晓芬
Original assignee: Hisense Home Appliances Group Co Ltd; Hisense Shandong Kitchen and Bathroom Co Ltd
Current assignee: Hisense Home Appliances Group Co Ltd; Hisense Shandong Kitchen and Bathroom Co Ltd
Priority date: 2022-03-15
Filing date: 2022-03-15
Publication date: 2022-05-13

Abstract

The embodiment of the application discloses a cooker and a temperature prediction method, relates to the technical field of household appliances, and can be used for obtaining a pot temperature value of a pot through the cooker and improving the accuracy of the temperature measurement in the pot. The method comprises the following steps: acquiring temperature influence parameters, wherein the temperature influence parameters comprise: pot bottom temperature value, firepower gear of the cooker and liquid volume in the cooker; and inputting the temperature influence parameters into a pre-trained temperature prediction model based on a long-time and short-time memory neural network to obtain a predicted temperature value in the pot.

Description

Kitchen range and temperature prediction method

Technical Field

The application relates to the technical field of household appliances, in particular to a stove and a temperature prediction method.

Background

With the development of economic society, the requirements of people on the quality of life are gradually improved. When cooking, people often want to acquire accurate temperature in a pot so as to make various delicious dishes at proper temperature. In addition, the timely acquisition of the temperature in the pot can also be used for monitoring the condition in the pot, so that the phenomena of dry burning of the pot, overhigh oil temperature in the pot and the like are prevented.

Generally, people know the temperature in the pot through eye sight and body feeling when making dishes, but the temperature obtained in the way is too dependent on experience and is not accurate enough.

Disclosure of Invention

The embodiment of the application provides a cooker and a temperature prediction method, which are used for improving the accuracy and convenience of obtaining a temperature value in a cooker.

In a first aspect, embodiments of the present application provide a cooktop comprising: and the temperature sensor is used for detecting the pot bottom temperature value of a pot placed on the stove. A controller connected to the temperature sensor and configured to: acquiring temperature influence parameters, wherein the temperature influence parameters comprise: pot bottom temperature value, firepower gear of the cooker and liquid volume in the cooker; and inputting the temperature influence parameters into a pre-trained temperature prediction model based on a long-time and short-time memory neural network to obtain a predicted temperature value in the pot.

The technical scheme provided by the embodiment of the application at least has the following beneficial effects: because the pot has good heat conductivity, the temperature value of the bottom of the pot directly influences the temperature value in the pot; in addition, when the firepower gears of the cooking utensils are different, the heating degree of the pot is also different, and the change of the firepower gears of the cooking utensils can also cause the change of the temperature value in the pot; in addition, considering that the specific heat capacity of liquid is higher, when more liquid exists in the pot, the temperature value in the pot and the temperature value at the bottom of the pot have larger difference, so that the temperature value at the bottom of the pot, the firepower gear of the stove and the volume of the liquid in the pot can be used as temperature influence parameters, and the temperature influence parameters are input into the temperature prediction model to predict the change of the temperature value in the pot. Further, considering that the temperature is a time-series related variable, the temperature in the pan at the current time is not only related to the current temperature-affecting parameter, but also affected by the historical time parameter. Compared with the conventional machine learning methods such as an artificial neural network algorithm particle swarm method and a fuzzy logic method, the long and short term memory neural network (LSTM) has a good effect on data prediction of a time sequence, and the problem of gradient disappearance or gradient explosion of a Recurrent Neural Network (RNN) is solved, so that the temperature value in the pot can be predicted more accurately by adopting a temperature prediction model based on the long and short term memory neural network. Therefore, the temperature value in the pot can be obtained through the stove, and the accuracy and the convenience of obtaining the temperature in the pot are improved.

In some embodiments, the controller is specifically configured to: normalizing the temperature influence parameters; inputting the temperature influence parameters after the normalization processing into a pre-trained temperature prediction model based on a long-time and short-time memory neural network to obtain a predicted value output by the temperature prediction model; and performing inverse normalization processing on the predicted value to obtain a predicted temperature value in the pot.

It should be understood that normalization processing is performed on the input temperature influence parameters, so that the speed of gradient reduction for solving the optimal solution in the training process of the temperature prediction model can be increased, and the training precision is improved. After normalization, the predicted value output by the temperature prediction model refers to the intermediate value predicted by the machine, so that inverse normalization is required to obtain the actual temperature value in the pot.

In some embodiments, the temperature influencing parameters further comprise one or more of: the range hood gear or the ambient temperature.

It should be noted that the ambient temperature refers to an indoor temperature value of a room where the cooker is located. The indoor temperature value will affect the temperature value in the pan to a certain extent. In addition, when the indoor temperature range hood is opened, obvious airflow is formed above the kitchen range, and the temperature in the pot of the pot arranged on the kitchen range is influenced. Therefore, when the temperature value in the cooker is predicted, the influence of the environment temperature and/or the gear of the smoke machine on the temperature value in the cooker can be considered.

In some embodiments, the temperature prediction model is trained according to the following steps: acquiring a plurality of groups of temperature influence parameters and pot temperature values corresponding to the plurality of groups of temperature influence parameters; generating a sample set by a plurality of groups of temperature influence parameters and the pan temperature corresponding to the temperature influence parameters, wherein the sample set comprises a plurality of samples, and one sample corresponds to one group of temperature influence parameters and the pan temperature corresponding to the temperature influence parameters; and training the temperature prediction model by using the sample set until the temperature prediction model meets the preset training termination condition.

In some embodiments, the controller is further configured to: if the predicted temperature value in the pot is larger than the first temperature threshold value, controlling the stove to reduce a firepower gear; if the predicted temperature value in the pot is smaller than the second temperature threshold value, controlling the stove to increase the firepower gear; wherein the second temperature threshold is less than or equal to the first temperature threshold.

It is understood that the fire stage is decreased when the temperature value in the pan is greater than the first temperature threshold value, and the fire stage is increased when the temperature value in the pan is less than the second temperature threshold value, so that the temperature in the pan can be controlled between the second temperature threshold value and the first temperature threshold value. Therefore, the temperature in the pot can be adjusted according to the requirements of users.

In some embodiments, the controller is further configured to: if the predicted temperature value in the pot is larger than the boiling point temperature threshold value, sending out prompt information for prompting that the liquid in the pot reaches the boiling point; or if the predicted temperature value in the pot is greater than the dry-burning temperature threshold value, sending out prompt information for prompting that the pot is in a dry-burning state.

Based on the method, the user can be reminded in time when the temperature value in the pot reaches the boiling point temperature threshold value so as to carry out the next cooking operation, such as adding food materials or closing the stove; in addition, when the temperature value in the pot is greater than the dry-burning temperature threshold value, the user is reminded in time so that the user can close the cooker in time, the damage of the dry-burning to the cooker or the pot is avoided, and the cooking safety is improved.

In a second aspect, an embodiment of the present application provides a temperature prediction method, where the method includes: acquiring temperature influence parameters, wherein the temperature influence parameters comprise: pot bottom temperature value, firepower gear of the cooker and liquid volume in the cooker; and inputting the temperature influence parameters into a pre-trained temperature prediction model based on a long-time and short-time memory neural network to obtain a predicted temperature value in the pot.

In some embodiments, the inputting the temperature influencing parameter into a pre-trained temperature prediction model based on a long-time and short-time memory neural network to obtain a predicted temperature value in the pan includes: normalizing the temperature influence parameters; inputting the temperature influence parameters after the normalization processing into a pre-trained temperature prediction model based on a long-time and short-time memory neural network to obtain a predicted value output by the temperature prediction model; and performing inverse normalization processing on the predicted value to obtain a predicted temperature value in the pot.

In some embodiments, the above method further comprises: if the predicted temperature value in the pot is larger than the first temperature threshold value, controlling the stove to reduce a firepower gear; if the predicted temperature value in the pot is smaller than the second temperature threshold value, controlling the stove to increase the firepower gear; wherein the second temperature threshold is less than or equal to the first temperature threshold.

In some embodiments, the above method further comprises: if the predicted temperature value in the pot is larger than the boiling point temperature threshold value, sending out prompt information for prompting that the liquid in the pot reaches the boiling point; or if the predicted temperature value in the pot is greater than the dry-heating temperature threshold value, sending out prompt information for prompting that the pot is in the dry-heating state.

In a third aspect, an embodiment of the present application provides a controller, including: one or more processors; one or more memories; wherein the one or more memories are for storing computer program code comprising computer instructions which, when executed by the one or more processors, cause the controller to perform the method provided in the second aspect and possible implementations.

In a fourth aspect, a computer-readable storage medium is provided, comprising computer instructions which, when run on a computer, cause the computer to perform the method provided in the second aspect and possible implementations.

In a fifth aspect, a computer program product is provided comprising computer instructions which, when run on a computer, cause the computer to perform the method provided in the second aspect and possible implementations described above.

It should be noted that all or part of the computer instructions may be stored on the computer readable storage medium. The computer readable storage medium may be packaged with or separately from a processor of the controller, which is not limited in this application.

The beneficial effects described in the second aspect to the fifth aspect in the present application may refer to the beneficial effect analysis of the first aspect, and are not described herein again.

Drawings

The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and not to limit the invention.

FIG. 1 is a schematic representation of a cooktop structure according to some embodiments;

fig. 2 is a block diagram of a hardware configuration of a cooktop according to some embodiments;

fig. 3 is a first schematic diagram of a smart home system according to some embodiments;

fig. 4 is a first schematic view of a management page of a terminal device according to some embodiments;

fig. 5 is a second schematic management page of a terminal device according to some embodiments;

fig. 6 is a third schematic view of a management page of a terminal device according to some embodiments;

fig. 7 is a schematic composition diagram of a smart home system according to some embodiments;

FIG. 8 is a first flowchart illustrating a temperature prediction method according to some embodiments;

fig. 9 is a fourth schematic view of a management page of a terminal device according to some embodiments;

FIG. 10 is a schematic diagram of a multi-layer LSTM network architecture in accordance with some embodiments;

FIG. 11 is a schematic diagram of the basic structure of a recurrent neuron of an LSTM network according to some embodiments;

FIG. 12 is a second flowchart of a temperature prediction method according to some embodiments;

fig. 13 is a first schematic diagram of a cooktop interacting with terminal equipment in accordance with some embodiments;

fig. 14 is a fifth management page diagram of a terminal device according to some embodiments;

FIG. 15 is a second schematic diagram of a cooktop interacting with terminal equipment in accordance with some embodiments;

FIG. 16 is a schematic flow diagram of a method of training a temperature prediction model, according to some embodiments;

fig. 17 is a hardware configuration diagram of a controller according to some embodiments.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless otherwise specified.

In the description of the present application, it is to be noted that the terms "connected" and "connected" are to be interpreted broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected, unless explicitly stated or limited otherwise. The specific meaning of the above terms in the present application can be understood in a specific case by those of ordinary skill in the art. In addition, when a pipeline is described, the terms "connected" and "connected" are used in this application to have a meaning of conducting. The specific meaning is to be understood in conjunction with the context.

Unless the context requires otherwise, throughout the description and the claims, the term "comprise" and its other forms, such as the third person's singular form "comprising" and the present participle form "comprising" are to be interpreted in an open, inclusive sense, i.e. as "including, but not limited to". In the description of the specification, the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" and the like are intended to indicate that a particular feature, structure, material, or characteristic associated with the embodiment or example is included in at least one embodiment or example of the present disclosure. The schematic representations of the above terms are not necessarily referring to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be included in any suitable manner in any one or more embodiments or examples.

In the embodiments of the present application, words such as "exemplary" or "for example" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.

As described in the background art, generally, a person knows the temperature in the pan through the eye and body sense during cooking, but the temperature obtained in this way is too empirical and not accurate enough. In order to solve this problem, people sometimes use smart pans. The intelligent pot sets up a temperature sensor in a certain position of pot to transmit the temperature value that this temperature sensor measured for the equipment of control firepower through wired or wireless mode and control by temperature change culinary art, but this kind of mode of utilizing intelligent pot temperature measurement, the suitability is relatively poor, only be applicable to specific intelligent pot, the cooking pot that will use can have a great variety, for example iron pan, pottery pot etc., can not guarantee during the culinary art that all pans all are the pans that have the temperature measurement function, and will make the culinary art cost obviously increase if all adopt the intelligent pot at the culinary art in-process of reality.

In view of the above, the embodiment of the present application provides a cooker and a temperature prediction method, in which a pot temperature of a pot placed on the cooker is obtained by obtaining a temperature influence parameter and inputting the temperature influence parameter to a pre-trained temperature influence model. Therefore, the temperature in the pot can be obtained through the stove, so that a more accurate temperature value in the pot can be obtained, and the convenience of obtaining the temperature in the pot is improved.

For further description of the scheme of the present application, fig. 1 is a schematic structural diagram of a cooktop provided in the embodiments of the present application.

Referring to fig. 1, a cooktop 100 may include: a temperature sensor 101 (not shown in fig. 1), a heating device 102, and a controller 103. The controller 103 may be connected to the temperature sensor 101 and the heating device 102 through a wired (e.g., signal line) or wireless (e.g., bluetooth or WiFi).

In some embodiments, the cooktop 100 can be a smart cooker, gas range, electromagnetic cooker, or the like. The embodiments of the present application do not limit this.

In some embodiments, temperature sensor 101 is a sensor that can detect temperature and can convert the detected temperature value into a usable output signal. Exemplarily, the temperature sensor 101 may be used for detecting a pot bottom temperature value of a pot placed on the hob 100.

In some embodiments, a heating device 102 is used to heat a pot placed on the cooktop 100 to complete a cooking operation. The actual heating manner of the heating device 102 varies with the type of the cooker 100, for example, when the cooker 100 is a gas cooker, the heating device 102 completes heating by burning gas to release heat; when the cooking appliance 100 is an electromagnetic cooker, the heating device 102 may perform heating according to the electromagnetic principle. The embodiment of the present application does not limit the specific heating manner of the heating device 102.

In some embodiments, the controller 103 refers to a device that can generate an operation control signal according to the instruction operation code and the timing signal, and instruct the cooking appliance 100 to execute the control instruction. Illustratively, the controller 103 may be a Central Processing Unit (CPU), a general purpose processor Network Processor (NP), a Digital Signal Processor (DSP), a microprocessor, a microcontroller, a Programmable Logic Device (PLD), or any combination thereof. The controller 103 may also be other means having a processing function, such as a circuit, a device, or a software module. In addition, the controller 103 may be installed on the surface of the cooktop 100 or integrated inside the cooktop 100, and the embodiment shown in fig. 1 is only an example, and the embodiment of the present application does not set any limitation thereto.

In some embodiments, controller 103 may be configured to control temperature sensor 101 to obtain a bottom of pot temperature value. Further, the controller 103 may also input the temperature value of the bottom of the pan into a pre-trained temperature prediction model to obtain the temperature value of the bottom of the pan.

In other embodiments, the controller 103 may also be used to control the heating device 102 to perform heating.

Fig. 2 exemplarily shows a hardware configuration block diagram of the cooker 100 in the embodiment of the present application.

As shown in fig. 2, in addition to the temperature sensor 101, the heating device 102, and the controller 103, the cooktop 100 may include one or more of: a display 104, a weight sensor 105, a gear adjustment switch 106, a voice prompt 107, a communication interface 108, and a memory 109.

In some embodiments, the display 104 is connected to the controller 103. The display 104 may be a liquid crystal display, an organic light-emitting diode (OLED) display. The particular type, size, resolution, etc. of the display 104 is not limiting, and those skilled in the art will appreciate that the display 104 may be modified in performance and configuration as desired. In some embodiments of the present application, the display 104 is used to display a control panel of the cooktop 100 to enable human-machine interaction functionality. For example, the cooktop 100 may feed back the current heating gear of the cooktop 100 via the display 104. For another example, the display 104 may be used to display a pot temperature value of a pot currently placed on the cooktop.

In some embodiments, a weight sensor 105 is coupled to the controller 103, and the weight sensor 105 is a sensor that can detect a weight and can convert a detected weight value into a usable output signal. In some embodiments of the present application, the weight sensor 105 may be used to detect the weight of an object placed on the cooktop 100 to determine whether a pot is placed on the cooktop 100.

In some embodiments, a gear adjustment switch 106 is connected to the heating device 102 and can be used to adjust the fire gear of the cooker 100. Illustratively, when the user adjusts the fire step adjustment switch 106, the fire step of the heating apparatus 100 is reset to the user-specified fire step. Further, the position adjustment switch 106 may also be connected to the controller 103 or integrated with the controller 103. For example, if the temperature in the pan is higher than the threshold, the controller 103 can control the gear adjustment switch 106 to lower the fire gear of the cooker 100 to lower the heating temperature of the cooker.

In some embodiments, the voice prompt device 107 is connected to the controller 103 and may be used for voice prompting after the cooking appliance 100 completes the related cooking operation, such as a cooking end prompt tone and a hot water boiling prompt tone. The content of the voice prompt may be preset by a manufacturer of the cooker 100, or may be set by a user through the display 104. For example, if it is detected that the heating device 102 is in an on state and it is detected that no pot is placed on the cooker 100, the controller 103 may play a prompt message through the voice prompt device 107.

In some embodiments, the communication interface 108 is a component for communicating with external devices or servers according to various communication protocol types. For example: the communication interface 108 may include at least one of a wireless communication technology (Wi-Fi) module, a bluetooth module, a wired ethernet module, a Near Field Communication (NFC) module, and other network communication protocol chips or near field communication protocol chips, and an infrared receiver. The communication interface 108 may be used for communicating with other devices or communication networks (e.g., ethernet, Radio Access Network (RAN), Wireless Local Area Networks (WLAN), etc.). Illustratively, the communication interface 108 is coupled to the controller 103 or integrated with the controller 103. The controller 103 may communicate with the terminal device through the communication interface 108. For example, if the cooking program is detected to be completed, the controller 103 may send a prompt message to the terminal device through the communication interface 108.

In some embodiments, the memory 109 is used to store applications and data, and the controller 103 performs various functions and data processing of the hob 100 by running the applications and data stored in the memory 109. The memory 109 mainly includes a program storage area and a data storage area, wherein the program storage area can store an operating system and application programs (such as a voice prompt function, an information display function, and the like) required by at least one function; the stored data area may store data created from use of the cooktop 100. Further, the memory 109 may include high speed random access memory, and may also include non-volatile memory, such as a magnetic disk storage device, a flash memory device, or other volatile solid state storage device. In some embodiments of the present application, the memory 109 may be used to store a pre-trained temperature prediction model.

Although not shown in fig. 2, the kitchen range 100 may further include a power supply device (such as a battery and a power management chip) for supplying power to each component, and the battery may be logically connected to the controller 103 through the power management chip, so as to implement functions of power consumption management and the like of the kitchen range 100 through the power supply device.

It is understood that the structure illustrated by the embodiment of the invention does not constitute a specific limitation on the cooker. In other embodiments of the present application, the cooktop can include more or fewer components than shown, or combine certain components, or split certain components, or a different arrangement of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

Fig. 3 is a schematic composition diagram of an intelligent home system provided in an embodiment of the present application. The smart home system may include a kitchen range 100, a server 200, a terminal device 300, and an Access Point (AP) 400.

The access point 400 may be used to provide a signal source for a Wi-Fi network. For example, the access point 400 may be a router. Alternatively, the access point 400 may also be a certain smart home device. After the cooker 100 and the terminal device 300 access the same access point 400, they join the Wi-Fi network created by the access point 400. Further, the cooktop 100 and the terminal equipment 300 can communicate with the server 200 on the network side through the access point 400 using the Wi-Fi network. Meanwhile, the cooktop 100 and the terminal equipment 300 in the same Wi-Fi network can also communicate with each other through the access point 400.

The server 200 may be a single server, or may be a server cluster including a plurality of servers. In some embodiments, the server cluster may also be a distributed cluster. The present application is not limited to the specific form of the server 200.

In some embodiments, server 200 may store a pre-trained temperature prediction model. Illustratively, the cooker 100 uploads the detected pot bottom temperature value to the server 200, the server 200 inputs the received pot bottom temperature value to the temperature prediction model, and obtains the predicted pot bottom temperature value, which is returned to the cooker 100, and the cooker 100 displays the received pot bottom temperature value on the display.

In some embodiments, the server 200 may further receive the intelligent recipe uploaded by the terminal device 300, analyze a temperature value required by the intelligent recipe and a duration corresponding to the temperature value, and transmit the temperature value and the duration corresponding to the temperature value to the cooker 100 to complete cooking.

The terminal device 300 may be a mobile phone, a tablet computer, a Personal Computer (PC), a Personal Digital Assistant (PDA), an intelligent watch, a netbook, a wearable electronic device, an Augmented Reality (AR) device, a Virtual Reality (VR) device, a robot, and the like, which is not limited in this application.

Taking the terminal device 300 as a mobile phone as an example, in some embodiments, a user may use the mobile phone to send a control instruction to the cooker 100.

Illustratively, the mobile phone may be installed with smart home APPs for controlling the smart home devices. Generally, a user can register a target account in the smart home APP. The user can register all the intelligent household equipment needing to be managed under the target account. A plurality of intelligent household devices under the same target account can be uniformly managed by a server of an intelligent household APP, and therefore each intelligent household device in a user home can be associated through one target account.

As shown in fig. 4, after the user opens the smart home APP in the mobile phone, the mobile phone may display a management interface 301 of the smart home APP, and the mobile phone may display, in the management interface 301, a device currently registered under the target account (for example, a range hood, a kitchen range, and an air conditioner shown in the management interface 301 of fig. 4).

As also shown in fig. 4, the above-described management interface 301 may include a detail expansion control 3011 for the cooktop. Further, if it is detected that the user clicks the detail expansion control 3011 of the kitchen range, it indicates that the mobile phone receives an input operation of the user for checking details of the kitchen range, and then the mobile phone may enter the kitchen range management interface 302 to display detailed information of the kitchen range. For example, the remaining cooking time, the current cooking stage, and the temperature value in the pan, etc., as shown in fig. 4.

Further, as shown in fig. 5, if it is detected that the user clicks the "parameter setting" control 3021 of the cooktop management interface 302, it indicates that the mobile phone receives an input instruction of the parameter setting of the user, and then the mobile phone may enter the parameter setting interface 303. The parameter setting interface 303 may include a variety of parameters that affect the temperature value within the pan, such as: fire gear, liquid volume, etc.

Further, as shown in fig. 6, the parameter setting interface 303 may include a "liquid volume" setting control 3031. If it is detected that the user clicks the setting control 3031 of the "liquid volume" in the parameter setting interface 303, it indicates that the mobile phone receives an instruction of the user for setting the parameter of the liquid volume, and further, the mobile phone pops up a text box 304 on the parameter setting interface 303, and sets the parameter liquid volume as the input value of the user according to the information changed or input by the user in the popped up text box 304.

In some possible implementations, referring to fig. 7, the smart home system may further include one or more of the following: range hood 500 and indoor temperature measuring device 600.

In some embodiments, range hood 500 is connected to cooktop 100 through access point 400. The range hood 500 is used for rapidly exhausting oil smoke harmful to human bodies, which is generated in the cooking process, out of the room, so as to achieve the purposes of reducing pollution and purifying air. Illustratively, the range hood 500 is turned on by default when the cooktop 100 is turned on; alternatively, range hood 500 is turned on in response to a user command. Further, when the cooker 100 is opened, if it is detected that the range hood 500 is in an open state, the server 200 acquires a range hood gear of the range hood 500 and transmits the range hood gear to the cooker 100, so that the cooker 100 can more accurately acquire a temperature value in the pot.

In some embodiments, the indoor temperature measuring device 600 is connected with the cooktop 100 through the access point 400, or the room temperature detecting device 600 is integrated on the cooktop 100. Illustratively, when the cooker 100 is turned on, the server 200 acquires the indoor temperature detected by the room temperature detection device 600 and transmits the indoor temperature to the cooker 100, so that the cooker 100 can accurately predict the temperature value in the pot.

The embodiments provided in the present application will be described in detail below with reference to the accompanying drawings.

As shown in fig. 8, an embodiment of the present application provides a temperature prediction method, including:

s101, a controller of the kitchen range obtains temperature influence parameters.

Wherein, the temperature influencing parameter is a relevant parameter influencing the temperature in the pot of the pot placed on the cooker.

In some embodiments, the temperature affecting parameter comprises: the temperature value of the bottom of the pan. Optionally, the cooker may obtain the temperature value of the pot bottom through a temperature sensor arranged on the cooker.

It should be understood that the temperature value of the bottom of the pot on the stove directly influences the temperature value in the pot. Because the pan generally has good heat conductivity, when the cooking utensils heat the pan, the temperature value in the pan can change along with the change of the temperature value at the bottom of the pan. However, due to the influence of a plurality of factors such as environment, the amount of food in the pot, the number of times of cooking and the like, the temperature value of the bottom of the pot and the temperature value in the pot have certain nonlinear difference. Therefore, in the embodiment of the application, the temperature value of the bottom of the pot is taken as a temperature influence parameter, and the temperature value in the pot is predicted by integrating other related parameters.

In some embodiments, the temperature-affecting parameters may include, in addition to the bottom of pot temperature value, one or more of the following:

(1) fire gear of stove

When the firepower gears of the cooker are different, the heating degree of the cooker is also different. The firepower gear of the stove directly influences the temperature value of the bottom of the pot, and further influences the temperature value in the pot. Therefore, the fire gear of the stove can be used as a temperature influence parameter.

In some embodiments, the cooktop may automatically acquire a fire gear through a controller of the cooktop, or the cooktop may receive input information of a user's terminal device to acquire a fire gear of the cooktop.

(2) Volume of liquid in pot

Considering that the specific heat capacity of liquid is higher, when more liquid exists in the cooker, the temperature value in the cooker and the temperature value at the bottom of the cooker have larger difference in the heating process of the cooker. Therefore, the volume of the liquid in the pot can be used as a temperature influence parameter.

Optionally, the cooker may receive input information of a terminal device of a user, and obtain the volume of the liquid in the pot from the received information. The volume of the liquid refers to the volume of water or oil in the pot, and when the food material in the pot is solid, the volume of the liquid is defaulted to be 0.

(3) Range hood gear

It should be understood that when the indoor temperature range hood is opened, a relatively obvious air flow is formed above the kitchen range to influence the temperature in the pot of the pot arranged on the kitchen range, so that the gear of the range hood can be used as a temperature influence parameter.

Optionally, the kitchen range and the range hood establish communication connection, and the kitchen range obtains a range hood gear set by the range hood through communication connection. Or the cooker can set the range hood gear as a default gear. Or the kitchen range can receive input information of terminal equipment of a user so as to obtain the range hood gear of the range hood.

(4) Ambient temperature

The ambient temperature refers to an indoor temperature value of a room in which the cooker is located. The indoor temperature value has influence on the temperature value in the pot to a certain extent, so that the environmental temperature can be used as an environmental influence parameter.

Optionally, communication connection is established between the stove and the indoor temperature measuring device, and the stove acquires the indoor temperature detected by the indoor temperature measuring device through communication connection. Or the stove is provided with an indoor temperature measuring device, and the stove can acquire the ambient temperature through the indoor temperature measuring device. Still alternatively, the cooktop may receive input information of a user's terminal device to obtain the ambient temperature. Illustratively, taking a terminal device as a mobile phone as an example, as shown in fig. 9, the terminal device displays a parameter setting interface 305, and the parameter setting interface 305 may include a setting control 3051 of "ambient temperature". If it is detected that the user clicks the setting control 3051 of "ambient temperature" in the parameter setting interface 305, it indicates that the mobile phone receives an instruction of setting an ambient temperature parameter from the user, and further, the mobile phone pops up a text box 306 on the parameter setting interface 305, and sets the ambient temperature of the parameter to a specified value according to information changed or input by the user in the popped up text box 306.

S102, the controller of the cooker inputs the temperature influence parameters into a pre-trained temperature prediction model based on a long-time memory neural network to obtain the temperature value in the pot predicted by the temperature prediction model.

Wherein, the Long Short Term Memory (LSTM) neural network is a special Recurrent Neural Network (RNN), and has a good effect on data prediction of time series. Fig. 10 is a schematic diagram of a multi-layer LSTM network structure. The LSTM network may include an input layer, a hidden layer, and an output layer, where the hidden layer may contain a plurality of convolutional layers for extracting feature information.

Further, the basic structure of the recurrent neurons of the LSTM network is shown in fig. 11. It can be seen that the cyclic neuron of the LSTM network introduces three gated cyclic units (shown as a forgetting gate, an input gate, and an output gate) for controlling the update and forgetting of memory, ensuring that important information is always memorized, and forgetting less important information, so as to realize storage and flow in the hidden layer.

The technical solution shown in fig. 8 brings at least the following beneficial effects: temperature influencing parameters such as a pot bottom temperature value, a firepower gear of a cooker, a liquid volume in the cooker and the like all influence the temperature value in the pot. Because the temperature is a time sequence related variable, the temperature in the pan at the current moment is not only related to the current temperature influence parameter, but also influenced by the parameter at the historical moment, and the long-time and short-time memory neural network has a good effect on predicting the data of the time sequence, so that the temperature influence parameter is input into a temperature prediction model based on the long-time and short-time memory neural network, the temperature value in the pan is obtained through a cooker, and the accuracy and the convenience for obtaining the temperature in the pan are improved.

In some embodiments, as shown in fig. 12, the step S102 is specifically implemented as:

and S1021, normalizing the temperature influence parameters.

Exemplarily, it is assumed that the temperature-influencing parameter is represented by x (i), wherein the maximum value of the temperature-influencing parameter is max (x), the minimum value is min (x), and the normalized temperature-influencing parameter is represented by x' (i). The normalization formula may select the following formula:

it should be understood that normalization processing is performed on the temperature influence parameters input by the input layer, so that the speed of solving the optimal solution by gradient descent in the training process can be increased, and the training precision is improved.

And S1022, inputting the temperature influence parameters after the normalization processing into a pre-trained temperature prediction model based on the long-time and short-time memory neural network to obtain a predicted value output by the temperature prediction model.

And S1023, performing inverse normalization processing on the predicted value to obtain a predicted temperature value in the pot.

It should be understood that after the input temperature influencing parameters are normalized, the predicted values output by the temperature prediction model refer to the predicted intermediate values of the machine, so that the inverse normalization is required to obtain the actual temperature value in the pan.

The use of the predicted pan temperature value is exemplified below.

In some embodiments, after determining the predicted temperature value in the pot, the controller of the cooker may control the display to display the predicted temperature value in the pot, so that the user can know the temperature in the pot in time.

In some embodiments, in a scene needing temperature control, for example, a scene needing oil temperature control to fry food, if the predicted temperature value in the pot is greater than a first temperature threshold value, the cooker lowers the fire gear; or if the predicted temperature value in the boiler is smaller than a second temperature threshold value, the fire gear of the stove is increased, wherein the second temperature threshold value is smaller than or equal to the first temperature threshold value.

For example, when the second temperature threshold is less than the first temperature threshold, assuming that the first temperature value is 130 ℃ and the second temperature threshold is 110 ℃, the cooker controller may adjust a fire gear according to the detected temperature value in the pot to control the temperature value in the pot to be maintained between 110 ℃ and 130 ℃ when the cooker is used. Or, when the second temperature threshold is equal to the first temperature threshold, if the first temperature threshold and the second temperature threshold are both 80 ℃, in the heating process of the cooker, the cooker controller can adjust the firepower gear according to the detected temperature value in the cooker, and control the temperature value in the cooker to be kept at about 80 ℃ so as to achieve a more stable heating effect. Therefore, the temperature in the cooker can be controlled within a specified range according to the requirements of users or the instructions of intelligent recipes.

In some embodiments, in the case that the cooking appliance starts the boiling point warning function, if the predicted temperature value in the pot is greater than the boiling point temperature threshold value, a prompt message for prompting that the liquid in the pot reaches the boiling point is sent out.

Optionally, the cooker itself may send out prompt information in the form of voice. Or the stove can send prompt information to the terminal equipment, and the terminal equipment can prompt the user in other modes such as characters, voice, music, vibration, animation and the like. The embodiments of the present application do not limit this.

Alternatively, the boiling point temperature threshold may be related to the liquid carried in the pan. The user can set a boiling point temperature threshold for the cooker according to the self requirement (namely, the type of liquid carried in the pot). Alternatively, the cooktop may default to the liquid carried in the pan being water, so that the default boiling point temperature threshold is 100 ℃.

Based on the temperature value, the user can be reminded in time when the temperature value in the pot reaches the boiling point temperature threshold value, so that the user can perform the next cooking operation, such as adding food materials or closing a fire gear of the cooker. Optionally, when the temperature value in the pot reaches the boiling point temperature threshold, the controller of the cooker can default to close the fire gear; or, when the temperature value in the pot reaches the boiling point temperature threshold, the cooker may perform an operation according to the received user instruction, for example, change a fire gear of the cooker according to the user instruction.

Exemplarily, referring to fig. 13, taking water boiling as an example, when the cooker 100 detects that the temperature in the pan is 100 ℃, the cooker 100 sends a prompt message "the temperature has reached 100 ℃" to the user terminal device 300. Accordingly, the terminal device displays a text popup window 307 on the terminal interface 300, which prompts the user that the water in the pot is boiling. Further, as shown in fig. 14, if it is detected that the user clicks the prompt pop-up window 307 of the terminal device, it indicates that the mobile phone receives a stove detail viewing instruction of the user, and then the mobile phone enters the stove management interface 308, where the stove management interface 308 includes a fire gear management control 3081. If the fire gear setting control 3081 is detected to be clicked by the user, a text box 309 pops up on the cooker management interface 308 by the mobile phone, and the fire gear of the cooker is set to a specified value according to information changed or input in the popped text box 309 by the user.

In some embodiments, when the cooktop turns on the intelligent cooking function, the cooktop can adjust the fire gear according to the electronic recipe to complete the cooking process. The electronic recipe related data can be prestored in the server, or prestored in the controller of the cooker, or input by a user from a terminal device, or acquired by the cooker through networking. The related data of the electronic menu may include: cooking temperature and cooking duration for one or more stages.

Further, each time cooking in one stage is finished, the cooker sends out prompt information for prompting a user that the current stage is finished. In this way, the user can perform cooking operations after a cooking phase is completed, according to the actual needs, for example: adding flavoring or supplementing food material.

In some embodiments, when the oven is turned on, if the predicted temperature value in the pot is greater than the dry-burning temperature value, the oven sends out a prompt message for prompting that the pot is in a dry-burning state.

Referring to fig. 15, when the cooker detects that the pot is in a dry-cooking state, the cooker 100 sends a prompt message for prompting that the pot is in the dry-cooking state to the user terminal device 300; thus, the terminal device 300 displays a text popup window on the terminal interface to prompt the user that the pot is in a dry-cooking state.

Further, when the cookware is detected to be in a dry-burning state, the cooker automatically closes the firepower gear, or the firepower gear is adjusted or closed according to the input of the terminal equipment of the user.

In a possible implementation manner, as shown in fig. 16, the temperature prediction model based on the long-term and short-term memory neural network may be trained according to the following steps:

sa1, acquiring a plurality of groups of temperature influence parameters and pot temperature values corresponding to the plurality of groups of temperature influence parameters.

In some embodiments, after obtaining a plurality of sets of temperature-affecting parameters and the pan temperature values corresponding to the plurality of sets of temperature-affecting parameters, missing value processing needs to be performed on the parameters to ensure sufficient data sample number and improve the prediction effect.

Sa2, and generating a sample set by a plurality of groups of temperature influence parameters and the temperature in the pan corresponding to the temperature influence parameters.

The sample set comprises a plurality of samples, and one sample corresponds to a group of temperature influence parameters and the temperature in the pot corresponding to the temperature influence parameters.

And Sa3, training the temperature prediction model by using the sample set until the temperature prediction model meets the preset training termination condition.

In some embodiments, the preset training termination condition includes: and when the length is long, the mean square error loss of the memory network is less than a preset threshold value, or the training reaches the maximum iteration number.

Illustratively, assuming that the number of iterations is N, the actual output data in the training is z_iTheoretical output data is z_i' then, the above mean square error loss expression is:

in some possible implementations, referring to fig. 11, during the operation of step Sa3, the specific implementation of each gate in the cyclic neuron of the corresponding LSTM network is described as follows:

the value of the forgetting gate can be calculated according to the following equation (1).

f_t＝σ(w_f[h_t-1，x_t]+b_f) Formula (1)

In the formula (1), f_tTo forget the information at time t, x_t、h_t-1Input and output of the neuron at time t-1, w_fWeight matrix being the forgetting gate of the neuron, b_fIs the neuron forgets the bias of the gate. σ is the Sigmoid activation function of the neuron, and the following is no longer redundantThe above-mentioned processes are described.

The value of the input gate can be calculated according to the following equation (2).

i_t＝σ(w_i[h_t-1，x_t]+b_i) Formula (2)

In the formula (2), i_tFor entering information at time t, x_t、h_t-1Input and output of the neuron at time t-1, w_iWeight matrix being input gates of neurons, b_iThe bias of the neuron input gate.

Control parameter C 'formed by new data'_tThe calculation can be performed according to the following formula (3).

C′_t＝tanh(w_c[h_t-1，x_t]+b_c) Formula (3)

In the formula (3), C'_tControl parameters formed for new data, x_t、h_t-1Input and output of the neuron at time t-1, respectively, b_cWeight matrix being input gates of neurons, b_cAn offset for cell information update.

For updated cell information state C_tThe calculation can be performed according to the following formula (4).

C_t＝f_t·C_t-1+i_t·C′_tFormula (4)

The value of the output gate can be calculated according to the following equation (5).

o_t＝σ(w_o[h_t-1，x_t]+b_o) Formula (5)

In the formula (5), o_tFor outputting information at time t of gate, x_t、h_t-1Input and output of the neuron at time t-1, w_oWeight matrix being output gates of neurons, b_oIs the bias of the neuron output gate.

the output of the neuron element at time t can be calculated according to the following equation (6).

h_t＝o_t·tanh(C_t) Formula (6)

In the formula (6), h_tIs the output of the neuron at time t, o_tFor outputting information at time t of gate, tanh is an activation function, C_tIs the updated cell information state.

The hidden layer can output the output result h_tAnd outputting the data to an output layer. The output layer can be paired with h_tDecoding is carried out to obtain a predicted value of the temperature in the pan. And further carrying out inverse normalization on the predicted value to obtain the actual predicted temperature value in the pot.

In some possible embodiments, after the sample set is generated at step Sa2, the sample set is divided into a training sample set and a testing sample set according to a preset ratio. Illustratively, the preset ratio of the training sample set to the testing sample set may be 8:2 or 7:3, which is not limited in the embodiments of the present application.

Further, the step Sa3 may be implemented as: and training the temperature prediction model by using the training sample set until the temperature prediction model meets the preset training termination condition.

After step Sa3, the trained temperature prediction model may also be tested. And inputting the test sample set into the trained LSTM network-based temperature prediction model, and evaluating the model prediction effect to obtain the temperature prediction model meeting the test passing conditions.

Optionally, when the model prediction effect is quantitatively evaluated, a curve fitting degree R may be selected as an evaluation index of the prediction accuracy of the pan temperature, and the fitting degree formula is as follows:

wherein Q is the sum of squares of residuals, Q ═ Σ (y-y')²Y is the actual temperature value in the pan, and y' is the predicted temperature value in the pan. It should be appreciated that the closer the fitness R is to 1, the better the representative model fits the data.

Alternatively, the test pass condition may be that the test error is less than the error threshold. The error threshold may be preset in the controller of the hob by a manager. For example, the error threshold may be 10%, or 20%, etc. The embodiment of the present application does not limit the specific value of the error threshold.

It can be seen that the foregoing describes the solution provided by the embodiments of the present application primarily from a methodological perspective. In order to implement the functions, the embodiments of the present application provide corresponding hardware structures and/or software modules for performing the respective functions. Those of skill in the art will readily appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the embodiment of the present application, the controller may be divided into function modules according to the method example, for example, each function module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.

The embodiment of the present application further provides a schematic diagram of a hardware structure of a controller, as shown in fig. 17, the controller 2000 includes a processor 2001, and optionally, a memory 2002 and a communication interface 2003 connected to the processor 2001. The processor 2001, memory 2002 and communication interface 2003 are connected by a bus 2004.

The processor 2001 may be a Central Processing Unit (CPU), a general purpose processor Network Processor (NP), a Digital Signal Processor (DSP), a microprocessor, a microcontroller, a Programmable Logic Device (PLD), or any combination thereof. The processor 2001 may also be any other means having a processing function such as a circuit, device or software module. The processor 2001 may also include a plurality of CPUs, and the processor 2001 may be one single-core (single-CPU) processor or a multi-core (multi-CPU) processor. A processor herein may refer to one or more devices, circuits, or processing cores that process data, such as computer program instructions.

Memory 2002 may be a read-only memory (ROM) or other type of static storage device that may store static information and instructions, a Random Access Memory (RAM) or other type of dynamic storage device that may store information and instructions, but is not limited to, electrically erasable programmable read-only memory (EEPROM), compact disk read-only memory (CD-ROM) or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory 2002 may be separate or integrated with the processor 2001. The memory 2002 may include, among other things, computer program code. The processor 2001 is configured to execute the computer program code stored in the memory 2002, thereby implementing the control method provided by the embodiment of the present application.

Communication interface 2003 may be used to communicate with other devices or communication networks (e.g., an Ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), etc.. communication interface 2003 may be a module, circuitry, transceiver, or any other device capable of communicating.

The bus 2004 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus 2004 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 17, but this does not mean only one bus or one type of bus.

Embodiments of the present invention also provide a computer-readable storage medium, where the computer-readable storage medium includes computer-executable instructions, and when the computer-executable instructions are executed on a computer, the computer is caused to execute the method provided in the foregoing embodiments.

The embodiment of the present invention further provides a computer program product, which can be directly loaded into the memory and contains software codes, and after being loaded and executed by the computer, the computer program product can implement the method provided by the above embodiment.

Those skilled in the art will recognize that, in one or more of the examples described above, the functions described in this invention may be implemented in hardware, software, firmware, or any combination thereof. When implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.

Through the above description of the embodiments, it is clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the above described functions.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules or units is only one logical function division, and there may be other division ways in actual implementation. For example, various elements or components may be combined or may be integrated into another device, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. Units described as separate parts may or may not be physically separate, and parts displayed as units may be one physical unit or a plurality of physical units, may be located in one place, or may be distributed to a plurality of different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or partially contributed to by the prior art, or all or part of the technical solutions may be embodied in the form of a software product, where the software product is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

The above description is only an embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A hob, characterized in that it comprises:

the temperature sensor is used for detecting the pot bottom temperature value of a pot placed on the stove;

a controller connected to the temperature sensor, the controller configured to:

acquiring a temperature influence parameter, wherein the temperature influence parameter comprises: the pot bottom temperature value, the firepower gear of the cooker and the volume of liquid in the cooker;

and inputting the temperature influence parameters into a pre-trained temperature prediction model based on a long-time and short-time memory neural network to obtain a predicted temperature value in the pot.

2. Hob according to claim 1,

the controller is specifically configured to:

normalizing the temperature influence parameters;

inputting the temperature influence parameters after the normalization processing into a pre-trained temperature prediction model based on a long-time and short-time memory neural network to obtain a predicted value output by the temperature prediction model;

and performing inverse normalization processing on the predicted value to obtain a predicted temperature value in the pot.

3. Hob according to claim 1, characterized in that the temperature influencing parameters further comprise one or more of the following: the range hood gear or the ambient temperature.

4. The hob according to any one of the claims 1 to 3, characterized in that the temperature prediction model is trained according to the following steps:

acquiring a plurality of groups of temperature influence parameters and pot temperature values corresponding to the plurality of groups of temperature influence parameters;

generating a sample set by the plurality of groups of temperature influence parameters and the pan temperature corresponding to the temperature influence parameters, wherein the sample set comprises a plurality of samples, and one sample corresponds to one group of temperature influence parameters and the pan temperature corresponding to the temperature influence parameters;

and training the temperature prediction model by using the sample set until the temperature prediction model meets a preset training termination condition.

5. Hob according to any of the claims 1 to 3, characterized in that the controller is further configured to:

if the predicted temperature value in the cooker is larger than a first temperature threshold value, controlling the cooker to reduce a firepower gear;

if the predicted temperature value in the pot is smaller than a second temperature threshold value, controlling the cooker to increase a firepower gear; wherein the second temperature threshold is less than or equal to the first temperature threshold.

6. The cooktop of any of claims 1 to 3, wherein the controller is further configured to:

if the predicted temperature value in the pot is larger than the boiling point temperature threshold value, sending out prompt information for prompting that the liquid in the pot reaches the boiling point; or,

and if the predicted temperature value in the pot is greater than the dry-burning temperature threshold value, sending out prompt information for prompting that the pot is in a dry-burning state.

7. A method of temperature prediction, the method comprising:

8. The method of claim 7, wherein inputting the temperature influencing parameters into a pre-trained long-time memory neural network-based temperature prediction model to obtain a predicted temperature value in the pot comprises:

normalizing the temperature influence parameters;

9. The method of claim 7, wherein the temperature affecting parameters further comprise one or more of: the range hood gear or the ambient temperature.

10. The method of claim 7, wherein the temperature prediction model is trained according to the following steps: