阅读说明：本技术 控制烹饪模式的方法、装置和烹饪器具 (Method and device for controlling cooking mode and cooking appliance ) 是由 罗晓宇 陈翀 岳冬 于 2019-01-14 设计创作，主要内容包括：本发明公开了一种控制烹饪模式的方法、装置和烹饪器具。其中,该方法包括：识别待烹饪的食物的种类,并获取操作对象定制的食物的至少一种口感数据；基于食物的种类,确定对应的深度确定性策略梯度模型；使用确定的深度确定性策略梯度模型处理食物的至少一种口感数据,获取调整数据,其中,调整数据用于调整烹饪参数；基于调整数据确定调整后的烹饪模式。上述方案采用强化学习的深度确定性策略梯度算法,根据用户需求制定个性化煮饭模式,解决了现有技术中由于烹饪器具的烹饪模式是出厂预设,导致烹饪的控制方法不灵活的技术问题。(The invention discloses a method and a device for controlling a cooking mode and a cooking appliance. Wherein, the method comprises the following steps: identifying the type of food to be cooked, and acquiring at least one type of taste data of the food customized by an operation object; determining a corresponding depth-determining policy gradient model based on the type of food; processing at least one sensory data of the food using the determined depth certainty strategy gradient model, obtaining adjustment data, wherein the adjustment data is used to adjust the cooking parameters; an adjusted cooking mode is determined based on the adjustment data. According to the scheme, the deep certainty strategy gradient algorithm of reinforcement learning is adopted, the personalized rice cooking mode is formulated according to the user requirement, and the technical problem that the cooking control method is inflexible due to the fact that the cooking mode of the cooking appliance is preset before the factory in the prior art is solved.)

1. A method of controlling a cooking mode, comprising:

identifying the type of food to be cooked, and acquiring at least one type of taste data of the food customized by an operation object;

determining a corresponding depth-determining policy gradient model based on the category of the food;

processing at least one sensory data of the food using the determined depth-deterministic strategic gradient model, obtaining adjustment data, wherein the adjustment data is used to adjust a cooking parameter;

an adjusted cooking mode is determined based on the adjustment data.

2. The method of claim 1, wherein prior to processing at least one sensory data of the food using the determined depth-deterministic policy gradient model, obtaining adjustment data, the method further comprises:

different depth certainty strategy gradient models are obtained by training different kinds of food, and corresponding cooking modes are formulated according to the different depth certainty strategy gradient models.

3. The method according to claim 1 or 2, wherein after identifying the kind of food to be cooked, the method further comprises:

judging whether an instruction for entering a cooking mode is received;

if the instruction is received, entering a step of acquiring at least one type of taste data of the food customized by an operation object;

and if the instruction is not received, controlling the cooking appliance to work according to a preset cooking mode.

4. The method according to claim 1 or 2, wherein after determining the adjusted cooking mode based on the adjustment data, the method further comprises:

controlling the cooking appliance to work according to the adjusted cooking mode;

and storing the association relationship between the type of the food and the corresponding cooking mode into a cooking database.

5. The method of claim 4, wherein after storing the association between the type of food and the corresponding cooking mode, the method further comprises:

if the cooking appliance is detected to be put in a new food, identifying the type of the new food;

querying whether a corresponding cooking mode exists in the cooking database based on the kind of the new food;

if the corresponding cooking mode is inquired, controlling the cooking appliance to work according to the inquired cooking mode;

and if the corresponding cooking mode is not inquired, controlling the cooking appliance to work according to the preset cooking mode.

6. The method of claim 5, wherein prior to controlling the cooking appliance to operate in the queried cooking mode, the method further comprises:

outputting inquiry information, wherein the inquiry information comprises a voice prompt and/or a text prompt for prompting the operation object to update the taste data;

if the operation object inputs new taste data based on the prompt information, processing the updated taste data by using a corresponding depth certainty strategy gradient model to obtain updated adjustment data;

and adjusting the inquired cooking mode based on the updated adjustment data.

7. The method of claim 6, wherein the cooking mode corresponding to the food item in the cooking database is updated based on the adjusted cooking mode.

8. The method of claim 1, wherein identifying the type of food to be cooked comprises:

acquiring a food image of the food to be cooked;

and identifying the food image by using a convolutional neural network model, and determining the type of food in the food image.

9. The method of claim 8, wherein identifying the food image using a convolutional neural network model, determining a type of food in the food image, comprises:

segmenting the food image to obtain a plurality of sub-pictures;

processing the plurality of sub-pictures using the convolutional neural network model, identifying food in each sub-picture;

carrying out contour extraction on the sub-pictures with the recognized food, and setting the areas without the recognized food as a preset background to obtain a plurality of processed sub-pictures;

and splicing the plurality of processed sub-pictures to obtain a restored food picture.

10. The method of claim 9, wherein after stitching the plurality of processed sub-pictures to obtain a reduced food picture, the method further comprises:

outputting the restored food picture, and extracting a food outline in the restored food picture;

determining the type of the food based on the food outline in the restored food picture.

11. An apparatus for controlling a cooking mode, comprising:

the identification module is used for identifying the type of food to be cooked and acquiring at least one type of taste data of the food customized by an operation object;

a first determination module to determine a corresponding depth certainty policy gradient model based on a category of the food;

a first adjustment module for processing at least one sensory data of the food using the determined depth-deterministic strategic gradient model, obtaining adjustment data, wherein the adjustment data is used to adjust a cooking parameter;

a second determination module for determining an adjusted cooking mode based on the adjustment data.

12. The apparatus of claim 11, further comprising:

the making module is used for training different types of food to obtain different depth certainty strategy gradient models and making corresponding cooking modes according to the different depth certainty strategy gradient models before the determined depth certainty strategy gradient models are used for processing at least one type of taste data of the food and obtaining adjustment data.

13. The apparatus of claim 11 or 12, further comprising:

the judging module is used for judging whether an instruction for entering a cooking mode is received or not after the type of food to be cooked is identified;

the execution module is used for entering the step of acquiring at least one type of taste data of the food customized by the operation object if the instruction is received; and if the instruction is not received, controlling the cooking appliance to work according to a preset cooking mode.

14. The apparatus of claim 11 or 12, further comprising:

the control module is used for controlling the cooking appliance to work according to the adjusted cooking mode after the adjusted cooking mode is determined based on the adjustment data;

and the storage module is used for storing the association relationship between the type of the food and the corresponding cooking mode into a cooking database.

15. The apparatus of claim 14, further comprising:

the new food identification module is used for identifying the type of the new food if the cooking appliance is detected to be put into the new food after the association relationship between the type of the food and the corresponding cooking mode is stored;

the new food inquiry module is used for inquiring whether a corresponding cooking mode exists in the cooking database or not based on the type of the new food;

the new food control module is used for controlling the cooking appliance to work according to the inquired cooking mode if the corresponding cooking mode is inquired; and if the corresponding cooking mode is not inquired, controlling the cooking appliance to work according to the preset cooking mode.

16. The apparatus of claim 15, further comprising:

the output module is used for outputting inquiry information before controlling the cooking appliance to work according to the inquired cooking mode, wherein the inquiry information comprises a voice prompt and/or a text prompt and is used for prompting the operation object to update the taste data;

a processing module, configured to, if the operation object inputs new taste data based on the prompt information, process the updated taste data using a corresponding depth certainty policy gradient model, and obtain updated adjustment data;

and the second adjusting module is used for adjusting the inquired cooking mode based on the updated adjusting data.

17. The apparatus of claim 16, further comprising an updating module for updating the cooking mode corresponding to the food item in the cooking database based on the adjusted cooking mode.

18. The apparatus of claim 11, wherein the identification module comprises:

the acquisition module is used for acquiring a food image of the food to be cooked;

and the third determination module is used for identifying the food image by using a convolutional neural network model and determining the type of food in the food image.

19. The apparatus of claim 18, wherein the third determining module comprises:

the segmentation module is used for segmenting the food image to obtain a plurality of sub-pictures;

the sub-picture processing module is used for processing the plurality of sub-pictures by using the convolutional neural network model and identifying food in each sub-picture;

the first extraction module is used for extracting the outline of the sub-picture with the recognized food, and setting the area without the recognized food as a preset background to obtain a plurality of processed sub-pictures;

and the splicing module is used for splicing the plurality of processed sub-pictures to obtain a restored food picture.

20. The apparatus of claim 19, further comprising:

the second extraction module is used for outputting the restored food picture after the plurality of processed sub-pictures are spliced to obtain the restored food picture, and extracting the food outline in the restored food picture;

and the third determining submodule is used for determining the type of the food based on the food outline in the restored food picture.

21. A storage medium comprising a stored program, wherein the apparatus on which the storage medium is located is controlled to perform the method of controlling a cooking mode according to any one of claims 1 to 10 when the program is run.

22. A processor for running a program, wherein the program when running performs the method of controlling a cooking mode of any one of claims 1 to 10.

23. A cooking appliance, comprising:

the image acquisition device is used for acquiring an image of food to be cooked;

a controller for executing a program, wherein the following processing steps are performed on data output from the image acquisition apparatus when the program is executed: identifying the type of food to be cooked, and acquiring at least one type of taste data of the food customized by an operation object; determining a corresponding depth-determining policy gradient model based on the category of the food; processing at least one sensory data of the food using the determined depth-deterministic strategic gradient model, obtaining adjustment data, wherein the adjustment data is used to adjust a cooking parameter; an adjusted cooking mode is determined based on the adjustment data.

Technical Field

The invention relates to the field of intelligent small household appliances, in particular to a method and a device for controlling a cooking mode and a cooking appliance.

Background

With the pace of life increasing, people stay in the kitchen for less and less time, and therefore, a cooking appliance which is simple and easy to operate, meets the taste of a user, and can perfectly exert the nutrition contained in food is urgently needed to be developed. The electric cooker is an essential cooking tool in life of people, and the traditional electric cooker usually adopts the same cooking mode aiming at different rice grain types, so that the taste of rice is good and bad; the control mode of the electric cooker is developed from simple mechanical control to the current microcomputer control, fuzzy control and the like, although the functions of the electric cooker are also developed from a single rice cooking function to multiple purposes and have different rice cooking modes, the cooking mode of the electric cooker is still a preset fixed mode from a factory, and the individual requirements of users cannot be fully met.

Aiming at the problem that in the prior art, a cooking mode of a cooking appliance is preset before factory installation, so that a cooking control method is inflexible, an effective solution is not provided at present.

Disclosure of Invention

The embodiment of the invention provides a method and a device for controlling a cooking mode and a cooking appliance, and at least solves the technical problem that in the prior art, the cooking mode of the cooking appliance is preset before a factory, so that the cooking control method is inflexible.

According to an aspect of an embodiment of the present invention, there is provided a method of controlling a cooking mode, including: identifying the type of food to be cooked, and acquiring at least one type of taste data of the food customized by an operation object; determining a corresponding depth-determining policy gradient model based on the type of food; processing at least one sensory data of the food using the determined depth certainty strategy gradient model, obtaining adjustment data, wherein the adjustment data is used to adjust the cooking parameters; an adjusted cooking mode is determined based on the adjustment data.

Optionally, before processing at least one of the sensory data of the food using the determined depth-deterministic policy gradient model to obtain the adjustment data, the method further comprises: different depth certainty strategy gradient models are obtained by training different kinds of food, and corresponding cooking modes are formulated according to the different depth certainty strategy gradient models.

Optionally, after identifying the type of food to be cooked, the method further comprises: judging whether an instruction for entering a cooking mode is received; if an instruction is received, entering a step of acquiring at least one type of taste data of food customized by an operation object; and if the instruction is not received, controlling the cooking appliance to work according to a preset cooking mode.

Optionally, after determining the adjusted cooking mode based on the adjustment data, the method further comprises: controlling the cooking appliance to work according to the adjusted cooking mode; and storing the association relationship between the type of the food and the corresponding cooking mode into a cooking database.

Optionally, after storing the association between the type of the food and the corresponding cooking mode, the method further includes: if the cooking appliance is detected to be put into new food, identifying the type of the new food; inquiring whether a corresponding cooking mode exists in a cooking database based on the type of the new food; if the corresponding cooking mode is inquired, controlling the cooking appliance to work according to the inquired cooking mode; and if the corresponding cooking mode is not inquired, controlling the cooking appliance to work according to the preset cooking mode.

Optionally, before controlling the cooking appliance to operate according to the queried cooking mode, the method further includes: outputting inquiry information, wherein the inquiry information comprises a voice prompt and/or a text prompt and is used for prompting an operation object to update the taste data; if the operation object inputs new taste data based on the prompt information, processing the updated taste data by using a corresponding depth certainty strategy gradient model, and acquiring updated adjustment data; and adjusting the inquired cooking mode based on the updated adjustment data.

Optionally, the cooking mode corresponding to the food in the cooking database is updated based on the adjusted cooking mode.

Optionally, identifying the type of food to be cooked includes: acquiring a food image of food to be cooked; and identifying the food image by using the convolutional neural network model, and determining the type of the food in the food image.

Optionally, identifying the food image using a convolutional neural network model, determining a type of food in the food image, comprising: segmenting the food image to obtain a plurality of sub-pictures; processing a plurality of sub-pictures by using a convolutional neural network model, and identifying food in each sub-picture; carrying out contour extraction on the sub-pictures with the recognized food, and setting the areas without the recognized food as a preset background to obtain a plurality of processed sub-pictures; and (4) splicing the plurality of processed sub-pictures to obtain a restored food picture.

Optionally, after the combining the plurality of processed sub-pictures to obtain the restored food picture, the method further includes: outputting the restored food picture, and extracting a food outline in the restored food picture; and determining the type of the food based on the food outline in the restored food picture.

According to another aspect of the embodiments of the present invention, there is also provided an apparatus for controlling a cooking mode, including: the identification module is used for identifying the type of food to be cooked and acquiring at least one type of taste data of the food customized by an operation object; a first determination module for determining a corresponding depth certainty strategy gradient model based on a category of food; a first adjusting module, configured to process at least one sensory data of the food using the determined depth certainty strategy gradient model, and obtain an adjustment data, where the adjustment data is used to adjust a cooking parameter; a second determination module for determining an adjusted cooking mode based on the adjustment data.

Optionally, the apparatus further comprises: the making module is used for training different types of food to obtain different depth certainty strategy gradient models and making corresponding cooking modes according to the different depth certainty strategy gradient models before the determined depth certainty strategy gradient models are used for processing at least one type of taste data of the food and obtaining adjustment data.

Optionally, the apparatus further comprises: the judging module is used for judging whether an instruction for entering a cooking mode is received or not after the type of food to be cooked is identified; the execution module is used for entering the step of acquiring at least one type of taste data of food customized by the operation object if the instruction is received; and if the instruction is not received, controlling the cooking appliance to work according to a preset cooking mode.

Optionally, the apparatus further comprises: the control module is used for controlling the cooking appliance to work according to the adjusted cooking mode after the adjusted cooking mode is determined based on the adjustment data; and the storage module is used for storing the association relationship between the type of the food and the corresponding cooking mode into a cooking database.

Optionally, the apparatus further comprises: the new food identification module is used for identifying the type of the new food if the cooking appliance is detected to be put into the new food after the incidence relation between the type of the food and the corresponding cooking mode is stored; the new food inquiry module is used for inquiring whether a corresponding cooking mode exists in the cooking database or not based on the type of the new food; the new food control module is used for controlling the cooking appliance to work according to the inquired cooking mode if the corresponding cooking mode is inquired; and if the corresponding cooking mode is not inquired, controlling the cooking appliance to work according to the preset cooking mode.

Optionally, the apparatus further comprises: the output module is used for outputting inquiry information before controlling the cooking appliance to work according to the inquired cooking mode, wherein the inquiry information comprises voice prompt and/or text prompt and is used for prompting an operation object to update the taste data; the processing module is used for processing the updated taste data by using a corresponding depth certainty strategy gradient model and acquiring updated adjustment data if the operation object inputs new taste data based on the prompt information; and the second adjusting module adjusts the inquired cooking mode based on the updated adjusting data.

Optionally, the apparatus further includes an updating module, configured to update the cooking mode corresponding to the food in the cooking database based on the adjusted cooking mode.

Optionally, the identification module comprises: the acquisition module is used for acquiring a food image of food to be cooked; and the third determining module is used for identifying the food image by using the convolutional neural network model and determining the type of the food in the food image.

Optionally, the third determining module includes: the segmentation module is used for segmenting the food image to obtain a plurality of sub-pictures; the sub-picture processing module is used for processing a plurality of sub-pictures by using the convolutional neural network model and identifying food in each sub-picture; the first extraction module is used for extracting the outline of the sub-picture with the recognized food, and setting the area without the recognized food as a preset background to obtain a plurality of processed sub-pictures; and the splicing module is used for splicing the plurality of processed sub-pictures to obtain the restored food picture.

Optionally, the apparatus further comprises: the second extraction module is used for outputting the restored food picture after the plurality of processed sub-pictures are spliced to obtain the restored food picture, and extracting the food outline in the restored food picture; and the third determining submodule is used for determining the type of the food based on the food outline in the restored food picture.

According to another aspect of the embodiments of the present invention, there is also provided a storage medium including a stored program, wherein when the program is executed, an apparatus in which the storage medium is controlled performs any one of the above methods of controlling a cooking mode.

According to another aspect of the embodiments of the present invention, there is also provided a processor for executing a program, wherein the program executes any one of the above methods for controlling a cooking mode.

According to another aspect of the embodiments of the present invention, there is also provided a cooking appliance including: the image acquisition device is used for acquiring an image of food to be cooked; a controller for executing the program, wherein the following processing steps are executed on the data output from the image acquisition device when the program is executed: identifying the type of food to be cooked, and acquiring at least one type of taste data of the food customized by an operation object; determining a corresponding depth-determining policy gradient model based on the type of food; processing at least one sensory data of the food using the determined depth certainty strategy gradient model, obtaining adjustment data, wherein the adjustment data is used to adjust the cooking parameters; an adjusted cooking mode is determined based on the adjustment data.

In the embodiment of the invention, the type of food to be cooked is identified, and at least one type of taste data of the food customized by an operation object is obtained; determining a corresponding depth-determining policy gradient model based on the type of food; processing at least one sensory data of the food using the determined depth certainty strategy gradient model, obtaining adjustment data, wherein the adjustment data is used to adjust the cooking parameters; an adjusted cooking mode is determined based on the adjustment data. According to the scheme, a deep certainty strategy gradient algorithm of reinforcement learning is adopted, different cooking modes corresponding to different original food types are adjusted according to a user preference training algorithm, and the technical problem that a cooking control method is inflexible due to the fact that the cooking modes of cooking appliances are preset in a factory in the prior art is solved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:

FIG. 1 is a flow chart of an alternative method of controlling a cooking mode according to an embodiment of the present application;

FIG. 2 is an alternative deep deterministic strategy gradient algorithm learning flow diagram according to an embodiment of the application;

FIG. 3 is a learning flow chart of an alternative depth deterministic strategy gradient algorithm for controlling an electric rice cooker according to an embodiment of the present application;

FIG. 4 is a flow chart of an alternative personalized cooking profile according to an embodiment of the present application; and

fig. 5 is a schematic diagram of an alternative apparatus for controlling a cooking mode according to an embodiment of the present application.

Detailed Description

In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, system, article, or device that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, apparatus, article, or device.