CN110941203A - Control method and device for cooking food and cooker - Google Patents
Control method and device for cooking food and cooker Download PDFInfo
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- CN110941203A CN110941203A CN201811115968.3A CN201811115968A CN110941203A CN 110941203 A CN110941203 A CN 110941203A CN 201811115968 A CN201811115968 A CN 201811115968A CN 110941203 A CN110941203 A CN 110941203A
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- 235000013305 food Nutrition 0.000 title claims abstract description 268
- 238000010411 cooking Methods 0.000 title claims abstract description 123
- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000004422 calculation algorithm Methods 0.000 claims description 22
- 238000000605 extraction Methods 0.000 claims description 19
- 238000001914 filtration Methods 0.000 claims description 15
- 238000012545 processing Methods 0.000 claims description 15
- 230000008569 process Effects 0.000 claims description 11
- 238000013135 deep learning Methods 0.000 claims description 9
- 238000012549 training Methods 0.000 claims description 9
- 230000008859 change Effects 0.000 claims description 7
- 238000010801 machine learning Methods 0.000 claims description 4
- 238000010606 normalization Methods 0.000 claims description 4
- 238000012217 deletion Methods 0.000 claims description 3
- 230000037430 deletion Effects 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 7
- 241000209094 Oryza Species 0.000 description 5
- 235000007164 Oryza sativa Nutrition 0.000 description 5
- 235000009566 rice Nutrition 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47J—KITCHEN EQUIPMENT; COFFEE MILLS; SPICE MILLS; APPARATUS FOR MAKING BEVERAGES
- A47J27/00—Cooking-vessels
- A47J27/004—Cooking-vessels with integral electrical heating means
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47J—KITCHEN EQUIPMENT; COFFEE MILLS; SPICE MILLS; APPARATUS FOR MAKING BEVERAGES
- A47J27/00—Cooking-vessels
- A47J27/08—Pressure-cookers; Lids or locking devices specially adapted therefor
- A47J27/0802—Control mechanisms for pressure-cookers
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47J—KITCHEN EQUIPMENT; COFFEE MILLS; SPICE MILLS; APPARATUS FOR MAKING BEVERAGES
- A47J36/00—Parts, details or accessories of cooking-vessels
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47J—KITCHEN EQUIPMENT; COFFEE MILLS; SPICE MILLS; APPARATUS FOR MAKING BEVERAGES
- A47J37/00—Baking; Roasting; Grilling; Frying
- A47J37/06—Roasters; Grills; Sandwich grills
- A47J37/0623—Small-size cooking ovens, i.e. defining an at least partially closed cooking cavity
- A47J37/0629—Small-size cooking ovens, i.e. defining an at least partially closed cooking cavity with electric heating elements
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- Engineering & Computer Science (AREA)
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- Automation & Control Theory (AREA)
- General Preparation And Processing Of Foods (AREA)
Abstract
The application discloses a control method and device for cooking food and a cooker. Wherein, the method comprises the following steps: acquiring food image data of an appliance interior of a cooker, wherein the food image data comprises at least one of: the food material types and the food material quantity of the food materials to be processed; inputting food image data into a food prediction model to obtain cooking data; and controlling the operation of the cooker based on the cooking data. The technical problem that the cooking effect is not ideal due to the fact that the cooking parameters set for different food materials are single when a user cooks food is solved.
Description
Technical Field
The application relates to the field of intelligent kitchenware, in particular to a control method and device for cooking food and a cooker.
Background
When the food is cooked by using the cooker at the present stage, due to different characteristics of different food materials, the cooking methods are different, for example, if the cooking time is too long, the food is too cooked and rotten, and the taste of the food is affected, and if the cooking time is short, the food cannot reach the expected cooked and rotten degree, and cannot be eaten. The user often selects fixed cooking parameters when cooking food, rather than pertinently setting the cooking parameters according to the characteristics of the cooking food materials, so that the cooking effect is not ideal.
Disclosure of Invention
The embodiment of the application provides a control method and device for cooking food and a cooker, and aims to at least solve the technical problem that the cooking effect is not ideal due to single cooking parameters set for different food materials when a user cooks the food.
According to an aspect of an embodiment of the present application, there is provided a control method of cooking food, including: acquiring food image data of an appliance interior of a cooker, wherein the food image data comprises at least one of: the food material types and the food material quantity of the food materials to be processed; inputting food image data into a food prediction model to obtain cooking data; and controlling the operation of the cooker based on the cooking data.
Optionally, before inputting the food image data into the food prediction model to obtain the cooking data, the method further comprises: obtaining the stored food material data, wherein the food material data comprises at least one of the following items: characteristics of different food materials, cooking parameters of a cooker and characteristics of different food materials after cooking; obtaining sample data based on the food material data; based on sample data, performing feature extraction on input parameters by using a deep learning algorithm; and constructing a food prediction model according to the extraction result, wherein the input parameter is at least one of the following parameters included in the sample data: food type, food characteristics, food quantity, and cooking result of the food.
Optionally, based on the food material data, obtaining sample data includes: filtering the food material data based on the filtering conditions to obtain a filtering result; traversing the filtering result, and deleting the filtering result according to the removing condition to obtain a deleting result; and carrying out normalization processing based on the deletion result to obtain sample data.
Optionally, the performing feature extraction on the input parameters by using a deep learning algorithm based on the sample data, and constructing the food prediction model according to the extraction result includes: based on sample data, performing feature extraction on input parameters input into the prediction model by using a deep learning algorithm to obtain an extraction result; and processing the extraction result based on a machine learning algorithm to generate a food prediction model.
Optionally, inputting the food image data into a food prediction model to obtain cooking data, including: identifying food image data, and acquiring food material parameters of food materials to be processed, wherein the food material parameters comprise at least one of the following parameters: food material type, food material quantity and target cooking result; matching food material parameters of food materials to be processed with sample data corresponding to the food prediction model; and if the matching is successful, acquiring the cooking data for processing the food material to be processed.
Optionally, after controlling the operation of the cooker based on the cooking data, the method further comprises: acquiring food material change data generated in the process of cooking food by a cooker; adding the food material change data serving as newly-added sample data to a sample library; and performing secondary training on the food prediction model based on the sample library added with new sample data.
According to another aspect of the embodiments of the present application, there is also provided a control apparatus for cooking food, including: the device comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring food image data inside an appliance of the cooker, and the food image data comprises at least one of the following data: the food material types and the food material quantity of the food materials to be processed; the processing module is used for inputting the food image data into the food prediction model to obtain cooking data; and the control module controls the cooker to work based on the cooking data.
According to still another aspect of embodiments of the present application, there is also provided a cooker including: the device comprises a collecting device for collecting food image data inside the cooking device, wherein the food image data comprises at least one of the following data: the food material types and the food material quantity of the food materials to be processed; the processor is connected with the acquisition device and used for inputting the food image data into the food prediction model to obtain cooking data; and the controller is connected with the processor and is used for controlling the operation of the cooker based on the cooking data.
According to still another aspect of the embodiments of the present application, there is also provided a storage medium including a stored program, wherein the program controls an apparatus in which the storage medium is located to perform the above control method for cooking food when the program is executed.
According to still another aspect of the embodiments of the present application, there is also provided a processor for executing a program, wherein the program executes the above control method for cooking food.
In an embodiment of the present application, acquiring food image data of an inside of an appliance of a cooker is employed, wherein the food image data includes at least one of: the food material types and the food material quantity of the food materials to be processed; inputting food image data into a food prediction model to obtain cooking data; the cooking device comprises a neural network algorithm, a food prediction model is obtained by training the collected food material data of the existing food materials through the neural network algorithm, the food materials to be processed are identified through the trained food prediction model in the cooking process to obtain cooking parameters aiming at the food materials to be processed, a cooking appliance is set through the obtained cooking parameters, the food materials to be processed are processed, the purpose of pointedly selecting the cooking parameters aiming at the characteristics of different food materials when the food materials are cooked through the cooking appliance is achieved, the good cooking effect is achieved, the user experience is improved, and the technical problem that the cooking effect is not ideal due to the fact that the cooking parameters set for different food materials by a user when the user cooks the food materials are single is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a flow chart of a control method of cooking food according to an embodiment of the present application;
FIG. 2 is a flow chart of a method of constructing a food prediction model according to an embodiment of the present application;
FIG. 3 is a block diagram of a control device for cooking food in accordance with an embodiment of the present application;
fig. 4 is a structural view of a cooker according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, 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 partial embodiments of the present application, but not all 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.
It should be noted that the terms "first," "second," and the like in the description and claims of this application 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 application 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, method, system, article, or apparatus 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, method, article, or apparatus.
In accordance with an embodiment of the present application, there is provided a method embodiment of a control method for cooking food, it is noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system, such as a set of computer executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that presented herein.
Fig. 1 is a flowchart of a control method for cooking food according to an embodiment of the present application, as shown in fig. 1, the method including the steps of:
step S102, collecting food image data inside an appliance of a cooker, wherein the food image data comprises at least one of the following data: the type and the number of the food materials to be processed.
According to an optional embodiment of the application, an image acquisition device is arranged inside the electric rice cooker, and the image acquisition device can be a high-definition camera and is used for acquiring the food material types and the food material quantity of food materials to be cooked.
And step S104, inputting the food image data into a food prediction model to obtain cooking data.
In some optional embodiments of the present application, before proceeding to step S104, the food prediction model needs to be constructed, and fig. 2 is a flowchart of a method for constructing a food prediction model according to an embodiment of the present application, as shown in fig. 2, the method includes the following steps:
step S202, the stored food material data is obtained, wherein the food material data comprises at least one of the following items: characteristics of different kinds of food materials, cooking parameters of a cooker, and characteristics of different food materials after cooking.
In step S202, the existing food material data is obtained, wherein the food material data includes at least one of the following: the characteristics of food materials, such as the cooking time of rice, can affect the degree of glutinous rice; the cooking parameters of the cooker refer to parameters set for achieving an ideal cooking effect of different food materials, such as set cooking time, set heating gear and other related parameters; the characteristic of different food materials after cooking is the cooking result of the same food under the condition of setting different cooking parameters.
Step S204, sample data is obtained based on the food material data.
In some optional embodiments of the present application, step S204 is implemented by: filtering the food material data based on the filtering conditions to obtain a filtering result; traversing the filtering result, and deleting the filtering result according to the removing condition to obtain a deleting result; and carrying out normalization processing based on the deletion result to obtain sample data.
According to an optional embodiment of the application, the sample data is preprocessed, and the food material data is further refined, for example, for the same food material, the food material of the same category with high quality and low quality is selected to respectively obtain the data of the food material, and then the average data of the food material of the category is obtained. In addition, the obtained food material data are traversed, the data with large food material data deviation are deleted, and then the remaining food material data are subjected to normalization processing to obtain the final food material data. Through the steps, the sample data for training the food prediction model can be more accurate, so that the data prediction result of the trained prediction model is more accurate.
And step S206, based on the sample data, performing feature extraction on the input parameters by using a deep learning algorithm.
Step S208, constructing a food prediction model according to the extraction result, wherein the input parameter is at least one of the following parameters included in the sample data: food type, food characteristics, food quantity, and cooking result of the food.
In some optional embodiments of the present application, step S206 and step S208 are implemented by: based on sample data, performing feature extraction on input parameters input into the prediction model by using a deep learning algorithm to obtain an extraction result; and processing the extraction result based on a machine learning algorithm to generate a food prediction model.
According to an optional embodiment of the application, based on the sample data obtained after the preprocessing, the food material type, the food material characteristics, the food material quantity and the rotten degree of the food material are input parameters of a food material prediction model, and the processing parameters of the cooking appliance on the food material are output parameters of the food material prediction model. And performing feature extraction on the input parameters by using a deep learning algorithm to reduce the number of the input parameters. And then training the extracted result by using a supervised learning algorithm to generate a food prediction model with the functions of self-adaptive learning and dynamic updating.
The supervised learning algorithm is one of machine learning algorithms, which consists of one target variable or result variable. These variables are predicted from a series of predictive variables, which are used to generate a function that maps input values to expected output values, and this training process continues until the model achieves the desired accuracy in the training data. The supervised learning algorithm comprises a regression algorithm, a decision tree algorithm, a random forest, a K-approach algorithm and the like.
In some optional embodiments of the present application, step S104 is implemented by: identifying food image data, and acquiring food material parameters of food materials to be processed, wherein the food material parameters comprise at least one of the following parameters: food material type, food material quantity and target cooking result; matching food material parameters of food materials to be processed with sample data corresponding to the food prediction model; and if the matching is successful, acquiring the cooking data for processing the food material to be processed.
According to an optional embodiment of the application, the food material to be processed is predicted through the trained food material prediction model, and the principle of obtaining the cooking data is to match the food material to be processed with sample data used for training the food material prediction model, and if the matching is successful, the cooking data corresponding to the food material in the sample data is used as the cooking data of the food material to be processed.
And step S106, controlling the cooker to work based on the cooking data.
And according to the obtained cooking data, the cooking parameters are manually set or automatically set by a cooking appliance so as to finish the cooking treatment of the food material to be treated.
In some optional embodiments of the present application, after step S106 is completed, the method further includes: acquiring food material change data generated in the process of cooking food by a cooker; adding the food material change data serving as newly-added sample data to a sample library; and performing secondary training on the food prediction model based on the sample library added with new sample data. In the cooking process, the generated food material change data is added to a food material data sample base, secondary prediction is carried out on the food prediction model based on the updated sample base, and through the step, sample data in the sample database is updated in real time, so that the accuracy of the prediction model prediction data is continuously kept at a higher accuracy.
Through the steps, the collected food material data of the existing food materials are trained through the neural network algorithm to obtain a food prediction model, the trained food prediction model is used for identifying the food materials to be processed in the cooking process to obtain cooking parameters aiming at the food materials to be processed, and the obtained cooking parameters are used for setting a cooking appliance to process the food materials to be processed. When the user cooks food by using the cooking appliance through the steps, the cooker automatically sets corresponding cooking parameters according to the identification of the types and the characteristics of food materials, or prompts the user to set corresponding cooking parameters according to the identification result, so that the user can automatically select corresponding cooking parameters aiming at the types and the characteristics of different food materials in the process of cooking food, a better cooking effect is achieved, and the use experience of the user is improved.
Fig. 3 is a block diagram of a control apparatus for cooking food according to an embodiment of the present application, as shown in fig. 3, the apparatus including:
an acquisition module 30 for acquiring food image data of an appliance interior of the cooker, wherein the food image data includes at least one of: the type and the number of the food materials to be processed.
According to an alternative embodiment of the present application, the collection module 30 may be a high definition camera disposed inside the cooking appliance.
And the processing module 32 is used for inputting the food image data into the food prediction model to obtain cooking data.
And a control module 34 for controlling the operation of the cooking device based on the cooking data.
It should be noted that, reference may be made to the description of the embodiment shown in fig. 1 to 2 for a preferred implementation of the embodiment shown in fig. 3, and details are not repeated here.
Fig. 4 is a structural view of a cooker according to an embodiment of the present application, as shown in fig. 4, the cooker including:
a collecting device 40 for collecting food image data of the inside of the cooking device, wherein the food image data includes at least one of: the type and the number of the food materials to be processed.
And the processor 42 is connected with the acquisition device and is used for inputting the food image data into the food prediction model to obtain cooking data.
And a controller 44 connected with the processor for controlling the operation of the cooker based on the cooking data.
In some optional embodiments of the present application, the cooking device may be a cooking utensil such as an electric rice cooker, a microwave oven, an electric oven, a pressure cooker, or an electric rice cooker.
It should be noted that, reference may be made to the description of the embodiment shown in fig. 1 to 2 for a preferred implementation of the embodiment shown in fig. 3, and details are not repeated here.
The embodiment of the application also provides a storage medium which comprises a stored program, wherein when the program runs, the device on which the storage medium is positioned is controlled to execute the control method for cooking food.
The storage medium stores a program for executing the following functions: acquiring food image data of an appliance interior of a cooker, wherein the food image data comprises at least one of: the food material types and the food material quantity of the food materials to be processed; inputting food image data into a food prediction model to obtain cooking data; and controlling the operation of the cooker based on the cooking data.
The embodiment of the application also provides a processor, which is used for running the program, wherein the control method for cooking food is executed when the program runs.
The processor is configured to execute a program that implements the following functions: acquiring food image data of an appliance interior of a cooker, wherein the food image data comprises at least one of: the food material types and the food material quantity of the food materials to be processed; inputting food image data into a food prediction model to obtain cooking data; and controlling the operation of the cooker based on the cooking data.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. 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, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. 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 application 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 computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (10)
1. A method of controlling cooking of food, comprising:
acquiring food image data of an appliance interior of a cooker, wherein the food image data comprises at least one of: the food material types and the food material quantity of the food materials to be processed;
inputting the food image data into a food prediction model to obtain cooking data;
and controlling the cooker to work based on the cooking data.
2. The method of claim 1, wherein prior to inputting the food image data into a food prediction model to obtain cooking data, the method further comprises:
obtaining stored food material data, wherein the food material data comprises at least one of the following: characteristics of different food materials, cooking parameters of the cooker, characteristics of different food materials after cooking;
obtaining sample data based on the food material data;
based on the sample data, performing feature extraction on input parameters by using a deep learning algorithm;
constructing the food prediction model according to the extraction result, wherein the input parameter is at least one of the following parameters included in the sample data: food type, food characteristics, food quantity, and cooking result of the food.
3. The method of claim 2, wherein obtaining sample data based on the food material data comprises:
filtering the food material data based on the filtering condition to obtain a filtering result;
traversing the filtering result, and deleting the filtering result according to a removing condition to obtain a deleting result;
and carrying out normalization processing based on the deletion result to obtain the sample data.
4. The method of claim 2, wherein feature extracting input parameters using a deep learning algorithm based on the sample data and constructing the food prediction model according to the extraction result comprises:
based on the sample data, performing feature extraction on the input parameters input into the prediction model by using a deep learning algorithm to obtain an extraction result;
and processing the extraction result based on a machine learning algorithm to generate the food prediction model.
5. The method of claim 2, wherein inputting the food image data into a food prediction model, resulting in cooking data, comprises:
identifying the food image data, and acquiring food material parameters of the food material to be processed, wherein the food material parameters comprise at least one of the following parameters: food material type, food material quantity and target cooking result;
matching the food material parameters of the food material to be processed with sample data corresponding to the food prediction model;
and if the matching is successful, acquiring the cooking data for processing the food material to be processed.
6. The method of claim 1, wherein after controlling the operation of the cooker based on the cooking data, the method further comprises:
acquiring food material change data generated in the food cooking process of the cooker;
adding the food material change data serving as newly-added sample data to a sample library;
and performing secondary training on the food prediction model based on the sample library added with new sample data.
7. A control device for cooking food, comprising:
an acquisition module for acquiring food image data of an appliance interior of a cooker, wherein the food image data includes at least one of: the food material types and the food material quantity of the food materials to be processed;
the processing module is used for inputting the food image data into a food prediction model to obtain cooking data;
and the control module controls the cooker to work based on the cooking data.
8. A cooker, comprising:
an acquisition device for acquiring food image data of an appliance interior of a cooker, wherein the food image data includes at least one of: the food material types and the food material quantity of the food materials to be processed;
the processor is connected with the acquisition device and used for inputting the food image data into a food prediction model to obtain cooking data;
and the controller is connected with the processor and used for controlling the cooker to work based on the cooking data.
9. A storage medium characterized by comprising a stored program, wherein the program executes the control method of cooking food according to any one of claims 1 to 6.
10. A processor, characterized in that it is configured to run a program, wherein the program when running executes a control method of cooking food according to any one of claims 1 to 6.
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| CN201811115968.3A CN110941203A (en) | 2018-09-25 | 2018-09-25 | Control method and device for cooking food and cooker |
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| CN201811115968.3A CN110941203A (en) | 2018-09-25 | 2018-09-25 | Control method and device for cooking food and cooker |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111493672A (en) * | 2020-04-26 | 2020-08-07 | 上海明略人工智能(集团)有限公司 | Food cooking method and device and cooking appliance |
| CN111671315A (en) * | 2020-05-08 | 2020-09-18 | 华帝股份有限公司 | Method for detecting humidity control abnormity of cooking equipment |
| CN112528941A (en) * | 2020-12-23 | 2021-03-19 | 泰州市朗嘉馨网络科技有限公司 | Automatic parameter setting system based on neural network |
| CN114202758A (en) * | 2021-01-26 | 2022-03-18 | 杭州食方科技有限公司 | Food information generation method and device, electronic equipment and medium |
| CN114903343A (en) * | 2021-02-09 | 2022-08-16 | 海信集团控股股份有限公司 | Automatic steaming and baking method and device and steaming and baking box thereof |
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| CN112528941A (en) * | 2020-12-23 | 2021-03-19 | 泰州市朗嘉馨网络科技有限公司 | Automatic parameter setting system based on neural network |
| CN112528941B (en) * | 2020-12-23 | 2021-11-19 | 芜湖神图驭器智能科技有限公司 | Automatic parameter setting system based on neural network |
| CN114202758A (en) * | 2021-01-26 | 2022-03-18 | 杭州食方科技有限公司 | Food information generation method and device, electronic equipment and medium |
| CN114903343A (en) * | 2021-02-09 | 2022-08-16 | 海信集团控股股份有限公司 | Automatic steaming and baking method and device and steaming and baking box thereof |
| CN114903343B (en) * | 2021-02-09 | 2024-05-14 | 海信集团控股股份有限公司 | Automatic steaming and baking method and device and steaming and baking oven thereof |
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