CN112401630A - Auxiliary cooking method and device - Google Patents
Auxiliary cooking method and device Download PDFInfo
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- CN112401630A CN112401630A CN202011145691.6A CN202011145691A CN112401630A CN 112401630 A CN112401630 A CN 112401630A CN 202011145691 A CN202011145691 A CN 202011145691A CN 112401630 A CN112401630 A CN 112401630A
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- 238000010411 cooking Methods 0.000 title claims abstract description 92
- 238000000034 method Methods 0.000 title claims abstract description 26
- 230000006399 behavior Effects 0.000 claims abstract description 65
- 238000010801 machine learning Methods 0.000 claims abstract description 41
- 238000007781 pre-processing Methods 0.000 claims abstract description 17
- 238000001914 filtration Methods 0.000 claims description 10
- 238000012163 sequencing technique Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 5
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- 229910052782 aluminium Inorganic materials 0.000 description 1
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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
- A47J27/002—Construction of cooking-vessels; Methods or processes of manufacturing specially adapted for 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
- A47J27/00—Cooking-vessels
- A47J27/04—Cooking-vessels for cooking food in steam; Devices for extracting fruit juice by means of steam ; Vacuum 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
- 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
- A47J36/00—Parts, details or accessories of cooking-vessels
- A47J36/32—Time-controlled igniting mechanisms or alarm devices
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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
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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
- A47J37/0664—Accessories
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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
- A47J27/04—Cooking-vessels for cooking food in steam; Devices for extracting fruit juice by means of steam ; Vacuum cooking vessels
- A47J2027/043—Cooking-vessels for cooking food in steam; Devices for extracting fruit juice by means of steam ; Vacuum cooking vessels for cooking food in steam
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- Engineering & Computer Science (AREA)
- Food Science & Technology (AREA)
- Manufacturing & Machinery (AREA)
- Cookers (AREA)
Abstract
The invention discloses an auxiliary cooking method and device, wherein the method comprises the following steps: acquiring the temperature of the kitchen ware in real time to obtain an original temperature time sequence, preprocessing the original temperature time sequence to obtain a first temperature time sequence, calculating a current cooking behavior according to the first temperature time sequence by using a preset machine learning model, calculating a target temperature according to the current cooking behavior, and controlling the working temperature of the kitchen ware according to the first temperature time sequence and the target temperature by using a closed-loop control model; the cooking behavior of the user can be automatically judged by implementing the invention, and the working temperature of the kitchen ware is set according to the cooking behavior of the user, thereby helping the user to complete the corresponding cooking task.
Description
Technical Field
The invention relates to the technical field of cooking, in particular to a method and a device for assisting cooking.
Background
The demand of people for pursuing high-quality diet is increasing day by day, but more and more new-generation users have little knowledge of the traditional cooking skill, so the traditional cooking skill faces the problem of propagation fault in the modern generation, and users lacking cooking experience are difficult to use common kitchen ware to finish the cooking work of the traditional menu.
Disclosure of Invention
The invention aims to provide an auxiliary cooking method and an auxiliary cooking device which can automatically help a user to finish the traditional menu cooking work.
In order to solve the above-mentioned problems, the present invention adopts a technical solution of:
an auxiliary cooking method comprising the steps of:
acquiring the temperature of the kitchen ware in real time to obtain an original temperature time sequence;
preprocessing an original temperature time sequence to obtain a first temperature time sequence;
calculating the current cooking behavior according to the first temperature time sequence by using a preset machine learning model;
calculating a target temperature according to the current cooking behavior;
the operating temperature of the cookware is controlled according to the first temperature time sequence and the target temperature using a closed-loop control model.
Preferably, the preprocessing the original temperature time series to obtain the first temperature time series includes: and carrying out digital filtering on the original temperature time sequence according to a preset filtering algorithm.
Preferably, the preset filtering algorithm is a digital kalman filtering algorithm.
Preferably, the preprocessing the original temperature time series to obtain the first temperature time series further includes: and integrating the original temperature time sequence according to a preset time window.
Preferably, the calculating, by using a preset machine learning model, the current cooking behavior of the user according to the first temperature time series includes: and sequencing the first temperature time sequence by a preset sequencing algorithm, and inputting a sequencing result into a preset machine learning model to calculate and obtain the current cooking behavior.
Preferably, the method further comprises: detecting the heat transfer introduction coefficient of the cooker, identifying the type of the current cooker, and selecting a corresponding machine learning model as a preset machine learning model according to the type of the current cooker.
Preferably, the preset machine learning model is Adaboost.
Preferably, the method further includes detecting a user operation, determining whether the user operation causes a temperature to deviate from a target temperature, if so, calculating a new current cooking behavior according to the first temperature time sequence, and adding the new current cooking behavior and the first temperature time sequence to a training sample set of a preset machine learning model.
The invention adopts another technical scheme that: an auxiliary cooking device comprising: the temperature acquisition module is used for acquiring the temperature of the kitchen ware in real time to obtain an original temperature time sequence;
the system comprises a preprocessing module, a first temperature time sequence and a second temperature time sequence, wherein the preprocessing module is used for preprocessing an original temperature time sequence to obtain the first temperature time sequence;
the behavior recognition module is used for calculating the current cooking behavior according to the first temperature time sequence by utilizing a preset machine learning model;
the temperature calculation module is used for calculating a target temperature according to the current cooking behavior;
and the closed-loop control module is used for controlling the working temperature of the kitchen ware according to the first temperature time sequence and the target temperature by using the closed-loop control model.
Preferably, the device further comprises a learning module, wherein the learning module is configured to determine whether a temperature deviates from a target temperature due to a user operation, and if so, calculate a new current cooking behavior according to the first temperature time sequence, and add the new current cooking behavior and the first temperature time sequence to the training sample set of the preset machine learning model.
Compared with the prior art, the invention has the beneficial effects that: on one hand, the method can acquire the temperature of the kitchen ware in real time to obtain an original temperature time sequence, preprocess the original temperature time sequence to obtain a first temperature time sequence, calculate the current cooking behavior according to the first temperature time sequence by using a preset machine learning model, calculate the target temperature according to the current cooking behavior, and control the working temperature of the kitchen ware according to the first temperature time sequence and the target temperature by using a closed-loop control model, so that a user is assisted in completing corresponding cooking tasks; on the other hand, the method can also judge whether the temperature deviates from the target temperature due to user operation, if so, a new current cooking behavior is calculated according to the first temperature time sequence, and the new current cooking behavior and the first temperature time sequence are added into the training sample set of the preset machine learning model, so that the machine learning model is adaptively adjusted according to the cooking behavior.
Drawings
Fig. 1 is a flowchart of an auxiliary cooking method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an auxiliary cooking device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The following detailed description of the implementation of the present invention is made with reference to specific embodiments:
fig. 1 is a flowchart of an auxiliary cooking method according to an embodiment of the present invention. As shown in fig. 1, the auxiliary cooking method includes the steps of:
and S1, acquiring the temperature of the kitchen ware in real time to obtain an original temperature time sequence.
And S2, preprocessing the original temperature time sequence to obtain a first temperature time sequence.
And S3, calculating the current cooking behavior according to the first temperature time sequence by using a preset machine learning model.
And S4, calculating the target temperature according to the current cooking behavior.
And S5, controlling the working temperature of the kitchen ware according to the first temperature time sequence and the target temperature by using the closed-loop control model.
Specifically, the kitchen ware can be a steam box, an oven, a steaming and baking all-in-one machine or a kitchen range. The cookware may comprise a variety of different cookware, such as marmite, aluminum pan, iron pan, or composite pan, among others. The temperature of the kitchen ware can be collected in real time according to a certain sampling window and sampling frequency, for example, the temperature can be collected by taking 1 second as a sampling period and taking 10 seconds as a time window, and an original temperature time sequence is obtained. The raw temperature time series may comprise 1 or more time windows, each time window comprising temperature data sampled at several time points.
In some embodiments, step S2 may include digitally filtering the raw temperature-time series according to a preset filtering algorithm, so as to filter invalid data in the raw temperature-time series, thereby improving the anti-interference capability and stability of the system. Preferably, the preset filtering algorithm is a digital kalman filtering algorithm.
In some embodiments, step S2 may further include: and integrating the original temperature time sequence according to a preset time window so as to obtain a smoother first temperature time sequence.
In some embodiments, step S3 includes: and sequencing the first temperature time sequence by a preset sequencing algorithm, and inputting a sequencing result into a preset machine learning model to calculate and obtain the current cooking behavior. The cooking behaviors may include, for example, a water boiling behavior, a hot pot behavior, a hot oil behavior, a stir cooking behavior, a frying behavior, a stewing behavior, a soup cooking behavior, a water boiling, a water drying, a small and medium fire juicing behavior, a small and medium fire stewing behavior, and a low and medium high temperature frying behavior. It should be noted that the correspondence between the cooking behaviors and the temperature data may be set according to a recipe, and each cooking behavior may correspond to a temperature range or a temperature data sequence.
In some embodiments, the predetermined machine learning model may be, for example, a BP neural network model. For example, a plurality of temperature data in a single time window in the first temperature time series may be used as input of the BP neural network model, probabilities of various cooking behaviors are used as output, historical temperature data and artificially labeled cooking behaviors are used as training samples, and the BP neural network model is trained by using the training samples, so as to obtain the preset machine learning model. Because the kitchen ware can use different cookers, the heat transfer introduction coefficients of the different cookers are different, and the temperature control is different, different training samples can be collected aiming at the different cookers so as to train different machine learning models.
In some embodiments, the method further comprises: detecting the heat transfer introduction coefficient of the cooker, identifying the type of the current cooker, and selecting a corresponding machine learning model as a preset machine learning model according to the type of the current cooker. Therefore, the automatic temperature control device can automatically adapt to different cookers, and the temperature control is more accurate.
In some embodiments, the method further includes detecting a user operation, determining whether the user operation causes a temperature to deviate from a target temperature, if so, calculating a new current cooking behavior according to the first temperature time series, and adding the new current cooking behavior and the first temperature time series to a training sample set of a preset machine learning model. Specifically, detecting the user operation and determining whether the user operation causes the temperature to deviate from the target temperature may include comparing the temperature data in the first temperature time series with the target temperature, and when the difference between the two is greater than a preset threshold, determining that the user operation causes the temperature to deviate from the target temperature, which indicates that the user performs a new cooking action, which causes a large change in the temperature of the kitchen ware, and the cooking action determined in step S3 has failed, and the current cooking action of the user must be determined again. The machine learning model may be, for example, AdaBoost, which is an adaptive reinforcement learning model. And adding the new current cooking behavior and the first temperature time sequence into the training sample set of the preset machine learning model, so that the Adaboost model can automatically learn the new training sample, thereby realizing the self-adaptive offline AI function and automatically learning the new cooking behavior of the user.
In some embodiments, the closed loop control model may be a PID control model. And carrying out accurate temperature control by utilizing a PID control model according to the first temperature time sequence and the target temperature which are acquired in real time. The control signal can be output through the PID control model to drive the executing component and control the power of the heating element or the power output element of the kitchen ware, so that the temperature of the kitchen ware is controlled.
In some embodiments, the above method can be implemented by the MicroPython programming, which has a rich algorithm library for implementing the various ranking algorithms and machine learning models described above. The MicroPython can be transplanted to an embedded SOC chip end supporting the MIT license protocol, such as single-chip microcomputers of STM32F4 series, ESP8266 series, RTL8710 series and the like.
In this embodiment, the temperature of the kitchen ware can be collected in real time to obtain an original temperature time sequence, the original temperature time sequence is preprocessed to obtain a first temperature time sequence, a preset machine learning model is used for calculating a current cooking behavior according to the first temperature time sequence, a target temperature is calculated according to the current cooking behavior, and a closed-loop control model is used for controlling the working temperature of the kitchen ware according to the first temperature time sequence and the target temperature, so that a user is assisted in completing corresponding cooking tasks. On the other hand, the method can also judge whether the temperature deviates from the target temperature due to user operation, if so, a new current cooking behavior is calculated according to the first temperature time sequence, and the new current cooking behavior and the first temperature time sequence are added into the training sample set of the preset machine learning model, so that the machine learning model is adaptively adjusted according to the cooking behavior.
Fig. 2 is a schematic structural diagram of an auxiliary cooking device according to an embodiment of the present invention. As shown in fig. 2, the auxiliary cooking apparatus includes: the temperature acquisition module is used for acquiring the temperature of the kitchen ware in real time to obtain an original temperature time sequence; the system comprises a preprocessing module, a first temperature time sequence and a second temperature time sequence, wherein the preprocessing module is used for preprocessing an original temperature time sequence to obtain the first temperature time sequence; the behavior recognition module is used for calculating the current cooking behavior according to the first temperature time sequence by utilizing a preset machine learning model; the temperature calculation module is used for calculating a target temperature according to the current cooking behavior; and the closed-loop control module is used for controlling the working temperature of the kitchen ware according to the first temperature time sequence and the target temperature by using the closed-loop control model. The closed-loop control module can output control signals to drive an executing component of the kitchen ware and control the power of a heating element or a power output element of the kitchen ware, so that the closed-loop control of the temperature of the kitchen ware is realized.
In this embodiment, supplementary cooking device can gather the temperature of kitchen utensils and appliances in real time and obtain the original temperature time series, carries out the preliminary treatment to original temperature time series in order to obtain first temperature time series, utilizes and predetermines machine learning model according to the current culinary art action of first temperature time series calculation, according to the current culinary art action calculation target temperature, uses closed loop control model according to the work temperature of first temperature time series and target temperature control kitchen utensils and appliances to supplementary user accomplishes corresponding culinary art task.
In some embodiments, the auxiliary cooking device further includes a learning module, where the learning module is configured to determine whether a temperature deviates from a target temperature due to a user operation, and if so, calculate a new current cooking behavior according to the first temperature time series, and add the new current cooking behavior and the first temperature time series to a training sample set of the preset machine learning model. The machine learning model may be, for example, AdaBoost, which is an adaptive reinforcement learning model. And adding the new current cooking behavior and the first temperature time sequence into the training sample set of the preset machine learning model, so that the Adaboost model can automatically learn the new training sample, thereby realizing the self-adaptive offline AI function and automatically learning the new cooking behavior of the user.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. An auxiliary cooking method, characterized by comprising the steps of:
acquiring the temperature of the kitchen ware in real time to obtain an original temperature time sequence;
preprocessing an original temperature time sequence to obtain a first temperature time sequence;
calculating the current cooking behavior according to the first temperature time sequence by using a preset machine learning model;
calculating a target temperature according to the current cooking behavior;
the operating temperature of the cookware is controlled according to the first temperature time sequence and the target temperature using a closed-loop control model.
2. The auxiliary cooking method according to claim 1, wherein the pre-processing the raw temperature time series to obtain a first temperature time series comprises: and carrying out digital filtering on the original temperature time sequence according to a preset filtering algorithm.
3. The auxiliary cooking method according to claim 2, wherein the preset filter algorithm is a digital kalman filter algorithm.
4. The auxiliary cooking method according to claim 2, wherein the pre-processing the raw temperature time series to obtain a first temperature time series further comprises: and integrating the original temperature time sequence according to a preset time window.
5. The auxiliary cooking method according to claim 1, wherein the calculating the current cooking behavior of the user according to the first temperature time series by using a preset machine learning model comprises: and sequencing the first temperature time sequence by a preset sequencing algorithm, and inputting a sequencing result into a preset machine learning model to calculate and obtain the current cooking behavior.
6. The auxiliary cooking method of claim 1, further comprising: and detecting a heat transfer introduction coefficient of the kitchen ware, judging the type of the front kitchen ware according to the heat transfer introduction coefficient, and selecting a corresponding machine learning model as a preset machine learning model according to the type of the current kitchen ware.
7. The auxiliary cooking method according to claim 1, wherein the preset machine learning model is Adaboost.
8. The auxiliary cooking method according to any one of claims 1 to 7, further comprising: and detecting user operation, judging whether the temperature deviates from the target temperature or not due to the user operation, if so, calculating a new current cooking behavior according to the first temperature time sequence, and adding the new current cooking behavior and the first temperature time sequence into a training sample set of a preset machine learning model.
9. An auxiliary cooking device, comprising:
the temperature acquisition module is used for acquiring the temperature of the kitchen ware in real time to obtain an original temperature time sequence;
the system comprises a preprocessing module, a first temperature time sequence and a second temperature time sequence, wherein the preprocessing module is used for preprocessing an original temperature time sequence to obtain the first temperature time sequence;
the behavior recognition module is used for calculating the current cooking behavior according to the first temperature time sequence by utilizing a preset machine learning model;
the temperature calculation module is used for calculating a target temperature according to the current cooking behavior;
and the closed-loop control module is used for controlling the working temperature of the kitchen ware according to the first temperature time sequence and the target temperature by using the closed-loop control model.
10. The auxiliary cooking device of claim 9, further comprising a learning module, wherein the learning module is configured to determine whether a user operation causes a temperature deviation from a target temperature, and if so, calculate a new current cooking behavior according to the first temperature time series, and add the new current cooking behavior and the first temperature time series to the training sample set of the preset machine learning model.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113378637A (en) * | 2021-05-11 | 2021-09-10 | 宁波方太厨具有限公司 | Kitchen electrical equipment control method based on user cooking action prediction |
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| CN110348622A (en) * | 2019-07-02 | 2019-10-18 | 创新奇智(成都)科技有限公司 | A kind of Time Series Forecasting Methods based on machine learning, system and electronic equipment |
| CN110836393A (en) * | 2019-10-17 | 2020-02-25 | 佛山市云米电器科技有限公司 | Method for controlling cooking target temperature based on mode identification, control device and cooker |
| CN110748927A (en) * | 2019-10-25 | 2020-02-04 | 佛山市顺德区美的洗涤电器制造有限公司 | Detection method, cooking utensil, cooking system and computer readable storage medium |
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