CN113384159A - Control method of cooking equipment - Google Patents
Control method of cooking equipment Download PDFInfo
- Publication number
- CN113384159A CN113384159A CN202110675602.7A CN202110675602A CN113384159A CN 113384159 A CN113384159 A CN 113384159A CN 202110675602 A CN202110675602 A CN 202110675602A CN 113384159 A CN113384159 A CN 113384159A
- Authority
- CN
- China
- Prior art keywords
- cooking
- temperature
- real
- value
- cloud
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000010411 cooking Methods 0.000 title claims abstract description 165
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000010438 heat treatment Methods 0.000 claims abstract description 65
- 238000013528 artificial neural network Methods 0.000 claims abstract description 14
- 210000002569 neuron Anatomy 0.000 claims description 69
- 238000004891 communication Methods 0.000 claims description 3
- 238000010025 steaming Methods 0.000 abstract description 6
- 235000013305 food Nutrition 0.000 description 8
- 235000014510 cooky Nutrition 0.000 description 5
- 238000001514 detection method Methods 0.000 description 5
- 230000001360 synchronised effect Effects 0.000 description 3
- 235000015895 biscuits Nutrition 0.000 description 2
- 238000011217 control strategy Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- FFRBMBIXVSCUFS-UHFFFAOYSA-N 2,4-dinitro-1-naphthol Chemical compound C1=CC=C2C(O)=C([N+]([O-])=O)C=C([N+]([O-])=O)C2=C1 FFRBMBIXVSCUFS-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Images
Classifications
-
- 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
- A47J36/321—Time-controlled igniting mechanisms or alarm devices the electronic control being performed over a network, e.g. by means of a handheld device
Abstract
The invention provides a control method of cooking equipment, which comprises the following steps: step S101, acquiring a menu instruction of cooking equipment, and uploading the menu instruction to a cloud; step S102, the cloud outputs cooking steps and heating temperatures corresponding to the cooking steps according to menu instructions; step S103, acquiring the real-time inner cavity temperature of the cooking equipment, and uploading the real-time inner cavity temperature to a cloud end; step S104, the cloud inputs the real-time inner cavity temperature into the artificial neural network model to obtain the real-time heating temperature of the cooking equipment; and S105, judging the running state of the cooking equipment and controlling and adjusting the heating temperature of the inner cavity of the cooking equipment according to the real-time heating temperature of the cooking equipment and the heating temperature corresponding to each cooking step. An artificial neural network model is established at the cloud end, the electric control cost of cooking equipment, particularly the material cost of an MCU (microprogrammed control unit) is reduced, and the complex cooking function of the steaming oven is realized by using the MCU (which has less internal resources and lower specification) with low cost.
Description
Technical Field
The invention relates to the technical field of cooking equipment, in particular to a control method of the cooking equipment.
Background
The steamer is becoming the indispensable electrical apparatus in modern kitchen, on the one hand both must satisfy the abundant polybasic culinary art demand of user, and it needs to integrate multiple intelligent menu and each various intelligent algorithm that the menu contains, and on the other hand needs control product cost, often utilizes the less low specification MCU of internal resource to realize the function of steamer, causes and just satisfies the culinary art demand but loses the possibility of follow-up function extension (for example menu extension, algorithm optimization). Based on the pain points, aiming at the steam oven with the Internet of things WIFI function, the cooking algorithm is transferred to the cloud end by utilizing the Internet of things technology, so that the steam oven is only used as a carrier for detecting the temperature of an inner cavity and performing heating regulation and control, the calculation load of an MCU (microprogrammed control unit) of the steam oven is greatly reduced, and on the basis, the artificial neural network technology is added to the cloud end to meet the requirements for expanding a cooking menu and iterating a temperature control algorithm.
Disclosure of Invention
The present invention has been made to solve one of the problems occurring in the related art to some extent, and therefore an object of the present invention is to provide a control method of a cooking apparatus, which reduces costs.
The above purpose is realized by the following technical scheme:
a control method of a cooking apparatus, comprising the steps of:
step S101, acquiring a menu instruction of cooking equipment, and uploading the menu instruction to a cloud;
step S102, the cloud outputs cooking steps and heating temperatures corresponding to the cooking steps according to menu instructions;
step S103, acquiring the real-time inner cavity temperature of the cooking equipment, and uploading the real-time inner cavity temperature to a cloud end;
step S104, the cloud inputs the real-time inner cavity temperature into the artificial neural network model to obtain the real-time heating temperature of the cooking equipment;
and S105, judging the running state of the cooking equipment and controlling and adjusting the heating temperature of the inner cavity of the cooking equipment according to the real-time heating temperature of the cooking equipment and the heating temperature corresponding to each cooking step.
As a further improvement of the present invention, in step S104, the method for inputting the real-time cavity temperature to the artificial neural network model by the cloud end to obtain the real-time heating temperature of the cooking device includes:
step S401, taking the real-time inner cavity temperature as an input layer neuron to obtain a hidden layer neuron;
step S402, obtaining an output layer neuron through a hidden layer neuron;
step S403, acquiring a real-time heating temperature of a cooking device of the cooking device through an output layer neuron.
As a further improvement of the present invention, in step S401, the real-time inner cavity temperature includes a first temperature value and a second temperature value, the first temperature value is a temperature at the top of the inner cavity of the cooking device, and the second temperature value is a temperature at the bottom of the inner cavity of the cooking device.
As a further improvement of the present invention, in step S401, the method for acquiring hidden layer neurons by using real-time lumen temperature as input layer neurons comprises:
acquiring a first hidden layer neuron according to a formula, wherein the first temperature value of the cooking device, the second temperature value of the cooking device, the first weight value, the second weight value, the first bias value and the first hidden layer neuron are respectively a first temperature value, a second temperature value, a first bias value and a second bias value;
acquiring a second hidden layer neuron according to a formula, wherein the first temperature value of the cooking device, the second temperature value of the cooking device, the third weight value, the fourth weight value, the second bias value and 2 are the second hidden layer neuron;
acquiring a third hidden layer neuron according to a formula, wherein the third hidden layer neuron is a first temperature value of the cooking device, a second temperature value of the cooking device, a fifth weight value, a sixth weight value, a third bias value and 3;
and acquiring a fourth hidden layer neuron according to a formula, wherein the fourth hidden layer neuron is a first temperature value of the cooking device, a second temperature value of the cooking device, a seventh weight value, an eighth weight value, a fourth bias value and 4.
As a further improvement of the present invention, in step S402, the method for acquiring output layer neurons by hidden layer neurons is:
a1, a2, a3 and a4 are input to formula obtain output layer neurons z1, z2, z3 and z4, respectively.
As a further improvement of the present invention, in step S403, the method for acquiring the real-time heating temperature of the cooking device through the neurons of the output layer is:
the real-time heating temperature of the cooking device is obtained, wherein the real-time heating temperature of the cooking device is a bias value, a ninth weight value, a tenth weight value, an eleventh weight value, a twelfth weight value, a first output layer neuron, a second output layer neuron, a third output layer neuron and a fourth output layer neuron.
As a further improvement of the present invention, before step S101, the following steps are further included:
and (4) enabling the cooking steps of different menu instructions and the heating temperature corresponding to each cooking step to reach the cloud.
As a further improvement of the present invention, in step S102, the cooking step output by the cloud according to the menu instruction includes: the number of steps corresponding to the menu instruction and the heating temperature corresponding to each cooking step.
As a further improvement of the present invention, in step S101 and step S103, the method for uploading the menu instruction to the cloud and uploading the real-time cavity temperature to the cloud includes:
and wireless communication connection is established between the cooking equipment and the cloud.
Compared with the prior art, the invention at least comprises the following beneficial effects:
1. the invention provides a control method of cooking equipment, which is characterized in that an artificial neural network model is established at the cloud end, the electric control cost of the cooking equipment, particularly the material cost of an MCU (microprogrammed control unit) is reduced, and the complex cooking function of a steaming oven is realized by using the MCU (which has less internal resources and lower specification) with low cost.
Drawings
Fig. 1 is a flowchart of a control method of a cooking apparatus in an embodiment.
Detailed Description
The present invention is illustrated by the following examples, but the present invention is not limited to these examples. Modifications to the embodiments of the invention or equivalent substitutions of parts of technical features without departing from the spirit of the invention are intended to be covered by the scope of the claims of the invention.
A control method of a cooking apparatus, comprising the steps of:
step S101, acquiring a menu instruction of cooking equipment, and uploading the menu instruction to a cloud;
step S102, the cloud outputs cooking steps and heating temperatures corresponding to the cooking steps according to menu instructions;
step S103, acquiring the real-time inner cavity temperature of the cooking equipment, and uploading the real-time inner cavity temperature to a cloud end;
step S104, the cloud inputs the real-time inner cavity temperature into the artificial neural network model to obtain the real-time heating temperature of the cooking equipment;
and S105, judging the running state of the cooking equipment and controlling and adjusting the heating temperature of the inner cavity of the cooking equipment according to the real-time heating temperature of the cooking equipment and the heating temperature corresponding to each cooking step.
The invention provides a control method of cooking equipment, wherein the cooking equipment is used as a carrier for detection and execution and is responsible for acquiring a menu instruction and an inner cavity temperature value and uploading the menu instruction and the inner cavity temperature value to a cloud end, and the cloud end is used as a processing unit and is responsible for calculating a heating regulation and control strategy according to the menu instruction and the inner cavity temperature value and issuing the heating regulation and control strategy to a heating regulation and control unit corresponding to the cooking equipment.
An artificial neural network model is established at the cloud end, the electric control cost of cooking equipment, particularly the material cost of an MCU (microprogrammed control unit) is reduced, and the complex cooking function of the steaming oven is realized by using the MCU (which has less internal resources and lower specification) with low cost.
In step S101, acquiring a menu instruction of a cooking apparatus includes acquiring a food material category in the cooking apparatus.
In step S102, the cloud outputs the cooking steps and the heating temperature corresponding to each step according to the food material category. For example, the food material in the cooking device is a biscuit, the cooking device uploads a menu instruction to the cloud, and the cloud acquires the cooking temperatures corresponding to five steps according to the food material, such as the biscuit, where the five steps are respectively a first step, a fourth step, a third step, a fourth step and a fifth step, the heating temperature of the first step is 50 ℃, the heating temperature of the second step is 90 ℃, the heating temperature of the third step is 120 ℃, the heating temperature of the fourth step is 150 ℃, and the heating temperature of the fifth step is 180 ℃.
In step S103, a real-time inner cavity temperature of the cooking apparatus is obtained, a plurality of temperature detection devices are arranged in the inner cavity of the cooking apparatus, the inner cavity temperature of the cooking apparatus is detected by the plurality of temperature detection devices, and an inner cavity temperature value detected by each temperature detection device is uploaded to the cloud.
In step S104, the temperature values of the inner cavity detected by the plurality of temperature detection devices are input to the artificial neural network model to obtain the real-time heating temperature of the cooking apparatus.
In step S105, by comparing the real-time heating temperature of the cooking device with the heating temperature corresponding to each cooking step, which cooking step the cooking device runs to can be obtained, and temperature regulation and control are performed on the cooking device according to the cooking step where the cooking device is located. For example, if the real-time heating temperature of the cooking device is obtained to be 80 ℃, that is, the cooking device is between the first step and the second step, the cooking device is controlled to be heated up, so that the cooking device reaches the second step; if the real-time heating temperature of the cooking device is 180 ℃, that is, the cooking device has reached the fifth step, the cooking device may be controlled to finish cooking.
In step S104, the cloud inputs the real-time inner cavity temperature into the artificial neural network model, and the method for obtaining the real-time heating temperature of the cooking device includes:
step S401, taking the real-time inner cavity temperature as an input layer neuron to obtain a hidden layer neuron;
step S402, obtaining an output layer neuron through a hidden layer neuron;
step S403, acquiring a real-time heating temperature of a cooking device of the cooking device through an output layer neuron.
In this embodiment, real-time inner chamber temperature includes first temperature value and second temperature value, first temperature value is the temperature at cooking equipment inner chamber top, the second temperature value is the temperature of cooking equipment inner chamber bottom.
In step S401, the method for acquiring hidden layer neurons by using real-time lumen temperature as input layer neurons includes:
obtaining a first hidden layer neuron according to a formula x1 × ω 11+ x2 × ω 21+ b1 ═ a1, wherein x1 is a first temperature value, x2 is a second temperature value, ω 11 is a first weight value, ω 21 is a second weight value, b1 is a first bias value, and a is a first hidden layer neuron;
obtaining a second hidden layer neuron according to the formula x1 × ω 12+ x2 × ω 22+ b2 ═ a2, where x1 is a first temperature value of the cooking appliance, x2 is a second temperature value of the cooking appliance, ω 12 is a third weight value, ω 22 is a fourth weight value, b1 is a second bias value, and a2 is the second hidden layer neuron;
obtaining a third hidden layer neuron according to a formula x1 × ω 13+ x2 × ω 23+ b3 ═ a3, where x1 is a first temperature value of the cooking appliance, x2 is a second temperature value of the cooking appliance, ω 13 is a fifth weight value, ω 23 is a sixth weight value, b1 is a third bias value, and a3 is the third hidden layer neuron;
a fourth hidden layer neuron is obtained according to the formula x1 × ω 14+ x2 × ω 24+ b4 ═ a4, where x1 is a first temperature value of the cooking appliance, x2 is a second temperature value of the cooking appliance, ω 14 is a seventh weight value, ω 24 is an eighth weight value, b1 is a fourth bias value, and a4 is the fourth hidden layer neuron.
In step S402, the method for acquiring output layer neurons by hidden layer neurons is:
respectively inputting a1, a2, a3 and a4 into formulas
Output layer neurons z1, z2, z3, and z4 are obtained.
In step S403, the method for acquiring the real-time heating temperature of the cooking device through the neurons of the output layer is as follows:
obtaining a real-time heating temperature of the cooking device according to z1 x ω 3+ z2 x ω 4+ z3 x ω 5+ z4 x ω 6+ b5 ═ y, wherein y is the real-time heating temperature of the cooking device, b5 is a bias value, ω 3 is a ninth weight value, ω 4 is a tenth weight value, ω 5 is an eleventh weight value, ω 6 is a twelfth weight value, z1 is a first output layer neuron, z2 is a second output layer neuron, z3 is a third output layer neuron, and z4 is a fourth output layer neuron.
The following steps are also included before step S101:
and (4) enabling the cooking steps of different menu instructions and the heating temperature corresponding to each cooking step to reach the cloud. When the menu instruction of the cooking equipment is acquired and uploaded to the cloud, the cloud can search the cooking steps corresponding to the menu instruction and the heating temperature corresponding to each step.
In step S102, the cooking step output by the cloud according to the menu instruction includes: the number of steps corresponding to the menu instruction and the heating temperature corresponding to each cooking step.
In steps S101 and S103, the method for uploading the menu instruction to the cloud and uploading the real-time cavity temperature to the cloud includes:
and wireless communication connection is established between the cooking equipment and the cloud.
The cloud artificial neural network enters a self-learning mode, and after the user puts food materials into the cooking equipment, the steps are executed as follows:
a. the user enters a self-learning mode by operating the panel of the cooking equipment, and the synchronous cloud also enters the self-learning mode.
b. The user sets up the combination heating mode of heating device and opens the heating through the cooking equipment panel, and synchronous high in the clouds also acquires this heating combination mode according to the menu instruction.
c. The user inputs the calibration value of the cooking state according to the cooking effect of the food material, the synchronous cloud records the calibration value of the cooking state,
for example: baking cookies, the whole process of baking dough from white to dark brown can be divided into 5 states, i.e. 5 cooking steps: the food cooking system is white 1, light yellow 2, golden yellow 3, light brown 4 and dark brown 5, a user inputs heating temperature corresponding to each cooking step according to the surface color of the food material, the cloud records the heating temperature corresponding to each cooking step, n groups of cooking step-heating temperature data are obtained by the cloud after repeated cooking for many times, and for example, 5 groups of cooking step-heating temperature data are obtained by baking a cookie for 5 times. The cloud end uses the obtained n groups of data as n training samples to train the artificial neural network model (namely, model parameters of omega 11, omega 21, omega 12, omega 22, omega 13, omega 23, omega 14, omega 24, omega 3, omega 4, omega 5, omega 6, b1, b2, b3, b4 and b5) to form a menu of the baked cookie, and the user can directly select the menu of the baked cookie next time to automatically cook the baked cookie.
The cooking equipment comprises a steaming and baking oven, an oven, a steaming and baking integrated machine, a micro-steaming and baking oven and the like.
The above preferred embodiments should be considered as examples of the embodiments of the present application, and technical deductions, substitutions, improvements and the like similar to, similar to or based on the embodiments of the present application should be considered as the protection scope of the present patent.
Claims (9)
1. A control method of a cooking apparatus, characterized by comprising the steps of:
step S101, acquiring a menu instruction of cooking equipment, and uploading the menu instruction to a cloud;
step S102, the cloud outputs cooking steps and heating temperatures corresponding to the cooking steps according to menu instructions;
step S103, acquiring the real-time inner cavity temperature of the cooking equipment, and uploading the real-time inner cavity temperature to a cloud end;
step S104, the cloud inputs the real-time inner cavity temperature into the artificial neural network model to obtain the real-time heating temperature of the cooking equipment;
and S105, judging the running state of the cooking equipment and controlling and adjusting the heating temperature of the inner cavity of the cooking equipment according to the real-time heating temperature of the cooking equipment and the heating temperature corresponding to each cooking step.
2. The method of claim 1, wherein in step S104, the cloud inputs the real-time cavity temperature into the artificial neural network model, and the method of obtaining the real-time heating temperature of the cooking device comprises:
step S401, taking the real-time inner cavity temperature as an input layer neuron to obtain a hidden layer neuron;
step S402, obtaining an output layer neuron through a hidden layer neuron;
step S403, acquiring a real-time heating temperature of a cooking device of the cooking device through an output layer neuron.
3. The method according to claim 2, wherein in step S401, the real-time inner cavity temperature includes a first temperature value and a second temperature value, the first temperature value is a temperature at a top of the inner cavity of the cooking apparatus, and the second temperature value is a temperature at a bottom of the inner cavity of the cooking apparatus.
4. The method for controlling a cooking apparatus according to claim 3, wherein in step S401, the method for obtaining hidden layer neurons using real-time lumen temperature as input layer neurons comprises:
obtaining a first hidden layer neuron according to a formula x1 × ω 11+ x2 × ω 21+ b1 ═ a1, where x1 is a first temperature value of the cooking device, x2 is a second temperature value of the cooking device, ω 11 is a first weight value, ω 21 is a second weight value, b1 is a first bias value, and a is the first hidden layer neuron;
obtaining a second hidden layer neuron according to the formula x1 × ω 12+ x2 × ω 22+ b2 ═ a2, where x1 is a first temperature value of the cooking appliance, x2 is a second temperature value of the cooking appliance, ω 12 is a third weight value, ω 22 is a fourth weight value, b1 is a second bias value, and a2 is the second hidden layer neuron;
obtaining a third hidden layer neuron according to a formula x1 × ω 13+ x2 × ω 23+ b3 ═ a3, where x1 is a first temperature value of the cooking appliance, x2 is a second temperature value of the cooking appliance, ω 13 is a fifth weight value, ω 23 is a sixth weight value, b1 is a third bias value, and a3 is the third hidden layer neuron;
a fourth hidden layer neuron is obtained according to the formula x1 × ω 14+ x2 × ω 24+ b4 ═ a4, where x1 is a first temperature value of the cooking appliance, x2 is a second temperature value of the cooking appliance, ω 14 is a seventh weight value, ω 24 is an eighth weight value, b1 is a fourth bias value, and a4 is the fourth hidden layer neuron.
5. The method of controlling a cooking apparatus according to claim 4, wherein in step S402, the method of obtaining the neurons of the output layer by the neurons of the hidden layer is:
respectively inputting a1, a2, a3 and a4 into formulas
Output layer neurons z1, z2, z3, and z4 are obtained.
6. The method for controlling a cooking apparatus according to claim 5, wherein in step S403, the method for obtaining the real-time heating temperature of the cooking apparatus through the neuron of the output layer comprises:
obtaining a real-time heating temperature of the cooking device according to z1 x ω 3+ z2 x ω 4+ z3 x ω 5+ z4 x ω 6+ b2 ═ y, wherein y is the real-time heating temperature of the cooking device, b2 is a bias value, ω 3 is a ninth weight value, ω 4 is a tenth weight value, ω 5 is an eleventh weight value, ω 6 is a twelfth weight value, z1 is a first output layer neuron, z2 is a second output layer neuron, z3 is a third output layer neuron, and z4 is a fourth output layer neuron.
7. The method for controlling a cooking apparatus according to claim 1, further comprising the step of, before the step S101:
and (4) enabling the cooking steps of different menu instructions and the heating temperature corresponding to each cooking step to reach the cloud.
8. The method for controlling a cooking apparatus according to claim 1, wherein in step S102, the cooking step output by the cloud according to the menu instruction comprises: the number of steps corresponding to the menu instruction and the heating temperature corresponding to each cooking step.
9. The method of claim 1, wherein in steps S101 and S103, the method of uploading the menu command to the cloud and the real-time cavity temperature to the cloud comprises:
and wireless communication connection is established between the cooking equipment and the cloud.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110675602.7A CN113384159A (en) | 2021-06-18 | 2021-06-18 | Control method of cooking equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110675602.7A CN113384159A (en) | 2021-06-18 | 2021-06-18 | Control method of cooking equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113384159A true CN113384159A (en) | 2021-09-14 |
Family
ID=77621902
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110675602.7A Pending CN113384159A (en) | 2021-06-18 | 2021-06-18 | Control method of cooking equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113384159A (en) |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH04364815A (en) * | 1991-06-12 | 1992-12-17 | Hitachi Home Tec Ltd | Rice cooker |
| JP2014202381A (en) * | 2013-04-02 | 2014-10-27 | 株式会社ハーマン | Heating cooker |
| WO2015018890A1 (en) * | 2013-08-09 | 2015-02-12 | Miele & Cie. Kg | Cooking device and method for operating a cooking device |
| CN104461501A (en) * | 2014-11-04 | 2015-03-25 | 百度在线网络技术（北京）有限公司 | Cloud intelligent cooking method, cloud intelligent cooking device and cloud server |
| CN106213971A (en) * | 2016-08-08 | 2016-12-14 | 宁波卡特马克炊具科技有限公司 | A kind of intelligent cooking system and cooking methods |
| CN107886170A (en) * | 2017-09-30 | 2018-04-06 | 珠海格力电器股份有限公司 | The control method of cooking apparatus, device and system, storage medium, processor |
| CN108021071A (en) * | 2017-12-30 | 2018-05-11 | 重庆羽狐科技有限公司 | Intelligent electric cooker control method based on cloud server |
| CN109349913A (en) * | 2018-10-23 | 2019-02-19 | 杭州若奇技术有限公司 | Cooking control method, cooking apparatus, Cloud Server and culinary art control system |
| CN112167995A (en) * | 2020-09-21 | 2021-01-05 | 珠海格力电器股份有限公司 | Multi-stage cooking control method and device, cooking equipment and storage medium |
| US20210116142A1 (en) * | 2019-10-22 | 2021-04-22 | Distech Controls Inc. | Thermostat and method using a neural network to adjust temperature measurements |
| US20210182667A1 (en) * | 2019-12-12 | 2021-06-17 | Lg Electronics Inc. | Cooking apparatus and control method thereof |
-
2021
- 2021-06-18 CN CN202110675602.7A patent/CN113384159A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH04364815A (en) * | 1991-06-12 | 1992-12-17 | Hitachi Home Tec Ltd | Rice cooker |
| JP2014202381A (en) * | 2013-04-02 | 2014-10-27 | 株式会社ハーマン | Heating cooker |
| WO2015018890A1 (en) * | 2013-08-09 | 2015-02-12 | Miele & Cie. Kg | Cooking device and method for operating a cooking device |
| CN104461501A (en) * | 2014-11-04 | 2015-03-25 | 百度在线网络技术（北京）有限公司 | Cloud intelligent cooking method, cloud intelligent cooking device and cloud server |
| CN106213971A (en) * | 2016-08-08 | 2016-12-14 | 宁波卡特马克炊具科技有限公司 | A kind of intelligent cooking system and cooking methods |
| CN107886170A (en) * | 2017-09-30 | 2018-04-06 | 珠海格力电器股份有限公司 | The control method of cooking apparatus, device and system, storage medium, processor |
| CN108021071A (en) * | 2017-12-30 | 2018-05-11 | 重庆羽狐科技有限公司 | Intelligent electric cooker control method based on cloud server |
| CN109349913A (en) * | 2018-10-23 | 2019-02-19 | 杭州若奇技术有限公司 | Cooking control method, cooking apparatus, Cloud Server and culinary art control system |
| US20210116142A1 (en) * | 2019-10-22 | 2021-04-22 | Distech Controls Inc. | Thermostat and method using a neural network to adjust temperature measurements |
| US20210182667A1 (en) * | 2019-12-12 | 2021-06-17 | Lg Electronics Inc. | Cooking apparatus and control method thereof |
| CN112167995A (en) * | 2020-09-21 | 2021-01-05 | 珠海格力电器股份有限公司 | Multi-stage cooking control method and device, cooking equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107045289A (en) | A kind of nonlinear neural network optimization PID control method of electric furnace temperature | |
| CN110742484B (en) | Industrialized intelligent rice cooking system | |
| CN107886170A (en) | The control method of cooking apparatus, device and system, storage medium, processor | |
| Purlis | Optimal design of bread baking: Numerical investigation on combined convective and infrared heating | |
| CN105615619B (en) | Cook electrical equipment and its control method and device | |
| CN110609523B (en) | Cooperative control method for units in primary tea leaf making process | |
| CN111513583A (en) | Control method of cooking device, server and storage medium | |
| CN110601573A (en) | Three-phase rectification control method based on improved self-adaptive fuzzy neural network | |
| CN104792397A (en) | Recipe generating system and kitchen scale | |
| Mahadevappa et al. | Fuzzy logic based process control strategy for effective sheeting of wheat dough in small and medium-sized enterprises | |
| CN113384159A (en) | Control method of cooking equipment | |
| CN106292291A (en) | A kind of electrical network automatic electricity generation control system controller parameter optimization method | |
| CN106136847A (en) | Electric cooker and control method thereof | |
| AU2017380531B2 (en) | Method of operating a cooking oven, in particular a steam cooking oven | |
| CN106403182A (en) | Control parameter determining method and device and control system for photovoltaic air-conditioning system | |
| WO2020074478A1 (en) | Method for preparing a cooking product, cooking device, and cooking device system | |
| CN206975521U (en) | Multistage oil temperature balance control device in a kind of heating furnace | |
| CN107581941A (en) | A kind of method and device for making cake | |
| CN115886580A (en) | Control method and device for radio frequency cooking equipment and radio frequency cooking equipment | |
| CN116802681A (en) | Method for determining the end of a cooking time of a food item and household cooking appliance | |
| CN107479507A (en) | The custom feature control method and system of a kind of intelligent appliance | |
| CN109527978A (en) | The control method and cooking equipment of culinary art mode | |
| Padhan et al. | Enhanced performance of PID load frequency controller for power systems | |
| CN110693303A (en) | Cooking device, control system applying same and intelligent cooking method | |
| CN117193427B (en) | Regulation and control method for Xinjiang rice flour automatic stir-frying mechanical equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210914 |