WO2021098427A1 - 烹饪设备的控制方法、装置、烹饪设备及存储介质 - Google Patents

烹饪设备的控制方法、装置、烹饪设备及存储介质 Download PDF

Info

Publication number
WO2021098427A1
WO2021098427A1 PCT/CN2020/121814 CN2020121814W WO2021098427A1 WO 2021098427 A1 WO2021098427 A1 WO 2021098427A1 CN 2020121814 W CN2020121814 W CN 2020121814W WO 2021098427 A1 WO2021098427 A1 WO 2021098427A1
Authority
WO
WIPO (PCT)
Prior art keywords
cooking
information
maturity
food material
volume
Prior art date
Application number
PCT/CN2020/121814
Other languages
English (en)
French (fr)
Inventor
杜海波
Original Assignee
广东美的厨房电器制造有限公司
美的集团股份有限公司
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Priority claimed from CN201911142151.XA external-priority patent/CN110780628B/zh
Priority claimed from CN201911143383.7A external-priority patent/CN110806699A/zh
Application filed by 广东美的厨房电器制造有限公司, 美的集团股份有限公司 filed Critical 广东美的厨房电器制造有限公司
Priority to EP20891069.5A priority Critical patent/EP4047428A4/en
Publication of WO2021098427A1 publication Critical patent/WO2021098427A1/zh
Priority to US17/750,107 priority patent/US20220273136A1/en

Links

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
    • A—HUMAN NECESSITIES
    • A23—FOODS OR FOODSTUFFS; TREATMENT THEREOF, NOT COVERED BY OTHER CLASSES
    • A23L—FOODS, FOODSTUFFS, OR NON-ALCOHOLIC BEVERAGES, NOT COVERED BY SUBCLASSES A21D OR A23B-A23J; THEIR PREPARATION OR TREATMENT, e.g. COOKING, MODIFICATION OF NUTRITIVE QUALITIES, PHYSICAL TREATMENT; PRESERVATION OF FOODS OR FOODSTUFFS, IN GENERAL
    • A23L5/00—Preparation or treatment of foods or foodstuffs, in general; Food or foodstuffs obtained thereby; Materials therefor
    • A23L5/10—General methods of cooking foods, e.g. by roasting or frying
    • 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
    • G05B15/00—Systems controlled by a computer
    • G05B15/02—Systems controlled by a computer electric
    • 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
    • G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • 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
    • G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • G05B19/0426—Programming the control sequence
    • A—HUMAN NECESSITIES
    • A23—FOODS OR FOODSTUFFS; TREATMENT THEREOF, NOT COVERED BY OTHER CLASSES
    • A23V—INDEXING SCHEME RELATING TO FOODS, FOODSTUFFS OR NON-ALCOHOLIC BEVERAGES AND LACTIC OR PROPIONIC ACID BACTERIA USED IN FOODSTUFFS OR FOOD PREPARATION
    • A23V2002/00—Food compositions, function of food ingredients or processes for food or foodstuffs
    • 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
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/26—Pc applications
    • G05B2219/2643—Oven, cooking

Definitions

  • the first aspect of this application is to propose a method for controlling cooking equipment.
  • the second aspect of the present application is to propose a method for controlling cooking equipment.
  • the third aspect of the present application is to provide a control device for cooking equipment.
  • the fourth aspect of the present application is to provide a control device for cooking equipment.
  • the fifth aspect of the application is to provide a cooking device.
  • the sixth aspect of this application is to provide a cooking device.
  • a method for controlling cooking equipment including: acquiring image information of ingredients; identifying image information, determining the category information of the ingredients and the volume information of the ingredients; according to the category information and volume The information determines the maturity of the ingredients.
  • the method for controlling cooking equipment obtains image information of the food material, obtains the size information and category information of the food material by recognizing the image information, and then obtains the volume information of the food material according to the size information, and calculates the food material by using the initial volume and the current volume of the food material
  • the volume change in the cooking process is determined by the pre-trained maturity model according to the volume change of the food material. On the one hand, it can monitor the volume change of the food in real time, and on the other hand, use the volume change It realizes the function of automatic maturity recognition, which greatly saves users' observation time and effectively reduces the difficulty of recognition. Compared with the prior art schemes that rely solely on color and other surface conditions to determine the maturity of food, the accuracy is higher, and it is used for subsequent control of cooking equipment. Provide a reliable basis.
  • the image information includes a color two-dimensional image and a three-dimensional three-dimensional image.
  • the category information of the food is recognized through the two-dimensional image, and the size information is recognized through the three-dimensional image. Further, the image is enhanced after the image is obtained. In order to improve the clarity of the image, it is helpful to accurately identify the category information and size information.
  • Image recognition algorithms can use local recognition algorithms, cloud recognition algorithms, deep learning methods, or pattern recognition methods.
  • the method for controlling cooking equipment in the above technical solution provided by this application may also have the following additional technical features:
  • the step of determining the maturity corresponding to the food material according to the category information and the volume information specifically includes: calculating the volume change of the food material according to the volume information; determining the maturity model according to the category information and the volume change The maturity of the ingredients.
  • the volume of the food material since the food material is half-cooked or after fermentation, the volume of the food material has changed to a certain extent, so there will be a certain error in judging the maturity of the food material only by the change of the volume value, but the volume change is only related to the cooking.
  • the process is related, so the initial volume of the food is adjusted, that is, the volume information obtained for the first time after the food is put into the cooking device.
  • the volume change of the food is determined according to the ratio or difference between the current volume and the initial volume, and the volume is changed according to the category information of the food.
  • the amount of change is compared with the data in the maturity model to obtain the maturity of the food material.
  • the step of recognizing image information and determining the volume information of the food material specifically includes: recognizing the image information and determining the three-dimensional information of the food material; determining the size information of the food material according to the three-dimensional information; determining the volume according to the size information information.
  • the three-dimensional image in the image information is recognized, the three-dimensional information of the food is determined, and the three-dimensional information is used to obtain the size information of the food, that is, the length, width, and height of the food, and the volume information of the food is calculated based on the size information , And then use the volume information to calculate the volume change, so as to use the volume change to automatically determine the maturity of the food.
  • the three-dimensional information is a three-dimensional cloud point
  • the three-dimensional cloud point includes: coordinate information, color information and/or laser reflection intensity, and then the size of the food material is calculated according to the coordinate information.
  • the step of obtaining the image information of the food material it further includes: constructing and saving a maturity model.
  • the steps of constructing and saving the maturity model specifically include: collecting volume data and cooking parameters corresponding to different types of ingredients; determining the volume change data of different types of ingredients according to the volume data; recording the volume change data of any ingredient, The corresponding relationship between the cooking parameters and the preset maturity level; and according to the corresponding relationship, the maturity model is constructed and saved.
  • the volume data and cooking parameters corresponding to different types of ingredients are collected.
  • the cooking parameters include mode, power, and duration.
  • the volume data of different types of ingredients are calculated based on the volume data. Different types of ingredients are in different cooking modes and Under power, the volume V0 of immature ingredients gradually changes to V1, V2, V3 and until Vn as the cooking time progresses.
  • the maturity of the ingredients at different cooking stages is determined by category information and volume changes.
  • the model divides the preset maturity levels of the ingredients into multiple maturity levels from raw to coke, for example, seven maturity levels: raw, three mature, five mature, seven mature, fully mature, over mature, and coked.
  • the maturity level needs to be set reasonably by the user based on experience or certain judgment standards.
  • the step of determining the maturity level corresponding to the food material according to the category information and volume information it further includes: obtaining the target maturity level of the food material; according to the category information and the target maturity level, passing the maturity level
  • the model obtains the corresponding cooking parameters; controls the cooking equipment to work according to the cooking parameters.
  • the target maturity level of the food material is obtained, that is, the maturity level of the food material required by the user, for example, steak is half-ripe, bread is fully cooked, etc.
  • the selection is already in the maturity model
  • the stored cooking parameters control the working of the cooking equipment according to the cooking parameters, so as to realize the automatic cooking of the ingredients, so that the ingredients can reach the target maturity level directly after cooking, avoid over-cooking or immature cooking, and the entire cooking process does not need to be supervised by the user, which can be no-cooking
  • Experienced users provide reliable cooking solutions that are simple, accurate, and easy to operate to meet the various needs of users and improve the practicability and popularization of cooking equipment.
  • it further includes: acquiring the current working time of the cooking device; determining the remaining working time according to the difference between the cooking time threshold corresponding to the cooking parameter and the current working time; and displaying the remaining working time.
  • the current working time of the cooking equipment is obtained, that is, the working time of the cooking equipment according to the cooking parameters, and the current maturity level of the ingredients is determined to reach the target maturity according to the difference between the cooking time threshold corresponding to the cooking parameter and the current working time
  • the remaining working time required for the level, and the remaining working time is displayed to remind the user of the cooking countdown, so that the user can intuitively understand the time required for cooking, which is beneficial to planning the cooking, thereby improving the cooking efficiency and improving the user experience.
  • the step of displaying the remaining working hours further includes: according to the current maturity level and the target maturity level of the ingredients, the maturity model is used to determine that the current maturity level reaches the target maturity level Remaining cooking time; compare the remaining cooking time with the remaining working time; adjust the cooking parameters based on the situation that the remaining cooking time is greater than or less than the remaining working time.
  • the cooking time is equal to the remaining working time, so as to ensure that the cooking equipment can make the ingredients reach the target maturity level after working according to the cooking parameters, realize the automatic dynamic adjustment of the cooking process, and improve the user experience.
  • it further includes: based on the fact that the current maturity level of the food material reaches the target maturity level, controlling the cooking equipment to stop working and issuing a prompt message; or determining the maturity level with the target according to the maturity model The volume change range corresponding to the degree level; based on the situation that the volume change meets the volume change range, the cooking equipment is controlled to stop working and a prompt message is issued.
  • the prompt method includes at least one of the following: voice, light, and image.
  • a method for controlling a cooking device which includes: acquiring image information of an ingredient and weight information of the ingredient; and determining the maturity of the ingredient according to the image information and weight information.
  • the volume of the food will expand or decrease during the cooking process, and the weight will also change due to the evaporation of water, so that the change in the maturity of the food from the inside to the outside will be integrated into the density information
  • the size information and category information of the ingredients are obtained by recognizing the image information, and then the volume information of the ingredients is obtained according to the size information, and the density information of the ingredients is calculated according to the volume information and weight information of the ingredients.
  • the maturity model determines the maturity level of the food material. On the one hand, it can monitor the volume and weight of the food material in real time, that is, the density change.
  • the density is used to realize the automatic maturity recognition function, which greatly saves the user's observation time and is effective. It reduces the difficulty of identification, and avoids the inaccuracy of judging the maturity of food by relying solely on color and surface temperature in traditional methods, and provides a reliable basis for subsequent control of the cooking equipment.
  • the method for controlling cooking equipment in the above technical solution provided by this application may also have the following additional technical features:
  • the step of determining the maturity corresponding to the food material according to the image information and weight information specifically includes: identifying the image information, determining the category information and volume information of the food material; and calculating the food material based on the volume information and weight information
  • the density information of the food; according to the category information and density information, the maturity level of the food material is determined through the maturity model.
  • the image information includes a color two-dimensional image and a three-dimensional three-dimensional image.
  • the category information of the food is recognized through the two-dimensional image, and the size information is recognized through the three-dimensional image. Further, the image is enhanced after the image is obtained. In order to improve the clarity of the image, it is helpful to accurately identify the category information and size information.
  • the step of recognizing image information and determining the volume information of the food material specifically includes: recognizing the image information and determining the three-dimensional information of the food material; determining the size information of the food material according to the three-dimensional information; determining the volume according to the size information information.
  • the three-dimensional image in the image information is recognized, the three-dimensional information of the food is determined, and the three-dimensional information is used to obtain the size information of the food, that is, the length, width, and height of the food, and the volume information of the food is calculated based on the size information , And then use the volume and weight to calculate the density, so as to use the density to automatically judge the maturity of the food.
  • the three-dimensional information is a three-dimensional cloud point
  • the three-dimensional cloud point includes: coordinate information, color information and/or laser reflection intensity, and then the size of the food material is calculated according to the coordinate information.
  • the step of obtaining the image information of the food material and the weight information of the food material it further includes: constructing and saving a maturity model.
  • the steps of constructing and saving the maturity model include: collecting volume data, weight data and cooking parameters corresponding to different types of ingredients; calculating the density data of different types of ingredients based on the volume data and weight data; recording the volume of any ingredient Correspondence between data, weight data, density data, cooking parameters and preset maturity levels; and build and save maturity models according to the corresponding relations.
  • the volume data, weight data and cooking parameters corresponding to different types of ingredients are collected.
  • the cooking parameters include mode, power and duration.
  • the density data of different types of ingredients are calculated. Under different cooking modes and powers, the volume and weight of immature ingredients gradually change as the cooking time progresses, and the density of immature ingredients ⁇ 0 will also gradually change to ⁇ 1, ⁇ 2, ⁇ 3 and ⁇ n as the ingredients are cooked.
  • the model divides the preset maturity levels of the ingredients into multiple maturity levels from raw to coke, for example, seven maturity levels: raw, three mature, five mature, seven mature, fully mature, over mature, and coked.
  • the maturity level needs to be set reasonably by the user based on experience or certain judgment standards.
  • the step of determining the maturity level of the food material through the maturity model according to the category information and density information it further includes: obtaining the target maturity level of the food material; according to the category information and the target maturity level Level, obtain the corresponding cooking parameters through the maturity model; control the work of the cooking equipment according to the cooking parameters.
  • the target maturity level of the food material is obtained, that is, the maturity level of the food material required by the user, for example, steak is half-ripe, bread is fully cooked, etc.
  • the selection is already in the maturity model
  • the stored cooking parameters control the working of the cooking equipment according to the cooking parameters, so as to realize the automatic cooking of the ingredients, so that the ingredients can reach the target maturity level directly after cooking, avoid over-cooking or immature cooking, and the entire cooking process does not need to be supervised by the user, which can be no-cooking
  • Experienced users provide reliable cooking solutions, simple, accurate, and easy to operate.
  • it further includes: acquiring the current working time of the cooking device; determining the remaining working time according to the difference between the cooking time threshold corresponding to the cooking parameter and the current working time; and displaying the remaining working time.
  • the current working time of the cooking equipment is obtained, that is, the working time of the cooking equipment according to the cooking parameters, and the current maturity level of the ingredients is determined to reach the target maturity according to the difference between the cooking time threshold corresponding to the cooking parameter and the current working time
  • the remaining working time required for the level, and the remaining working time is displayed to remind the user of the cooking countdown, so that the user can intuitively understand the time required for cooking, which is beneficial to planning the cooking, thereby improving the cooking efficiency and improving the user experience.
  • the step of displaying the remaining working hours further includes: according to the current maturity level and the target maturity level of the ingredients, the maturity model is used to determine that the current maturity level reaches the target maturity level Remaining cooking time; compare the remaining cooking time with the remaining working time; adjust the cooking parameters based on the situation that the remaining cooking time is greater than or less than the remaining working time.
  • the set cooking time threshold is deviated from the actual time required by the ingredients. This will cause the cooking to end and the ingredients are coking or not reaching the target maturity level. Therefore, According to the current maturity level of the ingredients and the target maturity level, through the relationship between the preset maturity level and cooking parameters in the maturity model, the cooking time corresponding to the current maturity level and the target maturity level are determined, and then the current maturity level is calculated. The remaining cooking time when the maturity level reaches the target maturity level, compare the remaining cooking time with the remaining working time. If the remaining cooking time does not match the remaining working time, it means that the cooking is likely to cause the ingredients to burn or not reach the target maturity level. Adjust the cooking parameters of the ingredients so that the remaining cooking time is equal to the remaining working time, so as to ensure that the cooking equipment can make the ingredients reach the target maturity level after working according to the cooking parameters, realize the automatic dynamic adjustment of the cooking process, and improve the user experience.
  • it further includes: based on the fact that the current maturity level of the food material reaches the target maturity level, controlling the cooking equipment to stop working and issuing a prompt message; or determining the maturity level with the target according to the maturity model The density range corresponding to the degree level; based on the situation that the density information meets the density range, the cooking device is controlled to stop working and a prompt message is issued.
  • the prompt method includes at least one of the following: voice, light, and image.
  • a control device for cooking equipment including a memory, a processor, and a computer program stored in the memory and running on the processor.
  • the processor executes the computer program to implement the first aspect Method of controlling cooking equipment. Therefore, the control device of the cooking device has all the beneficial effects of the control method of any one of the above-mentioned cooking devices.
  • a control device for cooking equipment including a memory, a processor, and a computer program stored in the memory and running on the processor.
  • the processor executes the computer program to implement the fourth aspect.
  • Method of controlling cooking equipment Therefore, the control device of the cooking device has all the beneficial effects of the control method of any one of the above-mentioned cooking devices.
  • a cooking device including: an image acquisition device for collecting image information of ingredients; and the third aspect proposes a control device for the cooking device, the control device is related to the image acquisition device and the gravity sensor connection.
  • the cooking equipment provided by the present application collects image information of ingredients through an image acquisition device, recognizes the image information to obtain the size information and category information of the ingredients, and then obtains the volume information of the ingredients according to the size information, and calculates the ingredients using the initial volume and the current volume of the ingredients
  • the volume change in the cooking process is determined by the pre-trained maturity model according to the volume change of the food material. On the one hand, it can monitor the volume change of the food in real time, and on the other hand, use the volume change It realizes the automatic maturity recognition function, which greatly saves the user's observation time and effectively reduces the difficulty of recognition. Compared with the existing technology, the accuracy of judging food maturity based on the surface state such as color is higher, and it is used for subsequent control of cooking equipment. Provide a reliable basis.
  • the image information includes a color two-dimensional image and a three-dimensional three-dimensional image
  • the category information of the food is recognized through the two-dimensional image
  • the size information is recognized through the three-dimensional image.
  • the image acquisition device includes one or more sets of cameras. The setting of multiple cameras can acquire images of food materials from multiple angles, which is beneficial to identify the size information and category information of the food materials.
  • the cooking equipment includes but is not limited to at least one of the following: an oven, a steamer, a microwave oven, and the like.
  • a cooking device including: an image acquisition device for collecting image information of ingredients; a gravity sensor for collecting weight information of the ingredients; and a fourth aspect proposes a control device for the cooking device , The control device is connected with the image acquisition device and the gravity sensor.
  • the cooking equipment provided in this application collects image information of ingredients through an image acquisition device, collects weight information of ingredients through a gravity sensor, recognizes the size information and category information of the ingredients in the image information, and then obtains the volume of the ingredients based on the size information, and then according to The volume information and weight information of the ingredients calculate the density information of the ingredients. According to the density of the ingredients, the maturity level of the ingredients is determined in the pre-trained maturity model. On the one hand, the volume and weight changes of the ingredients can be monitored in real time, that is, the density changes.
  • the automatic maturity recognition function is realized by using density, which greatly saves the user's observation time, effectively reduces the difficulty of recognition, and avoids the inaccuracy of the traditional method of judging the maturity of food solely by color and surface temperature.
  • the work of cooking equipment provides a reliable basis.
  • a computer-readable storage medium on which a computer program is stored.
  • the steps of the control method of the cooking device proposed in the first aspect or the second aspect is proposed.
  • the steps of the control method of the cooking equipment. Therefore, the computer-readable storage medium has all the beneficial effects of the cooking device control method proposed in the first aspect or the cooking device control method proposed in the second aspect.
  • Fig. 1 shows a schematic flow chart of a control method of a cooking device according to an embodiment of the present application
  • Fig. 2 shows a schematic flow chart of a control method of a cooking device according to another embodiment of the present application
  • FIG. 3 shows a schematic flowchart of a method for controlling a cooking device according to another embodiment of the present application
  • FIG. 4 shows a schematic flow chart of a control method of a cooking device according to another embodiment of the present application
  • FIG. 5 shows a schematic flow chart of a control method of a cooking device according to another embodiment of the present application
  • Fig. 6 shows a schematic flow chart of a control method of a cooking device according to another embodiment of the present application
  • FIG. 7 shows a schematic flowchart of a control method of a cooking device according to a specific embodiment of the present application.
  • FIG. 8 shows a schematic flowchart of a control method of a cooking device according to another embodiment of the present application.
  • FIG. 9 shows a schematic flowchart of a method for controlling a cooking device according to another embodiment of the present application.
  • FIG. 10 shows a schematic flowchart of a control method of a cooking device according to another embodiment of the present application.
  • FIG. 11 shows a schematic flowchart of a method for controlling a cooking device according to another embodiment of the present application.
  • FIG. 12 shows a schematic flowchart of a control method of a cooking device according to another embodiment of the present application.
  • FIG. 13 shows a schematic flowchart of a method for controlling a cooking device according to another embodiment of the present application.
  • Fig. 14 shows a schematic flowchart of a method for controlling a cooking device according to another specific embodiment of the present application
  • Fig. 16 shows a schematic block diagram of a control device of a cooking device according to an embodiment of the present application
  • Fig. 17 shows a schematic block diagram of a cooking device according to an embodiment of the present application.
  • a method for controlling a cooking device includes:
  • Step S102 acquiring image information of the food material
  • Step S104 identifying the image information, and determining the category information of the food material and the volume information of the food material;
  • Step S106 Determine the maturity corresponding to the food material according to the category information and the volume information.
  • the image information of the ingredients is acquired, the size information and category information of the ingredients are obtained by recognizing the image information, and then the volume information of the ingredients is obtained according to the size information, and the initial volume and current volume of the ingredients are used to calculate the ingredients during the cooking process.
  • the volume change of the food material the maturity level of the food material is determined in the pre-trained maturity model.
  • the volume change of the food material can be monitored in real time.
  • the volume change is used to realize the automatic
  • the maturity recognition function greatly saves the user's observation time and effectively reduces the difficulty of recognition. Compared with the prior art schemes that rely solely on color and other surface conditions to determine the maturity of the food, the accuracy is higher, and it provides a reliable basis for subsequent control of the cooking equipment. .
  • the image information includes a color two-dimensional image and a three-dimensional three-dimensional image.
  • the category information of the food is recognized through the two-dimensional image, and the size information is recognized through the three-dimensional image.
  • the image is enhanced after the image is obtained. In order to improve image clarity, it is beneficial to accurately identify category information and size information.
  • Image recognition algorithms can use local recognition algorithms, cloud recognition algorithms, deep learning methods, or pattern recognition methods.
  • Step S202 construct and save a maturity model
  • Step S204 obtaining image information of the food material
  • Step S206 identifying the image information, and determining the category information of the food material and the three-dimensional information of the food material;
  • Step S208 Determine the size information of the food material according to the three-dimensional information
  • Step S212 Calculate the volume change of the food material according to the volume information
  • a method for controlling a cooking device includes:
  • Step S304 Determine the volume change data of different types of ingredients according to the volume data
  • Step S408 according to the category information and the target maturity level, obtain the corresponding cooking parameters through the maturity model
  • Step S410 controlling the cooking device to work according to the cooking parameters
  • Step S414 Calculate the volume change of the food material according to the volume information
  • the corresponding cooking parameters are obtained through the maturity model, and the cooking equipment is controlled. If the cooking equipment is in the working state, the acquisition is automatically skipped In the step of cooking parameters, the image information of the ingredients is acquired in real time, and the volume information of the ingredients is monitored according to the image information until the cooking is completed.
  • a method for controlling a cooking device includes:
  • Step S502 construct and save a maturity model
  • Step S504 Obtain the image information of the food material and the target maturity level of the food material
  • Step S506 Identify the image information, and determine the category information and volume information of the ingredients
  • Step S508 Determine the volume change of different types of ingredients according to the volume information
  • Step S510 according to the category information and the volume change, the maturity level of the food material is determined through the maturity model
  • Step S512 acquiring the current working time of the cooking device
  • Step S514 Determine the remaining working time according to the difference between the cooking time threshold corresponding to the cooking parameter and the current working time;
  • step S520 the cooking device is controlled to stop working, and a prompt message is issued.
  • the current working time of the cooking device is acquired, that is, the working time of the cooking device according to the cooking parameter, and the current maturity level of the ingredient is determined to reach the target maturity according to the difference between the cooking time threshold corresponding to the cooking parameter and the current working time
  • the remaining working time required for the level, and the remaining working time is displayed to remind the user of the cooking countdown, so that the user can intuitively understand the time required for cooking, which is conducive to cooking planning, improving cooking efficiency, and whether the current maturity level is
  • the cooking of the ingredients is completed.
  • the cooking device is controlled to stop working and a prompt message is issued to remind the user that the cooking is over, so as to realize the automatic control function of the cooking device.
  • the method includes:
  • Step S602 Obtain the image information of the food material and the target maturity level of the food material
  • Step S604 Identify the image information, and determine the category information and volume information of the ingredients;
  • Step S606 Determine the maturity corresponding to the food material according to the image information and the volume information;
  • Step S610 Determine the remaining working time according to the difference between the cooking time threshold corresponding to the cooking parameter and the current working time;
  • Step S612 according to the current maturity level and the target maturity level of the ingredients, determine the remaining cooking time until the current maturity level reaches the target maturity level through the maturity model;
  • Step S614 whether the remaining cooking time is equal to the remaining working time, if yes, go to step S616, if not, go to step S618;
  • Step S616, display the remaining working time, and go to step S620;
  • Step S620 Determine the volume change range corresponding to the target maturity level according to the maturity model
  • Step S622 whether the volume change amount meets the volume change amount range, if yes, go to step S624, if not, go to step S602;
  • step S624 the cooking device is controlled to stop working, and a prompt message is issued.
  • the cooking time is equal to the remaining working time, so as to ensure that the cooking equipment can make the ingredients reach the target maturity level after working according to the cooking parameters, realize the automatic dynamic adjustment of the cooking process, and improve the user experience.
  • an oven is used as a cooking device, and the control method of the cooking device includes:
  • Step S708 mark the corresponding relationship between maturity and heating time
  • Step S710 simulate the maturity and volume change rate model according to the marked heating time
  • Step S712 Recognize the food ingredient category through the two-dimensional image, and retrieve the corresponding maturity and volume change rate models in the training library according to different food ingredient categories;
  • Step S7108 it is judged whether the volume change rate of the food material reaches the volume change rate range corresponding to the target maturity in the model, if yes, go to step S720, if not, go to step S714;
  • step S720 the heating is completed.
  • the three-dimensional camera is installed on the top or side of the oven, and the field of view can cover the bakeware area.
  • the model can divide the food from raw to over-cooked into multiple maturity stages. Specifically, the model divides the maturity of the food material from raw to coking. There are seven maturity stages, including raw, three-ripe, five-ripe, seven-ripe, full-ripe, over-ripe and coking seven maturity points. The labeling of these maturity levels requires people to judge based on experience or certain judgment standards. As the food is heated, the volume V0 of raw food gradually changes to V1, V2, V3 and up to Vn.
  • RV1 V1/V0
  • RV1 V1/V0
  • the ingredients are put into the oven.
  • the 3D camera is used to generate 2D RGB (color) image information.
  • the category of the ingredients is identified through image recognition technology, and the specific heating mode and heating time that have been trained in the model are selected according to the category of the ingredients. And control the oven to work in the corresponding specific heating mode and heating time.
  • the three-dimensional camera continuously collects three-dimensional image information, and calculates the volume or length, width and height of the food, and monitors the volume change rate of the food in real time during the heating process and compares it with the model library
  • To compare the maturity of the ingredients use the relationship between the maturity in the training model and the heating time to estimate the time required for the current maturity to reach the target maturity, and display the countdown.
  • the three-dimensional heating process The camera will continue to collect, and the oven will continue to update the maturity information and time information.
  • the oven is controlled to stop heating, and the heating is completed through voice/image prompts.
  • the system can provide maturity judgments without user supervision or for users without cooking experience, so as to realize automatic ripeness recognition and automatic Cooking function.
  • a method for controlling a cooking device includes:
  • Step S802 Obtain the image information of the food material and the weight information of the food material;
  • Step S804 Determine the maturity corresponding to the food material according to the image information and the weight information.
  • the weight will also change due to the evaporation of water, so that the maturity change of the food material from the inside to the outside will be integrated into the density information, through identification
  • the image information obtains the size information and category information of the ingredients, and then obtains the volume information of the ingredients according to the size information, and calculates the density information of the ingredients according to the volume information and weight information of the ingredients.
  • the density of the ingredients in the pre-trained maturity model Determine the maturity level of the food material.
  • the volume and weight of the food material can be monitored in real time, that is, the density change.
  • the density is used to realize the automatic maturity recognition function, which greatly saves the user's observation time and effectively reduces the difficulty of recognition. Moreover, it avoids the inaccuracy of judging the maturity of the food by relying solely on color and surface temperature in the traditional method, and provides a reliable basis for the subsequent control of the cooking equipment.
  • the image recognition algorithm can be a local recognition algorithm, a cloud recognition algorithm, a deep learning method, or a pattern recognition method.
  • a method for controlling a cooking device includes:
  • Step S902 construct and save the maturity model
  • Step S904 acquiring image information of the food material and weight information of the food material
  • Step S906 identifying the image information, and determining the category information and volume information of the food;
  • Step S908 Calculate the density information of the food material according to the volume information and the weight information
  • Step S910 Determine the maturity corresponding to the food material through the maturity model according to the category information and the density information.
  • the step of recognizing image information and determining the volume information of the food material specifically includes: recognizing the image information and determining the three-dimensional information of the food material; determining the size information of the food material according to the three-dimensional information; and determining the volume information according to the size information.
  • the three-dimensional information is a three-dimensional cloud point
  • the three-dimensional cloud point includes: coordinate information, color information and/or laser reflection intensity, and then the size of the food material is calculated according to the coordinate information.
  • the image information includes color two-dimensional images and three-dimensional three-dimensional images.
  • the category information of the food is recognized through the two-dimensional image, and the size information is recognized through the three-dimensional image.
  • image enhancement is performed to improve the image. Clarity is conducive to accurate identification of category information and size information.
  • a method for controlling a cooking device includes:
  • Step S1002 collecting volume data, weight data and cooking parameters corresponding to different types of ingredients
  • Step S1004 Calculate density data of different types of food materials according to the volume data and weight data
  • Step S1006 Record the corresponding relationship between the volume data, weight data, density data, cooking parameters of any food material and the preset maturity level;
  • Step S1008 construct and save the maturity model according to the corresponding relationship
  • Step S1010 obtaining image information of the food material and weight information of the food material
  • Step S1012 identifying the image information, and determining the category information and volume information of the ingredients
  • Step S1014 Calculate the density information of the food material according to the volume information and the weight information
  • Step S1016 Determine the maturity corresponding to the food material through the maturity model according to the category information and the density information.
  • the volume data, weight data, and cooking parameters corresponding to different types of ingredients are collected.
  • the cooking parameters include mode, power, and duration.
  • the density data of different types of ingredients are calculated.
  • the volume data, weight data, and density data of immature ingredients gradually change with the cooking time.
  • the volume data, weight data, density data, and cooking parameters of any ingredient are recorded separately from the preset maturity level. For example, in the baking mode and 1000W power, the density of sweet potatoes is 1.1 ⁇ 1.2g/cm 3 after cooking for 10 minutes, the maturity level is three mature, and the density of sweet potatoes is 0.95 ⁇ 1.0g/ after 60 minutes of cooking. cm 3 , the maturity level is fully cooked. Construct a maturity model based on the above corresponding relationship and save it in the system, so that the maturity of the ingredients in different cooking stages can be determined through category information and density information when cooking ingredients.
  • the model divides the preset maturity levels of the ingredients into multiple maturity levels from raw to coke, for example, seven maturity levels: raw, three mature, five mature, seven mature, fully mature, over mature, and coked.
  • the maturity level needs to be set reasonably by the user based on experience or certain judgment standards.
  • a method for controlling a cooking device includes:
  • Step S1102 construct and save the maturity model
  • Step S1104 Obtain the image information of the food material, the weight information of the food material, and the target maturity level of the food material;
  • Step S1106 Identify the image information, and determine the category information of the food
  • Step S1108 according to the category information and the target maturity level, obtain the corresponding cooking parameters through the maturity model
  • Step S1110 controlling the operation of the cooking device according to the cooking parameter
  • Step S1112 identifying the image information, and determining the volume information of the ingredients
  • Step S1114 Calculate the density information of the food material according to the volume information and the weight information
  • Step S1116 According to the category information and density information, the maturity level of the food material is determined through the maturity model.
  • the target maturity level of the food material is obtained, that is, the maturity level of the food material required by the user, and the cooking parameters that have been stored in the maturity model are selected according to the category information and the target maturity level, and the cooking equipment is controlled to work according to the cooking parameters , So as to realize the automatic cooking of the ingredients, make the ingredients directly reach the target maturity level after cooking, avoid over-cooking or immature cooking, and the entire cooking process does not need to be supervised by the user, which can provide reliable cooking solutions for users without cooking experience, simple and accurate, Easy to operate.
  • the corresponding cooking parameters are obtained through the maturity model, and the cooking equipment is controlled. If the cooking equipment is in the working state, the acquisition is automatically skipped In the step of cooking parameters, the image information of the ingredients is obtained in real time, and the density information of the ingredients is monitored according to the image information and weight information until the cooking is completed.
  • a method for controlling a cooking device includes:
  • Step S1202 construct and save the maturity model
  • Step S1204 Obtain the image information of the food material, the weight information of the food material, and the target maturity level of the food material;
  • Step S1206 Identify the image information, and determine the category information and volume information of the ingredients
  • Step S1208 Calculate the density information of the food material according to the volume information and the weight information
  • Step S1210 determine the maturity corresponding to the food through the maturity model
  • Step S1212 acquiring the current working time of the cooking device
  • Step S1214 Determine the remaining working time according to the difference between the cooking time threshold corresponding to the cooking parameter and the current working time;
  • Step S1216 display the remaining working time
  • Step S1218 whether the current maturity level of the food material reaches the target maturity level, if yes, go to step S1220, if not, go to step S1204;
  • step S1220 the cooking device is controlled to stop working, and a prompt message is issued.
  • the current working time of the cooking device is acquired, that is, the working time of the cooking device according to the cooking parameter, and the current maturity level of the ingredient is determined to reach the target maturity according to the difference between the cooking time threshold corresponding to the cooking parameter and the current working time
  • the remaining working time required for the level, and the remaining working time is displayed to remind the user of the cooking countdown, which is convenient for the user to intuitively understand the time required for cooking, which is conducive to planning cooking, improving cooking efficiency, and when the density information meets the target mature
  • the density range corresponds to the degree level, it indicates that the cooking of the ingredients is completed.
  • the cooking equipment is controlled to stop working and a prompt message is issued to remind the user that the cooking is over.
  • a method for controlling a cooking device includes:
  • Step S1302 Obtain the image information of the food material, the weight information of the food material, and the target maturity level of the food material;
  • Step S1304 Determine the maturity corresponding to the ingredients according to the image information and weight information
  • Step S1306 acquiring the current working time of the cooking device
  • Step S1308 Determine the remaining working time according to the difference between the cooking time threshold corresponding to the cooking parameter and the current working time;
  • Step S1310 according to the current maturity level of the ingredients and the target maturity level, determine the remaining cooking time for the current maturity level to reach the target maturity level through the maturity model;
  • Step S1312 whether the remaining cooking time is equal to the remaining working time, if yes, go to step S1314, if not, go to step S1316;
  • Step S1314 display the remaining working time, go to step S1318;
  • Step S1316 adjusting cooking parameters
  • Step S1318 according to the maturity model, determine the density range corresponding to the target maturity level
  • Step S1320 whether the density information meets the density range, if yes, go to step S1322, if not, go to step S1302;
  • step S1322 the cooking device is controlled to stop working, and a prompt message is issued.
  • the set cooking time threshold is deviated from the actual time required by the ingredients, which will cause the ingredients to be coked or not reach the target maturity level after the cooking is over.
  • the cooking time corresponding to the current maturity level and the target maturity level are determined, and then the current maturity level is calculated.
  • the remaining cooking time when the maturity level reaches the target maturity level compare the remaining cooking time with the remaining working time. If the remaining cooking time does not match the remaining working time, it means that the cooking is likely to cause the ingredients to burn or not reach the target maturity level. Adjust the cooking parameters of the ingredients so that the remaining cooking time is equal to the remaining working time, so as to ensure that the cooking equipment can make the ingredients reach the target maturity level after working according to the cooking parameters, realize the automatic dynamic adjustment of the cooking process, and improve the user experience.
  • an oven is used as a cooking device, and the control method of the cooking device includes:
  • Step S1402 obtaining volume data, weight data and heating mode of a specific food material
  • Step S1404 controlling the cooking device to heat in a corresponding heating mode
  • Step S1406 recording the corresponding relationship between the food density data and the heating time
  • Step S1408 mark the corresponding relationship between maturity and heating time
  • Step S1410 simulate the maturity and density change model according to the marked heating time
  • Step S1412 Recognize the food ingredient category through the two-dimensional image, and retrieve the corresponding maturity and density change model in the training library according to different food ingredient categories;
  • Step S1414 using the three-dimensional image to detect the initial volume of the food, the weight sensor detects the initial weight of the food, converts it into the density of the food, and continuously monitors the change in the density of the food during the heating process;
  • step S1416 the density change is compared with the maturity of the food material and the density change model to judge the maturity of the food material;
  • Step S1418 judging whether the density of the food material reaches the density range corresponding to the target maturity in the model, if yes, go to step S1420, if not, go to step S1414;
  • step S1420 heating is completed.
  • the three-dimensional camera is installed on the top or side of the oven, and the field of view can cover the bakeware area.
  • the model can divide the ingredients from raw to over-cooked into multiple maturity stages. Specifically, the model divides the maturity of the ingredients into seven from raw to coking. There are seven maturity stages, including raw, three-ripe, five-ripe, seven-ripe, full-ripe, over-ripe and coking seven maturity points. The labeling of these maturity levels requires people to judge based on experience or certain judgment standards.
  • the density ⁇ 0 of raw ingredients gradually changes to ⁇ 1, ⁇ 2, ⁇ 3 and ⁇ n as the ingredients are cooked.
  • a corresponding relationship between the heating time and the density of the ingredients in a specific cooking mode is generated
  • the corresponding relationship between the maturity and density of the food in a specific heating mode that is, the maturity and density change model (maturity model) is trained and deployed locally on the system or on a remote server. Different food materials and different heating modes correspond to different maturity and density change models.
  • the density change of the food material is monitored in real time and compared with the maturity level in the model library to judge the maturity level of the food material through training the model
  • the relationship between the maturity and the heating time is estimated, and the time required for the current maturity to reach the target maturity is estimated, and the countdown is displayed.
  • the three-dimensional camera will continue to collect during the cooking process, and the oven will continue to update the maturity information and time information.
  • the oven is controlled to stop heating, and the heating is completed through voice/image prompts.
  • the system can provide maturity judgments without user supervision or for users without cooking experience, so as to realize automatic ripeness recognition and automatic Cooking function.
  • a cooking device control device 100 including a memory 102, a processor 104, and a computer stored in the memory 102 and capable of running on the processor 104
  • the processor 104 executes the computer program
  • the processor 104 implements the cooking device control method of the embodiment of the first aspect. Therefore, the control device 100 of the cooking device has all the beneficial effects of the control method of the cooking device of the embodiment of the first aspect.
  • a control device 200 for cooking equipment which includes a memory 202, a processor 204, and a computer stored in the memory 202 and capable of running on the processor 204
  • the processor 204 executes the computer program
  • the processor 204 implements the cooking device control method of the embodiment of the second aspect. Therefore, the control device 200 of the cooking device has all the beneficial effects of the control method of the cooking device of the embodiment of the second aspect.
  • a cooking device including: an image acquisition device, and the control device of the cooking device proposed in the embodiment of the third aspect.
  • the image acquisition device is used to collect image information of the food, and the control device is connected to the image acquisition device.
  • the cooking equipment includes but is not limited to at least one of the following: an oven, a steamer, a microwave oven, etc.
  • the image acquisition device is a three-dimensional camera device, And the three-dimensional camera device includes one or more sets of cameras.
  • the cooking equipment provided in this embodiment collects image information of ingredients through an image acquisition device, recognizes the image information to obtain size information and category information of the ingredients, and then obtains the volume information of the ingredients according to the size information, and uses the initial volume and current volume of the ingredients to calculate The volume change of the ingredients during the cooking process, according to the volume change of the ingredients, determine the maturity of the ingredients in the pre-trained maturity model. On the one hand, it can monitor the volume changes of the ingredients in real time, and on the other hand, use the volume The amount of change realizes the automatic maturity recognition function, which greatly saves the user's observation time and effectively reduces the difficulty of recognition. Compared with the existing technology, the accuracy of the solution to judge the maturity of the food solely based on the surface state such as color is higher. It is for the subsequent control of cooking equipment Work provides a reliable basis.
  • the image information includes a color two-dimensional image and a three-dimensional three-dimensional image
  • the category information of the food is recognized through the two-dimensional image
  • the size information is recognized through the three-dimensional image.
  • the image acquisition device includes one or more sets of cameras. The setting of multiple cameras can acquire images of food materials from multiple angles, which is beneficial to identify the size information and category information of the food materials.
  • a cooking device 300 including: an image acquisition device 302, a gravity sensor 304, and the cooking device control device 200 according to the embodiment of the fourth aspect. .
  • the image acquisition device 302 is used to collect the image information of the food
  • the gravity sensor 304 is used to collect the weight information of the food
  • the control device is connected with the image acquisition device 302 and the gravity sensor 304
  • the cooking equipment 300 includes but is not limited to at least one of the following Species: oven, steamer, microwave oven, etc.
  • the image acquisition device 302 is a three-dimensional camera device
  • the three-dimensional camera device includes one or more sets of cameras.
  • the cooking device 300 collects image information of the ingredients through the image acquisition device 302, collects the weight information of the ingredients through the gravity sensor 304, recognizes the size information and category information of the ingredients in the image information, and then obtains the size information of the ingredients according to the size information. Volume, and then calculate the density information of the ingredients according to the volume information and weight information of the ingredients. According to the density of the ingredients, determine the maturity of the ingredients in the pre-trained maturity model. On the one hand, it can monitor the volume and weight changes of the ingredients in real time. , That is, density change.
  • the density is used to realize the automatic maturity recognition function, which greatly saves the user's observation time, effectively reduces the difficulty of recognition, and avoids the inaccuracy of the traditional method of judging the maturity of food solely relying on color and surface temperature , Provide a reliable basis for the follow-up control of the cooking equipment.
  • the image information includes a color two-dimensional image and a three-dimensional three-dimensional image.
  • the category information of the food is recognized through the two-dimensional image, and the size information is recognized through the three-dimensional image.
  • multiple cameras can be set up to obtain the food at multiple angles. The image is conducive to identifying the size information and category information of the ingredients.
  • a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the cooking device control method of the embodiment of the first aspect or the second aspect is implemented The steps of the control method of the cooking device of the embodiment.
  • the computer-readable storage medium provided in the present application implements the steps of the cooking device control method of the embodiment of the first aspect or the cooking device control method of the embodiment of the second aspect when the computer program is executed by the processor, so the computer-readable storage The medium includes all the beneficial effects of the cooking device control method of the embodiment of the first aspect or the cooking device control method of the embodiment of the second aspect.
  • connection can be a fixed connection, a detachable connection, or an integral connection; it can be directly connected or indirectly connected through an intermediate medium.

Landscapes

  • Engineering & Computer Science (AREA)
  • Food Science & Technology (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Health & Medical Sciences (AREA)
  • Nutrition Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Polymers & Plastics (AREA)
  • General Engineering & Computer Science (AREA)
  • Electric Stoves And Ranges (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

一种烹饪设备的控制方法、装置、烹饪设备及存储介质，其中方法包括：获取食材的图像信息（S102）；识别图像信息，确定食材的类别信息和食材的体积信息（S104）；根据类别信息和体积信息确定食材对应的成熟度（S106）。该控制方法通过识别图像信息得到食材的体积信息和类别信息，利用食材的初始体积和当前体积计算食材在烹饪过程中的体积变化量，根据食材的体积变化量，在预先训练好的成熟度模型中确定食材对应的成熟度，不仅能够实时监测食材的体积变化，且利用体积变化量实现了自动成熟度识别功能，大大节省用户观察时间，相对于现有技术中单纯依靠颜色等表面状态判断食品成熟度的方案准确度更高。

Description

烹饪设备的控制方法、装置、烹饪设备及存储介质
本申请要求于2019年11月20日提交到中国国家知识产权局、申请号为“201911143383.7”、申请名称为“烹饪设备的控制方法、装置、烹饪设备及存储介质”的中国专利申请的优先权、于2019年11月20日提交到中国国家知识产权局、申请号为“201911142151.X”、申请名称为“烹饪设备的控制方法、装置、烹饪设备及存储介质”的中国专利申请的优先权、其全部内容通过引用结合在本申请中。
技术领域
本申请涉及烹饪设备技术领域，具体而言，涉及一种烹饪设备的控制方法、一种烹饪设备的控制装置、一种烹饪设备及一种计算机可读存储介质。
背景技术
食物在烹饪过程中存在一些表面状态改变，例如，外形、颜色、气味等，有经验的烹饪者可以利用这些变化，判断食物的成熟度，但对于初学者而言则无法依靠这些表面状态的变化对成熟度进行准确地判断。相关技术中，利用高光谱图像获取加热食物的表面纹理和色泽信息变化，进而判断食物的成熟度变化，但该方法存在较多缺点：第一，只有少数食物烹饪中会有明显颜色和纹理变化；第二，不同烹饪方式、是否添加佐料等对食物的颜色变化影响较大；第三，由于加热不均匀，食物的表面温度变化并不能反映出食物的内部温度，食物的表面颜色不一定能反映出食物的成熟度，从而导致成熟度误判。
发明内容
本申请旨在至少解决现有技术或相关技术中存在的技术问题之一。
为此，本申请第一方面在于提出了一种烹饪设备的控制方法。
本申请的第二方面在于提出了一种烹饪设备的控制方法。
本申请的第三方面在于提出了一种烹饪设备的控制装置。
本申请的第四方面在于提出了一种烹饪设备的控制装置。
本申请的第五方面在于提出了一种烹饪设备。
本申请的第六方面在于提出了一种烹饪设备。
本申请的第七方面在于提出了一种计算机可读存储介质。
有鉴于此，根据本申请的第一方面，提出了一种烹饪设备的控制方法，包括：获取食材的图像信息；识别图像信息，确定食材的类别信息和食材的体积信息；根据类别信息和体积信息确定食材对应的成熟度。
本申请提供的烹饪设备的控制方法，获取食材的图像信息，通过识别图像信息得到食材的尺寸信息和类别信息，进而根据尺寸信息得出食材的体积信息，利用食材的初始体积和当前体积计算食材在烹饪过程中的体积变化量，根据食材的体积变化量，在预先训练好的成熟度模型中确定食材对应的成熟度，一方面，能够实时监测食材的体积变化，另一方面，利用体积变化量实现了成熟度自动识别功能，大大节省用户观察时间，有效降低识别难度，而且相对于现有技术中单纯依靠颜色等表面状态判断食品成熟度的方案准确度更高，为后续控制烹饪设备工作提供可靠的依据。
具体地，图像信息包括彩色的二维图像和立体的三维图像，通过二维图像对食材的类别信息进行识别，通过三维图像对尺寸信息进行识别，进一步地，在获得图像后进行图像增强处理，以提高图像清晰度，有利于对类别信息和尺寸信息进行准确地识别。图像识别算法可以通过本地识别算法、云端识别算法、深度学习的方式或模式识别的方式。
另外，根据本申请提供的上述技术方案中的烹饪设备的控制方法，还可以具有如下附加技术特征：
在上述技术方案中，进一步地，根据类别信息和体积信息确定食材对应的成熟度的步骤，具体包括：根据体积信息计算食材的体积变化量；根据类别信息和体积变化量，通过成熟度模型确定食材对应的成熟度。
在该技术方案中，由于食材是半熟的或者经过发酵处理后，食材本身体积已经有一定变化，那么仅依靠体积值的变化来判断食材成熟度会存在一定误差，而体积变化量却只与烹饪过程相关，故而调取食材的初始体积，即食材放入烹饪设备后首次获得的体积信息，根据当前体积和初始体积的 比值或差值确定食材的体积变化量，按照食材的类别信息，将体积变化量和成熟度模型中的数据进行比对，得到食材的成熟度。通过上述技术方案，不仅能够便于用户监控食材在不同烹饪阶段的体积变化，而且整个成熟度判定过程无需人工干预，即使没有烹饪经验的用户也能快速、准确的了解食材当前的成熟度等级，有利于用户进行高效的烹饪规划。
在上述任一技术方案中，进一步地，识别图像信息，确定食材的体积信息的步骤，具体包括：识别图像信息，确定食材的三维信息；根据三维信息确定食材的尺寸信息；根据尺寸信息确定体积信息。
在该技术方案中，对图像信息中的三维图像进行识别，确定食材的三维信息，利用三维信息得到食材的尺寸信息，也即食材的长、宽、高，根据尺寸信息计算出食材的体积信息，进而利用体积信息计算体积变化量，以便于利用体积变化量自动判断食材成熟度。
具体地，三维信息为三维云点，三维云点包括：坐标信息以及颜色信息和/或激光反射强度，进而根据坐标信息计算出食材尺寸。
在上述任一技术方案中，进一步地，获取食材的图像信息的步骤之前，还包括：构建并保存成熟度模型。
进一步地，构建并保存成熟度模型的步骤具体包括：采集不同类别食材对应的体积数据和烹饪参数；根据体积数据，确定不同类别食材的体积变化量数据；记录任一食材的体积变化量数据、烹饪参数分别与预设成熟度等级之间的对应关系；以及根据对应关系，构建并保存成熟度模型。
在该技术方案中，采集不同类别食材对应的体积数据和烹饪参数，其中，烹饪参数包括模式、功率和时长，根据体积数据计算不同类别食材的体积变化量数据，不同类别食材在不同烹饪模式和功率下，未成熟食材的体积V0随着烹饪时长的推进逐渐变到V1、V2、V3直至Vn，不同烹饪时长的体积相对食材的体积变化率RV1＝V1/V0，RV2＝V2/V0至RVn＝Vn/0，记录任一食材的体积变化量数据和烹饪参数分别与预设成熟度等级之间的对应关系，根据上述对应关系构建成熟度模型，并将其保存在***中，以便于在烹饪食材时通过类别信息和体积变化量确定食材在不同烹饪阶段的成熟度。
具体地，模型中将食材的预设成熟度等级从生到焦划分成多个成熟度阶段，例如，生、三成熟、五成熟、七成熟、全熟、过熟和焦化七个熟度等级，成熟度等级需要用户根据经验或者一定判断标准合理设置。
另外，考虑到食材本身具备一定的成熟度，还可以采用当前体积与上一次测得体积的比值计算体积变化率，即RV1＝V1/V0，RV2＝V2/V1……RVn＝Vn/n-1。
在上述任一技术方案中，进一步地，根据类别信息和体积信息确定食材对应的成熟度的步骤之前，还包括：获取食材的目标成熟度等级；根据类别信息和目标成熟度等级，通过成熟度模型获取对应的烹饪参数；根据烹饪参数控制烹饪设备工作。
在该技术方案中，获取食材的目标成熟度等级，即用户需要的食材成熟度，例如，牛排七分熟、面包全熟等，根据类别信息和目标成熟度等级，选择已经在成熟度模型中存储的烹饪参数，按照烹饪参数控制烹饪设备工作，从而实现食材的自动烹饪，使得食材烹饪后直接达到目标成熟度等级，避免烹饪过火或没有成熟，且整个烹饪过程无需用户看管，能够为无烹饪经验的用户提供可靠的烹饪方案，简单准确，容易操作，满足用户多种需求，提高烹饪设备的实用性和推广性。
需要说明的是，当成熟度模型中一种食材预先训练有多种烹饪参数时，则将全部烹饪参数推送给用户，根据用户确定的参数控制烹饪设备工作，例如用户需要将鱼加热到全熟，而模型中存在参数一：烘烤、2000W、10min，参数二：蒸、1000W、20min，此时用户可根据需求进行选择参数一或参数二。
在上述任一技术方案中，进一步地，还包括：获取烹饪设备的当前工作时长；根据烹饪参数对应的烹饪时长阈值与当前工作时长的差值，确定剩余工作时长；显示剩余工作时长。
在该技术方案中，获取烹饪设备的当前工作时长，即烹饪设备按照烹饪参数工作的时长，根据烹饪参数对应的烹饪时长阈值与当前工作时长的差值，确定食材当前成熟度等级达到目标成熟度等级所需的剩余工作时长，并显示剩余工作时长，以提示用户烹饪倒计时，便于用户直观了解到烹饪 所需的时间，有利于对烹饪进行规划，从而提高烹饪效率，提升用户的使用体验。
在上述任一技术方案中，进一步地，显示剩余工作时长的步骤之前，还包括：根据食材的当前成熟度等级与目标成熟度等级，通过成熟度模型确定当前成熟度等级达到目标成熟度等级的剩余烹饪时长；比较剩余烹饪时长与剩余工作时长；基于剩余烹饪时长大于或小于剩余工作时长的情况，调整烹饪参数。
在该技术方案中，若设置的烹饪时长阈值与食材实际所需的时长存在偏差，这样会造成烹饪结束而食材焦化或未达到目标成熟等级，因此，根据食材的当前成熟度等级与目标成熟度等级，通过成熟度模型中预设成熟度等级与烹饪参数的关系，确定当前成熟度等级和目标成熟度等级分别对应的烹饪时长，进而计算出当前成熟度等级达到目标成熟度等级的剩余烹饪时长，比较剩余烹饪时长与剩余工作时长，若剩余烹饪时长与剩余工作时长不符，说明此次烹饪容易造成食材焦化或未达到目标成熟等级的情况，此时对食材的烹饪参数进行调节，以使剩余烹饪时长与剩余工作时长相等，从而保证烹饪设备按照烹饪参数工作后能够使食材达到目标成熟度等级，实现烹饪过程的自动动态调节，提升用户体验。
在上述任一技术方案中，进一步地，还包括：基于食材的当前成熟度等级达到目标成熟度等级的情况，控制烹饪设备停止工作，并发出提示信息；或根据成熟度模型，确定与目标成熟度等级对应的体积变化量范围；基于体积变化量满足体积变化量范围的情况，控制烹饪设备停止工作，并发出提示信息。
在该技术方案中，当食材的当前成熟度等级达到目标成熟度等级时，或者，当体积变化量满足目标成熟度等级对应的体积变化量范围时，说明食材烹饪完毕，此时控制烹饪设备停止工作，并发出提示信息，以提示用户烹饪结束。通过上述方案，无需借助以往的人工方式确认食材的生熟程度，自动根据设置的烹饪参数对食材进行烹饪，节约了用户的观察时间，为用户带来极大的便利，大大增强用户的使用感受。
具体地，提示方式包括以下至少一种：语音、灯光、图像。
根据本申请的第二方面，提出了一种烹饪设备的控制方法，包括：获取食材的图像信息和食材的重量信息；根据图像信息和重量信息，确定食材对应的成熟度。
本申请提供的烹饪设备的控制方法，由于在烹饪过程中，食材的体积会膨胀或者减小，重量由于水分的蒸发也会发生变化，使得食材从里到外的成熟度变化会综合到密度信息上，通过识别图像信息得到食材的尺寸信息和类别信息，进而根据尺寸信息得出食材的体积信息，根据食材的体积信息和重量信息计算食材的密度信息，根据食材的密度，在预先训练好的成熟度模型中确定食材对应的成熟度，一方面，能够实时监测食材的体积和重量变化，也即密度变化，另一方面，利用密度实现了自动成熟度识别功能，大大节省用户观察时间，有效降低识别难度，而且避免了传统方法中单纯依靠颜色和表面温度判断食品成熟度的不准确性，为后续控制烹饪设备工作提供可靠的依据。
另外，根据本申请提供的上述技术方案中的烹饪设备的控制方法，还可以具有如下附加技术特征：
在上述技术方案中，进一步地，根据图像信息和重量信息，确定食材对应的成熟度的步骤，具体包括：识别图像信息，确定食材的类别信息和体积信息；根据体积信息和重量信息，计算食材的密度信息；根据类别信息和密度信息，通过成熟度模型确定食材对应的成熟度。
在该技术方案中，考虑到食材是半熟的或者经过发酵处理后，食材本身体积已经有一定变化，那么依靠体积来判断食材成熟度会存在一定误差，而食材的密度却是一个累积量，能够综合反映食材实际的成熟度，故而通过图像信息确定食材的类别信息和体积信息，根据公式密度(ρ)＝重量(m)/体积(V)，得到相应食材的密度信息，按照食材的类别信息，将食材密度和成熟度模型中的数据进行比对，得到食材的成熟度，从而无需人工观察识别，为无烹饪经验的用户提供成熟度判断功能，且有效提升成熟度识别的准确性，为后续控制烹饪设备工作提供可靠的依据。
具体地，图像信息包括彩色的二维图像和立体的三维图像，通过二维图像对食材的类别信息进行识别，通过三维图像对尺寸信息进行识别，进 一步地，在获得图像后进行图像增强处理，以提高图像清晰度，有利于对类别信息和尺寸信息进行准确地识别。
在上述任一技术方案中，进一步地，识别图像信息，确定食材的体积信息的步骤，具体包括：识别图像信息，确定食材的三维信息；根据三维信息确定食材的尺寸信息；根据尺寸信息确定体积信息。
在该技术方案中，对图像信息中的三维图像进行识别，确定食材的三维信息，利用三维信息得到食材的尺寸信息，也即食材的长、宽、高，根据尺寸信息计算出食材的体积信息，进而利用体积和重量计算密度，以便于利用密度自动判断食材成熟度。
具体地，三维信息为三维云点，三维云点包括：坐标信息以及颜色信息和/或激光反射强度，进而根据坐标信息计算出食材尺寸。
在上述任一技术方案中，进一步地，获取食材的图像信息和食材的重量信息的步骤之前，还包括：构建并保存成熟度模型。
进一步地，构建并保存成熟度模型的步骤具体包括：采集不同类别食材对应的体积数据、重量数据和烹饪参数；根据体积数据和重量数据，计算不同类别食材的密度数据；记录任一食材的体积数据、重量数据、密度数据、烹饪参数分别与预设成熟度等级之间的对应关系；以及根据对应关系，构建并保存成熟度模型。
在该技术方案中，采集不同类别食材对应的体积数据、重量数据和烹饪参数，其中，烹饪参数包括模式、功率和时长，根据体积数据和重量数据，计算不同类别食材的密度数据，不同类别食材在不同烹饪模式和功率下，未成熟食材的体积和重量随着烹饪时长的推进逐渐变化，则未成熟食材的密度ρ0也随着食材被烹饪，密度逐渐变到ρ1、ρ2、ρ3一直到ρn，记录任一食材的体积数据、重量数据、密度数据、烹饪参数分别与预设成熟度等级之间的对应关系，根据上述对应关系构建成熟度模型，并将其保存在***中，以便于在烹饪食材时通过类别信息和密度信息确定食材在不同烹饪阶段的成熟度。
具体地，模型中将食材的预设成熟度等级从生到焦划分成多个成熟度阶段，例如，生、三成熟、五成熟、七成熟、全熟、过熟和焦化七个熟度 等级，成熟度等级需要用户根据经验或者一定判断标准合理设置。
在上述任一技术方案中，进一步地，根据类别信息和密度信息，通过成熟度模型确定食材对应的成熟度的步骤之前，还包括：获取食材的目标成熟度等级；根据类别信息和目标成熟度等级，通过成熟度模型获取对应的烹饪参数；根据烹饪参数控制烹饪设备工作。
在该技术方案中，获取食材的目标成熟度等级，即用户需要的食材成熟度，例如，牛排七分熟、面包全熟等，根据类别信息和目标成熟度等级，选择已经在成熟度模型中存储的烹饪参数，按照烹饪参数控制烹饪设备工作，从而实现食材的自动烹饪，使得食材烹饪后直接达到目标成熟度等级，避免烹饪过火或没有成熟，且整个烹饪过程无需用户看管，能够为无烹饪经验的用户提供可靠的烹饪方案，简单准确，容易操作。
需要说明的是，当成熟度模型中一种食材预先训练有多种烹饪参数时，则将全部烹饪参数推送给用户，根据用户确定的参数控制烹饪设备工作，例如用户需要将鱼加热到全熟，而模型中存在参数一：烘烤、2000W、10min，参数二：蒸、1000W、20min，此时用户可根据需求进行选择参数一或参数二。
在上述任一技术方案中，进一步地，还包括：获取烹饪设备的当前工作时长；根据烹饪参数对应的烹饪时长阈值与当前工作时长的差值，确定剩余工作时长；显示剩余工作时长。
在该技术方案中，获取烹饪设备的当前工作时长，即烹饪设备按照烹饪参数工作的时长，根据烹饪参数对应的烹饪时长阈值与当前工作时长的差值，确定食材当前成熟度等级达到目标成熟度等级所需的剩余工作时长，并显示剩余工作时长，以提示用户烹饪倒计时，便于用户直观了解到烹饪所需的时间，有利于对烹饪进行规划，从而提高烹饪效率，提升用户的使用体验。
在上述任一技术方案中，进一步地，显示剩余工作时长的步骤之前，还包括：根据食材的当前成熟度等级与目标成熟度等级，通过成熟度模型确定当前成熟度等级达到目标成熟度等级的剩余烹饪时长；比较剩余烹饪时长与剩余工作时长；基于剩余烹饪时长大于或小于剩余工作时长的情况， 调整烹饪参数。
在该技术方案中，由于食材本身具备一定的成熟度或其它因素，导致设置的烹饪时长阈值与食材实际所需的时长存在偏差，这样会造成烹饪结束而食材焦化或未达到目标成熟等级，因此，根据食材的当前成熟度等级与目标成熟度等级，通过成熟度模型中预设成熟度等级与烹饪参数的关系，确定当前成熟度等级和目标成熟度等级分别对应的烹饪时长，进而计算出当前成熟度等级达到目标成熟度等级的剩余烹饪时长，比较剩余烹饪时长与剩余工作时长，若剩余烹饪时长与剩余工作时长不符，说明此次烹饪容易造成食材焦化或未达到目标成熟等级的情况，此时对食材的烹饪参数进行调节，以使剩余烹饪时长与剩余工作时长相等，从而保证烹饪设备按照烹饪参数工作后能够使食材达到目标成熟度等级，实现烹饪过程的自动动态调节，提升用户体验。
在上述任一技术方案中，进一步地，还包括：基于食材的当前成熟度等级达到目标成熟度等级的情况，控制烹饪设备停止工作，并发出提示信息；或根据成熟度模型，确定与目标成熟度等级对应的密度范围；基于密度信息满足密度范围的情况，控制烹饪设备停止工作，并发出提示信息。
在该技术方案中，当食材的当前成熟度等级达到目标成熟度等级时，或者，当密度信息满足目标成熟度等级对应的密度范围时，说明食材烹饪完毕，此时控制烹饪设备停止工作，并发出提示信息，以提示用户烹饪结束。通过上述方案，无需借助以往的烘培以人工的方式确认食材的生熟程度，自动根据设置的烹饪参数对食材进行烹饪，节约了用户的观察时间，为用户带来极大的便利，大大增强用户的使用感受。
具体地，提示方式包括以下至少一种：语音、灯光、图像。
根据本申请的第三方面，提出了一种烹饪设备的控制装置，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，处理器执行计算机程序时实现第一方面提出的烹饪设备的控制方法。因此该烹饪设备的控制装置具备上述任一项的烹饪设备的控制方法的全部有益效果。
根据本申请的第四方面，提出了一种烹饪设备的控制装置，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，处理器 执行计算机程序时实现第四方面提出的烹饪设备的控制方法。因此该烹饪设备的控制装置具备上述任一项的烹饪设备的控制方法的全部有益效果。
根据本申请的第五方面，提出了一种烹饪设备，包括：图像采集装置，用于采集食材的图像信息；及第三方面提出烹饪设备的控制装置，控制装置与图像采集装置和重力传感器相连接。
本申请提供的烹饪设备，通过图像采集装置采集食材的图像信息，识别图像信息得到食材的尺寸信息和类别信息，进而根据尺寸信息得出食材的体积信息，利用食材的初始体积和当前体积计算食材在烹饪过程中的体积变化量，根据食材的体积变化量，在预先训练好的成熟度模型中确定食材对应的成熟度，一方面，能够实时监测食材的体积变化，另一方面，利用体积变化量实现了自动成熟度识别功能，大大节省用户观察时间，有效降低识别难度，而且相对于现有技术中单纯依靠颜色等表面状态判断食品成熟度的方案准确度更高，为后续控制烹饪设备工作提供可靠的依据。
进一步地，图像信息包括彩色的二维图像和立体的三维图像，通过二维图像对食材的类别信息进行识别，通过三维图像对尺寸信息进行识别。图像采集装置包括一组或多组摄像头，设置多个摄像头能够获取多个角度的食材图像，有利于识别食材尺寸信息和类别信息。
具体地，烹饪设备包括但不限于以下至少一种：烤箱、蒸箱和微波炉等。
根据本申请的第六方面，提出了一种烹饪设备，包括：图像采集装置，用于采集食材的图像信息；重力传感器，用于采集食材的重量信息；及第四方面提出烹饪设备的控制装置，控制装置与图像采集装置和重力传感器相连接。
本申请提供的烹饪设备，通过图像采集装置采集食材的图像信息，通过重力传感器采集食材的重量信息，识别图像信息中食材的尺寸信息和类别信息，进而根据尺寸信息得出食材的体积，再根据食材的体积信息和重量信息计算食材的密度信息，根据食材的密度，在预先训练好的成熟度模型中确定食材对应的成熟度，一方面，能够实时监测食材的体积和重量变化，即密度变化，另一方面，利用密度实现了自动成熟度识别功能，大大 节省用户观察时间，有效降低识别难度，而且避免了传统方法中单纯依靠颜色和表面温度判断食品成熟度的不准确性，为后续控制烹饪设备工作提供可靠的依据。
根据本申请的第七方面，提出了一种计算机可读存储介质，其上存储有计算机程序，计算机程序被处理器执行时实现第一方面提出的烹饪设备的控制方法的步骤或第二方面提出的烹饪设备的控制方法的步骤。因此该计算机可读存储介质具备第一方面提出的烹饪设备的控制方法或第二方面提出的烹饪设备的控制方法的全部有益效果。
本申请的附加方面和优点将在下面的描述部分中变得明显，或通过本申请的实践了解到。
附图说明
本申请的上述和/或附加的方面和优点从结合下面附图对实施例的描述中将变得明显和容易理解，其中：
图1示出了本申请一个实施例的烹饪设备的控制方法流程示意图；
图2示出了本申请又一个实施例的烹饪设备的控制方法流程示意图；
图3示出了本申请又一个实施例的烹饪设备的控制方法流程示意图；
图4示出了本申请又一个实施例的烹饪设备的控制方法流程示意图；
图5示出了本申请又一个实施例的烹饪设备的控制方法流程示意图；
图6示出了本申请又一个实施例的烹饪设备的控制方法流程示意图；
图7示出了本申请一个具体实施例的烹饪设备的控制方法流程示意图；
图8示出了本申请又一个实施例的烹饪设备的控制方法流程示意图；
图9示出了本申请又一个实施例的烹饪设备的控制方法流程示意图；
图10示出了本申请又一个实施例的烹饪设备的控制方法流程示意图；
图11示出了本申请又一个实施例的烹饪设备的控制方法流程示意图；
图12示出了本申请又一个实施例的烹饪设备的控制方法流程示意图；
图13示出了本申请又一个实施例的烹饪设备的控制方法流程示意图；
图14示出了本申请又一个具体实施例的烹饪设备的控制方法流程示 意图；
图15示出了本申请一个实施例的烹饪设备的控制装置示意框图；
图16示出了本申请一个实施例的烹饪设备的控制装置示意框图；
图17示出了本申请一个实施例的烹饪设备示意框图。
具体实施方式
为了能够更清楚地理解本申请的上述目的、特征和优点，下面结合附图和具体实施方式对本申请进行进一步的详细描述。需要说明的是，在不冲突的情况下，本申请的实施例及实施例中的特征可以相互组合。
在下面的描述中阐述了很多具体细节以便于充分理解本申请，但是，本申请还可以采用其他不同于在此描述的其他方式来实施，因此，本申请的保护范围并不限于下面公开的具体实施例的限制。
下面参照图1至图17描述根据本申请一些实施例的烹饪设备的控制方法、烹饪设备的控制装置、烹饪设备和计算机可读存储介质。
实施例一
如图1所示，根据本申请第一方面的实施例，提出了一种烹饪设备的控制方法，该方法包括：
步骤S102，获取食材的图像信息；
步骤S104，识别图像信息，确定食材的类别信息和食材的体积信息；
步骤S106，根据类别信息和体积信息确定食材对应的成熟度。
在该实施例中，获取食材的图像信息，通过识别图像信息得到食材的尺寸信息和类别信息，进而根据尺寸信息得出食材的体积信息，利用食材的初始体积和当前体积计算食材在烹饪过程中的体积变化量，根据食材的体积变化量，在预先训练好的成熟度模型中确定食材对应的成熟度，一方面，能够实时监测食材的体积变化，另一方面，利用体积变化量实现了自动成熟度识别功能，大大节省用户观察时间，有效降低识别难度，而且相对于现有技术中单纯依靠颜色等表面状态判断食品成熟度的方案准确度更高，为后续控制烹饪设备工作提供可靠的依据。
具体地，图像信息包括彩色的二维图像和立体的三维图像，通过二维 图像对食材的类别信息进行识别，通过三维图像对尺寸信息进行识别，进一步地，在获得图像后进行图像增强处理，以提高图像清晰度，有利于对类别信息和尺寸信息进行准确地识别，图像识别算法可以通过本地识别算法、云端识别算法、深度学习的方式或模式识别的方式。
实施例二
如图2所示，根据本申请的一个实施例，提出了一种烹饪设备的控制方法，该方法包括：
步骤S202，构建并保存成熟度模型；
步骤S204，获取食材的图像信息；
步骤S206，识别图像信息，确定食材的类别信息和食材的三维信息；
步骤S208，根据三维信息确定食材的尺寸信息；
步骤S210，根据尺寸信息确定体积信息；
步骤S212，根据体积信息计算食材的体积变化量；
步骤S214，根据类别信息和体积变化量，通过成熟度模型确定食材对应的成熟度。
在该实施例中，由于食材是半熟的或者经过发酵处理后，食材本身体积已经有一定变化，那么仅依靠体积来判断食材成熟度会存在一定误差，而体积变化量却只与烹饪过程相关，故而调取食材的初始体积，即食材放入烹饪设备后首次获得的体积信息，根据当前体积和初始体积的比值或差值确定食材的体积变化量，按照食材的类别信息，将体积变化量和成熟度模型中的数据进行比对，得到食材的成熟度。通过上述实施例，不仅能够便于用户监控食材在不同烹饪阶段的体积变化，而且整个成熟度判定过程无需人工干预，即使没有烹饪经验的用户也能快速、准确的了解食材当前的成熟度等级，有利于用户进行高效的烹饪规划。
具体地，三维信息为三维云点，三维云点包括：坐标信息以及颜色信息和/或激光反射强度，进而根据坐标信息计算出食材尺寸。
实施例三
如图3所示，根据本申请的一个实施例，提出了一种烹饪设备的控制方法，该方法包括：
步骤S302，采集不同类别食材对应的体积数据和烹饪参数；
步骤S304，根据体积数据，确定不同类别食材的体积变化量数据；
步骤S306，记录任一食材的体积变化量数据、烹饪参数分别与预设成熟度等级之间的对应关系；
步骤S308，根据对应关系，构建并保存成熟度模型；
步骤S310，获取食材的图像信息；
步骤S312，识别图像信息，确定食材的类别信息和食材的体积信息；
步骤S314，根据体积信息计算食材的体积变化量；
步骤S316，根据类别信息和体积变化量，通过成熟度模型确定食材对应的成熟度。
在该实施例中，采集不同类别食材对应的体积数据和烹饪参数，其中，烹饪参数包括模式、功率和时长，根据体积数据计算不同类别食材的体积变化量数据，不同类别食材在不同烹饪模式和功率下，未成熟食材的体积V0随着烹饪时长的推进逐渐变到V1、V2、V3直至Vn，不同烹饪时长的体积相对食材的体积变化率RV1＝V1/V0，RV2＝V2/V0至RVn＝Vn/V0，记录任一食材的体积变化量数据和烹饪参数分别与预设成熟度等级之间的对应关系，例如，面包在烘烤模式、1000W的功率下，烹饪20min后面包体积变化率为1.1～1.2，成熟度等级为三成熟，烹饪60min后面包体积变化率为1.6～1.7，成熟度等级为过熟，牛排在烘烤模式、1500W的功率下，烹饪20min后牛排体积变化率为0.8～0.9，成熟度等级为七成熟，根据上述对应关系构建成熟度模型，并将其保存在***中，以便于在烹饪食材时通过类别信息和体积变化量确定食材在不同烹饪阶段的成熟度。
具体地，模型中将食材的预设成熟度等级从生到焦划分成多个成熟度阶段，例如，生、三成熟、五成熟、七成熟、全熟、过熟和焦化七个熟度等级，成熟度等级需要用户根据经验或者一定判断标准合理设置。
进一步地，考虑到待烹饪食材本身具备一定的成熟度，还可以采用当前体积与上一次测得体积的比值计算体积变化率，即RV1＝V1/V0，RV2＝V2/V1……RVn＝Vn/n-1。
实施例四
如图4所示，根据本申请的一个实施例，提出了一种烹饪设备的控制方法，该方法包括：
步骤S402，构建并保存成熟度模型；
步骤S404，获取食材的图像信息和食材的目标成熟度等级；
步骤S406，识别图像信息，确定食材的类别信息；
步骤S408，根据类别信息和目标成熟度等级，通过成熟度模型获取对应的烹饪参数；
步骤S410，根据烹饪参数控制烹饪设备工作；
步骤S412，识别图像信息，确定食材的体积信息；
步骤S414，根据体积信息计算食材的体积变化量；
步骤S416，根据类别信息和体积变化量，通过成熟度模型确定食材对应的成熟度。
在该实施例中，获取食材的目标成熟度等级，即用户需要的食材成熟度，例如，牛排七分熟、面包全熟等，根据类别信息和目标成熟度等级，选择已经在成熟度模型中存储的烹饪参数，按照烹饪参数控制烹饪设备工作，从而实现食材的自动烹饪，使得食材烹饪后直接达到目标成熟度等级，避免烹饪过火或没有成熟，且整个烹饪过程无需用户看管，能够为无烹饪经验的用户提供可靠的烹饪方案，简单准确，容易操作，满足用户多种需求，提高烹饪设备的实用性和推广性。
需要说明的是，当成熟度模型中一种食材预先训练有多种烹饪参数时，则将全部烹饪参数推送给用户，根据用户确定的参数控制烹饪设备工作，例如用户需要将鱼加热到全熟，而模型中存在参数一：烘烤、2000W、10min，参数二：蒸、1000W、20min，此时用户可根据需求进行选择参数一或参数二。
另外，仅在首次获取食材的图像信息后，根据类别信息和目标成熟度等级，通过成熟度模型获取对应的烹饪参数，并控制烹饪设备工作，若烹饪设备处于工作状态下，则自动跳过获取烹饪参数的步骤，实时获取食材的图像信息，根据图像信息监测食材的体积信息，直至烹饪结束。
实施例五
如图5所示，根据本申请的一个实施例，提出了一种烹饪设备的控制方法，该方法包括：
步骤S502，构建并保存成熟度模型；
步骤S504，获取食材的图像信息和食材的目标成熟度等级；
步骤S506，识别图像信息，确定食材的类别信息和体积信息；
步骤S508，根据体积信息，确定不同类别食材的体积变化量；
步骤S510，根据类别信息和体积变化量，通过成熟度模型确定食材对应的成熟度；
步骤S512，获取烹饪设备的当前工作时长；
步骤S514，根据烹饪参数对应的烹饪时长阈值与当前工作时长的差值，确定剩余工作时长；
步骤S516，显示剩余工作时长；
步骤S518，食材的当前成熟度等级是否达到目标成熟度等级，若是，进入步骤S520，若否，进入步骤S504；
步骤S520，控制烹饪设备停止工作，并发出提示信息。
在该实施例中，获取烹饪设备的当前工作时长，即烹饪设备按照烹饪参数工作的时长，根据烹饪参数对应的烹饪时长阈值与当前工作时长的差值，确定食材当前成熟度等级达到目标成熟度等级所需的剩余工作时长，并显示剩余工作时长，以提示用户烹饪倒计时，便于用户直观的了解到烹饪所需的时间，有利于对烹饪进行规划，提高烹饪效率，而且在当前成熟度等级是否达到目标成熟度等级时，说明食材烹饪完毕，此时控制烹饪设备停止工作，并发出提示信息，以提示用户烹饪结束，实现烹饪设备的自动控制功能。通过上述方案，无需借助以往的人工方式确认食材的生熟程度，自动根据设置的烹饪参数对食材进行烹饪，节约了用户的观察时间，为用户带来极大的便利，大大增强用户的使用感受。
实施例六
如图6所示，根据本申请的一个实施例，提出了一种烹饪设备的控制方法，该方法包括：
步骤S602，获取食材的图像信息和食材的目标成熟度等级；
步骤S604，识别图像信息，确定食材的类别信息和体积信息；
步骤S606，根据图像信息和体积信息，确定食材对应的成熟度；
步骤S608，获取烹饪设备的当前工作时长；
步骤S610，根据烹饪参数对应的烹饪时长阈值与当前工作时长的差值，确定剩余工作时长；
步骤S612，根据食材的当前成熟度等级与目标成熟度等级，通过成熟度模型确定当前成熟度等级达到目标成熟度等级的剩余烹饪时长；
步骤S614，剩余烹饪时长是否等于剩余工作时长，若是，进入步骤S616，若否，进入步骤S618；
步骤S616，显示剩余工作时长，进入步骤S620；
步骤S618，调整烹饪参数；
步骤S620，根据成熟度模型，确定与目标成熟度等级对应的体积变化量范围；
步骤S622，体积变化量是否满足体积变化量范围，若是，进入步骤S624，若否，进入步骤S602；
步骤S624，控制烹饪设备停止工作，并发出提示信息。
在该实施例中，若设置的烹饪时长阈值与食材实际所需的时长存在偏差，这样会造成烹饪结束而食材焦化或未达到目标成熟等级，因此，根据食材的当前成熟度等级与目标成熟度等级，通过成熟度模型中预设成熟度等级与烹饪参数的关系，确定当前成熟度等级和目标成熟度等级分别对应的烹饪时长，进而计算出当前成熟度等级达到目标成熟度等级的剩余烹饪时长，比较剩余烹饪时长与剩余工作时长，若剩余烹饪时长与剩余工作时长不符，说明此次烹饪容易造成食材焦化或未达到目标成熟等级的情况，此时对食材的烹饪参数进行调节，以使剩余烹饪时长与剩余工作时长相等，从而保证烹饪设备按照烹饪参数工作后能够使食材达到目标成熟度等级，实现烹饪过程的自动动态调节，提升用户体验。
实施例七
如图7所示，根据本申请的一个具体实施例，以烤箱作为烹饪设备，烹饪设备的控制方法包括：
步骤S702，获取特定食材的初始体积和加热模式；
步骤S704，控制烹饪设备在相应的加热模式下加热；
步骤S706，记录食材的体积变化率与加热时间的对应关系；
步骤S708，标注成熟度与加热时间的对应关系；
步骤S710，根据标注的加热时间模拟出成熟度与体积变化率模型；
步骤S712，通过二维图像识别食材类别，根据不同食材类别调取训练库中的相应的成熟度与体积变化率模型；
步骤S714，利用三维图像探测食材的初始体积，并在加热过程中监控食物体积相对于初始体积的变化率；
步骤S716，将体积变化率情况与食材对应的成熟度与体积变化率模型做对比，判断食材成熟度；
步骤S718，判断食材的体积变化率是否达到模型中目标成熟度对应的体积变化率范围，若是，进入步骤S720，若否，进入步骤S714；
步骤S720，完成加热。
在该实施例中，将三维摄像头安装于烤箱顶部或者侧面，视野可以覆盖烤盘区域。使用前需要预先训练不同食物从生到熟的体积变化率模型，模型中可以将食物从生到过熟分成多个成熟度阶段，具体地，模型中将食材的成熟度从生到焦化共计分成七个成熟度阶段，包括生、三成熟、五成熟、七成熟、全熟、过熟和焦化七个熟度点，这些成熟度的标注需要人根据经验或者一定判断标准去判断。生食物的体积V0随着食物被加热，体积逐渐变到V1、V2、V3一直到Vn，不同加热时间的体积相对生食物的体积变化率RV1＝V1/V0，RV2＝V2/V0一直到RVn＝Vn/V0生成一个特定食物和特定加热模式下的加热时间和食物体积的变化率RV对应关系，同时食物在烹饪过程中，需要人工标注不同时间点对应的成熟度，生成一个特定食物和特定烹饪模式下的烹饪时间和成熟度的对应关系。结合两个对应关系，即可得到特定食物和特定烹饪模式下食物体积变化率和成熟度的对应关系，即成熟度与体积变化率模型(成熟度模型)，即可根据体积变化率判断食物成熟度。这一模型训练和布置在***本地上，也可以是远程服务器上。不同食材、不同加热模式对应不同的成熟度与体积变化率模型。
完成模型训练后，食材放入烤箱，首先利用三维摄像头生成二维RGB(色彩)图像信息，通过图像识别技术识别食材的类别，根据食材类别选择模型中已经训练过的特定加热模式和加热时间，并控制烤箱工作在相应特定的加热模式和加热时间，加热过程中三维摄像头持续采集三维图像信息，并计算食材体积或者长宽高信息，在加热过程中实时监控食材的体积变化率并与模型库中的成熟度做对比，以此判断食材的成熟程度，通过训练模型中的成熟度和加热时间的关系，预估当前成熟度到目标成熟度需要的时间，并显示该倒计时，加热过程中三维摄像头会持续采集，并且烤箱持续更新熟度信息和时间信息。当当前食材的成熟度达到目标成熟度，控制烤箱停止加热，并通过声音/图像提示加热完成，***可以无需用户看管或者为无烹饪经验的用户提供成熟度判断，从而实现自动熟度识别和自动烹饪功能。
实施例八
如图8所示，根据本申请第二方面的实施例，提出了一种烹饪设备的控制方法，该方法包括：
步骤S802，获取食材的图像信息和食材的重量信息；
步骤S804，根据图像信息和重量信息，确定食材对应的成熟度。
在该实施例中，由于在烹饪过程中，食材的体积会膨胀或者减小，重量由于水分的蒸发也会发生变化，使得食材从里到外的成熟度变化会综合到密度信息上，通过识别图像信息得到食材的尺寸信息和类别信息，进而根据尺寸信息得出食材的体积信息，根据食材的体积信息和重量信息计算食材的密度信息，根据食材的密度，在预先训练好的成熟度模型中确定食材对应的成熟度，一方面，能够实时监测食材的体积和重量变化，也即密度变化，另一方面，利用密度实现了自动成熟度识别功能，大大节省用户观察时间，有效降低识别难度，而且避免了传统方法中单纯依靠颜色和表面温度判断食品成熟度的不准确性，为后续控制烹饪设备工作提供可靠的依据。
具体地，图像识别算法可以通过本地识别算法、云端识别算法、深度学习的方式或模式识别的方式。
实施例九
如图9所示，根据本申请的一个实施例，提出了一种烹饪设备的控制方法，该方法包括：
步骤S902，构建并保存成熟度模型；
步骤S904，获取食材的图像信息和食材的重量信息；
步骤S906，识别图像信息，确定食材的类别信息和体积信息；
步骤S908，根据体积信息和重量信息，计算食材的密度信息；
步骤S910，根据类别信息和密度信息，通过成熟度模型确定食材对应的成熟度。
在该实施例中，考虑到食材是半熟的或者经过发酵处理后，食材本身体积已经有一定变化，那么依靠体积变化来判断食材成熟度会存在一定误差，而食材的密度却是一个累积量，能够综合反映食材的从里到外的成熟度，故而通过图像信息确定食材的类别信息和体积信息，根据公式密度(ρ)＝重量(m)/体积(V)，得到相应食材的密度信息，按照食材的类别信息，将食材密度和成熟度模型中的数据进行比对，得到食材的成熟度，从而无需人工观察识别，为无烹饪经验的用户提供成熟度判断功能，有效提升成熟度识别的准确性，为后续控制烹饪设备工作提供可靠的依据。
进一步地，识别图像信息，确定食材的体积信息的步骤，具体包括：识别图像信息，确定食材的三维信息；根据三维信息确定食材的尺寸信息；根据尺寸信息确定体积信息。
具体地，三维信息为三维云点，三维云点包括：坐标信息以及颜色信息和/或激光反射强度，进而根据坐标信息计算出食材尺寸。图像信息包括彩色的二维图像和立体的三维图像，通过二维图像对食材的类别信息进行识别，通过三维图像对尺寸信息进行识别，进一步地，在获得图像后进行图像增强处理，以提高图像清晰度，有利于对类别信息和尺寸信息进行准确地识别。
实施例十
如图10所示，根据本申请的一个实施例，提出了一种烹饪设备的控制方法，该方法包括：
步骤S1002，采集不同类别食材对应的体积数据、重量数据和烹饪参数；
步骤S1004，根据体积数据和重量数据，计算不同类别食材的密度数据；
步骤S1006，记录任一食材的体积数据、重量数据、密度数据、烹饪参数分别与预设成熟度等级之间的对应关系；
步骤S1008，根据对应关系，构建并保存成熟度模型；
步骤S1010，获取食材的图像信息和食材的重量信息；
步骤S1012，识别图像信息，确定食材的类别信息和体积信息；
步骤S1014，根据体积信息和重量信息，计算食材的密度信息；
步骤S1016，根据类别信息和密度信息，通过成熟度模型确定食材对应的成熟度。
在该实施例中，采集不同类别食材对应的体积数据、重量数据和烹饪参数，其中，烹饪参数包括模式、功率和时长，根据体积数据和重量数据，计算不同类别食材的密度数据，不同类别食材在不同烹饪模式和功率下，未成熟食材的体积数据、重量数据和密度数据随烹饪时长逐渐变化，记录任一食材的体积数据、重量数据、密度数据、烹饪参数分别与预设成熟度等级之间的对应关系，例如，红薯在烘烤模式、1000W的功率下，烹饪10min后红薯密度为1.1～1.2g/cm 3，成熟度等级为三成熟，烹饪60min后红薯密度为0.95～1.0g/cm 3，成熟度等级为全熟，根据上述对应关系构建成熟度模型，并将其保存在***中，以便于在烹饪食材时通过类别信息和密度信息确定食材在不同烹饪阶段的成熟度。
具体地，模型中将食材的预设成熟度等级从生到焦划分成多个成熟度阶段，例如，生、三成熟、五成熟、七成熟、全熟、过熟和焦化七个熟度等级，成熟度等级需要用户根据经验或者一定判断标准合理设置。
实施例十一
如图11所示，根据本申请的一个实施例，提出了一种烹饪设备的控制方法，该方法包括：
步骤S1102，构建并保存成熟度模型；
步骤S1104，获取食材的图像信息、食材的重量信息和食材的目标成熟度等级；
步骤S1106，识别图像信息，确定食材的类别信息；
步骤S1108，根据类别信息和目标成熟度等级，通过成熟度模型获取对应的烹饪参数；
步骤S1110，根据烹饪参数控制烹饪设备工作；
步骤S1112，识别图像信息，确定食材的体积信息；
步骤S1114，根据体积信息和重量信息，计算食材的密度信息；
步骤S1116，根据类别信息和密度信息，通过成熟度模型确定食材对应的成熟度。
在该实施例中，获取食材的目标成熟度等级，即用户需要的食材成熟度，根据类别信息和目标成熟度等级，选择已经在成熟度模型中存储的烹饪参数，按照烹饪参数控制烹饪设备工作，从而实现食材的自动烹饪，使得食材烹饪后直接达到目标成熟度等级，避免烹饪过火或没有成熟，且整个烹饪过程无需用户看管，能够为无烹饪经验的用户提供可靠的烹饪方案，简单准确，容易操作。
需要说明的是，当成熟度模型中一种食材预先训练有多种烹饪参数时，则将全部烹饪参数推送给用户，根据用户确定的参数控制烹饪设备工作，例如用户需要将鱼加热到全熟，而模型中存在参数一：烘烤、2000W、10min，参数二：蒸、1000W、20min，此时用户可根据需求进行选择参数一或参数二。
另外，仅在首次获取食材的图像信息后，根据类别信息和目标成熟度等级，通过成熟度模型获取对应的烹饪参数，并控制烹饪设备工作，若烹饪设备处于工作状态下，则自动跳过获取烹饪参数的步骤，实时获取食材的图像信息，根据图像信息和重量信息监测食材的密度信息，直至烹饪结束。
实施例十二
如图12所示，根据本申请的一个实施例，提出了一种烹饪设备的控制方法，该方法包括：
步骤S1202，构建并保存成熟度模型；
步骤S1204，获取食材的图像信息、食材的重量信息和食材的目标成熟度等级；
步骤S1206，识别图像信息，确定食材的类别信息和体积信息；
步骤S1208，根据体积信息和重量信息，计算食材的密度信息；
步骤S1210，根据类别信息和密度信息，通过成熟度模型确定食材对应的成熟度；
步骤S1212，获取烹饪设备的当前工作时长；
步骤S1214，根据烹饪参数对应的烹饪时长阈值与当前工作时长的差值，确定剩余工作时长；
步骤S1216，显示剩余工作时长；
步骤S1218，食材的当前成熟度等级是否达到目标成熟度等级，若是，进入步骤S1220，若否，进入步骤S1204；
步骤S1220，控制烹饪设备停止工作，并发出提示信息。
在该实施例中，获取烹饪设备的当前工作时长，即烹饪设备按照烹饪参数工作的时长，根据烹饪参数对应的烹饪时长阈值与当前工作时长的差值，确定食材当前成熟度等级达到目标成熟度等级所需的剩余工作时长，并显示剩余工作时长，以提示用户烹饪倒计时，便于用户直观的了解到烹饪所需的时间，有利于对烹饪进行规划，提高烹饪效率，而且当密度信息满足目标成熟度等级对应的密度范围时，说明食材烹饪完毕，此时控制烹饪设备停止工作，并发出提示信息，以提示用户烹饪结束。通过上述方案，无需借助以往的烘培以人工的方式确认食材的生熟程度，自动根据设置的烹饪参数对食材进行烹饪，节约了用户的观察时间，为用户带来极大的便利，大大增强用户的使用感受。
实施例十三
如图13所示，根据本申请的一个实施例，提出了一种烹饪设备的控制方法，该方法包括：
步骤S1302，获取食材的图像信息、食材的重量信息和食材的目标成熟度等级；
步骤S1304，根据图像信息和重量信息，确定食材对应的成熟度；
步骤S1306，获取烹饪设备的当前工作时长；
步骤S1308，根据烹饪参数对应的烹饪时长阈值与当前工作时长的差值，确定剩余工作时长；
步骤S1310，根据食材的当前成熟度等级与目标成熟度等级，通过成熟度模型确定当前成熟度等级达到目标成熟度等级的剩余烹饪时长；
步骤S1312，剩余烹饪时长是否等于剩余工作时长，若是，进入步骤S1314，若否，进入步骤S1316；
步骤S1314，显示剩余工作时长，进入步骤S1318；
步骤S1316，调整烹饪参数；
步骤S1318，根据成熟度模型，确定与目标成熟度等级对应的密度范围；
步骤S1320，密度信息是否满足密度范围，若是，进入步骤S1322，若否，进入步骤S1302；
步骤S1322，控制烹饪设备停止工作，并发出提示信息。
在该实施例中，由于食材本身具备一定的成熟度或其它因素，导致设置的烹饪时长阈值与食材实际所需的时长存在偏差，这样会造成烹饪结束而食材焦化或未达到目标成熟等级，因此，根据食材的当前成熟度等级与目标成熟度等级，通过成熟度模型中预设成熟度等级与烹饪参数的关系，确定当前成熟度等级和目标成熟度等级分别对应的烹饪时长，进而计算出当前成熟度等级达到目标成熟度等级的剩余烹饪时长，比较剩余烹饪时长与剩余工作时长，若剩余烹饪时长与剩余工作时长不符，说明此次烹饪容易造成食材焦化或未达到目标成熟等级的情况，此时对食材的烹饪参数进行调节，以使剩余烹饪时长与剩余工作时长相等，从而保证烹饪设备按照烹饪参数工作后能够使食材达到目标成熟度等级，实现烹饪过程的自动动态调节，提升用户体验。
实施例十四
如图14所示，根据本申请的一个具体实施例，以烤箱作为烹饪设备，烹饪设备的控制方法包括：
步骤S1402，获取特定食材的体积数据、重量数据和加热模式；
步骤S1404，控制烹饪设备在相应的加热模式下加热；
步骤S1406，记录食材密度数据与加热时间的对应关系；
步骤S1408，标注成熟度与加热时间的对应关系；
步骤S1410，根据标注的加热时间模拟出成熟度与密度变化模型；
步骤S1412，通过二维图像识别食材类别，根据不同食材类别调取训练库中的相应的成熟度与密度变化模型；
步骤S1414，利用三维图像探测食材的初始体积，重量传感器检测食材初始重量，转换成食材密度，并在加热过程中持续监控食材密度变化；
步骤S1416，密度变化情况与食材对应的成熟度与密度变化模型做对比，判断食材成熟度；
步骤S1418，判断食材的密度是否达到模型中目标成熟度对应的密度范围，若是，进入步骤S1420，若否，进入步骤S1414；
步骤S1420，完成加热。
在该实施例中，将三维摄像头安装于烤箱顶部或者侧面，视野可以覆盖烤盘区域。使用前需要预先训练不同食材从生到熟的密度变化模型，模型中可以将食材从生到过熟分成多个成熟度阶段，具体地，模型中将食材的成熟度从生到焦化共计分成七个成熟度阶段，包括生、三成熟、五成熟、七成熟、全熟、过熟和焦化七个熟度点，这些成熟度的标注需要人根据经验或者一定判断标准去判断。生食材的密度ρ0随着食材被烹饪，密度逐渐变到ρ1、ρ2、ρ3一直到ρn，根据食材密度受热的密度变化，生成一个特定食材和特定烹饪模式下的加热时间和食材密度的对应关系，同时食材在烹饪过程中，需要人工标注不同加热时间点对应的成熟度，生成一个特定食材和特定加热模式下的加热时间和成熟度的对应关系，结合两个对应关系，即可得到特定食材和特定加热模式下食材成熟度与密度的对应关系，即成熟度与密度变化模型(成熟度模型)这一模型训练和布置在***本地上，也可以是远程服务器上。不同食材、不同加热模式对应不同的成熟度与密度变化模型。
完成模型训练后，食材放入烤箱，首先利用三维摄像头生成二维RGB (色彩)图像信息，通过图像识别技术识别食材的类别，根据食材类别选择模型中已经训练过的特定加热模式和加热时间，并控制烤箱工作在相应特定的加热模式和加热时间，烹饪过程中三维摄像头持续采集三维图像信息，并计算食材体积或者长宽高信息，重量传感器持续探测食材的重量信息，根据公式密度(ρ)＝质量(m)除以体积(V)，得到食材的密度，在加热过程中实时监控食材的密度变化并与模型库中的成熟度做对比，以此判断食材的成熟程度，通过训练模型中的成熟度和加热时间的关系，预估当前成熟度到目标成熟度需要的时间，并显示该倒计时，烹饪过程中三维摄像头会持续采集，并且烤箱持续更新熟度信息和时间信息。当当前食材的成熟度达到目标成熟度，控制烤箱停止加热，并通过声音/图像提示加热完成，***可以无需用户看管或者为无烹饪经验的用户提供成熟度判断，从而实现自动熟度识别和自动烹饪功能。
实施例十五
如图15所示，根据本申请第三方面的实施例，提出了一种烹饪设备的控制装置100，包括存储器102、处理器104及存储在存储器102上并可在处理器104上运行的计算机程序，处理器104执行计算机程序时实现第一方面实施例的烹饪设备的控制方法。因此该烹饪设备的控制装置100具备第一方面实施例的烹饪设备的控制方法的全部有益效果。
实施例十六
如图16所示，根据本申请第四方面的实施例，提出了一种烹饪设备的控制装置200，包括存储器202、处理器204及存储在存储器202上并可在处理器204上运行的计算机程序，处理器204执行计算机程序时实现第二方面实施例的烹饪设备的控制方法。因此该烹饪设备的控制装置200具备第二方面实施例的烹饪设备的控制方法的全部有益效果。
实施例十七
根据本申请第四方面的实施例，提出了一种烹饪设备，包括：图像采集装置，及上述第三方面实施例提出的烹饪设备的控制装置。
具体地，图像采集装置用于采集食材的图像信息，控制装置与图像采集装置相连接，烹饪设备包括但不限于以下至少一种：烤箱、蒸箱和微波 炉等，图像采集装置为三维摄像装置，且三维摄像装置包括一组或多组摄像头。
本实施例提供的烹饪设备，通过图像采集装置采集食材的图像信息，识别图像信息得到食材的尺寸信息和类别信息，进而根据尺寸信息得出食材的体积信息，利用食材的初始体积和当前体积计算食材在烹饪过程中的体积变化量，根据食材的体积变化量，在预先训练好的成熟度模型中确定食材对应的成熟度，一方面，能够实时监测食材的体积变化，另一方面，利用体积变化量实现了自动成熟度识别功能，大大节省用户观察时间，有效降低识别难度，而且相对于现有技术中单纯依靠颜色等表面状态判断食品成熟度的方案准确度更高，为后续控制烹饪设备工作提供可靠的依据。
进一步地，图像信息包括彩色的二维图像和立体的三维图像，通过二维图像对食材的类别信息进行识别，通过三维图像对尺寸信息进行识别。图像采集装置包括一组或多组摄像头，设置多个摄像头能够获取多个角度的食材图像，有利于识别食材尺寸信息和类别信息。
实施例十八
如图17所示，根据本申请第六方面的实施例，提出了一种烹饪设备300，包括：图像采集装置302，重力传感器304，及上述第四方面实施例提出的烹饪设备的控制装置200。
具体地，图像采集装置302用于采集食材的图像信息，重力传感器304用于采集食材的重量信息，控制装置与图像采集装置302和重力传感器304相连接，烹饪设备300包括但不限于以下至少一种：烤箱、蒸箱和微波炉等，图像采集装置302为三维摄像装置，且三维摄像装置包括一组或多组摄像头。
本实施例提供的烹饪设备300，通过图像采集装置302采集食材的图像信息，通过重力传感器304采集食材的重量信息，识别图像信息中食材的尺寸信息和类别信息，进而根据尺寸信息得出食材的体积，再根据食材的体积信息和重量信息计算食材的密度信息，根据食材的密度，在预先训练好的成熟度模型中确定食材对应的成熟度，一方面，能够实时监测食材的体积和重量变化，即密度变化，另一方面，利用密度实现了自动成熟度识别功能，大大节省用户观察时间，有效降低识别难度，而且避免了传统 方法中单纯依靠颜色和表面温度判断食品成熟度的不准确性，为后续控制烹饪设备工作提供可靠的依据。
进一步地，图像信息包括彩色的二维图像和立体的三维图像，通过二维图像对食材的类别信息进行识别，通过三维图像对尺寸信息进行识别，另外设置多个摄像头能够获取多个角度的食材图像，有利于识别食材尺寸信息和类别信息。
实施例十九
根据本申请第七方面的实施例，提出了一种计算机可读存储介质，其上存储有计算机程序，计算机程序被处理器执行时实现第一方面实施例的烹饪设备的控制方法或第二方面实施例的烹饪设备的控制方法的步骤。
本申请提供的计算机可读存储介质，计算机程序被处理器执行时实现第一方面实施例的烹饪设备的控制方法或第二方面实施例的烹饪设备的控制方法的步骤，因此该计算机可读存储介质包括第一方面实施例的烹饪设备的控制方法或第二方面实施例的烹饪设备的控制方法的全部有益效果。
在本说明书的描述中，术语“第一”、“第二”仅用于描述的目的，而不能理解为指示或暗示相对重要性，除非另有明确的规定和限定；术语“连接”、“安装”、“固定”等均应做广义理解，例如，“连接”可以是固定连接，也可以是可拆卸连接，或一体地连接；可以是直接相连，也可以通过中间媒介间接相连。对于本领域的普通技术人员而言，可以根据具体情况理解上述术语在本申请中的具体含义。
在本说明书的描述中，术语“一个实施例”、“一些实施例”、“具体实施例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或特点包含于本申请的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不一定指的是相同的实施例或实例。而且，描述的具体特征、结构、材料或特点可以在任何的一个或多个实施例或示例中以合适的方式结合。
以上所述仅为本申请的优选实施例而已，并不用于限制本申请，对于本领域的技术人员来说，本申请可以有各种更改和变化。凡在本申请的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本申请的保护范围之内。

Claims (23)

  1. 一种烹饪设备的控制方法，其中，包括：
    获取食材的图像信息；
    识别所述图像信息，确定所述食材的类别信息和所述食材的体积信息；
    根据所述类别信息和所述体积信息确定所述食材对应的成熟度。
  2. 根据权利要求1所述的烹饪设备的控制方法，其中，所述根据所述类别信息和所述体积信息确定所述食材对应的成熟度的步骤，具体包括：
    根据所述体积信息计算所述食材的体积变化量；
    根据所述类别信息和所述体积变化量，通过成熟度模型确定所述食材对应的成熟度。
  3. 根据权利要求2所述的烹饪设备的控制方法，其中，所述识别所述图像信息，确定所述食材的体积信息的步骤，具体包括：
    识别所述图像信息，确定所述食材的三维信息；
    根据所述三维信息确定所述食材的尺寸信息；
    根据所述尺寸信息确定所述体积信息。
  4. 根据权利要求2所述的烹饪设备的控制方法，其中，所述获取食材的图像信息的步骤之前，还包括：
    构建并保存所述成熟度模型。
  5. 根据权利要求4所述的烹饪设备的控制方法，其中，所述构建并保存所述成熟度模型的步骤，具体包括：
    采集不同类别食材对应的体积数据和烹饪参数；
    根据所述体积数据，确定所述不同类别食材的体积变化量数据；
    记录任一所述食材的所述体积变化量数据、所述烹饪参数分别与预设成熟度等级之间的对应关系；以及
    根据所述对应关系，构建并保存所述成熟度模型。
  6. 根据权利要求2至5中任一项所述的烹饪设备的控制方法，其中，所述根据所述类别信息和所述体积信息确定所述食材对应的成熟度的步骤之前，还包括：
    获取所述食材的目标成熟度等级；
    根据所述类别信息和所述目标成熟度等级，通过所述成熟度模型获取 对应的烹饪参数；
    根据所述烹饪参数控制所述烹饪设备工作。
  7. 根据权利要求6所述的烹饪设备的控制方法，其中，还包括：
    获取所述烹饪设备的当前工作时长；
    根据所述烹饪参数对应的烹饪时长阈值与所述当前工作时长的差值，确定剩余工作时长；
    显示所述剩余工作时长。
  8. 根据权利要求7所述的烹饪设备的控制方法，其中，所述显示所述剩余工作时长的步骤之前，还包括：
    根据所述食材的当前成熟度等级与所述目标成熟度等级，通过所述成熟度模型确定所述当前成熟度等级达到所述目标成熟度等级的剩余烹饪时长；
    比较所述剩余烹饪时长与所述剩余工作时长；
    基于所述剩余烹饪时长大于或小于所述剩余工作时长的情况，调整所述烹饪参数。
  9. 根据权利要求6所述的烹饪设备的控制方法，其中，还包括：
    基于所述食材的当前成熟度等级达到所述目标成熟度等级的情况，控制所述烹饪设备停止工作，并发出提示信息；或
    根据所述成熟度模型，确定与所述目标成熟度等级对应的体积变化量范围；
    基于所述体积变化量满足所述体积变化量范围的情况，控制所述烹饪设备停止工作，并发出提示信息。
  10. 一种烹饪设备的控制方法，其中，包括：
    获取食材的图像信息和所述食材的重量信息；
    根据所述图像信息和所述重量信息，确定所述食材对应的成熟度。
  11. 根据权利要求10所述的烹饪设备的控制方法，其中，所述根据所述图像信息和所述重量信息，确定所述食材对应的成熟度的步骤，具体包括：
    识别所述图像信息，确定所述食材的类别信息和体积信息；
    根据所述体积信息和所述重量信息，计算所述食材的密度信息；
    根据所述类别信息和所述密度信息，通过成熟度模型确定所述食材对 应的成熟度。
  12. 根据权利要求11所述的烹饪设备的控制方法，其中，所述识别所述图像信息，确定所述食材的体积信息的步骤，具体包括：
    识别所述图像信息，确定所述食材的三维信息；
    根据所述三维信息确定所述食材的尺寸信息；
    根据所述尺寸信息确定所述体积信息。
  13. 根据权利要求11所述的烹饪设备的控制方法，其中，所述获取食材的图像信息和所述食材的重量信息的步骤之前，还包括：
    构建并保存所述成熟度模型。
  14. 根据权利要求13所述的烹饪设备的控制方法，其中，所述构建并保存所述成熟度模型的步骤，具体包括：
    采集不同类别食材对应的体积数据、重量数据和烹饪参数；
    根据所述体积数据和重量数据，计算所述不同类别食材的密度数据；
    记录任一所述食材的所述体积数据、所述重量数据、所述密度数据、所述烹饪参数分别与预设成熟度等级之间的对应关系；以及
    根据所述对应关系，构建并保存所述成熟度模型。
  15. 根据权利要求11所述的烹饪设备的控制方法，其中，所述根据所述类别信息和所述密度信息，通过成熟度模型确定所述食材对应的成熟度的步骤之前，还包括：
    获取所述食材的目标成熟度等级；
    根据所述类别信息和所述目标成熟度等级，通过所述成熟度模型获取对应的烹饪参数；
    根据所述烹饪参数控制所述烹饪设备工作。
  16. 根据权利要求15所述的烹饪设备的控制方法，其中，还包括：
    获取所述烹饪设备的当前工作时长；
    根据所述烹饪参数对应的烹饪时长阈值与所述当前工作时长的差值，确定剩余工作时长；
    显示所述剩余工作时长。
  17. 根据权利要求16所述的烹饪设备的控制方法，其中，所述显示所述剩余工作时长的步骤之前，还包括：
    根据所述食材的当前成熟度等级与所述目标成熟度等级，通过所述成 熟度模型确定所述当前成熟度等级达到所述目标成熟度等级的剩余烹饪时长；
    比较所述剩余烹饪时长与所述剩余工作时长；
    基于所述剩余烹饪时长大于或小于所述剩余工作时长的情况，调整所述烹饪参数。
  18. 根据权利要求15至17中任一项所述的烹饪设备的控制方法，其中，还包括：
    基于所述食材的当前成熟度等级达到所述目标成熟度等级的情况，控制所述烹饪设备停止工作，并发出提示信息；或
    根据所述成熟度模型，确定与所述目标成熟度等级对应的密度范围；
    基于所述密度信息满足所述密度范围的情况，控制所述烹饪设备停止工作，并发出所述提示信息。
  19. 一种烹饪设备的控制装置，其中，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如权利要求1至9中任一项所述的烹饪设备的控制方法。
  20. 一种烹饪设备的控制装置，其中，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如权利要求10至18中任一项所述的烹饪设备的控制方法。
  21. 一种烹饪设备，其中，包括：
    图像采集装置，用于采集所述食材的图像信息；及
    如权利要求19所述的烹饪设备的控制装置，所述控制装置与所述图像采集装置相连接。
  22. 一种烹饪设备，其中，包括：
    图像采集装置，用于采集所述食材的图像信息；
    重量传感器，用于采集所述食材的重量信息；及
    如权利要求20所述的烹饪设备的控制装置，所述控制装置与所述图像采集装置和所述重量传感器相连接。
  23. 一种计算机可读存储介质，其上存储有计算机程序，其中，所述 计算机程序被执行时，实现如权利要求1至9中任一项所述的烹饪设备的控制方法或权利要求10至18中任一项所述的烹饪设备的控制方法或的步骤。
PCT/CN2020/121814 2019-11-20 2020-10-19 烹饪设备的控制方法、装置、烹饪设备及存储介质 WO2021098427A1 (zh)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP20891069.5A EP4047428A4 (en) 2019-11-20 2020-10-19 CONTROL METHOD AND DEVICE FOR COOKING APPLIANCES, COOKING APPLIANCES AND STORAGE MEDIUM
US17/750,107 US20220273136A1 (en) 2019-11-20 2022-05-20 Control Method and Device for Cooking Equipment, Cooking Equipment and Storage Medium

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
CN201911142151.X 2019-11-20
CN201911143383.7 2019-11-20
CN201911142151.XA CN110780628B (zh) 2019-11-20 2019-11-20 烹饪设备的控制方法、装置、烹饪设备及存储介质
CN201911143383.7A CN110806699A (zh) 2019-11-20 2019-11-20 烹饪设备的控制方法、装置、烹饪设备及存储介质

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US17/750,107 Continuation US20220273136A1 (en) 2019-11-20 2022-05-20 Control Method and Device for Cooking Equipment, Cooking Equipment and Storage Medium

Publications (1)

Publication Number Publication Date
WO2021098427A1 true WO2021098427A1 (zh) 2021-05-27

Family

ID=75981194

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/121814 WO2021098427A1 (zh) 2019-11-20 2020-10-19 烹饪设备的控制方法、装置、烹饪设备及存储介质

Country Status (3)

Country Link
US (1) US20220273136A1 (zh)
EP (1) EP4047428A4 (zh)
WO (1) WO2021098427A1 (zh)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230075208A1 (en) * 2021-09-07 2023-03-09 Whirlpool Corporation Generative food doneness prediction

Citations (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040195231A1 (en) * 2003-04-03 2004-10-07 Bond Leonard J. System and technique for ultrasonic determination of degree of cooking
US20140203012A1 (en) * 2013-01-23 2014-07-24 Whirlpool Corporation Microwave oven multiview silhouette volume calculation for mass estimation
CN105444222A (zh) * 2015-12-11 2016-03-30 美的集团股份有限公司 微波炉的烹饪控制方法、***、云服务器和微波炉
CN106102479A (zh) * 2014-03-11 2016-11-09 皇家飞利浦有限公司 控制食物烹饪过程的方法和装置
CN106372198A (zh) * 2016-08-31 2017-02-01 乐视控股（北京）有限公司 一种基于图像识别技术的数据提取方法及其移动终端
CN109426167A (zh) * 2017-08-29 2019-03-05 佛山市顺德区美的电热电器制造有限公司 烹饪机及其烹饪控制方法和装置、以及可读存储介质
CN109541985A (zh) * 2018-10-16 2019-03-29 珠海格力电器股份有限公司 一种烹饪器具的控制方法及烹饪器具
CN109691898A (zh) * 2017-10-24 2019-04-30 佛山市顺德区美的电热电器制造有限公司 一种食物加热的控制方法、装置、煎烤机及存储介质
CN109727657A (zh) * 2019-01-15 2019-05-07 海南大学 个性化食材口味的节材节能环保炊具定制***
CN109998360A (zh) * 2019-04-11 2019-07-12 上海长膳智能科技有限公司 一种用于自动烹饪食物的方法和装置
CN110084372A (zh) * 2019-04-04 2019-08-02 宁波方太厨具有限公司 基于自适应学习的智能菜谱生成方法及智能烹饪方法
CN110123149A (zh) * 2019-05-10 2019-08-16 九阳股份有限公司 一种烹饪设备的烹饪控制方法以及烹饪设备
CN110780628A (zh) * 2019-11-20 2020-02-11 广东美的厨房电器制造有限公司 烹饪设备的控制方法、装置、烹饪设备及存储介质
CN110806699A (zh) * 2019-11-20 2020-02-18 广东美的厨房电器制造有限公司 烹饪设备的控制方法、装置、烹饪设备及存储介质

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102400018B1 (ko) * 2017-09-29 2022-05-19 삼성전자주식회사 자동 조리 장치 및 방법

Patent Citations (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040195231A1 (en) * 2003-04-03 2004-10-07 Bond Leonard J. System and technique for ultrasonic determination of degree of cooking
US20140203012A1 (en) * 2013-01-23 2014-07-24 Whirlpool Corporation Microwave oven multiview silhouette volume calculation for mass estimation
CN106102479A (zh) * 2014-03-11 2016-11-09 皇家飞利浦有限公司 控制食物烹饪过程的方法和装置
CN105444222A (zh) * 2015-12-11 2016-03-30 美的集团股份有限公司 微波炉的烹饪控制方法、***、云服务器和微波炉
CN106372198A (zh) * 2016-08-31 2017-02-01 乐视控股（北京）有限公司 一种基于图像识别技术的数据提取方法及其移动终端
CN109426167A (zh) * 2017-08-29 2019-03-05 佛山市顺德区美的电热电器制造有限公司 烹饪机及其烹饪控制方法和装置、以及可读存储介质
CN109691898A (zh) * 2017-10-24 2019-04-30 佛山市顺德区美的电热电器制造有限公司 一种食物加热的控制方法、装置、煎烤机及存储介质
CN109541985A (zh) * 2018-10-16 2019-03-29 珠海格力电器股份有限公司 一种烹饪器具的控制方法及烹饪器具
CN109727657A (zh) * 2019-01-15 2019-05-07 海南大学 个性化食材口味的节材节能环保炊具定制***
CN110084372A (zh) * 2019-04-04 2019-08-02 宁波方太厨具有限公司 基于自适应学习的智能菜谱生成方法及智能烹饪方法
CN109998360A (zh) * 2019-04-11 2019-07-12 上海长膳智能科技有限公司 一种用于自动烹饪食物的方法和装置
CN110123149A (zh) * 2019-05-10 2019-08-16 九阳股份有限公司 一种烹饪设备的烹饪控制方法以及烹饪设备
CN110780628A (zh) * 2019-11-20 2020-02-11 广东美的厨房电器制造有限公司 烹饪设备的控制方法、装置、烹饪设备及存储介质
CN110806699A (zh) * 2019-11-20 2020-02-18 广东美的厨房电器制造有限公司 烹饪设备的控制方法、装置、烹饪设备及存储介质

Also Published As

Publication number Publication date
EP4047428A4 (en) 2022-12-21
US20220273136A1 (en) 2022-09-01
EP4047428A1 (en) 2022-08-24

Similar Documents

Publication Publication Date Title
CN110780628B (zh) 烹饪设备的控制方法、装置、烹饪设备及存储介质
CN110806699A (zh) 烹饪设备的控制方法、装置、烹饪设备及存储介质
US11517146B2 (en) Cooking assistance appliance
CN107806656B (zh) 食物加热控制方法和食物加热装置
CN203914599U (zh) 一种智能烤箱
CN109213015B (zh) 一种烹饪器具的控制方法及烹饪器具
CN110664259B (zh) 烹饪设备及其控制方法、控制***、计算机可读存储介质
CN111148944B (zh) 自动烹饪设备和方法
CN109541985A (zh) 一种烹饪器具的控制方法及烹饪器具
CN109445485A (zh) 一种烹饪器具的控制方法及烹饪器具
CN113133682A (zh) 烹饪设备、烹饪曲线的调整方法和调整装置
JP6265868B2 (ja) 加熱調理器、加熱調理システム、及び、加熱調理器の制御方法
CN206596942U (zh) 一种智能烹饪***
CN104042124A (zh) 一种智能烤箱及其工作控制方法
CN112361387B (zh) 燃气灶火力功率控制方法、火力功率控制***及燃气灶
CN205608417U (zh) 一种智能烹饪***
WO2021098427A1 (zh) 烹饪设备的控制方法、装置、烹饪设备及存储介质
CN110123149A (zh) 一种烹饪设备的烹饪控制方法以及烹饪设备
WO2021098474A1 (zh) 烹饪设备及其控制方法、加热控制方法和服务器
CN110222720A (zh) 一种具有短视频获取功能的烹饪设备
TW201923657A (zh) 烘豆輔助裝置及烘豆裝置
AU2016403900B2 (en) System for the preparation of at least one food product and method for operating the relevant system
CN112426060A (zh) 控制方法、烹饪器具、服务器和可读存储介质
JP2021034223A (ja) 加熱調理器
CN110836393A (zh) 基于模式识别控制烹饪目标温度的方法、控制装置及灶具

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20891069

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2020891069

Country of ref document: EP

Effective date: 20220517

NENP Non-entry into the national phase

Ref country code: DE