Oven control method, medium and electronic device

Abstract

The invention discloses an oven control method, a medium and an electronic device, and relates to the technical field of smart home / smart home, and the method comprises the steps: firstly obtaining an oven internal food material image collected by an image collection device, and extracting first food material information such as the size, the volume and the number of to-be-cooked food materials from the oven internal food material image; meanwhile, responding to a user voice interaction instruction to determine second food material information such as target taste, cooking degree, cooking duration and quantity of the to-be-cooked food materials; and generating a menu cooking instruction according to the first food material information and the second food material information, and controlling the oven to execute. According to the method, the visual information acquired by the image acquisition device and the semantic information of user voice interaction are fused, so that the problem of inaccurate oven instruction generation is effectively solved, more accurate cooking instruction generation meeting user requirements is realized, and the intelligence of oven cooking is improved.

Description

Technical Field

[0001] This application relates to the field of smart home / intelligent home technology, and more specifically, to an oven control method, medium, and electronic device. Background Technology

[0002] With the improvement of living standards and the popularization of healthy eating concepts, ovens have become a popular choice for family kitchens due to their advantages such as precise temperature control, multiple cooking modes, and multi-functionality.

[0003] Existing ovens require users to issue cooking instructions via voice or have instructions automatically issued after an image acquisition device recognizes the ingredients during the baking process.

[0004] However, this control method that relies on a single modality results in the oven's insufficient adaptability in complex cooking scenarios. For example, voice commands are easily misrecognized due to interference from environmental noise, and image recognition has high requirements for the shape of the ingredients and lighting. Once the data of a certain modality is missing or of poor quality, it is easy to cause cooking failure, affecting the user experience and the reliability of the equipment. Summary of the Invention

[0005] This application provides an oven control method, medium, and electronic device to solve the problem of inaccurate oven command generation.

[0006] In a first aspect, this application provides an oven control method, wherein the oven is equipped with an image acquisition device, including:

[0007] The image acquisition device acquires an image of the food inside the oven, the image of which includes first food information of the food to be cooked, the first food information including at least one of the following: food size, food volume, and food quantity.

[0008] In response to a voice interaction command sent by a user, the cooking requirement information of the user is determined. The cooking requirement information includes second ingredient information of the ingredients to be cooked. The second ingredient information includes at least one of the following: target texture, target doneness, target cooking time, and quantity of the ingredients.

[0009] Based on the first ingredient information and the second ingredient information, generate recipe cooking instructions;

[0010] Control the oven to execute the recipe cooking instructions.

[0011] Optionally, generating recipe cooking instructions based on the first ingredient information and the second ingredient information includes:

[0012] The information of the first ingredient is parsed and processed to determine the first cooking plan;

[0013] The second ingredient information is analyzed and processed to determine the second cooking method;

[0014] The recipe cooking instructions are generated based on the first cooking scheme and the second cooking scheme.

[0015] Optionally, generating the recipe cooking instructions based on the first cooking scheme and the second cooking scheme includes:

[0016] Determine the temperature deviation between the first heating temperature associated with the first cooking scheme and the second heating temperature associated with the second cooking scheme;

[0017] Determine the duration deviation between the first heating time associated with the first cooking scheme and the second heating time associated with the second cooking scheme;

[0018] When the temperature deviation value is within a preset temperature deviation range and the duration deviation value is within a preset duration deviation range, the first cooking scheme and the second cooking scheme are fused to obtain a fused third cooking scheme, and the recipe cooking instruction is generated according to the third cooking scheme.

[0019] Optionally, the method further includes:

[0020] If the temperature deviation value is not within the preset temperature deviation range, or the time deviation value is not within the preset time deviation range, the difference between the first cooking scheme and the second cooking scheme is determined.

[0021] Based on the differences between the first cooking plan and the second cooking plan, a recipe inquiry instruction is generated, which is used to confirm the user's preferred recipe;

[0022] The system receives the user's voice selection command, obtains the target cooking plan, and generates the recipe cooking command based on the target cooking plan.

[0023] Optionally, if the first cooking scheme includes a first numerical parameter, and the second cooking scheme includes a second numerical parameter, then the fusion process of the first cooking scheme and the second cooking scheme to obtain a fused third cooking scheme includes:

[0024] The first numerical parameter and the second numerical parameter are averaged to obtain a third numerical parameter. The third cooking scheme includes the third numerical parameter.

[0025] or,

[0026] The third cooking scheme includes the fourth numerical parameter by multiplying the first numerical parameter by a preset first coefficient and then adding the second numerical parameter by multiplying it by a preset second coefficient.

[0027] Optionally, if the first cooking scheme further includes a first non-numerical parameter, and the second cooking scheme further includes a second non-numerical parameter, the step of fusing the first cooking scheme and the second cooking scheme to obtain a fused third cooking scheme includes:

[0028] The first non-numerical parameter and the second non-numerical parameter are compared to obtain the comparison result;

[0029] Based on the comparison results, a voice inquiry request is generated, which is used to request the user to select a target non-numerical parameter based on the comparison results;

[0030] or,

[0031] According to the preset parameter priority sorting rules, the first sorting result of the first non-numerical parameter and the second sorting result of the second non-numerical parameter are determined respectively.

[0032] Based on the first sorting result and the second sorting result, a third non-numerical parameter is determined from the first non-numerical parameter and the second non-numerical parameter. The third cooking scheme includes the third non-numerical parameter.

[0033] Optionally, the step of analyzing and processing the first ingredient information to determine the first cooking plan includes:

[0034] Obtain a preset first recipe recommendation database, which contains the size, volume, quantity of different ingredients and the corresponding relationship of the first preset cooking scheme;

[0035] Based on the size, volume or quantity of the ingredients, the first recipe recommendation database is matched to obtain a first matching result. The first matching result is used to indicate whether a first candidate cooking scheme is obtained from the first preset cooking scheme.

[0036] If the first matching result indicates that a first candidate cooking scheme is obtained from the first preset cooking scheme, then the first cooking scheme is determined according to the first candidate cooking scheme.

[0037] Optionally, the step of analyzing and processing the second ingredient information to determine the second cooking method includes:

[0038] Obtain a preset second recipe recommendation database, which contains the correspondence between the quantity, taste, doneness, and cooking time of different ingredients and the second preset cooking scheme;

[0039] Based on the target taste, target doneness, target cooking time, or the quantity of ingredients, the second recipe recommendation database is matched to obtain a second matching result. The second matching result is used to indicate whether a second candidate cooking scheme is obtained from the second preset cooking scheme.

[0040] If the second matching result indicates that a second candidate cooking scheme is obtained from the second preset cooking scheme, then the second cooking scheme is determined according to the second candidate cooking scheme.

[0041] Optionally, determining the user's cooking needs information in response to a voice interaction command sent by the user includes:

[0042] In response to the voice interaction command sent by the user, the voice interaction command is parsed and processed to obtain voice interaction corpus;

[0043] The voice interaction information is extracted and processed to obtain the cooking requirement information.

[0044] Optionally, after acquiring the image of the food inside the oven captured by the image acquisition device, the method further includes:

[0045] The food image is subjected to quality assessment processing to obtain the quality defect type of the food image;

[0046] If the quality defect type is uneven lighting or overexposure, then the food image is subjected to image enhancement processing to obtain the processed food image.

[0047] If the quality defect type is insufficient image integrity, the image acquisition device is controlled to make fine adjustments according to a preset adjustment angle, and the image acquisition device is controlled to re-acquire the food image inside the oven, repeating the quality assessment steps until a food image that meets the conditions is obtained.

[0048] Secondly, this application provides an oven control device, wherein the oven is equipped with an image acquisition device, including:

[0049] The acquisition module is used to acquire food images inside the oven captured by the image acquisition device. The food images include first food information of the food to be cooked, which includes at least one of the following: food size, food volume, and food quantity.

[0050] The determination module is used to determine the user's cooking needs information in response to the voice interaction command sent by the user. The cooking needs information includes second ingredient information of the ingredients to be cooked, and the second ingredient information includes at least one of the following: target taste, target doneness, target cooking time, and quantity of the ingredients.

[0051] The generation module is used to generate recipe cooking instructions based on the first ingredient information and the second ingredient information;

[0052] The control module is used to control the oven to execute the recipe cooking instructions.

[0053] Optionally, the device further includes: a processing module;

[0054] The processing module is used to parse and process the first ingredient information to determine the first cooking scheme;

[0055] The processing module is also used to parse and process the second ingredient information to determine the second cooking scheme;

[0056] The generation module is specifically used to generate the recipe cooking instructions based on the first cooking scheme and the second cooking scheme.

[0057] Optionally, the determining module is further configured to determine a temperature deviation value between a first heating temperature associated with the first cooking scheme and a second heating temperature associated with the second cooking scheme;

[0058] The determining module is further configured to determine the duration deviation value between the first heating time associated with the first cooking scheme and the second heating time associated with the second cooking scheme;

[0059] The generation module is specifically used to merge the first cooking scheme and the second cooking scheme when the temperature deviation value is within a preset temperature deviation range and the duration deviation value is within a preset duration deviation range, to obtain a merged third cooking scheme, and to generate the recipe cooking instructions based on the third cooking scheme.

[0060] Optionally, the determining module is further configured to determine the difference between the first cooking scheme and the second cooking scheme when the temperature deviation value is not within the preset temperature deviation range or the time deviation value is not within the preset time deviation range.

[0061] The generation module is further configured to generate a recipe query instruction based on the difference between the first cooking scheme and the second cooking scheme, and the recipe query instruction is used to confirm the user's preferred recipe.

[0062] The device further includes: a receiving module;

[0063] The receiving module is used to receive the voice selection command from the user, obtain the target cooking scheme, and generate the recipe cooking command based on the target cooking scheme.

[0064] Optionally, the processing module is further configured to perform averaging on the first numerical parameter and the second numerical parameter to obtain a third numerical parameter, wherein the third cooking scheme includes: the third numerical parameter;

[0065] or,

[0066] The processing module is further configured to multiply the first numerical parameter by a preset first coefficient, and then add the second numerical parameter multiplied by a preset second coefficient to obtain a fourth numerical parameter. The third cooking scheme includes the fourth numerical parameter.

[0067] Optionally, the processing module is further configured to compare the first non-numerical parameter and the second non-numerical parameter to obtain a comparison result;

[0068] The generation module is further configured to generate a voice inquiry request based on the comparison result, the voice inquiry request being used to request the user to select a target non-numerical parameter based on the comparison result;

[0069] or,

[0070] The determining module is further configured to determine the first sorting result of the first non-numerical parameter and the second sorting result of the second non-numerical parameter according to a preset parameter priority sorting rule;

[0071] The determining module is further configured to determine a third non-numerical parameter from the first non-numerical parameter and the second non-numerical parameter based on the first sorting result and the second sorting result, wherein the third cooking scheme includes the third non-numerical parameter.

[0072] Optionally, the acquisition module is further configured to acquire a preset first recipe recommendation database, wherein the first recipe recommendation database contains the correspondence between the size, volume, quantity of different ingredients and the first preset cooking scheme;

[0073] The processing module is further configured to perform matching processing on the first recipe recommendation database based on the size, volume or quantity of the ingredients to obtain a first matching result. The first matching result is used to indicate whether a first candidate cooking scheme is obtained from the first preset cooking scheme.

[0074] If the determining module indicates that a first candidate cooking scheme is obtained from the first preset cooking scheme, it is specifically used to determine the first cooking scheme based on the first candidate cooking scheme.

[0075] Optionally, the acquisition module is further configured to acquire a preset second recipe recommendation database, wherein the second recipe recommendation database contains the correspondence between the quantity, taste, doneness, and cooking time of different ingredients and the second preset cooking scheme;

[0076] The processing module is further configured to perform matching processing on the second recipe recommendation database based on the target taste, target doneness, target cooking time or the quantity of ingredients to obtain a second matching result. The second matching result is used to indicate whether a second candidate cooking scheme is obtained from the second preset cooking scheme.

[0077] If the determining module indicates that a second candidate cooking scheme is obtained from the second preset cooking scheme, it is specifically used to determine the second cooking scheme based on the second candidate cooking scheme.

[0078] Optionally, the processing module is further configured to respond to the voice interaction command sent by the user, parse the voice interaction command, and obtain voice interaction corpus.

[0079] The processing module is also used to extract and process the voice interaction data to obtain the cooking requirement information.

[0080] Optionally, the processing module is further configured to perform quality assessment processing on the food image to obtain the quality defect type of the food image;

[0081] If the quality defect type is uneven lighting or overexposure, the processing module is further used to perform image enhancement processing on the food image to obtain a processed food image.

[0082] If the quality defect type is insufficient image integrity, the processing module is further configured to control the image acquisition device to make fine adjustments according to a preset adjustment angle, and control the image acquisition device to re-acquire the food image inside the oven, repeating the quality assessment steps until a food image that meets the conditions is obtained.

[0083] Thirdly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0084] The memory stores computer-executed instructions;

[0085] The processor executes computer execution instructions stored in the memory to implement the oven control method as described in the first aspect and various possible implementations of the first aspect above.

[0086] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions thereon, which, when executed by a processor, are used to implement the oven control method as described in the first aspect and various possible implementations of the first aspect.

[0087] Fifthly, this application provides a program product including a computer program that, when executed by a processor, implements the oven control method described above.

[0088] The oven control method, medium, and electronic device provided in this application first acquire images of the food inside the oven captured by an image acquisition device, and extract first ingredient information such as the size, volume, and quantity of the ingredients to be cooked. Simultaneously, in response to user voice interaction commands, second ingredient information such as the target texture, doneness, cooking time, and quantity of the ingredients to be cooked is determined. Then, based on these two types of information, a recipe cooking command is generated and the oven is controlled to execute it. This method solves the problem of inaccurate oven command generation by fusing objective physical information of the ingredients recognized by camera vision with subjective cooking needs information parsed from voice interaction. It achieves precise matching between cooking commands and the actual state of the ingredients and user needs, effectively improving the rationality of the cooking plan and the intelligent interaction level of the oven. It also optimizes the user experience and avoids command deviations caused by single-modal information. Attached Figure Description

[0089] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0090] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0091] Figure 1 This is a schematic diagram of the hardware environment for an interaction method of a smart device according to an embodiment of this application;

[0092] Figure 2 A flowchart illustrating an oven control method provided in this application. Figure 1 ;

[0093] Figure 3 A flowchart illustrating an oven control method provided in this application. Figure 2 ;

[0094] Figure 4 A flowchart illustrating an oven control method provided in this application. Figure 3 ;

[0095] Figure 5 This application provides a schematic diagram of the structure of an oven control device;

[0096] Figure 6 This is a structural schematic diagram of an oven control device provided in this application. Detailed Implementation

[0097] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0098] It should be noted that the terms "first," "second," etc., used in this application's specification are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0099] According to one aspect of the embodiments of this application, an oven control method is provided. This oven control method is widely used in whole-house intelligent digital control application scenarios such as smart homes, smart home ecosystems, and intelligencehouse ecosystems. Optionally, in this embodiment, the above-mentioned oven control method can be applied to, for example... Figure 1 The hardware environment shown consists of terminal device 102 and server 104. For example... Figure 1As shown, server 104 is connected to terminal device 102 via a network and can be used to provide services (such as application services) to the terminal or clients installed on the terminal. A database can be set up on the server or independently of the server to provide data storage services for server 104. Cloud computing and / or edge computing services can be configured on the server or independently of the server to provide data processing services for server 104.

[0100] The aforementioned network may include, but is not limited to, at least one of the following: wired network, wireless network. The aforementioned wired network may include, but is not limited to, at least one of the following: wide area network, metropolitan area network, local area network. The aforementioned wireless network may include, but is not limited to, at least one of the following: Wi-Fi (Wireless Fidelity), Bluetooth. The terminal device 102 may not be limited to PC, mobile phone, tablet computer, smart air conditioner, smart range hood, smart refrigerator, smart oven, smart stove, smart washing machine, smart water heater, smart washing equipment, smart dishwasher, smart projector, smart TV, smart clothes rack, smart curtains, smart audio-visual equipment, smart socket, smart speaker, smart speaker box, smart fresh air equipment, smart kitchen and bathroom equipment, smart bathroom equipment, smart robot vacuum cleaner, smart window cleaning robot, smart mopping robot, smart air purifier, smart steam oven, smart microwave oven, smart water heater, smart air purifier, smart water dispenser, smart door lock, etc.

[0101] With the improvement of living standards and the popularization of healthy eating concepts, ovens have become a popular choice for family kitchens due to their advantages such as precise temperature control, multiple cooking modes and multi-functionality. They can not only meet baking needs, but also realize diverse operations such as steaming, baking and air frying, which are favored by consumers who pursue convenience and a healthy life.

[0102] Existing ovens require users to issue cooking instructions via voice or have instructions automatically issued after an image acquisition device recognizes the ingredients during the baking process.

[0103] However, this control method that relies on a single modality results in the oven's insufficient adaptability in complex cooking scenarios. For example, voice commands are easily misrecognized due to interference from environmental noise, and image recognition has high requirements for the shape of the ingredients and lighting. Once the data of a certain modality is missing or of poor quality, it is easy to cause cooking failure, affecting the user experience and the reliability of the equipment.

[0104] To address the aforementioned issues, this application provides an oven control method. First, it acquires images of the food inside the oven from an image acquisition device, extracting first-level ingredient information such as size, volume, type, and quantity. Simultaneously, it responds to user voice interaction commands to determine second-level ingredient information, including ingredient type, quantity, target texture, doneness, and cooking time. Then, it generates recipe cooking instructions based on these two types of information and controls the oven to execute them. This method effectively solves the problem of inaccurate oven instruction generation by fusing visual information acquired by the image acquisition device with semantic information from user voice interaction, achieving more precise and user-relevant cooking instruction generation, and improving the intelligence and accuracy of oven cooking.

[0105] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0106] Figure 2 A flowchart illustrating an oven control method provided in this application embodiment. Figure 1 .like Figure 2 As shown, the oven control method provided in this embodiment includes:

[0107] S101. Acquire an image of the food inside the oven captured by the image acquisition device. The food image includes first food information of the food to be cooked. The first food information includes at least one of the following: food size, food volume, and food quantity.

[0108] The purpose of this step is to acquire objective physical information about the food inside the oven using an image acquisition device.

[0109] For example, the image acquisition device captures images of chicken wings in the oven and determines the first ingredient information of the chicken wings as follows: Quantity: 3 pieces; Size: 5cm; Volume: 10cm³ / piece.

[0110] S102. In response to the voice interaction command sent by the user, determine the user's cooking requirement information. The cooking requirement information includes the second ingredient information of the ingredients to be cooked. The second ingredient information includes at least one of the following: target taste, target doneness, target cooking time, and quantity of ingredients.

[0111] The purpose of this step is to extract the user's subjective cooking needs by parsing the voice interaction commands sent by the user, and to transform the vague natural language needs into structured cooking needs information.

[0112] For example, if a user sends a voice interaction command: "Roast 3 chicken wings, crispy and fully cooked, cooking time not exceeding 20 minutes", then we can first determine that the ingredient to be cooked is chicken wings, and then determine the second ingredient information for the chicken wings: ingredient quantity: 3; target texture: crispy; target degree of cooking: fully cooked; target cooking time: ≤20 minutes.

[0113] S103. Generate recipe cooking instructions based on the first ingredient information and the second ingredient information.

[0114] The purpose of this step is to integrate the objective physical information of the ingredients obtained from image acquisition with the subjective cooking needs of the user obtained from voice analysis to generate recipe cooking instructions that combine the actual state of the ingredients with the user's personalized needs.

[0115] Understandably, firstly, the information about the ingredients can serve as objective physical data of the ingredients inside the oven, representing their true state and forming the basis for ensuring that cooking instructions conform to the characteristics of the ingredients.

[0116] Secondly, the second ingredient information can serve as the user's subjective cooking needs, such as the user wanting the cooking time to be no more than 20 minutes.

[0117] Therefore, by integrating the information of the first ingredient and the second ingredient, it is possible to avoid not only ignoring user preferences when generating instructions based solely on objective ingredient information, but also to avoid deviating from the actual ingredients when generating instructions based solely on subjective needs. Ultimately, this results in recipe cooking instructions that balance rationality and personalization, ensuring that the cooking effect output by the oven is consistent with the user's expectations.

[0118] S104. Control the oven to execute recipe cooking instructions.

[0119] The purpose of this step is to translate the generated recipe cooking instructions into actual cooking actions of the oven, driving the oven to perform baking operations according to the parameters in the instructions.

[0120] Understandably, recipe cooking instructions are the optimal decision-making solution that integrates objective information about ingredients with subjective needs of users. Remaining only at the data level cannot produce actual cooking results. Secondly, as the execution terminal, the oven needs to receive clear control signals to activate the hardware modules to work. Otherwise, it cannot autonomously complete operations such as temperature adjustment and time control.

[0121] Therefore, by controlling the oven to execute recipe cooking instructions, it can be ensured that the oven operates strictly according to the adaptation scheme, avoiding errors from manual operation, meeting the user's cooking needs, and thus realizing the intelligent baking function of the oven.

[0122] This embodiment provides an oven control method that acquires images of food inside the oven using an image acquisition device, extracts the food to be cooked and its first ingredient information, and simultaneously responds to user voice interaction commands to determine the second ingredient information. Based on these two types of information, a recipe cooking command is generated and the oven is controlled to execute it. This method solves the problem of inaccurate oven command generation by fusing the visual information of the food acquired by the image acquisition device with the semantic information of the cooking needs conveyed by the user's voice interaction. It enables the oven to accurately generate cooking commands based on the actual condition of the food and the user's personalized needs, thus improving the intelligence and precision of cooking.

[0123] Figure 3 A flowchart illustrating an oven control method provided in this application embodiment. Figure 2 .like Figure 3 As shown, in Figure 2 Based on the embodiments, the implementation of the oven control method is described in detail. The oven control method shown in this embodiment includes:

[0124] S201. Obtain an image of the food inside the oven captured by the image acquisition device. The food image includes first food information of the food to be cooked. The first food information includes at least one of the following: food size, food volume, and food quantity.

[0125] Optionally, after acquiring the food image, this application also provides a possible implementation method for the food image, including:

[0126] The first step is to perform quality assessment processing on the food images to obtain the types of quality defects in the food images.

[0127] Understandably, the complex internal environment of an oven, including strong light from heating elements, steam, and obstructions from food placement, can easily lead to various quality issues in the captured images. These quality issues directly affect the accurate extraction of primary information about the ingredients, such as size, volume, type, and quantity. For example, blurry images make it impossible to clearly distinguish the edges of ingredients, thus hindering accurate size measurements; color deviations can cause errors in identifying the type of ingredient, mistaking one similarly colored ingredient for another.

[0128] Therefore, by performing quality assessment processing on food images and identifying the types of defects, image quality problems can be detected in advance, and targeted measures can be taken to repair or adjust the images, thereby improving image quality and ensuring the accuracy of food information extraction.

[0129] The second step is to perform image enhancement processing on the food image if the quality defect is uneven lighting or overexposure. This will result in a processed food image.

[0130] Among them, uneven lighting defects refer to significant differences in brightness between different areas in the food image, with some areas being too bright and others too dark, resulting in an image quality problem that fails to clearly present the overall details of the food.

[0131] Overexposure defect: refers to an image quality problem in which the light intensity in a food image exceeds the dynamic range of the image sensor, resulting in the loss of surface details and the appearance of large areas of pure white.

[0132] Understandably, uneven lighting or overexposure can directly lead to the loss of details in food images. For example, uneven lighting makes it impossible to see the edges of food, and overexposure makes it impossible to distinguish the types of food. If such images are used directly to extract the first food information, it is very easy to cause problems such as size measurement errors and misjudgment of type.

[0133] Therefore, by performing image enhancement processing on food images, lighting defects can be specifically repaired, and the complete details of the food in the image can be restored. This ensures that the optimized image meets the accuracy requirements for food information extraction, thereby guaranteeing the accuracy of the subsequent image-based cooking solution generation logic and avoiding overall process deviations caused by image defects.

[0134] The third step is to control the image acquisition device to make fine adjustments according to the preset adjustment angle, and control the image acquisition device to re-acquire the food images inside the oven, repeating the quality assessment steps until a food image that meets the conditions is obtained.

[0135] Insufficient image integrity refers to a situation where the main body of the food in the oven captured by the image acquisition device is partially obscured, the edges are truncated, or it is not fully included in the picture, resulting in an image quality defect that fails to fully present the overall characteristics of the food.

[0136] The preset adjustment angles can be, for example, ±5° to the left horizontally, ±5° to the right horizontally, ±10° to the up vertically, and ±10° to the down vertically.

[0137] Understandably, firstly, insufficient image integrity cannot be repaired by algorithms; it can only be addressed by adjusting the shooting angle and re-capturing the image. Secondly, fine-tuning the angle according to a preset setting avoids the inefficiency caused by blind adjustments, ensuring the camera quickly focuses on the complete area of ​​the food. Finally, repeatedly performing the quality assessment step verifies whether the re-captured image meets the requirements, preventing incomplete images from being obtained due to a single improper adjustment.

[0138] S202. In response to the voice interaction command sent by the user, the voice interaction command is parsed and processed to obtain the voice interaction corpus.

[0139] Understandably, voice interaction commands exist in audio form; therefore, it is necessary to convert the voice interaction commands sent by the user into text form that can be recognized and analyzed.

[0140] S203. Extract and process the voice interaction information to obtain cooking requirement information. The cooking requirement information includes the second ingredient information of the ingredients to be cooked. The second ingredient information includes at least one of the following: target taste, target doneness, target cooking time, and quantity of ingredients.

[0141] Understandably, voice interaction corpora may contain a lot of redundant information unrelated to cooking. Therefore, by extracting and processing the voice interaction corpora, redundant information can be filtered out, thereby obtaining the cooking-related elements that users truly expect.

[0142] S204. Analyze and process the information of the first ingredient to determine the first cooking plan.

[0143] The purpose of this step is to determine the first cooking plan for the ingredients to be cooked in the currently acquired image based on the size, volume, or quantity of the ingredients extracted by the image acquisition device.

[0144] Optionally, this application provides a possible implementation method, including:

[0145] The first step is to obtain a preset first recipe recommendation database, which contains the size, volume, quantity of different ingredients and the corresponding relationship with the first preset cooking scheme.

[0146] The purpose of this step is to obtain relevant information about different ingredients and corresponding cooking methods.

[0147] For example, the first recipe recommendation database includes: sweet potato, sweet potato size 15cm, volume 200cm³, quantity 3, and the corresponding cooking program is: heat at 180℃ for 28 minutes, hot air mode.

[0148] The second step involves matching the first recipe recommendation database based on the size, volume, or quantity of the ingredients to obtain the first matching result. The first matching result is used to indicate whether a first candidate cooking scheme has been obtained from the first preset cooking scheme.

[0149] The purpose of this step is to match the objective physical information extracted by the image acquisition device with the first recipe recommendation database.

[0150] Optionally, this application provides a matching method in the matching process, including: on the premise that the first ingredient type matches any ingredient already stored in the first recipe recommendation database, further determining whether the actual extracted ingredient size, ingredient volume or ingredient quantity is within a preset deviation range from the preset size, preset volume or preset quantity corresponding to the ingredient in the first recipe recommendation database; if the deviation range requirement is met, then the first preset cooking scheme corresponding to the first recipe recommendation database is determined as the first candidate cooking scheme.

[0151] The third step is to determine the first cooking scheme based on the first candidate cooking scheme if the first matching result indicates that a first candidate cooking scheme is obtained from the first preset cooking scheme.

[0152] Understandably, the first candidate cooking solution was determined from the first recipe recommendation database. This solution was obtained through professional culinary practice and verification, and it matches the size, volume, type, and quantity of the ingredients extracted by the image acquisition device. Therefore, this candidate solution can be selected as the final first cooking solution.

[0153] Optionally, this application provides a possible implementation method when the first matching result indicates that no first candidate cooking scheme is obtained from the first preset cooking scheme, including: generating a first voice prompt message, the first voice prompt message being used to prompt the user that no suitable cooking scheme has been matched at present, and that the user may consider re-entering the relevant ingredient information or using other cooking methods.

[0154] Optionally, this application provides a feasible implementation method for situations where there are multiple first candidate cooking solutions: generating a second voice prompt message, which is used to provide feedback to the user on the multiple first candidate cooking solutions currently matched, and guide the user to select the final first cooking solution from them.

[0155] S205. Analyze and process the information of the second ingredient to determine the second cooking scheme.

[0156] The purpose of this step is to determine a second cooking plan for the ingredients mentioned in the current voice based on the type, quantity, taste, or cooking time of the ingredients extracted from the voice interaction command.

[0157] Optionally, this application provides a possible implementation method, including:

[0158] The first step is to obtain a preset second recipe recommendation database, which contains the correspondence between the quantity, taste, doneness, and cooking time of different ingredients and the second preset cooking scheme.

[0159] The purpose of this step is to obtain relevant information about different ingredients and corresponding cooking methods.

[0160] For example, the second recipe recommendation database includes: sweet potato, with a soft and glutinous texture, fully cooked, and a cooking time of 183℃ for 25 minutes, using the hot air mode.

[0161] The second step involves matching the second recipe recommendation database based on the target taste, target doneness, target cooking time, or number of ingredients to obtain a second matching result. The second matching result is used to indicate whether a second candidate cooking scheme is obtained from the second preset cooking scheme.

[0162] The purpose of this step is to match the types, quantities, tastes, or cooking times of ingredients extracted from voice interaction commands with the second recipe recommendation database.

[0163] Optionally, this application provides a matching method in the matching process, including: on the premise that the second ingredient type matches any ingredient already stored in the second recipe recommendation database, further determining whether the user's desired taste, target doneness, or target cooking time is within a preset deviation range from the preset taste, doneness, and cooking time corresponding to the ingredient in the second recipe recommendation database; if the range requirement is met, then the second preset cooking scheme corresponding to the second recipe recommendation database is determined as the second candidate cooking scheme.

[0164] Third, if the second matching result indicates that a second candidate cooking scheme is obtained from the second preset cooking scheme, then the second cooking scheme is determined according to the second candidate cooking scheme.

[0165] Understandably, the second candidate cooking solution is determined from the second recipe recommendation database. This solution has been tested, verified, and optimized, and it matches the type, quantity, taste, or cooking time of ingredients extracted from the voice interaction command. Therefore, this second candidate cooking solution can be selected as the second cooking solution.

[0166] Optionally, this application provides a possible implementation method when the second matching result indicates that no second candidate cooking scheme has been obtained from the second preset cooking scheme, including: generating third voice prompt information, the third voice prompt information being used to provide feedback to the user that no cooking scheme that matches the user's needs has been matched.

[0167] Optionally, this application provides a feasible implementation method for situations where there are multiple second candidate cooking solutions: generating a fourth voice prompt message, which is used to provide feedback to the user on the multiple currently matched second candidate cooking solutions and guide the user to select the final second cooking solution from them.

[0168] S206. Generate recipe cooking instructions based on the first cooking scheme and the second cooking scheme.

[0169] The purpose of this step is to integrate the selected and determined first cooking scheme with the second cooking scheme, and transform them into specific and operable recipe cooking instructions.

[0170] S207. Control the oven to execute recipe cooking instructions.

[0171] The explanation of step S207 is the same as that in the above embodiments, and will not be repeated here.

[0172] This embodiment provides an oven control method. First, it acquires images of the food inside the oven from an image acquisition device, extracting first ingredient information of the food to be cooked. Next, it responds to and parses user voice interaction commands to obtain voice interaction data and second ingredient information of the food to be cooked. Then, it determines corresponding first and second cooking schemes based on the first and second ingredient information respectively. Finally, it combines the two to generate a recipe cooking command and controls the oven to execute it. This method solves the problem of inaccurate oven command generation by fusing objective physical information of the food from camera vision recognition with subjective cooking needs information from user voice interaction parsing. It achieves accurate matching of cooking commands with the actual state of the food and user needs, effectively improving the rationality of the cooking scheme and the intelligent interaction level of the oven. It also optimizes the user experience and avoids command deviations caused by single-modal information.

[0173] Figure 4 A schematic flowchart of oven control provided in this application embodiment. Figure 3 .like Figure 4 As shown, based on the above embodiments, the process of generating recipe cooking instructions according to the first cooking scheme and the second cooking scheme is explained. The oven control method shown in this embodiment includes:

[0174] S301. Determine the temperature deviation value between the first heating temperature associated with the first cooking scheme and the second heating temperature associated with the second cooking scheme.

[0175] The purpose of this step is to quantify the difference between the first heating temperature of the first cooking scheme and the second heating temperature of the second cooking scheme.

[0176] For example, assuming the first heating temperature of the first cooking scheme is 180°C and the second heating temperature of the second cooking scheme is 200°C, then the temperature difference between the two can be determined to be 20°C.

[0177] S302. Determine the duration deviation value between the first heating time associated with the first cooking scheme and the second heating time associated with the second cooking scheme.

[0178] The purpose of this step is to quantify the difference between the first heating time of the first cooking scheme and the second heating time of the second cooking scheme.

[0179] For example, assuming the first heating time of the first cooking program is 25 minutes and the second heating time of the second cooking program is 15 minutes, then the time difference between the two can be determined to be 10 minutes.

[0180] S303. When the temperature deviation value is within the preset temperature deviation range and the time deviation value is within the preset time deviation range, the first cooking scheme and the second cooking scheme are merged to obtain the merged third cooking scheme, and a recipe cooking instruction is generated according to the third cooking scheme.

[0181] The preset temperature deviation range can be, for example, 10℃, and the preset duration deviation range can be, for example, 10 minutes.

[0182] Understandably, when the temperature deviation and time deviation between the first and second cooking schemes are both within the corresponding preset range, the parameters of the first and second cooking schemes can be fused to generate a third cooking scheme that takes into account both the adaptability of ingredients and user preferences. Based on this, executable recipe cooking instructions can be output to ensure that the final cooking result not only conforms to the physical characteristics of the ingredients but also meets the personalized needs of the user.

[0183] Optionally, when the first cooking scheme includes a first numerical parameter and the second cooking scheme includes a second numerical parameter, this application determines the third cooking scheme in several possible ways, including:

[0184] The first method involves averaging the first and second numerical parameters to obtain the third numerical parameter. The third cooking scheme includes the third numerical parameter.

[0185] For example, assuming the first numerical parameter of the first cooking program includes heating at 180°C for 25 minutes, and the second numerical parameter of the second cooking program includes heating at 190°C for 22 minutes, then the third cooking program can be determined as heating at (180°C + 190°C) / 2 = 185°C for (25 minutes + 22 minutes) / 2 = 23.5 minutes.

[0186] The second method involves multiplying the first numerical parameter by a preset first coefficient, then adding the second numerical parameter multiplied by a preset second coefficient to obtain the fourth numerical parameter. The third cooking method includes the fourth numerical parameter.

[0187] For example, suppose the first numerical parameter of the first cooking program includes heating at 180℃ for 25 minutes, and the second numerical parameter of the second cooking program includes heating at 190℃ for 22 minutes. It is known that the preset first coefficient for the numerical parameter in the first cooking program is 0.4, and the preset second coefficient for the numerical parameter in the second cooking program is 0.6. Then, the temperature parameter can be determined as 180℃×0.4+190℃×0.6=186℃, and the duration parameter is 25 minutes×0.4+22 minutes×0.6=23.2 minutes. That is, the third cooking program includes heating at 186℃ for 23.2 minutes.

[0188] Optionally, before averaging the first and second numerical parameters, this application performs attribute classification and verification processing on the first and second numerical parameters to ensure that their numerical attribute categories are consistent, so that the average is calculated only for numerical parameters of the same category (such as temperature and temperature, duration and duration).

[0189] Optionally, when the first cooking scheme includes a first non-numerical parameter and the second cooking scheme includes a second non-numerical parameter, this application determines the third cooking scheme in several possible ways, including:

[0190] The first method involves comparing the first non-numerical parameter and the second non-numerical parameter to obtain a comparison result; based on the comparison result, a voice query request is generated, which is used to ask the user to select a target non-numerical parameter based on the comparison result.

[0191] For example, suppose the first non-numerical parameter of the first cooking solution is the hot air mode, and the second non-numerical parameter of the second cooking solution is the convection baking mode. The hot air mode and the convection baking mode are compared and processed to obtain a comparison result of "parameter inconsistency". Based on the comparison result, a voice query request is generated. The voice query request is used to ask the user to select one of the "hot air mode and convection baking mode" as the target non-numerical parameter for cooking based on the comparison result of "hot air mode and convection baking mode".

[0192] The second method involves determining the first sorting result of the first non-numerical parameter and the second sorting result of the second non-numerical parameter according to the preset parameter priority sorting rules; based on the first sorting result and the second sorting result, determining the third non-numerical parameter from the first non-numerical parameter and the second non-numerical parameter, and the third cooking scheme includes: the third non-numerical parameter.

[0193] For example, suppose the first non-numerical parameter of the first cooking scheme is the defrosting mode, and the second non-numerical parameter of the second cooking scheme is the low-temperature fermentation mode without humidification. The preset parameter priority ranking rule is that "the parameter priority corresponding to the image acquisition device is lower than the parameter priority corresponding to the user's voice interaction command". According to this rule, the first ranking result of the defrosting mode is determined to be the second position, and the second ranking result of the low-temperature fermentation mode without humidification is determined to be the first position. Based on the first ranking result and the second ranking result, the low-temperature fermentation mode without humidification, which ranks higher among the defrosting mode and the low-temperature fermentation mode without humidification, is selected as the third non-numerical parameter. The third cooking scheme includes the third non-numerical parameter of the low-temperature fermentation mode without humidification.

[0194] Optionally, before averaging the first non-numerical parameter and the second non-numerical parameter, this application further categorizes the first non-numerical parameter and the second non-numerical parameter to ensure that their attributes belong to the same dimensional range and to avoid cross-attribute fusion, so that only non-numerical parameters within the same dimensional range are processed.

[0195] For example, if the heating mode (such as steam mode or baking mode) is not distinguished first, and temperature and time parameters are directly mixed in, it will lead to the situation where "steam mode, 100℃, 15min" and "baking mode, 180℃, 25min" are mistakenly grouped together in subsequent data calculations, which violates the premise that "steaming and baking cannot be converted", and the final calculation results will be completely meaningless.

[0196] S304. If the temperature deviation value is not within the preset temperature deviation range, or the time deviation value is not within the preset time deviation range, determine the difference between the first cooking scheme and the second cooking scheme.

[0197] Understandably, when it is determined that the temperature deviation between the first cooking program and the second cooking program is not within the preset temperature deviation range, or when it is determined that the time deviation between the first cooking program and the second cooking program is not within the preset time deviation range, the specific differences between the first cooking program and the second cooking program in core parameters, such as heating temperature and heating time, can be determined. This provides a clear basis for providing feedback to the user on conflicts, guiding the user to adjust their needs or select a program, and avoiding the direct generation of unreasonable cooking instructions due to parameter conflicts.

[0198] S305. Based on the difference between the first cooking plan and the second cooking plan, generate a recipe inquiry instruction. The recipe inquiry instruction is used to confirm the user's preferred recipe.

[0199] Understandably, based on the specific differences between the first and second cooking schemes, targeted recipe query instructions can be generated to clearly inform users of the parameter conflict points between the two schemes, and guide users to confirm their preferred cooking scheme or adjust their needs, ensuring that the final generated cooking instructions match the user's true wishes and avoiding cooking failures caused by directly executing unreasonable schemes due to parameter conflicts.

[0200] S306. Receive the user's voice selection command, obtain the target cooking plan, and generate the recipe cooking command based on the target cooking plan.

[0201] The purpose of this step is to receive the user's voice selection instructions regarding the differences between the two options, clarify the user's preferred target cooking option, and generate the final recipe cooking instructions based on that option, ensuring that the cooking instructions match the user's subjective choice.

[0202] This embodiment provides an oven control method. First, the heating temperature deviation and heating time deviation between the first cooking scheme and the second cooking scheme are determined. If the temperature and time deviations are both within a preset range, the two schemes are merged to obtain a third cooking scheme and a recipe cooking instruction is generated. If any deviation exceeds the preset range, the difference between the two schemes is determined, a recipe query instruction is generated for the user to select a preferred scheme, and after receiving the user's voice selection instruction, the target cooking scheme is obtained and a recipe cooking instruction is generated.

[0203] This method compares the core parameter deviations of two cooking programs and processes them according to different scenarios. When the deviation is reasonable, the program is integrated; when the deviation is too large, the user has the option to choose. This solves the problem of inaccurate oven command generation, achieves accurate matching between cooking commands and the actual state of ingredients and user needs, improves the rationality and intelligent interaction level of oven cooking programs, and at the same time protects the user's right to choose cooking programs, thus optimizing the overall user experience.

[0204] Optionally, based on the above embodiments, this application provides a method for fusing multi-source data to determine recipe cooking instructions, including:

[0205] The first step is to perform deduplication and fusion processing on the first ingredient information and the second ingredient information to obtain the ingredient fusion information.

[0206] The second step is to determine the target cooking scheme corresponding to the ingredient integration information from the pre-set recipe recommendation database. The third step is to pre-store the mapping relationship between ingredient attribute information and corresponding cooking instructions in the recipe recommendation database.

[0207] For example, assuming the first ingredient information includes ingredient size (5cm in diameter), ingredient volume (20cm³), and ingredient quantity (10 pieces), and the second ingredient information includes target texture (crispy on the outside and tender on the inside), target doneness (fully cooked), target cooking time (25 minutes), and ingredient quantity (10 pieces), then the ingredient fusion information can be determined first, including: ingredient size diameter 5cm, ingredient volume 20cm³, ingredient quantity 10 pieces, target texture crispy on the outside and tender on the inside, target doneness fully cooked, and target cooking time 25 minutes; then, based on this ingredient fusion information and the preset third recipe recommendation database, the corresponding cooking plan is determined to be: 185℃ hot air mode, heating for 23.5 minutes.

[0208] The third step is to generate recipe cooking instructions based on the target cooking plan.

[0209] The purpose of this step is to control the oven to perform the corresponding heating operation according to the parameter requirements of the target cooking program.

[0210] Understandably, since the target cooking plan is only a parameterized description, it cannot be directly recognized and executed by the oven. As the execution terminal, the oven can only respond to instructions with fixed formats and clear operating steps. Therefore, by converting the target cooking plan into recipe cooking instructions, a linkage between the plan and the equipment can be established, ensuring that the oven's operation strictly matches the preset cooking parameters, thereby stably outputting cooking results that meet expectations.

[0211] Figure 5 This is a schematic diagram of the structure of an oven control device provided in this application. Figure 5 As shown, the oven is equipped with an image acquisition device. This application provides an oven control device 400, which includes:

[0212] The acquisition module 401 is used to acquire food images inside the oven captured by the image acquisition device. The food images include first food information of the food to be cooked. The first food information includes at least one of the following: food size, food volume, and food quantity.

[0213] The determining module 402 is used to respond to the voice interaction command sent by the user and determine the user's cooking requirement information. The cooking requirement information includes the second ingredient information of the ingredients to be cooked. The second ingredient information includes at least one of the following: target taste, target doneness, target cooking time, and quantity of ingredients.

[0214] The generation module 403 is used to generate recipe cooking instructions based on the first ingredient information and the second ingredient information;

[0215] Control module 404 is used to control the oven to execute recipe cooking instructions.

[0216] Optionally, the device may also include: a processing module 405;

[0217] Processing module 405 is used to parse and process the information of the first ingredient to determine the first cooking scheme;

[0218] The processing module 405 is also used to parse and process the information of the second ingredient to determine the second cooking scheme;

[0219] The generation module 403 is specifically used to generate recipe cooking instructions based on the first cooking scheme and the second cooking scheme.

[0220] Optionally, the determining module 402 is further configured to determine a temperature deviation value between a first heating temperature associated with a first cooking scheme and a second heating temperature associated with a second cooking scheme;

[0221] The determining module 402 is further configured to determine the duration deviation value between the first heating time associated with the first cooking scheme and the second heating time associated with the second cooking scheme;

[0222] The generation module 403 is specifically used to merge the first cooking scheme and the second cooking scheme when the temperature deviation value is within a preset temperature deviation range and the time deviation value is within a preset time deviation range, to obtain a merged third cooking scheme, and to generate a recipe cooking instruction based on the third cooking scheme.

[0223] Optionally, the determining module 402 is further configured to determine the difference between the first cooking scheme and the second cooking scheme when the temperature deviation value is not within the preset temperature deviation range or the time deviation value is not within the preset time deviation range.

[0224] The generation module 403 is also used to generate a recipe query instruction based on the difference between the first cooking scheme and the second cooking scheme. The recipe query instruction is used to confirm the user's preferred recipe.

[0225] The device also includes: a receiving module 404;

[0226] The receiving module 404 is used to receive voice selection instructions from the user, obtain the target cooking scheme, and generate recipe cooking instructions based on the target cooking scheme.

[0227] Optionally, the processing module 405 is also used to perform averaging on the first numerical parameter and the second numerical parameter to obtain a third numerical parameter. The third cooking scheme includes: the third numerical parameter.

[0228] or,

[0229] The processing module 405 is further configured to multiply the first numerical parameter by a preset first coefficient, and then add the second numerical parameter multiplied by a preset second coefficient to obtain a fourth numerical parameter. The third cooking scheme includes the fourth numerical parameter.

[0230] Optionally, the processing module 405 is also used to compare the first non-numerical parameter and the second non-numerical parameter to obtain a comparison result;

[0231] The generation module 403 is also used to generate a voice query request based on the comparison results. The voice query request is used to ask the user to select a target non-numerical parameter based on the comparison results.

[0232] or,

[0233] The determining module 402 is also used to determine the first sorting result of the first non-numerical parameter and the second sorting result of the second non-numerical parameter according to the preset parameter priority sorting rules;

[0234] The determining module 402 is further configured to determine a third non-numerical parameter from the first non-numerical parameter and the second non-numerical parameter based on the first sorting result and the second sorting result, wherein the third cooking scheme includes the third non-numerical parameter.

[0235] Optionally, the acquisition module 401 is also used to acquire a preset first recipe recommendation database, which contains the size, volume, quantity of different ingredients and the corresponding relationship of the first preset cooking scheme;

[0236] The processing module 405 is also used to perform matching processing on the first recipe recommendation database based on the size, volume or quantity of ingredients to obtain a first matching result. The first matching result is used to indicate whether a first candidate cooking scheme is obtained from the first preset cooking scheme.

[0237] The determining module 402 is specifically used to determine the first cooking scheme based on the first candidate cooking scheme if the first matching result indicates that a first candidate cooking scheme is obtained from the first preset cooking scheme.

[0238] Optionally, the acquisition module 401 is also used to acquire a preset second recipe recommendation database, which contains the correspondence between the quantity, taste, doneness, cooking time and second preset cooking scheme of different ingredients;

[0239] The processing module 405 is also used to perform matching processing on the second recipe recommendation database based on the target taste, target doneness, target cooking time or ingredient quantity, to obtain a second matching result. The second matching result is used to indicate whether a second candidate cooking scheme is obtained from the second preset cooking scheme.

[0240] The determining module 402 is specifically used to determine the second cooking scheme based on the second candidate cooking scheme if the second matching result indicates that a second candidate cooking scheme is obtained from the second preset cooking scheme.

[0241] Optionally, the processing module 405 is also used to respond to the voice interaction command sent by the user, parse and process the voice interaction command, and obtain the voice interaction corpus.

[0242] The processing module 405 is also used to extract and process the voice interaction information to obtain cooking demand information.

[0243] Optionally, the processing module 405 is also used to perform quality assessment processing on the food image to obtain the quality defect type of the food image;

[0244] The processing module 405, if the quality defect type is uneven lighting or overexposure defect, is also used to perform image enhancement processing on the food image to obtain the processed food image.

[0245] If the quality defect type is insufficient image integrity, the processing module 405 is also used to control the image acquisition device to make fine adjustments according to the preset adjustment angle, and to control the image acquisition device to re-acquire the food image inside the oven, repeating the quality assessment steps until a food image that meets the conditions is obtained.

[0246] The oven control device provided in this application embodiment is similar in principle and technical effect to the implementation of each part of the aforementioned oven control method, and will not be described again here.

[0247] Figure 6 This is a structural schematic diagram of an oven control device provided in this application. Figure 6 As shown, this application provides an oven control device 500, which includes: a receiver 501, a transmitter 502, a processor 503, and a memory 504.

[0248] Receiver 501 is used to receive instructions and data;

[0249] Transmitter 502 is used to send commands and data;

[0250] Memory 504 is used to store instructions executed by the computer;

[0251] Processor 503 is used to execute computer execution instructions stored in memory 504 to implement the various steps of the test method in the above embodiments. For details, please refer to the relevant descriptions in the foregoing test method embodiments.

[0252] Optionally, the memory 504 can be either standalone or integrated with the processor 503.

[0253] When the memory 504 is set up independently, the electronic device also includes a bus for connecting the memory 504 and the processor 503.

[0254] The implementation principle and technical effects of the electronic device provided in this embodiment can be found in the foregoing embodiments, and will not be repeated here.

[0255] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the method of any of the foregoing embodiments.

[0256] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the method of any of the foregoing embodiments.

[0257] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0258] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor. The memory may include high-speed RAM, and may also include non-volatile memory (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk, or optical disc, etc.

[0259] The aforementioned storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0260] An exemplary storage medium is coupled to a processor, allowing the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. Both the processor and the storage medium can reside in an application-specific integrated circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic device or host device.

[0261] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0262] The above are merely preferred embodiments of this application. It should be noted that those skilled in the art can make several improvements and modifications without departing from the principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. An oven control method, characterized in that, The oven is equipped with an image acquisition device, and the method includes: The image acquisition device acquires an image of the food inside the oven, the image of which includes first food information of the food to be cooked, the first food information including at least one of the following: food size, food volume, and food quantity. In response to a voice interaction command sent by a user, the cooking requirement information of the user is determined. The cooking requirement information includes second ingredient information of the ingredients to be cooked. The second ingredient information includes at least one of the following: target texture, target doneness, target cooking time, and quantity of the ingredients. Based on the first ingredient information and the second ingredient information, generate recipe cooking instructions; Control the oven to execute the recipe cooking instructions.

2. The method according to claim 1, characterized in that, The step of generating recipe cooking instructions based on the first ingredient information and the second ingredient information includes: The information of the first ingredient is parsed and processed to determine the first cooking plan; The second ingredient information is analyzed and processed to determine the second cooking method; The recipe cooking instructions are generated based on the first cooking scheme and the second cooking scheme.

3. The method according to claim 2, characterized in that, The step of generating the recipe cooking instructions based on the first cooking scheme and the second cooking scheme includes: Determine the temperature deviation between the first heating temperature associated with the first cooking scheme and the second heating temperature associated with the second cooking scheme; Determine the duration deviation between the first heating time associated with the first cooking scheme and the second heating time associated with the second cooking scheme; When the temperature deviation value is within a preset temperature deviation range and the duration deviation value is within a preset duration deviation range, the first cooking scheme and the second cooking scheme are fused to obtain a fused third cooking scheme, and the recipe cooking instruction is generated according to the third cooking scheme.

4. The method according to claim 3, characterized in that, The method further includes: If the temperature deviation value is not within the preset temperature deviation range, or the time deviation value is not within the preset time deviation range, the difference between the first cooking scheme and the second cooking scheme is determined. Based on the differences between the first cooking plan and the second cooking plan, a recipe inquiry instruction is generated, which is used to confirm the user's preferred recipe; The system receives the user's voice selection command, obtains the target cooking plan, and generates the recipe cooking command based on the target cooking plan.

5. The method according to claim 3, characterized in that, If the first cooking scheme includes a first numerical parameter, and the second cooking scheme includes a second numerical parameter, then the process of fusing the first cooking scheme and the second cooking scheme to obtain a fused third cooking scheme includes: The first numerical parameter and the second numerical parameter are averaged to obtain a third numerical parameter. The third cooking scheme includes the third numerical parameter. or, The third cooking scheme includes the fourth numerical parameter by multiplying the first numerical parameter by a preset first coefficient and then adding the second numerical parameter by multiplying it by a preset second coefficient.

6. The method according to claim 3, characterized in that, If the first cooking scheme further includes a first non-numerical parameter, and the second cooking scheme further includes a second non-numerical parameter, the step of fusing the first cooking scheme and the second cooking scheme to obtain a fused third cooking scheme includes: The first non-numerical parameter and the second non-numerical parameter are compared to obtain the comparison result; Based on the comparison results, a voice inquiry request is generated, which is used to request the user to select a target non-numerical parameter based on the comparison results; or, According to the preset parameter priority sorting rules, the first sorting result of the first non-numerical parameter and the second sorting result of the second non-numerical parameter are determined respectively. Based on the first sorting result and the second sorting result, a third non-numerical parameter is determined from the first non-numerical parameter and the second non-numerical parameter. The third cooking scheme includes the third non-numerical parameter.

7. The method according to any one of claims 2-6, characterized in that, The step of analyzing and processing the first ingredient information to determine the first cooking plan includes: Obtain a preset first recipe recommendation database, which contains the size, volume, quantity of different ingredients and the corresponding relationship of the first preset cooking scheme; Based on the size, volume or quantity of the ingredients, the first recipe recommendation database is matched to obtain a first matching result. The first matching result is used to indicate whether a first candidate cooking scheme is obtained from the first preset cooking scheme. If the first matching result indicates that a first candidate cooking scheme is obtained from the first preset cooking scheme, then the first cooking scheme is determined according to the first candidate cooking scheme.

8. The method according to any one of claims 2-6, characterized in that, The step of analyzing and processing the second ingredient information to determine the second cooking plan includes: Obtain a preset second recipe recommendation database, which contains the correspondence between the quantity, taste, doneness, and cooking time of different ingredients and the second preset cooking scheme; Based on the target taste, target doneness, target cooking time, or the quantity of ingredients, the second recipe recommendation database is matched to obtain a second matching result. The second matching result is used to indicate whether a second candidate cooking scheme is obtained from the second preset cooking scheme. If the second matching result indicates that a second candidate cooking scheme is obtained from the second preset cooking scheme, then the second cooking scheme is determined according to the second candidate cooking scheme.

9. The method according to claim 1, characterized in that, The process of responding to a user's voice interaction command and determining the user's cooking needs includes: In response to the voice interaction command sent by the user, the voice interaction command is parsed and processed to obtain voice interaction corpus; The voice interaction information is extracted and processed to obtain the cooking requirement information.

10. The method according to claim 1, characterized in that, After acquiring the image of the food inside the oven captured by the image acquisition device, the method further includes: The food image is subjected to quality assessment processing to obtain the quality defect type of the food image; If the quality defect type is uneven lighting or overexposure, then the food image is subjected to image enhancement processing to obtain the processed food image. If the quality defect type is insufficient image integrity, the image acquisition device is controlled to make fine adjustments according to a preset adjustment angle, and the image acquisition device is controlled to re-acquire the food image inside the oven, repeating the quality assessment steps until a food image that meets the conditions is obtained.

Images