Cooking control method and system, cooking equipment and cloud processing unit

Abstract

The invention provides a cooking control method and system, cooking equipment and a cloud processing unit. The image data containing food information is acquired through the image acquisition unit in the cooking equipment, non-contact measurement is realized based on a visual identification technology, the problem of precision reduction caused by pollution and abrasion of a traditional weighing sensor is avoided, and the method can adapt to accurate gram weight analysis of food in various shapes and textures. And meanwhile, the cloud generates a corresponding cooking strategy based on the gram weight data of the food, and issues the cooking strategy to the cooking equipment to automatically cook the food, so that the effect of accurately controlling the cooking process according to the weight of the food is achieved, and the user experience is improved.

Description

Technical Field

[0001] This article relates to the field of smart home appliance technology, and in particular to a cooking control method, system, cooking equipment and cloud processing unit. Background Technology

[0002] The cooking effect of cooking equipment on food is greatly affected by the quality of the food. For example, when heating food, the heating power and time required for the same food of different weights are completely different. Without accurate quality, it is impossible to precisely control the cooking equipment.

[0003] Traditional contact weighing technology relies on strain gauges or cantilever sensors installed on the base or legs of cooking equipment to calculate weight by converting metal deformation into electrical signals when weighing food inside the equipment. However, this method has significant drawbacks in complex cooking scenarios. The cooking process is prone to splattering grease, high humidity, food residue, etc., which can penetrate the sensitive areas of the strain gauges or sensors, affecting measurement accuracy and consequently impacting the subsequent cooking results.

[0004] Therefore, a smart device cooking control solution is needed. Summary of the Invention

[0005] To address the aforementioned technical problems, this paper provides the following technical solutions:

[0006] In a first aspect, a cooking control method is provided, the method comprising:

[0007] When cooking is started, the image acquisition unit integrated inside the cooking equipment acquires image data of the food in real time;

[0008] The image data is uploaded to the cloud processing unit so that the cloud processing unit can analyze the image data, determine the weight of the food, and generate a cooking strategy based on the weight data.

[0009] Receive the cooking strategy sent by the cloud processing unit;

[0010] The cooking strategy is used to control the cooking equipment to perform cooking operations.

[0011] The cooking equipment acquires image data containing food information through its image acquisition unit, and achieves non-contact measurement based on visual recognition technology. This avoids the accuracy degradation problems caused by contamination and wear of traditional weighing sensors, and can accurately analyze the weight of foods of various shapes and textures. Simultaneously, the cloud generates corresponding cooking strategies based on the food's weight data and sends them to the cooking equipment for automatic cooking. This achieves precise control of the cooking process based on food weight, enhancing the user experience.

[0012] In some embodiments, the cooking device further includes a temperature sensor, and the method further includes;

[0013] The temperature sensor collects temperature data inside the cooking device to obtain a first temperature.

[0014] The first temperature is uploaded to the cloud processing unit so that the cloud processing unit can analyze the image data, combine it with the temperature data, determine the weight of the food, and generate a cooking strategy based on the weight.

[0015] By monitoring the temperature data inside the cooking equipment in real time and applying it to the cloud for food weight analysis, the accuracy of food weight measurement is improved.

[0016] In some embodiments, the method further includes:

[0017] During the cooking process, the first temperature is updated in real time, and the updated first temperature is uploaded to the cloud processing unit so that the cloud processing unit can adjust the cooking strategy.

[0018] By monitoring the temperature data inside the cooking equipment in real time and applying it to the cloud for food weight analysis, the accuracy of food weight measurement is improved, while cooking strategies are optimized to enhance cooking results.

[0019] In some embodiments, the method further includes:

[0020] Receive the weight data sent by the cloud processing unit;

[0021] Before the cooking operation is started, the weight data is displayed on the display unit of the cooking device.

[0022] And / or, the cooking device is a microwave oven;

[0023] And / or, the cloud processing unit is an intelligent agent.

[0024] By displaying the food weight data calculated in the cloud in real time before the cooking operation is performed, users can intuitively verify the accuracy of the weighing, avoid overheating or underheating due to system misjudgment, and enhance the credibility of intelligent cooking.

[0025] In some embodiments, the cooking device is a microwave oven, and the image acquisition unit has high temperature resistance and anti-interference characteristics.

[0026] It effectively overcomes the complex environment inside a microwave oven, such as high temperature, moisture, and microwave interference, ensuring the stability and accuracy of measurements.

[0027] Secondly, a cooking control method is provided, the method comprising:

[0028] Receives image data of food sent by the cooking device, the image data being acquired in real time through the image acquisition unit integrated inside the cooking device when cooking is started;

[0029] Analyze the image data to determine the weight of the food, and generate a cooking strategy based on the weight data;

[0030] The cooking strategy is sent to the cooking device so that the cooking device can control the cooking device to perform cooking operations based on the cooking strategy.

[0031] Leveraging the powerful computing and storage capabilities of the cloud processing unit, the weight of food is analyzed and calculated based on image and temperature data sent by the cooking equipment, enabling high-precision and intelligent weight measurement and cooking control without contact with the food.

[0032] In some embodiments, the method is applied to an intelligent agent, and / or the analysis of the image data, determination of the weight of the food, and generation of a cooking strategy based on the weight data, includes:

[0033] Based on the image data, target detection is performed on the food to construct the spatial structural features of the food.

[0034] Based on the information of the spatial structure features and the preset density parameters of the food, the weight of the food is determined;

[0035] A cooking strategy is generated based on the weight data.

[0036] The cloud-based AI agent uses AI algorithms such as image recognition models to analyze the weight of food and transmits the results back to the cooking equipment, linking the control system to achieve precise cooking.

[0037] In some embodiments, the image data of the food includes multi-angle image data;

[0038] Based on the image data, target detection is performed on the food, and the spatial structural features of the food are constructed, including:

[0039] Based on the image data, target detection is performed on the food, and the spatial structural features of the food are generated through multi-angle three-dimensional reconstruction technology.

[0040] Leveraging the powerful computing and storage resources of the cloud, and based on reconstruction technology, it maintains high-precision and intelligent weight measurement and cooking control even when food is obscured, without touching the food.

[0041] In some embodiments, the method further includes:

[0042] The first temperature sent by the cooking device is obtained, wherein the first temperature is temperature data inside the cooking device collected by a temperature sensor inside the cooking device;

[0043] The preset density parameters of the food are corrected based on the first temperature.

[0044] During the cooking process, cloud-based algorithms are continuously optimized based on real-time temperature data to improve the accuracy and efficiency of weight analysis. Furthermore, personalized cooking strategy recommendations are achieved through the accumulation of extensive user data.

[0045] Thirdly, a cooking control system is provided, the system comprising:

[0046] The cooking equipment is used to acquire image data of food in real time through an image acquisition unit integrated within the cooking equipment when cooking is started; upload the image data to a cloud processing unit; and receive cooking strategies sent by the cloud processing unit; and control the cooking equipment to perform cooking operations based on the cooking strategies.

[0047] The cloud processing unit is used to receive image data of food sent by the cooking device. The image data is acquired in real time by the image acquisition unit integrated inside the cooking device when the cooking operation is started. The unit analyzes the image data, determines the weight of the food, and generates a cooking strategy based on the weight data. The unit then sends the cooking strategy to the cooking device.

[0048] Fourthly, a cooking apparatus is provided for implementing the cooking control method described in the first aspect above.

[0049] Fifthly, a cloud processing unit is provided for implementing the cooking control method described in the second aspect above.

[0050] In a sixth aspect, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the cooking control method as described in the first or second aspect above.

[0051] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and do not limit this document. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments or prior art described herein, the accompanying drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments recorded in the embodiments of this article. For those skilled in the art, other drawings can be obtained based on these drawings.

[0053] Figure 1 This is a flowchart illustrating a cooking control method provided in this article;

[0054] Figure 2 This is another flowchart illustrating a cooking control method presented in this article;

[0055] Figure 3 This is an interactive schematic diagram of a cooking control system provided in this article;

[0056] Figure 4 This is a schematic diagram of a computer-readable storage medium provided in this disclosure;

[0057] Figure 5 This is a schematic diagram of the structure of a computing device provided in this disclosure. Detailed Implementation

[0058] This document describes several technical solutions with different concepts. Each concept has one or more embodiments, and different concepts can be combined to form more embodiments. Those skilled in the art, after reading this document, can combine different concepts to obtain new technical solutions, and these new technical solutions should also fall within the scope of this document.

[0059] The technical solutions of these different concepts will be introduced in turn below. Some concepts may appear in multiple technical solutions of different concepts. For these concepts, this article will explain them when they first appear and will not repeat them in the following text.

[0060] The heating effect of food is greatly affected by its weight; for example, 300g of potatoes and 500g of potatoes require completely different heating power and time. Therefore, accurately measuring the weight of food in cooking equipment is a challenge.

[0061] Traditionally, contact weighing methods are used, which use strain gauges or cantilever sensors installed at the bottom of the food tray to sense changes in the weight of the food. However, this method has problems such as complex hardware modifications that increase costs, and is easily affected by food residue and oil stains, which can lead to a decrease in accuracy and seriously affect the cooking results.

[0062] To address the aforementioned technical problems, this paper presents a cooking control method.

[0063] The cooking equipment acquires image data containing food information through its image acquisition unit, sends the image data to the cloud, and then uses cloud AI to identify and reconstruct the food, extract its volume features, calculate its weight by combining the food density, and generate corresponding cooking strategies.

[0064] In this process, visual recognition is used to capture food information, and cloud-based AI is combined to process the information captured by the cooking equipment, creating a non-contact food weighing method. This method is resistant to environmental interference and avoids the accuracy degradation caused by contamination and wear of traditional weighing sensors, providing an accurate and reliable data foundation for food weight detection, thereby improving the accuracy of food weight detection. Simultaneously, throughout the process, cloud-based AI generates a cooking strategy based on the food's weight and sends it to the cooking equipment for automatic cooking, achieving precise control of the cooking process based on food weight and enhancing the user experience.

[0065] The following embodiments, in conjunction with the accompanying drawings, will illustrate a cooking control method provided herein.

[0066] Figure 1 This is a flowchart illustrating a cooking control method according to an exemplary embodiment. The cooking control method can be applied to cooking equipment.

[0067] The cooking equipment mentioned in this article generally refers to devices that interact with an Internet of Things (IoT) platform. It typically refers to electrical appliances used in various environments, such as homes or office buildings, for various purposes. Specifically, it can be cooking appliances / household appliances with a specific cooking function, such as microwave ovens, electric pressure cookers, electric frying pans, blenders, juicers, and stir-fry machines. Furthermore, cooking appliances can also be multifunctional cooking appliances with various cooking functions.

[0068] The cooking device used in this embodiment is a microwave oven. For ease of description, it will be referred to as a microwave oven below.

[0069] The cooking equipment described in this article has the conventional structure and functions of cooking equipment currently on the market (such as product casing, power interface, basic equipment functions, etc.), and will not be described in detail here.

[0070] like Figure 1 As shown, the method includes steps 101 and 104.

[0071] Step 101: When the cooking process is started, the image data of the food is acquired in real time through the image acquisition unit integrated inside the cooking device.

[0072] In some embodiments, the microwave oven includes a main control chip, an image acquisition unit, a control module, and a temperature sensor.

[0073] The image acquisition unit is a device used to acquire food information at a preset frequency when cooking is started. It may be, but is not limited to, a camera or other anti-interference imaging devices.

[0074] When the cooking equipment is a microwave oven, the cooking environment inside is complex, with high temperatures, moisture, and microwave interference. Ordinary cameras are easily blurred by moisture and interfered with by microwaves, making them unsuitable for the requirements. Therefore, the image acquisition unit described in this article has high-temperature resistance and anti-interference characteristics. For example, the camera described here is an anti-fog, high-temperature resistant camera (hereinafter referred to as the camera), which has high-definition shooting capabilities and its lens is treated with an anti-fog coating, enabling it to work normally in high-temperature and moisture environments. This effectively overcomes the complex environment of high temperature, moisture, and microwave interference inside the microwave oven, ensuring the stability and accuracy of the measurement.

[0075] An anti-fog and high-temperature resistant camera is integrated into the microwave oven, employing an anti-fouling coating to capture real-time images of food. In some cases, the camera is integrated into multiple locations within the microwave oven, such as the top or side wall. This multi-angle shooting provides multi-angle image data, which is used for subsequent extraction of the food's spatial structure features. Multi-view compensation addresses the limitation of shooting angles within the microwave oven.

[0076] When the cooking process is started, the anti-fog and high-temperature resistant camera is activated and captures multi-angle images of the food at a preset frequency (such as 5 frames per second) to obtain image data.

[0077] The activation of the camera can be triggered by the following methods to determine when the microwave cooking process begins:

[0078] The camera is activated by detecting door closure. A built-in magnetic sensor or mechanical switch in the microwave door frame monitors the door's closing status in real time, and automatically activates when a door closure signal is triggered. For example, when a user places food into the microwave and closes the door, the camera will start.

[0079] The camera can be activated via user commands, including physical button activation or contactless activation. Physical button activation: The camera is activated when the user presses the "Start" button or the dedicated "Weigh" button on the control panel. Contactless activation includes voice command activation, where the system is woken up by recognizing commands such as "Start Cooking" or "Start" through the built-in microphone.

[0080] Through intelligent sensing, if the microwave oven has an integrated human body sensor module, it will automatically activate the camera after a certain delay once the motion sensor detects the user approaching and placing food inside.

[0081] The camera can also be triggered by sensing weight changes. If the microwave oven integrates basic weighing functions (such as chassis strain gauges), the camera will be activated when a sudden increase in weight is detected inside the cavity.

[0082] In addition, there are other ways to trigger the camera to start cooking, forming a multi-level fault tolerance mechanism to ensure that images are captured immediately when cooking begins.

[0083] After the camera captures image data including food information, it connects to the main control chip via its built-in wireless communication module, transmitting the image data to the main control chip in real time. The wireless communication module can be, but is not limited to, Wi-Fi or Bluetooth.

[0084] Step 102: Upload the image data to the cloud processing unit so that the cloud processing unit can analyze the image data, determine the weight of the food, and generate a cooking strategy based on the weight data.

[0085] The main control chip is responsible for coordinating the operation of various devices inside the microwave oven and has a built-in network communication module.

[0086] After receiving image data transmitted from the camera, the main control chip sends the image data to the cloud processing unit through the network communication module. The cloud processing unit calculates the weight of the food and generates a cooking strategy based on the image data.

[0087] The cloud processing unit can be an intelligent agent. Utilizing the powerful computing and storage resources of the cloud, AI algorithms can be continuously optimized, improving the accuracy and efficiency of weight analysis. Furthermore, it eliminates the need to integrate complex AI computing modules into local devices, reducing microwave oven hardware costs and development complexity. Simultaneously, the interaction between the cloud and the cooking equipment supports remote upgrades and maintenance, facilitating functional iterations and problem fixing on the device side.

[0088] It is understandable that the functions corresponding to the programs of cooking equipment are implemented through cloud AI. When the cooking equipment needs to update the program or use a new program, it can call the cloud AI to achieve this.

[0089] In some embodiments, the cooking device further includes a temperature sensor, and the method further includes;

[0090] The temperature sensor collects temperature data inside the cooking device to obtain a first temperature.

[0091] The first temperature is uploaded to the cloud processing unit so that the cloud processing unit can analyze the image data, combine it with the temperature data, determine the weight of the food, and generate a cooking strategy based on the weight.

[0092] The temperature sensor in the microwave oven is used to monitor the temperature inside the microwave oven in real time, obtain the first temperature, and feed the first temperature data back to the main control chip.

[0093] After receiving the first temperature from the temperature sensor, the main control chip uploads it to the cloud as needed to assist the cloud AI in optimizing the parameters of the weight analysis algorithm. It can be understood that the cloud processing unit calculates food weight based on the food's spatial structure characteristics (such as volume) and density. However, the oven temperature affects the food's appearance; for example, food expansion at high temperatures leads to a decrease in apparent density, which in turn reduces the food's weight. Therefore, the weight calculation process needs to be corrected to improve its measurement accuracy.

[0094] This embodiment improves the accuracy of food weight measurement by monitoring the temperature data inside the cooking equipment in real time and applying it to the cloud for food weight analysis.

[0095] In some embodiments, the method further includes:

[0096] During the cooking process, the first temperature is updated in real time, and the updated first temperature is uploaded to the cloud processing unit so that the cloud processing unit can adjust the cooking strategy.

[0097] During cooking, the temperature sensor continuously monitors the temperature and feeds it back to the main control chip. The main control chip can upload new temperature data to the cloud processing unit periodically or when the temperature change exceeds the threshold, according to preset rules, so that it can dynamically optimize the heating strategy.

[0098] It is understandable that cooking strategies are generated based on the weight of food, and the cooking strategies need to be adjusted accordingly when the weight of food changes.

[0099] As an example, when the cooking strategy is a heating strategy, the bread expands at high temperatures, causing a 10% decrease in density, which in turn reduces the calculated weight and shortens the heating time.

[0100] By monitoring the temperature data inside the cooking equipment in real time and applying it to the cloud for food weight analysis, the accuracy of food weight measurement is improved, while cooking strategies are optimized to enhance cooking results.

[0101] Step 103: Receive the cooking strategy sent by the cloud processing unit.

[0102] The microwave oven's main control chip receives cooking strategies sent by the cloud processing unit, which are then used to cook food based on these strategies.

[0103] In some embodiments, the method further includes:

[0104] Receive the weight data sent by the cloud processing unit;

[0105] Before the cooking operation is started, the weight data is displayed on the display unit of the cooking device.

[0106] The display unit may be, but is not limited to, the microwave oven's control panel, indicator lights, etc.

[0107] The microwave oven's main control chip receives food weight data from the cloud and sends it to the control panel for display. On one hand, by displaying the cloud-calculated food weight data in real-time before cooking, users can intuitively verify the accuracy of the weighing, avoiding overheating or underheating due to system misjudgment and enhancing the reliability of intelligent cooking. On the other hand, automatically displaying the weight replaces manual input by the user, simplifying the operation process and improving efficiency. Furthermore, if an abnormal weight is detected, the user can terminate the error-prevention strategy.

[0108] Step 104: Control the cooking equipment to perform cooking operations based on the cooking strategy.

[0109] The cooking strategy may include, but is not limited to, heating strategies, defrosting strategies, fermentation strategies, steam-assisted strategies, and other cooking-related strategies applicable to the current cooking equipment. For example, the cooking strategy delivered from the cloud may be: Stage 1: 800W, 120s; Stage 2: defrosting mode, 300g weight.

[0110] After receiving the weight data of the food and the cooking strategy, the main control chip of the microwave oven converts the cooking strategy into control commands such as cooking parameters or cooking mode instructions to control the microwave oven's cooking operation.

[0111] The main control chip sends control commands to the microwave oven's control module. The microwave oven's control module then automatically adjusts the cooking parameters and starts the cooking program based on the control commands, achieving precise cooking.

[0112] For example, when the cooking strategy is a heating strategy, the main control chip converts the heating strategy into a heating command, such as a power of 800W and a time of 120s. The control module then automatically adjusts the heating power and time of the microwave oven according to the heating command and starts the heating operation.

[0113] Through the above embodiments, image data containing food information is acquired by the image acquisition unit within the cooking equipment. Non-contact measurement is achieved based on visual recognition technology, avoiding the accuracy degradation problems caused by contamination and wear of traditional weighing sensors. This allows for precise weight analysis of foods of various shapes and textures. Simultaneously, the cloud generates corresponding cooking strategies based on the food's weight data and sends them to the cooking equipment for automatic cooking. This achieves precise control of the cooking process based on food weight, enhancing the user experience.

[0114] Figure 2 This is a flowchart illustrating a cooking control method according to an exemplary embodiment. The cooking control method can be applied to a cloud processing unit.

[0115] The method includes the following steps 201 to 203.

[0116] Step 201: Receive image data of food sent by the cooking device. The image data is acquired in real time by the image acquisition unit integrated inside the cooking device when the cooking operation is started.

[0117] The cloud processing unit is a computing unit deployed on a cloud server, possessing powerful computing and storage capabilities. It can be, but is not limited to, a cloud-based AI agent, a distributed service cluster, or a near-edge computing platform. In this embodiment, the cloud processing unit is exemplified by a cloud-based AI agent; for ease of understanding, it will be referred to as an agent, meaning the cooking control method is applied to the agent.

[0118] The intelligent agent receives image data and initial temperature data sent by the microwave oven, runs a weight analysis algorithm, calculates the weight of the food, generates a heating strategy, and sends the results back to the main control chip.

[0119] Step 202: Analyze the image data, determine the weight of the food, and generate a cooking strategy based on the weight data.

[0120] After receiving image data sent by the microwave oven, the intelligent agent first preprocesses the image, including denoising and contrast enhancement.

[0121] The processed image data is analyzed using deep learning algorithms (such as convolutional neural networks) to determine the weight of the food.

[0122] In some embodiments, analyzing the image data, determining the weight of the food, and generating a cooking strategy based on the weight data includes:

[0123] Based on the image data, target detection is performed on the food to construct the spatial structural features of the food.

[0124] Based on the information of the spatial structure features and the preset density parameters of the food, the weight of the food is determined;

[0125] A cooking strategy is generated based on the weight data.

[0126] An intelligent agent is a cloud-based computing device or system capable of performing image segmentation, 3D reconstruction, and weight calculation. Deep learning algorithms are used to perform object detection and segmentation on images, extracting spatial structural features of food, such as its outline and volume.

[0127] The preset density parameters corresponding to the food are obtained from the preset food density database. Based on the preset density parameters and volume characteristics, the weight of the food is calculated.

[0128] The heating power and time required for 300g potatoes and 500g potatoes are completely different. Therefore, after calculating the weight of the food, a cooking strategy is generated based on the weight of the food, and the result is sent back to the main control chip.

[0129] The cloud-based AI agent uses AI algorithms such as image recognition models to analyze the weight of food and transmits the results back to the cooking equipment, linking the control system to achieve precise cooking.

[0130] In some embodiments, the image data of the food includes multi-angle image data;

[0131] Based on the image data, target detection is performed on the food, and the spatial structural features of the food are constructed, including:

[0132] Based on the image data, target detection is performed on the food, and the spatial structural features of the food are generated through multi-angle three-dimensional reconstruction technology.

[0133] By simultaneously capturing multiple frames of images of the food from different perspectives using multi-angle cameras inside the microwave oven (such as the top and sides), and employing 3D reconstruction technology, accurate spatial structural features of the food are obtained. Even for areas where the food is obscured, the reconstruction technology automatically completes the geometric structure, ensuring that the cooking control scheme presented in this paper remains usable and accurate even in scenarios where the food is partially obscured.

[0134] Subsequently, by combining a pre-set food density database and the effect of temperature data on density, the food volume is calculated, and the food weight is obtained. Based on the weight, a corresponding heating strategy is generated.

[0135] Leveraging the powerful computing and storage resources of the cloud, and based on reconstruction technology, it maintains high-precision and intelligent weight measurement and cooking control even when food is obscured, without touching the food.

[0136] In some embodiments, the method further includes:

[0137] The first temperature sent by the cooking device is obtained, wherein the first temperature is temperature data inside the cooking device collected by a temperature sensor inside the cooking device;

[0138] The preset density parameters of the food are corrected based on the first temperature.

[0139] During the heating process, the temperature sensor continuously monitors the temperature and feeds it back to the main control chip. The main control chip can upload new temperature data to the cloud AI intelligent agent periodically or when the temperature change exceeds the threshold, according to preset rules.

[0140] The cloud-based AI agent receives temperature data uploaded by the main control chip, runs a weight analysis algorithm, corrects the calculation of food weight, and generates a new cooking strategy.

[0141] The results are then sent back to the main control chip.

[0142] During the cooking process, cloud-based algorithms are continuously optimized based on real-time temperature data to improve the accuracy and efficiency of weight analysis. Furthermore, personalized cooking strategy recommendations are achieved through the accumulation of extensive user data.

[0143] Step 203: Send the cooking strategy to the cooking device so that the cooking device controls the cooking device to perform cooking operations based on the cooking strategy.

[0144] The cloud-based AI agent sends cooking strategies to the cooking equipment and its linkage control system to achieve precise cooking.

[0145] Through the above embodiments, the powerful computing and storage capabilities of the cloud processing unit are utilized to analyze and calculate the weight of food based on the image and temperature data sent by the cooking equipment, thereby achieving high-precision and intelligent weight measurement and cooking control without contact with the food.

[0146] Figure 3 This is an interactive schematic diagram of a cooking control system illustrated in this article according to an exemplary embodiment.

[0147] The system includes:

[0148] The cooking equipment is used to acquire image data of food in real time through an image acquisition unit integrated within the cooking equipment when cooking is started; upload the image data to a cloud processing unit; and receive cooking strategies sent by the cloud processing unit; and control the cooking equipment to perform cooking operations based on the cooking strategies.

[0149] The cloud processing unit is used to receive image data of food sent by the cooking device. The image data is acquired in real time by the image acquisition unit integrated inside the cooking device when the cooking operation is started. The unit analyzes the image data, determines the weight of the food, and generates a cooking strategy based on the weight data. The unit then sends the cooking strategy to the cooking device.

[0150] The cooking equipment and cloud processing unit implement the cooking method as described above, and their specific embodiments are basically the same, so they will not be repeated here.

[0151] This article also discloses a cooking device for implementing the cooking control method described above.

[0152] This article also discloses a cloud processing unit for implementing the cooking control method described above.

[0153] Figure 4 This is a schematic diagram of a computer-readable storage medium 140 provided in this disclosure, on which a computer program is stored, which, when executed by a processor, implements the method of any embodiment of this disclosure.

[0154] This disclosure also provides a computing device, including a memory and a processor; the memory is used to store computer instructions that can be executed on the processor, and the processor is used to implement the methods of any embodiment of this disclosure when executing the computer instructions.

[0155] Figure 5 This is a schematic diagram of the structure of a computing device provided in this disclosure, such as... Figure 5 As shown, the computing device 15 may include, but is not limited to: a processor 151, a memory 152, and a bus 153 connecting different system components (including the memory 152 and the processor 151).

[0156] The memory 152 stores computer instructions that can be executed by the processor 151, enabling the processor 151 to perform the training method of the aesthetic image generation model according to any embodiment of this disclosure. The memory 152 may include a random access memory unit (RAM) 1521, a cache memory unit (Cache) 1522, and / or a read-only memory unit (ROM) 1523. The memory 152 may also include a program tool 1525 having a set of program modules 1524, including but not limited to: an operating system, one or more application programs, other program modules, and program data. One or more combinations of these program modules may include an implementation of a network environment.

[0157] Bus 153 may include, for example, a data bus, an address bus, and a control bus. The computing device 15 can also communicate with external devices 155 via I / O interface 154, such as a keyboard or a Bluetooth device. The computing device 15 can also communicate with one or more networks via network adapter 156, such as a local area network (LAN), a wide area network (WAN), or a public network. As shown in the figure, the network adapter 156 can also communicate with other modules of the computing device 15 via bus 153.

[0158] Furthermore, although the operations of the methods disclosed herein are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all of the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0159] While the spirit and principles of this disclosure have been described with reference to several specific embodiments, it should be understood that this disclosure is not limited to the disclosed specific embodiments, and the division of aspects does not imply that features in these aspects cannot be combined for benefit; such division is merely for convenience of expression. This disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

1. A cooking control method, characterized in that, The method includes: When cooking is started, the image acquisition unit integrated inside the cooking equipment acquires image data of the food in real time; The image data is uploaded to the cloud processing unit so that the cloud processing unit can analyze the image data, determine the weight of the food, and generate a cooking strategy based on the weight data. Receive the cooking strategy sent by the cloud processing unit; The cooking strategy is used to control the cooking equipment to perform cooking operations.

2. The cooking control method as described in claim 1, characterized in that, The cooking device also includes a temperature sensor. The method further includes; The temperature sensor collects temperature data inside the cooking device to obtain a first temperature. The first temperature is uploaded to the cloud processing unit so that the cloud processing unit can analyze the image data, combine it with the temperature data, determine the weight of the food, and generate a cooking strategy based on the weight.

3. The cooking control method as described in claim 2, characterized in that, The method further includes: During the cooking process, the first temperature is updated in real time, and the updated first temperature is uploaded to the cloud processing unit so that the cloud processing unit can adjust the cooking strategy.

4. The cooking control method as described in claim 1, characterized in that, The method further includes: Receive the weight data sent by the cloud processing unit; Before the cooking operation is started, the weight data is displayed on the display unit of the cooking device. And / or, the cooking device is a microwave oven; And / or, the cloud processing unit is an intelligent agent.

5. A cooking control method, characterized in that, The method includes: Receives image data of food sent by the cooking device, the image data being acquired in real time through the image acquisition unit integrated inside the cooking device when cooking is started; Analyze the image data to determine the weight of the food, and generate a cooking strategy based on the weight data; The cooking strategy is sent to the cooking device so that the cooking device can control the cooking device to perform cooking operations based on the cooking strategy.

6. The cooking control method as described in claim 5, characterized in that, The method is applied to an intelligent agent, and / or to analyze the image data, determine the weight of the food, and generate a cooking strategy based on the weight data, including: Based on the image data, target detection is performed on the food to construct the spatial structural features of the food. Based on the information of the spatial structure features and the preset density parameters of the food, the weight of the food is determined; A cooking strategy is generated based on the weight data.

7. The cooking control method as described in claim 6, characterized in that, The image data of the food includes multi-angle image data; Based on the image data, target detection is performed on the food, and the spatial structural features of the food are constructed, including: Based on the image data, target detection is performed on the food, and the spatial structural features of the food are generated through multi-angle three-dimensional reconstruction technology. Alternatively, the method may further include: The first temperature sent by the cooking device is obtained, wherein the first temperature is temperature data inside the cooking device collected by a temperature sensor inside the cooking device; The preset density parameters of the food are corrected based on the first temperature.

8. A cooking control system, characterized in that, The system includes: The cooking equipment is used to acquire image data of food in real time through an image acquisition unit integrated within the cooking equipment when cooking is started; upload the image data to a cloud processing unit; and receive cooking strategies sent by the cloud processing unit; and control the cooking equipment to perform cooking operations based on the cooking strategies. The cloud processing unit is used to receive image data of food sent by the cooking device. The image data is acquired in real time by the image acquisition unit integrated inside the cooking device when the cooking operation is started. The unit analyzes the image data, determines the weight of the food, and generates a cooking strategy based on the weight data. The unit then sends the cooking strategy to the cooking device.

9. A cooking device, characterized in that, Used to implement the cooking control method as described in any one of claims 1 to 4.

10. A cloud processing unit, characterized in that, Used to implement the cooking control method as described in any one of claims 5 to 7.

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