A food processing control method and system based on artificial intelligence

Abstract

This invention provides an artificial intelligence-based food processing control method and system. The invention relates to the field of food processing control technology. The method includes using a food data processing model to determine the ripeness of each food item and the corresponding heating zone of the grill; determining the heating power of the corresponding heating zone of the grill and the turning speed of each food item based on the ripeness and heating zone of each food item; determining the heating time of the corresponding heating zone of the grill based on the heating power and turning speed of each food item; and controlling the grill based on the heating power, heating time, and turning speed of the corresponding heating zone of the grill. This method accurately controls the food processing process and improves the stability of food processing.

Description

Technical Field

[0001] This invention relates to the field of food processing technology, and more specifically to a food processing control method and system based on artificial intelligence. Background Technology

[0002] In traditional food processing, especially for grilled foods, reliance on human experience and manual control is crucial. Specifically, chefs or operators must determine the grilling time, temperature, and frequency of turning based on the type, size, thickness of the food, and their own experience. Human judgment is heavily influenced by individual experience and skill, making consistency between operators difficult to guarantee. For example, different chefs might have different opinions on the grilling time for the same steak, leading to inconsistent doneness and texture. Due to the uncontrollable nature of human factors, the doneness and texture of food often fluctuate significantly, making it difficult to achieve a consistent standard.

[0003] Therefore, how to accurately control the food processing process and improve the stability of food processing is an urgent problem to be solved. Summary of the Invention

[0004] The main technical problem solved by this invention is to accurately control the food processing process and improve the stability of food processing.

[0005] According to a first aspect, the present invention provides an artificial intelligence-based food processing control method, comprising: acquiring barbecue videos of various foods in a barbecue oven; determining the ripeness of each food and the heating zone of the barbecue oven corresponding to each food using a food data processing model based on the barbecue videos of the various foods in the barbecue oven; determining the heating power of the heating zone of the barbecue oven corresponding to each food and the turning speed of each food based on the ripeness of each food and the heating zone of the barbecue oven corresponding to each food; determining the heating time of the heating zone of the barbecue oven corresponding to each food based on the heating power of the heating zone of the barbecue oven corresponding to each food and the turning speed of each food; and controlling the barbecue oven based on the heating power of the heating zone of the barbecue oven corresponding to each food, the heating time of the heating zone of the barbecue oven corresponding to each food, and the turning speed of each food.

[0006] In one possible implementation, determining the heating time of the heating zone of the barbecue oven corresponding to each food based on the heating power of the heating zone of the barbecue oven corresponding to each food and the turning speed of each food includes:

[0007] Construct a heating area knowledge graph, which includes multiple nodes and multiple edges between nodes. Each node represents the heating area of ​​a barbecue grill corresponding to a certain food. The node features of each node include the heating power of the heating area of ​​the barbecue grill corresponding to each food and the flipping speed of each food. The features of the edges between nodes represent the distance between the heating areas of the barbecue grill corresponding to different foods.

[0008] The heating time of the heating area of ​​the barbecue grill corresponding to each food is determined by processing the knowledge graph of the heating area based on the graph autoencoder.

[0009] In one possible implementation, the food data processing model is a recurrent neural network model. The input of the food data processing model is a video of the various foods being grilled in a barbecue grill, and the output of the food data processing model is the doneness of each food and the heating zone of the barbecue grill corresponding to each food.

[0010] In one possible implementation, the input of the graph autoencoder is the heating region knowledge graph, and the output of the graph autoencoder is the heating time of the heating region of the grill corresponding to each food.

[0011] According to a second aspect, the present invention provides an artificial intelligence-based food processing control system, comprising:

[0012] The acquisition module is used to acquire videos of various foods being grilled in a barbecue grill;

[0013] The processing module is used to determine the doneness of each food and the corresponding heating zone of the barbecue oven based on the barbecue video of the various foods in the barbecue oven using a food data processing model.

[0014] The food information determination module is used to determine the heating power of the heating area of ​​the barbecue oven corresponding to each food and the turning speed of each food based on the ripeness of each food and the heating area of ​​the barbecue oven corresponding to each food.

[0015] The time determination module is used to determine the heating time of the heating area of ​​the barbecue oven corresponding to each food based on the heating power of the heating area of ​​the barbecue oven corresponding to each food and the turning speed of each food.

[0016] The control module is used to control the barbecue oven based on the heating power of the heating area of ​​the barbecue oven corresponding to each food, the heating time of the heating area of ​​the barbecue oven corresponding to each food, and the turning speed of each food.

[0017] In one possible implementation, the time determination module is further configured to:

[0018] Construct a heating area knowledge graph, which includes multiple nodes and multiple edges between nodes. Each node represents the heating area of ​​a barbecue grill corresponding to a certain food. The node features of each node include the heating power of the heating area of ​​the barbecue grill corresponding to each food and the flipping speed of each food. The features of the edges between nodes represent the distance between the heating areas of the barbecue grill corresponding to different foods.

[0019] The heating time of the heating area of ​​the barbecue grill corresponding to each food is determined by processing the knowledge graph of the heating area based on the graph autoencoder.

[0020] In one possible implementation, the food data processing model is a recurrent neural network model. The input of the food data processing model is a video of the various foods being grilled in a barbecue grill, and the output of the food data processing model is the doneness of each food and the heating zone of the barbecue grill corresponding to each food.

[0021] In one possible implementation, the input of the graph autoencoder is the heating region knowledge graph, and the output of the graph autoencoder is the heating time of the heating region of the grill corresponding to each food.

[0022] According to a third aspect, embodiments of the present invention provide an electronic device, including: a processor; a memory; and a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method described above, the method including: acquiring videos of various foods being grilled in a grill; determining the ripeness of each food and the heating zone of the grill corresponding to each food using a food data processing model based on the videos of the various foods being grilled in the grill; determining the heating power of the heating zone of the grill corresponding to each food and the turning speed of each food based on the ripeness of each food and the heating zone of the grill corresponding to each food; determining the heating time of the heating zone of the grill corresponding to each food based on the heating power of the heating zone of the grill corresponding to each food and the turning speed of each food; and controlling the grill based on the heating power of the heating zone of the grill corresponding to each food, the heating time of the heating zone of the grill corresponding to each food and the turning speed of each food.

[0023] According to a fourth aspect, this embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned artificial intelligence-based food processing control method. The method includes: acquiring videos of various foods being grilled in a barbecue oven; determining the maturity of each food and the heating zone of the barbecue oven corresponding to each food using a food data processing model based on the videos of the various foods being grilled in the barbecue oven; determining the heating power of the heating zone of the barbecue oven corresponding to each food and the turning speed of each food based on the maturity of each food and the heating zone of the barbecue oven corresponding to each food; determining the heating time of the heating zone of the barbecue oven corresponding to each food based on the heating power of the heating zone of the barbecue oven corresponding to each food and the turning speed of each food; and controlling the barbecue oven based on the heating power of the heating zone of the barbecue oven corresponding to each food, the heating time of the heating zone of the barbecue oven corresponding to each food, and the turning speed of each food.

[0024] This invention provides an artificial intelligence-based food processing control method and system. The method includes acquiring videos of various foods being grilled in a barbecue oven; using a food data processing model to determine the doneness of each food and the corresponding heating zone of the barbecue oven based on the grilling videos; determining the heating power of the heating zone of the barbecue oven corresponding to each food and the turning speed of each food based on the doneness of each food and the heating zone of the barbecue oven corresponding to each food; determining the heating time of the heating zone of the barbecue oven corresponding to each food based on the heating power of the heating zone of the barbecue oven corresponding to each food and the turning speed of each food; and controlling the barbecue oven based on the heating power of the heating zone of the barbecue oven corresponding to each food, the heating time of the heating zone of the barbecue oven corresponding to each food, and the turning speed of each food. This method can accurately control the food processing process and improve the stability of food processing. Attached Figure Description

[0025] Figure 1 A schematic diagram illustrating an application scenario of an artificial intelligence-based food processing control method provided in an embodiment of the present invention;

[0026] Figure 2 A schematic flowchart of an artificial intelligence-based food processing control method provided in an embodiment of the present invention;

[0027] Figure 3 This is a flowchart illustrating a process for determining the probability value of a fuzzy test based on multiple data input by a user at different time points, as provided in an embodiment of the present invention.

[0028] Figure 4 A schematic diagram of an artificial intelligence-based food processing control system provided in an embodiment of the present invention;

[0029] Figure 5 A schematic diagram of an electronic device provided in an embodiment of the present invention;

[0030] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0031] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings. Similar elements in different embodiments are referred to by associated similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of the invention. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, certain operations related to the present invention are not shown or described in the specification. This is to avoid obscuring the core parts of the invention with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0032] Figure 1 This is a schematic diagram illustrating an application scenario of an artificial intelligence-based food processing control method provided in an embodiment of the present invention. Figure 1 The application scenarios of the food processing control method based on artificial intelligence can include servers 11, networks 12, terminals 13 and storage devices 14.

[0033] In some embodiments, server 11 may be a single server or a group of servers. Server 11 can access information and / or data stored in terminal 13 or storage device 14 via network 12. In some embodiments, server 11 may be used to perform... Figure 2 The food processing control method based on artificial intelligence is shown in the figure.

[0034] Network 12 can facilitate the exchange of information and / or data. In some embodiments, network 12 can be any form of wired or wireless network, or any combination thereof.

[0035] Terminal 13 may refer to one or more terminal devices used by a user. In some embodiments, terminal 13 may include one or more combinations of mobile devices, tablet computers, laptop computers, etc.

[0036] Storage device 14 can store data and / or instructions, for example, storage device 14 can store data instructions for food processing control methods based on artificial intelligence.

[0037] In this embodiment of the invention, the following are provided: Figure 2 The illustrated food processing control method based on artificial intelligence can effectively handle fuzzy testing. The method includes steps S1-S5:

[0038] Step S1: Obtain videos of various foods being grilled in a barbecue grill.

[0039] The video shows various foods being grilled on a barbecue grill, captured by a camera or other video capture device. For example, the video might include steak, chicken wings, vegetables, and other foods being grilled on a barbecue grill.

[0040] Step S2: Based on the grilling videos of the various foods in the grill, a food data processing model is used to determine the doneness of each food and the corresponding heating zone of the grill for each food.

[0041] The food data processing model is a recurrent neural network model. The input to the model is a video of the various foods being grilled in a barbecue grill, and the output is the doneness of each food and the corresponding heating zone of the barbecue grill. The recurrent neural network model is one implementation method of AI.

[0042] The heating zone of a barbecue grill corresponds to the area of ​​the grill that is used to heat that food. For example, zone A corresponds to steak, and zone B corresponds to chicken wings.

[0043] Recurrent Neural Network (RNN) models are used to process sequential data, capture information from the sequence, and output results based on the relationships between preceding and following data points. By processing continuous time segments of grilling videos of various foods on a barbecue grill using an RNN model, the output can comprehensively consider the relationships between different time points, making the output features more accurate and comprehensive. RNN models are a method for implementing artificial intelligence.

[0044] Barbecue videos consist of a series of consecutive video frames. Recurrent neural networks (RNNs) can process this sequential data, capturing the relationships between different points in time within the video. During the grilling of a steak, an RNN can capture the changes in the steak as it cooks from raw to well-done, thus more accurately assessing its doneness.

[0045] In some embodiments, the food data processing model includes a region segmentation layer, a maturity determination layer, and a heating zone determination layer. Each of these layers comprises a recurrent neural network. The input to the region segmentation layer is a video of the various foods being grilled in a barbecue grill, and the output of the region segmentation layer is a video of the heating zone of the barbecue grill and a video of the food being placed in the heating zone. The input to the maturity determination layer is a video of the food being placed in the heating zone, and the output of the maturity determination layer is the maturity level of each food item. The input to the heating zone determination layer is a video of the heating zone of the barbecue grill and a video of the food being placed in the heating zone, and the output of the heating zone determination layer is the heating zone of the barbecue grill corresponding to each food item.

[0046] Through a hierarchical design, the food data processing model can handle complex tasks more effectively, improving overall accuracy and robustness. The region segmentation layer, maturity determination layer, and heating region determination layer are each responsible for different tasks. This progressive refinement and task separation ensures that the final output features are more accurate and comprehensive.

[0047] Step S3: Based on the ripeness of each food and the heating area of ​​the barbecue oven corresponding to each food, determine the heating power of the heating area of ​​the barbecue oven corresponding to each food and the turning speed of each food.

[0048] In some embodiments, a deep neural network model can be used to determine the heating power of the heating zone of the grill corresponding to each food item and the flipping speed of each food item. The input to the deep neural network model is the doneness of each food item and the heating zone of the grill corresponding to each food item; the output of the deep neural network model is the heating power of the heating zone of the grill corresponding to each food item and the flipping speed of each food item. For example, if a steak is currently 70% done and the heating zone is area A, the deep neural network model determines a heating power of 1500W and a flipping speed of once per minute. As another example, if chicken wings are 85% done and the heating zone is area B, the deep neural network model determines a heating power of 1200W and a flipping speed of twice per minute. The deep neural network model includes deep neural networks (DNNs). A deep neural network can include multiple processing layers, each consisting of multiple neurons, and each neuron performs matrix transformations on the data. The parameters used in the matrix can be obtained through training. The deep neural network model is one implementation method of AI.

[0049] Step S4: Determine the heating time of the heating zone of the barbecue oven corresponding to each food based on the heating power of the heating zone of the barbecue oven corresponding to each food and the turning speed of each food.

[0050] In some embodiments, Figure 3This invention provides a flowchart illustrating the process of determining the heating time of the heating zone of a barbecue grill corresponding to each type of food. The determination of the heating time of the heating zone of the barbecue grill corresponding to each type of food includes steps S21-S22:

[0051] Step S21: Construct a heating area knowledge graph. The heating area knowledge graph includes multiple nodes and multiple edges between the nodes. Each node represents the heating area of ​​a barbecue grill corresponding to a type of food. The node features of each node include the heating power of the heating area of ​​the barbecue grill corresponding to each type of food and the flipping speed of each type of food. The features of the edges between nodes represent the distance between the heating areas of the barbecue grill corresponding to different foods.

[0052] The heating area knowledge graph is a graph data structure where each node represents the heating area of ​​a barbecue grill corresponding to a specific food. The node features of each node include the heating power and rotation speed of the heating area for each food. The edge features between nodes represent the distance between the heating areas of different foods. For example, the heating area knowledge graph includes two nodes: Steak heating area node A and Chicken Wing heating area node B. The node features of Steak heating area node A include a heating power of 1500W and a rotation speed of once per minute. The node features of Chicken Wing heating area node B include a heating power of 1200W and a rotation speed of twice per minute. The edge feature between Steak and Chicken Wing includes a heating area distance of 10cm.

[0053] Step S22: Based on the graph autoencoder, process the knowledge graph of the heating area to determine the heating time of the heating area of ​​the barbecue oven corresponding to each food.

[0054] The input of the graph autoencoder is the heating area knowledge graph, and the output of the graph autoencoder is the heating time of the heating area of ​​the barbecue oven corresponding to each food.

[0055] A Graph Autoencoder (GAE) is a deep learning model used to process graph-structured data. It combines the ideas of Graph Convolutional Networks (GCNs) and autoencoders, enabling it to capture information about nodes and edges in a graph structure and encode and decode node features.

[0056] In the heating region knowledge graph, each node represents the heating region of a grill corresponding to a specific food. Node features include heating power and tumbling speed. These features directly affect the heating effect of the food. Edges between nodes represent the distances between the heating regions of grills corresponding to different foods. This distance information reflects the mutual influence between the heating of different foods, such as heat transfer. By processing the graph data using a graph autoencoder, the heating time for each food can be predicted. This helps in planning the cooking process in advance, avoiding overheating or underheating. The graph autoencoder captures the relationships between nodes through graph convolution operations, generating low-dimensional embeddings of the nodes. These embeddings contain information about the mutual influence between nodes, which helps determine the heating time.

[0057] Step S5: Control the barbecue oven based on the heating power of the heating area of ​​the barbecue oven corresponding to each food, the heating time of the heating area of ​​the barbecue oven corresponding to each food, and the turning speed of each food.

[0058] Once the heating power, heating time, and turning speed of the grill for each food item are determined, the grill is controlled. The grill can be configured to set the heating power, heating time, and turning speed.

[0059] Based on the same inventive concept Figure 4 This invention provides a schematic diagram of an artificial intelligence-based food processing control system, which includes:

[0060] Module 41 is used to acquire videos of various foods being grilled in a barbecue grill.

[0061] Processing module 42 is used to determine the doneness of each food and the heating zone of the barbecue oven corresponding to each food by using a food data processing model based on the barbecue video of the various foods in the barbecue oven.

[0062] The food information determination module 43 is used to determine the heating power of the heating area of ​​the barbecue oven corresponding to each food and the turning speed of each food based on the ripeness of each food and the heating area of ​​the barbecue oven corresponding to each food.

[0063] The time determination module 44 is used to determine the heating time of the heating area of ​​the barbecue oven corresponding to each food based on the heating power of the heating area of ​​the barbecue oven corresponding to each food and the turning speed of each food.

[0064] The control module 45 is used to control the barbecue oven based on the heating power of the heating area of ​​the barbecue oven corresponding to each food, the heating time of the heating area of ​​the barbecue oven corresponding to each food, and the turning speed of each food.

[0065] Based on the same inventive concept, embodiments of the present invention provide an electronic device, such as... Figure 5 As shown, it includes:

[0066] The system includes: a processor 51; a memory 52; and a computer program; wherein the computer program is stored in the memory 52 and configured to be executed by the processor 51 to implement the artificial intelligence-based food processing control method provided above, the method comprising: acquiring videos of various foods being grilled in a grill; using a food data processing model to determine the ripeness of each food and the heating zone of the grill corresponding to each food based on the videos of the various foods being grilled in the grill; determining the heating power of the heating zone of the grill corresponding to each food and the turning speed of each food based on the ripeness of each food and the heating zone of the grill corresponding to each food; determining the heating time of the heating zone of the grill corresponding to each food based on the heating power of the heating zone of the grill corresponding to each food and the turning speed of each food; and controlling the grill based on the heating power of the heating zone of the grill corresponding to each food, the heating time of the heating zone of the grill corresponding to each food and the turning speed of each food.

[0067] Based on the same inventive concept, this embodiment provides a computer-readable storage medium storing a computer program. When executed by processor 51, the program implements the aforementioned artificial intelligence-based food processing control method. The method includes: acquiring videos of various foods being grilled in a grill; using a food data processing model to determine the ripeness of each food and the heating zone of the grill corresponding to each food based on the videos of the various foods being grilled in the grill; determining the heating power of the heating zone of the grill corresponding to each food and the turning speed of each food based on the ripeness of each food and the heating zone of the grill corresponding to each food; determining the heating time of the heating zone of the grill corresponding to each food based on the heating power of the heating zone of the grill corresponding to each food and the turning speed of each food; and controlling the grill based on the heating power of the heating zone of the grill corresponding to each food, the heating time of the heating zone of the grill corresponding to each food, and the turning speed of each food.

[0068] The artificial intelligence-based food processing control method provided in this application can be applied to terminal devices (such as mobile phones), tablets, laptops, ultra-mobile personal computers (UMPCs), handheld computers, netbooks, personal digital assistants (PDAs), wearable devices (such as smartwatches, smart glasses, or smart helmets), augmented reality (AR) / virtual reality (VR) devices, smart home devices, in-vehicle computers, and other electronic devices. This application does not impose any limitations on this.

[0069] Taking mobile phone 100 as an example of the aforementioned electronic devices, Figure 6 A structural schematic diagram of mobile phone 100 is shown.

[0070] like Figure 6 As shown, the mobile phone 100 may include a processing module 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, a headphone jack 170D, a sensor module 180, buttons 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc.

[0071] The processing module 110 may include one or more processing units, such as an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). These different processing units may be independent devices or integrated into one or more processors.

[0072] The processing module 110 can be used to: acquire videos of various foods being grilled in a barbecue oven; determine the doneness of each food and the heating zone of the barbecue oven corresponding to each food using a food data processing model based on the videos of various foods being grilled in a barbecue oven; determine the heating power of the heating zone of the barbecue oven corresponding to each food and the turning speed of each food based on the doneness of each food and the heating zone of the barbecue oven corresponding to each food; determine the heating time of the heating zone of the barbecue oven corresponding to each food based on the heating power of the heating zone of the barbecue oven corresponding to each food and the turning speed of each food; and control the barbecue oven based on the heating power of the heating zone of the barbecue oven corresponding to each food, the heating time of the heating zone of the barbecue oven corresponding to each food, and the turning speed of each food.

[0073] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0074] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0075] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0076] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0077] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A food processing control method based on artificial intelligence, characterized in that, include: Get videos of various foods being grilled on a barbecue grill; Based on the grilling videos of the various foods in the barbecue grill, a food data processing model is used to determine the ripeness of each food and the corresponding heating area of ​​the barbecue grill. The food data processing model is a recurrent neural network model. The input of the food data processing model is the grilling video of the various foods in the barbecue grill, and the output of the food data processing model is the ripeness of each food and the corresponding heating area of ​​the barbecue grill. The food data processing model includes a region segmentation layer, a ripeness determination layer, and a heating area determination layer. Each of the region segmentation layer, ripeness determination layer, and heating area determination layer includes a recurrent neural network. The input of the region segmentation layer is the grilling video of the various foods in the barbecue grill, and the output of the region segmentation layer is the heating area video of the barbecue grill and the placement area video of the food during heating. The input of the ripeness determination layer is the placement area video of the food during heating, and the output of the ripeness determination layer is the ripeness of each food. The input of the heating area determination layer is the heating area video of the barbecue grill and the placement area video of the food during heating, and the output of the heating area determination layer is the heating area of ​​the barbecue grill corresponding to each food. The heating power of the heating zone of the barbecue grill corresponding to each food and the turning speed of each food are determined based on the ripeness of each food and the heating zone of the barbecue grill corresponding to each food. The heating time of the heating zone of the barbecue grill corresponding to each food is determined based on the heating power of the heating zone of each food and the turning speed of each food, including: A heating area knowledge graph is constructed, which includes multiple nodes and multiple edges between the nodes. Each node represents the heating area of ​​a barbecue grill corresponding to a certain food. The node features of each node include the heating power of the heating area of ​​the barbecue grill corresponding to each food and the flipping speed of each food. The features of the edges between nodes represent the distance between the heating areas of the barbecue grills corresponding to different foods. The heating time of the heating area of ​​the barbecue oven corresponding to each food is determined by processing the knowledge graph of the heating area based on the graph autoencoder. The barbecue grill is controlled based on the heating power of the heating area of ​​the barbecue grill corresponding to each food, the heating time of the heating area of ​​the barbecue grill corresponding to each food, and the turning speed of each food.

2. The food processing control method based on artificial intelligence as described in claim 1, characterized in that, The input of the graph autoencoder is the heating area knowledge graph, and the output of the graph autoencoder is the heating time of the heating area of ​​the barbecue oven corresponding to each food.

3. A food processing control system based on artificial intelligence, characterized in that, include: The acquisition module is used to acquire videos of various foods being grilled in a barbecue grill; The processing module is used to determine the ripeness of each food and the corresponding heating area of ​​the barbecue oven based on the barbecue videos of the various foods being grilled in the barbecue oven using a food data processing model. The food data processing model is a recurrent neural network model. The input of the food data processing model is the barbecue videos of the various foods being grilled in the barbecue oven, and the output of the food data processing model is the ripeness of each food and the corresponding heating area of ​​the barbecue oven. The food data processing model includes a region segmentation layer, a ripeness determination layer, and a heating area determination layer. Each of the region segmentation layer, ripeness determination layer, and heating area determination layer includes a recurrent neural network. The input of the region segmentation layer is the barbecue videos of the various foods being grilled in the barbecue oven, and the output of the region segmentation layer is the heating area video of the barbecue oven and the placement area video of the food during heating. The input of the ripeness determination layer is the placement area video of the food during heating, and the output of the ripeness determination layer is the ripeness of each food. The input of the heating area determination layer is the heating area video of the barbecue oven and the placement area video of the food during heating, and the output of the heating area determination layer is the heating area of ​​the barbecue oven corresponding to each food. The food information determination module is used to determine the heating power of the heating area of ​​the barbecue oven corresponding to each food and the turning speed of each food based on the ripeness of each food and the heating area of ​​the barbecue oven corresponding to each food. The time determination module is used to determine the heating time of the heating zone of the barbecue oven corresponding to each food based on the heating power of the heating zone of the barbecue oven corresponding to each food and the turning speed of each food, including: A heating area knowledge graph is constructed, which includes multiple nodes and multiple edges between the nodes. Each node represents the heating area of ​​a barbecue grill corresponding to a certain food. The node features of each node include the heating power of the heating area of ​​the barbecue grill corresponding to each food and the flipping speed of each food. The features of the edges between nodes represent the distance between the heating areas of the barbecue grills corresponding to different foods. The heating time of the heating area of ​​the barbecue oven corresponding to each food is determined by processing the knowledge graph of the heating area based on the graph autoencoder. The control module is used to control the barbecue oven based on the heating power of the heating area of ​​the barbecue oven corresponding to each food, the heating time of the heating area of ​​the barbecue oven corresponding to each food, and the turning speed of each food.

4. The artificial intelligence-based food processing control system as described in claim 3, characterized in that, The input of the graph autoencoder is the heating area knowledge graph, and the output of the graph autoencoder is the heating time of the heating area of ​​the barbecue oven corresponding to each food.

5. An electronic device, characterized in that, include: processor; Memory; And a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement the artificial intelligence-based food processing control method as described in any one of claims 1 to 2.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the artificial intelligence-based food processing control method as described in any one of claims 1 to 2.

Images