Food processing control method and system based on artificial intelligence

Abstract

The invention provides a food processing control method and system based on artificial intelligence, and relates to the technical field of food processing control, the method comprises the following steps: using a food data processing model to determine the maturity of each kind of food and the heating area of a barbecue oven corresponding to each kind of food; based on the maturity of each kind of food and the heating area of the barbecue oven corresponding to each kind of food, the heating power of the heating area of the barbecue oven corresponding to each kind of food and the overturning speed of each kind of food are determined; determining 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 overturning speed of each food; the barbecue oven is controlled based on the heating power of the heating area of the barbecue oven corresponding to each kind of food, the heating time of the heating area of the barbecue oven corresponding to each kind of food and the overturning speed of each kind of food, the food processing process is accurately controlled, and the food processing stability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of food processing, and in particular to a food processing control method and system based on artificial intelligence. Background Art

[0002] In the traditional food processing process, especially the processing of grilled food, it usually relies on human experience and manual control. Specifically, the chef or operator needs to decide the grilling time, temperature and flipping frequency according to the type, size, thickness and personal experience of the food. Manual judgment is greatly affected by personal experience and skills, and consistency between different operators is difficult to ensure. For example, different chefs may have different opinions on the grilling time of the same steak, resulting in inconsistent final maturity and taste. Due to the uncontrollability of human factors, the maturity and taste of food often fluctuate greatly, making it difficult to achieve consistent standards.

[0003] Therefore, how to accurately control the food processing process and improve the stability of food processing is a problem that needs to be solved urgently. Summary of the invention

[0004] The main technical problem solved by the present 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 a food processing control method based on artificial intelligence, comprising: obtaining barbecue videos of multiple foods in a barbecue oven; determining the maturity of each food and the heating area of ​​the barbecue oven corresponding to each food using a food data processing model based on the barbecue videos of the multiple foods in the barbecue oven; determining the heating power of the heating area of ​​the barbecue oven corresponding to each food and the flipping speed of each food based on the maturity of each food and the heating area of ​​the barbecue oven corresponding to each food; determining 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 flipping speed of each food; controlling 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 flipping speed of each food.

[0006] In a possible implementation, the determining 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 includes: Constructing a heating area knowledge graph, wherein the heating area knowledge graph includes a plurality of nodes and a plurality of edges between the plurality of nodes, wherein each node represents a heating area of ​​a barbecue oven corresponding to a type of food, and the node features of each node include the heating power of the heating area of ​​the barbecue oven corresponding to each type of food and the flipping speed of each type of food, and the features of the edges between the nodes represent the distances between the heating areas of the barbecue oven corresponding to different types of food; The heating area knowledge graph is processed based on the graph autoencoder to determine the heating time of the heating area of ​​the barbecue oven corresponding to each food.

[0007] 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 barbecue video of the multiple foods in a barbecue oven, and the output of the food data processing model is the maturity of each food and the heating area of ​​the barbecue oven corresponding to each food.

[0008] In a possible implementation, 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 grill corresponding to each food.

[0009] According to a second aspect, the present invention provides a food processing control system based on artificial intelligence, comprising: An acquisition module is used to acquire barbecue videos of various foods in a barbecue oven; A processing module, configured to determine the maturity 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 plurality of foods in the barbecue oven; A food information determination module, 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 maturity of each food and the heating area of ​​the barbecue oven corresponding to each food; A time determination module, 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; 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.

[0010] In a possible implementation manner, the time determination module is further used to: Constructing a heating area knowledge graph, wherein the heating area knowledge graph includes a plurality of nodes and a plurality of edges between the plurality of nodes, wherein each node represents a heating area of ​​a barbecue oven corresponding to a type of food, and the node features of each node include the heating power of the heating area of ​​the barbecue oven corresponding to each type of food and the flipping speed of each type of food, and the features of the edges between the nodes represent the distances between the heating areas of the barbecue oven corresponding to different types of food; The heating area knowledge graph is processed based on the graph autoencoder to determine the heating time of the heating area of ​​the barbecue oven corresponding to each food.

[0011] 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 barbecue video of the multiple foods in a barbecue grill, and the output of the food data processing model is the maturity of each food and the heating area of ​​the barbecue grill corresponding to each food.

[0012] In a possible implementation, 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 grill corresponding to each food.

[0013] According to a third aspect, an embodiment of the present invention provides an electronic device, comprising: 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 as described above, the method comprising: obtaining barbecue videos of multiple foods in a barbecue oven; determining the maturity of each food and the heating area of ​​the barbecue oven corresponding to each food using a food data processing model based on the barbecue videos of the multiple foods in the barbecue oven; determining the heating power of the heating area of ​​the barbecue oven corresponding to each food and the flipping speed of each food based on the maturity of each food and the heating area of ​​the barbecue oven corresponding to each food; determining 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 flipping speed of each food; controlling 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 flipping speed of each food.

[0014] According to a fourth aspect, the present embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned artificial intelligence-based food processing control method, the method comprising: obtaining barbecue videos of a plurality of foods in a barbecue oven; determining the maturity of each food and the heating area of ​​the barbecue oven corresponding to each food using a food data processing model based on the barbecue videos of the plurality of foods in the barbecue oven; determining the heating power of the heating area of ​​the barbecue oven corresponding to each food and the flipping speed of each food based on the maturity of each food and the heating area of ​​the barbecue oven corresponding to each food; determining 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 flipping speed of each food; controlling 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 flipping speed of each food.

[0015] The present invention provides a food processing control method and system based on artificial intelligence. The method comprises obtaining barbecue videos of multiple foods in a barbecue oven; determining the maturity of each food and the heating area of ​​the barbecue oven corresponding to each food using a food data processing model based on the barbecue videos of the multiple foods in the barbecue oven; determining 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 maturity of each food and the heating area of ​​the barbecue oven corresponding to each food; determining 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; and controlling 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. The method can accurately control the food processing process and improve the stability of food processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of an application scenario of a food processing control method based on artificial intelligence provided by an embodiment of the present invention; Figure 2 A schematic flow chart of a food processing control method based on artificial intelligence provided by an embodiment of the present invention; Figure 3 A schematic diagram of a flow chart of determining a probability value of a fuzzy test based on multiple data input by a user at different time points provided by an embodiment of the present invention; Figure 4 A schematic diagram of a food processing control system based on artificial intelligence provided by an embodiment of the present invention; Figure 5A schematic diagram of an electronic device provided by an embodiment of the present invention; Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present invention better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present invention are not shown or described in the specification, this is to avoid the core part of the present invention being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0018] Figure 1 A schematic diagram of an application scenario of an artificial intelligence-based food processing control method provided in an embodiment of the present invention. Figure 1 The application scenario of the food processing control method based on artificial intelligence may include a server 11, a network 12, a terminal 13 and a storage device 14.

[0019] In some embodiments, the server 11 may be a single server or a server group. The server 11 may access information and / or data stored in the terminal 13 or the storage device 14 through the network 12. In some embodiments, the server 11 may be used to execute Figure 2 The artificial intelligence-based food processing control method shown in .

[0020] The network 12 may facilitate the exchange of information and / or data. In some embodiments, the network 12 may be any form of wired or wireless network, or any combination thereof.

[0021] 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 a mobile device, a tablet computer, a laptop computer, and the like.

[0022] The storage device 14 may store data and / or instructions. For example, the storage device 14 may store data instructions of a food processing control method based on artificial intelligence.

[0023] In an embodiment of the present invention, there is provided Figure 2The food processing control method based on artificial intelligence shown in the figure can effectively deal with fuzzy testing. The food processing control method based on artificial intelligence includes steps S1 to S5: Step S1, obtaining a barbecue video of a plurality of foods in a barbecue oven.

[0024] The barbecue video of various foods in the barbecue oven is a barbecue video of various foods in the barbecue oven obtained by a camera or other video acquisition equipment. For example, the barbecue video content includes the barbecue process of foods such as steak, chicken wings, and vegetables on the barbecue oven.

[0025] Step S2, based on the barbecue video of the plurality of foods in the barbecue oven, using a food data processing model to determine the maturity of each food and the heating area of ​​the barbecue oven corresponding to each food.

[0026] The food data processing model is a recurrent neural network model, the input of which is a barbecue video of the multiple foods in a barbecue oven, and the output of which is the maturity of each food and the heating area of ​​the barbecue oven corresponding to each food. The recurrent neural network model is an implementation of AI.

[0027] The heating area of ​​the barbecue oven corresponding to the food indicates the area corresponding to the heating of the food by the barbecue oven. For example, the heating area corresponding to the steak is area A, and the heating area corresponding to the chicken wings is area B.

[0028] The recurrent neural network model includes a recurrent neural network (RNN). The recurrent neural network model can process sequence data, capture sequence information, and output results based on the correlation between the previous and next data in the sequence. By processing the barbecue video of the plurality of foods in the barbecue oven in a continuous time period through the recurrent neural network model, it is possible to output features that comprehensively consider the correlation between the sequences at each time point, so that the output features are more accurate and comprehensive. The recurrent neural network model is a way to implement artificial intelligence.

[0029] Barbecue videos are composed of a series of continuous video frames. Recurrent neural networks can process this sequence data and capture the correlation between different time points in the video. During the barbecue process of steak, recurrent neural networks can capture the changes in the steak from raw to cooked, thereby more accurately evaluating the maturity of the steak.

[0030] In some embodiments, the food data processing model includes a region segmentation layer, a maturity determination layer, and a heating region determination layer. The region segmentation layer, the maturity determination layer, and the heating region determination layer all include a recurrent neural network. The input of the region segmentation layer is a barbecue video of the multiple foods in a barbecue oven, and the output of the region segmentation layer is a heating region video of the barbecue oven and a placement region video of the foods when they are heated. The input of the maturity determination layer is a placement region video of the foods when they are heated, and the output of the maturity determination layer is the maturity of each food. The input of the heating region determination layer is a heating region video of the barbecue oven and a placement region video of the foods when they are heated, and the output of the heating region determination layer is the heating region of the barbecue oven corresponding to each food.

[0031] Through hierarchical design, the food data processing model can handle complex tasks more effectively and improve overall accuracy and robustness. The region segmentation layer, maturity determination layer, and heating region determination layer are responsible for different tasks. Through gradual refinement and task separation, the final output features are ensured to be more accurate and comprehensive.

[0032] Step S3, determining 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 maturity of each food and the heating area of ​​the barbecue oven corresponding to each food.

[0033] In some embodiments, the heating power of the heating area of ​​the grill corresponding to each food and the flipping speed of each food can be determined by a deep neural network model. The input of the deep neural network model is the maturity of each food and the heating area of ​​the grill corresponding to each food, and the output of the deep neural network model is the heating power of the heating area of ​​the grill corresponding to each food and the flipping speed of each food. As an example, the current maturity of the steak is 70%, and the heating area is zone A. The heating power determined by the deep neural network model is 1500W, and the flipping speed is once per minute. For another example, the maturity of the chicken wings is 85%, the heating area is zone B, the heating power determined by the deep neural network model is 1200W, and the flipping speed is twice per minute. The deep neural network model includes a deep neural network (Deep Neural Networks, DNN). The deep neural network may include multiple processing layers, each processing layer is composed of multiple neurons, and each neuron performs a matrix transformation on the data. The parameters used by the matrix can be obtained through training. The deep neural network model is an implementation of AI.

[0034] Step S4, determining 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.

[0035] In some embodiments, Figure 3A schematic diagram of a flow chart of determining the heating time of the heating area of ​​the barbecue oven corresponding to each food provided by an embodiment of the present invention, wherein the determining the heating time of the heating area of ​​the barbecue oven corresponding to each food comprises steps S21-S22: Step S21, constructing a heating area knowledge graph, the heating area knowledge graph includes multiple nodes and multiple edges between the multiple nodes, each node represents a heating area of ​​a barbecue grill corresponding to a food, the node features of each node include the heating power of the heating area of ​​the barbecue grill corresponding to each food, the flipping speed of each food, and the features of the edges between the nodes represent the distances between the heating areas of the barbecue grills corresponding to different foods.

[0036] The heating area knowledge graph is a graph data structure, in which each node represents the heating area of ​​the grill corresponding to a food, and the node features of each node include the heating power and flipping speed of the heating area of ​​the grill corresponding to each food. The features of the edges between nodes represent the distances between the heating areas of the grill corresponding to different foods. For example, the heating area knowledge graph includes two nodes, steak heating area A node and chicken wing heating area B node. The node features of steak heating area A node include heating power 1500W and flipping speed once per minute. The node features of chicken wing heating area B node include heating power 1200W and flipping speed twice per minute. The features of the edge from steak to chicken wing include the heating area distance of 10cm.

[0037] Step S22: Processing the heating area knowledge graph based on the graph autoencoder to determine the heating time of the heating area of ​​the barbecue grill corresponding to each food.

[0038] 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 grill corresponding to each food.

[0039] Graph Autoencoder (GAE) is a deep learning model for processing graph structure data. Graph Autoencoder combines the ideas of Graph Convolutional Network (GCN) and Autoencoder, and can capture the information of nodes and edges in the graph structure and encode and decode node features.

[0040] In the heating area knowledge graph, each node represents the heating area of ​​the grill corresponding to a food, and the node features include heating power and flipping speed. These features directly affect the heating effect of the food. The edges between the nodes represent the distances between the heating areas of the grill 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 through the graph autoencoder, the heating time of each food can be predicted. This helps to plan the cooking process in advance and avoid overheating or underheating. The graph autoencoder captures the relationship between nodes through graph convolution operations and generates low-dimensional embeddings of the nodes. These embeddings contain information about the mutual influence between nodes, which helps to determine the heating time.

[0041] Step S5, controlling 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.

[0042] After determining 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, the barbecue oven is controlled. The barbecue oven can be configured to set the heating power, heating time, and turning speed.

[0043] Based on the same inventive concept, Figure 4 A schematic diagram of a food processing control system based on artificial intelligence provided by an embodiment of the present invention, the food processing control system based on artificial intelligence includes: An acquisition module 41 is used to acquire barbecue videos of various foods in a barbecue oven; A processing module 42 is used to determine the maturity of each food and the heating area of ​​the barbecue oven corresponding to each food by using a food data processing model based on the barbecue video of the multiple foods in the barbecue oven; A food information determination module 43, for determining 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 maturity of each food and the heating area of ​​the barbecue oven corresponding to each food; A time determination module 44, configured 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; 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.

[0044] Based on the same inventive concept, an embodiment of the present invention provides an electronic device, such as Figure 5 As shown, including: The invention comprises: a processor 51; a memory 52; and a computer program; wherein the computer program is stored in the memory 52 and is configured to be executed by the processor 51 to implement the food processing control method based on artificial intelligence as provided above, the method comprising: obtaining barbecue videos of a plurality of foods in a barbecue oven; determining the maturity of each food and the heating area of ​​the barbecue oven corresponding to each food using a food data processing model based on the barbecue videos of the plurality of foods in the barbecue oven; determining 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 maturity of each food and the heating area of ​​the barbecue oven corresponding to each food; determining 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; and controlling 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.

[0045] Based on the same inventive concept, the present embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by the processor 51, implements the aforementioned artificial intelligence-based food processing control method, the method comprising: obtaining barbecue videos of a plurality of foods in a barbecue oven; determining the maturity of each food and the heating area of ​​the barbecue oven corresponding to each food using a food data processing model based on the barbecue videos of the plurality of foods in the barbecue oven; determining 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 maturity of each food and the heating area of ​​the barbecue oven corresponding to each food; determining 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; controlling 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.

[0046] The artificial intelligence-based food processing control method provided in the embodiments of the present application can be applied to terminal devices (such as mobile phones), tablet computers, laptops, ultra-mobile personal computers (UMPCs), handheld computers, netbooks, personal digital assistants (PDAs), wearable devices (such as smart watches, smart glasses or smart helmets, etc.), augmented reality (AR) and virtual reality (VR) devices, smart home devices, car computers and other electronic devices, and the embodiments of the present application do not impose any restrictions on this.

[0047] Taking the mobile phone 100 as an example of the electronic device, Figure 6 A schematic structural diagram of the mobile phone 100 is shown.

[0048] 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, an earphone interface 170D, a sensor module 180, a button 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc.

[0049] The processing module 110 may include one or more processing units, for example, the processing module 110 may include an application processor (AP), a modem processor, a graphics processor (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), etc. Different processing units may be independent devices or integrated into one or more processors.

[0050] The processing module 110 can be used to: obtain barbecue videos of multiple foods in a barbecue oven; determine the maturity of each food and the heating area of ​​the barbecue oven corresponding to each food using a food data processing model based on the barbecue videos of the multiple foods in the barbecue oven; determine the heating power of the heating area of ​​the barbecue oven corresponding to each food and the flipping speed of each food based on the maturity of each food and the heating area of ​​the barbecue oven corresponding to each food; 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 flipping speed of each food; 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 flipping speed of each food.

[0051] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, 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, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0052] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0053] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0054] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0055] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A food processing control method based on artificial intelligence, characterized in that: include: Get videos of various foods grilling on the grill; Based on the barbecue videos of the multiple foods in the barbecue oven, use a food data processing model to determine the maturity of each food and the heating area of ​​the barbecue oven corresponding to each food; Determining 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 maturity of each food and the heating area of ​​the barbecue oven corresponding to each food; Determining 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; The barbecue oven is controlled 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.

2. The food processing control method based on artificial intelligence as claimed in claim 1, characterized in that: The step of determining 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 comprises: Constructing a heating area knowledge graph, wherein the heating area knowledge graph includes a plurality of nodes and a plurality of edges between the plurality of nodes, wherein each node represents a heating area of ​​a barbecue oven corresponding to a food, and the node features of each node include the heating power of the heating area of ​​the barbecue oven corresponding to each food and the turning speed of each food, and the features of the edges between the nodes represent the distances between the heating areas of the barbecue oven corresponding to different foods; The heating area knowledge graph is processed based on the graph autoencoder to determine the heating time of the heating area of ​​the barbecue oven corresponding to each food.

3. The food processing control method based on artificial intelligence as claimed in claim 1, characterized in that: The food data processing model is a recurrent neural network model, the input of the food data processing model is the barbecue video of the multiple foods in the barbecue oven, and the output of the food data processing model is the maturity of each food and the heating area of ​​the barbecue oven corresponding to each food.

4. The food processing control method based on artificial intelligence as claimed in claim 2, 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 grill corresponding to each food.

5. A food processing control system based on artificial intelligence, characterized in that: include: An acquisition module is used to acquire barbecue videos of various foods in a barbecue oven; A processing module, configured to determine the maturity 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 plurality of foods in the barbecue oven; A food information determination module, 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 maturity of each food and the heating area of ​​the barbecue oven corresponding to each food; A time determination module, 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; 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.

6. The food processing control system based on artificial intelligence as claimed in claim 5, characterized in that: The time determination module is also used for: Constructing a heating area knowledge graph, wherein the heating area knowledge graph includes a plurality of nodes and a plurality of edges between the plurality of nodes, wherein each node represents a heating area of ​​a barbecue oven corresponding to a food, and the node features of each node include the heating power of the heating area of ​​the barbecue oven corresponding to each food and the turning speed of each food, and the features of the edges between the nodes represent the distances between the heating areas of the barbecue oven corresponding to different foods; The heating area knowledge graph is processed based on the graph autoencoder to determine the heating time of the heating area of ​​the barbecue oven corresponding to each food.

7. The food processing control system based on artificial intelligence as claimed in claim 5, characterized in that: The food data processing model is a recurrent neural network model, the input of the food data processing model is the barbecue video of the multiple foods in the barbecue oven, and the output of the food data processing model is the maturity of each food and the heating area of ​​the barbecue oven corresponding to each food.

8. The food processing control system based on artificial intelligence as claimed in claim 6, 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 grill corresponding to each food.

9. An electronic device, characterized in that: include: processor; Memory; And a computer program; wherein the computer program is stored in the memory and is 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 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the artificial intelligence-based food processing control method as described in any one of claims 1 to 4 is implemented.

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