Heating method and device for dynamically controlling cooking degree of dishes and electronic equipment

Abstract

The invention provides a heating method and device for dynamically controlling the cooking degree of dishes and electronic equipment, and relates to the technical field of food material analysis, and the method comprises the steps: obtaining food material images according to a preset time interval; analyzing the food material image based on a ripening degree identification model, and outputting a real-time ripening degree numerical value of the current food material; comparing the real-time ripening degree value with a preset NI target ripening degree value to obtain a ripening degree difference value; according to the ripening degree difference value, a corresponding dynamic heating curve is matched from a ripening degree-heating curve database, the dynamic heating curve is converted into a control instruction, a temperature control system is driven to adjust heating parameters until the ripening degree difference value is not smaller than zero, and dish heating is completed. According to the method, the resource consumption and the monitoring real-time performance of the system are balanced by adopting a time-sharing variable-period image acquisition strategy, and the ripening degree recognition robustness is improved by combining a two-channel convolutional neural network of hue evolution characteristics and morphological parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of food analysis, and in particular to a heating method, device and electronic equipment for dynamically controlling the degree of cooked food. Background Art

[0002] The heating control of traditional cooking equipment mostly relies on preset fixed temperature curves or manual experience judgment, which is difficult to adapt to the dynamic changes in the characteristics of ingredients, especially in the precise control of the degree of doneness. The feedback mechanism based on a single sensor (such as a temperature probe) in the existing technology can only monitor the local thermodynamic state and cannot quantify the changes in the internal structure of the ingredients, resulting in a delayed judgment of the degree of doneness and large errors. For complex ingredients or special-shaped ingredients, traditional methods lack the ability to coordinate and regulate the heating differences in multiple regions, which can easily cause local overcooking or undercooking. In addition, the existing system does not consider the coupling effect of the user's personalized taste requirements and the physical property differences of the ingredients, and cannot achieve dynamic target value correction. In terms of abnormal working condition processing, the traditional solution lacks real-time recognition and emergency response mechanisms for nonlinear changes such as coking and carbonization, posing a safety hazard. Therefore, there is an urgent need for a heating control method that integrates multimodal perception, dynamic modeling and intelligent decision-making. By analyzing the characteristics of the morphology and color evolution of the ingredients in real time, an adaptive closed-loop control system is constructed to solve the technical bottleneck of precise control of the degree of doneness during the cooking process.

[0003] Therefore, a method, device and electronic device for predicting user dietary preferences are proposed. Summary of the Invention

[0004] This specification provides a heating method, device and electronic equipment for dynamically controlling the degree of doneness of dishes. It adopts a time-varying period image acquisition strategy to balance system resource consumption and real-time monitoring, and combines a dual-channel convolutional neural network with hue evolution characteristics and morphological parameters to improve the robustness of doneness recognition.

[0005] This specification provides a heating method for dynamically controlling the degree of doneness of dishes, including:

[0006] Acquire food images at preset time intervals;

[0007] Analyze the food image based on the maturity recognition model and output the real-time maturity value of the current food;

[0008] Compare the real-time maturity value with the preset NI target maturity value to obtain the maturity difference;

[0009] According to the degree of doneness difference, a corresponding dynamic heating curve is matched from the degree of doneness-heating curve database, and the dynamic heating curve is converted into a control instruction to drive the temperature control system to adjust the heating parameters until the degree of doneness difference is not less than zero, thereby completing the heating of the dish.

[0010] Optionally, acquiring food images at preset time intervals includes:

[0011] acquiring food images at first preset time intervals;

[0012] It is determined whether the real-time maturity value of the current food meets a preset threshold value, and when the real-time maturity value of the current food meets the preset threshold value, the food image is obtained according to a second preset time interval.

[0013] Optionally, parsing the food image based on a maturity recognition model and outputting a real-time maturity value of the current food includes:

[0014] The hue change characteristics of the food surface are extracted through HSV color space conversion, and the Hu moment algorithm is used to quantify the food morphological expansion rate to obtain the food morphological characteristics;

[0015] The surface color change characteristics of the food and the morphological characteristics of the food are input into a convolutional neural network model to output a real-time degree of maturity value of the current food.

[0016] Optionally, the dynamic adjustment method of the NI target maturity value includes:

[0017] Get user taste preference parameters;

[0018] Mapping the user taste preference parameter to a target maturity value coefficient;

[0019] The NI target degree of maturity value is corrected through a regression model based on the initial moisture content and protein content of the ingredients.

[0020] Optionally, the method for generating the maturity-heating curve database includes:

[0021] Establish a baseline heating curve based on the heat conduction model and food type;

[0022] Through the reinforcement learning model, the deviation between the actual doneness and the target value in the historical cooking data is used to obtain the iterative optimization curve parameters, and a doneness-heating curve database is constructed.

[0023] Optionally, the adjustment method of the temperature control system includes:

[0024] For different types of composite food, the system can control the different zones and divide the heating areas into independent zones through thermal imaging data.

[0025] The weight ratio of microwave and heat conduction heating is dynamically allocated according to the density difference of ingredients.

[0026] Optionally, also include:

[0027] Determine whether the food image is partially carbonized. When the food image is partially carbonized, trigger an emergency cooling instruction of the temperature control system and push a manual intervention prompt.

[0028] This specification provides a heating device for dynamically controlling the degree of doneness of dishes, comprising:

[0029] An acquisition module, configured to acquire food images at preset time intervals;

[0030] An analysis module is used to analyze the food image based on a maturity recognition model and output a real-time maturity value of the current food;

[0031] A comparison module is used to compare the real-time maturity value with the preset NI target maturity value to obtain the maturity difference;

[0032] The heating module is used to match the corresponding dynamic heating curve from the maturity-heating curve database according to the maturity difference, and convert the dynamic heating curve into a control instruction to drive the temperature control system to adjust the heating parameters until the maturity difference is not less than zero, thereby completing the heating of the dish.

[0033] Optionally, the acquisition module includes:

[0034] acquiring food images at first preset time intervals;

[0035] It is determined whether the real-time maturity value of the current food meets a preset threshold value, and when the real-time maturity value of the current food meets the preset threshold value, the food image is obtained according to a second preset time interval.

[0036] Optionally, the parsing module includes:

[0037] The hue change characteristics of the food surface are extracted through HSV color space conversion, and the Hu moment algorithm is used to quantify the food morphological expansion rate to obtain the food morphological characteristics;

[0038] The surface color change characteristics of the food and the morphological characteristics of the food are input into a convolutional neural network model to output a real-time degree of maturity value of the current food.

[0039] Optionally, the dynamic adjustment method of the NI target maturity value includes:

[0040] Get user taste preference parameters;

[0041] Mapping the user taste preference parameter to a target maturity value coefficient;

[0042] The NI target degree of maturity value is corrected through a regression model based on the initial moisture content and protein content of the ingredients.

[0043] Optionally, the method for generating the maturity-heating curve database includes:

[0044] Establish a baseline heating curve based on the heat conduction model and food type;

[0045] Through the reinforcement learning model, the deviation between the actual doneness and the target value in the historical cooking data is used to obtain the iterative optimization curve parameters, and a doneness-heating curve database is constructed.

[0046] Optionally, the adjustment method of the temperature control system includes:

[0047] For different types of composite food, the system can control the different zones and divide the heating areas into independent zones through thermal imaging data.

[0048] The weight ratio of microwave and heat conduction heating is dynamically allocated according to the density difference of ingredients.

[0049] Optionally, also include:

[0050] Determine whether the food image is partially carbonized. When the food image is partially carbonized, trigger an emergency cooling instruction of the temperature control system and push a manual intervention prompt.

[0051] This specification also provides an electronic device, wherein the electronic device includes:

[0052] processor; and,

[0053] A memory storing computer executable instructions, which, when executed, cause the processor to perform any of the above methods.

[0054] This specification also provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, any of the above methods is implemented.

[0055] In the present invention, a time-varying periodic image acquisition strategy is adopted to balance system resource consumption and real-time monitoring, and a dual-channel convolutional neural network combining hue evolution characteristics and morphological parameters is used to improve the robustness of doneness recognition; user preference mapping and food intrinsic property compensation mechanism are introduced to achieve personalized taste customization; through reinforcement learning optimized dynamic heating curve library and thermal field zoning control technology, the problem of uneven heating of complex ingredients is effectively solved; integrated carbonization real-time detection and emergency cooling protocol form a multi-layer safety protection system, ultimately achieving adaptive optimization of the cooking process and stable quality control. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0057] Figure 1 A schematic diagram of the principle of a heating method for dynamically controlling the degree of doneness of dishes provided in an embodiment of this specification;

[0058] Figure 2 A schematic diagram of the structure of a heating device for dynamically controlling the degree of doneness of dishes provided in an embodiment of this specification;

[0059] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification;

[0060] Figure 4 A schematic diagram of a computer-readable medium provided in accordance with an embodiment of this specification. DETAILED DESCRIPTION

[0061] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are for illustrative purposes only, and those skilled in the art will readily appreciate other obvious variations. The basic principles of the present invention defined in the following description may be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the present invention.

[0062] The following is combined with Figure 1-4 The exemplary embodiments of the present invention are described more fully. However, the exemplary embodiments can be implemented in various forms, and the present invention should not be construed as being limited to the embodiments set forth herein. On the contrary, providing these exemplary embodiments enables the present invention to be more comprehensive and complete, and more conveniently conveys the inventive concept to those skilled in the art. In the figures, the same reference numerals represent the same or similar elements, components, or parts, and thus their repeated description will be omitted.

[0063] Under the premise of being consistent with the technical concept of the present invention, the features, structures, characteristics or other details described in a specific embodiment do not exclude that they can be combined in one or more other embodiments in a suitable manner.

[0064] In the description of specific embodiments, the features, structures, characteristics, or other details of the present invention are described to enable those skilled in the art to fully understand the embodiments. However, this does not preclude those skilled in the art from practicing the technical solutions of the present invention without one or more of the specific features, structures, characteristics, or other details.

[0065] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0066] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0067] The term "and / or" or "and / or" includes all combinations of any one or more of the associated listed items.

[0068] Figure 1 A schematic diagram of the principle of a heating method for dynamically controlling the degree of doneness of a dish provided in an embodiment of this specification, the method may include:

[0069] S110: Acquire food images according to preset time intervals;

[0070] Optionally, the S110 includes:

[0071] acquiring food images at first preset time intervals;

[0072] It is determined whether the real-time maturity value of the current food meets a preset threshold value, and when the real-time maturity value of the current food meets the preset threshold value, the food image is obtained according to a second preset time interval.

[0073] In a specific implementation of the present specification, the first trigger period and the second trigger period of the image acquisition module are pre-set during the initialization phase. After the cooking process is started, the timer controls the camera to perform cyclic shooting based on the first trigger period, and simultaneously transmits the captured food images to the maturity analysis module. The food surface color change data is extracted through the image recognition algorithm and mapped to a real-time maturity value. When it is detected that the value continues to exceed the preset safety threshold, the control center sends an instruction to the timer to switch to the second trigger period. At this time, the image acquisition module automatically adjusts the shooting frequency and continues to monitor the food status until the end of the cooking process. The entire process achieves precise control of the cooking process by dynamically adjusting the monitoring frequency.

[0074] S120: Analyze the food image based on the maturity recognition model and output a real-time maturity value of the current food;

[0075] Optionally, the S120 includes:

[0076] The hue change characteristics of the food surface are extracted through HSV color space conversion, and the Hu moment algorithm is used to quantify the food morphological expansion rate to obtain the food morphological characteristics;

[0077] The surface color change characteristics of the food and the morphological characteristics of the food are input into a convolutional neural network model to output a real-time degree of maturity value of the current food.

[0078] In a specific implementation of the present specification, after the image acquisition module obtains the original image of the food, the preprocessing unit is called to perform HSV color space conversion, and the hue distribution offset between consecutive frames is extracted through hue channel histogram statistics as the surface color evolution feature. The food contour is simultaneously binarized and the normalized Hu moment descriptor is calculated, and the numerical change trend of each order moment is recorded to generate a morphological expansion feature vector; the hue offset and the Hu moment feature vector are spatially aligned and input into a pre-trained convolutional neural network. The network contains a multi-branch feature fusion structure, and the front end uses parallel convolution layers to process color and morphological data respectively, and realizes feature interaction through a cross-channel attention mechanism. Finally, the fully connected layer outputs the normalized degree of doneness prediction value, and the prediction result is fed back to the control center in real time to trigger subsequent cooking decisions.

[0079] S130: Compare the real-time maturity value with the preset NI target maturity value to obtain a maturity difference;

[0080] Optionally, the dynamic adjustment method of the NI target maturity value includes:

[0081] Get user taste preference parameters;

[0082] Mapping the user taste preference parameter to a target maturity value coefficient;

[0083] The NI target degree of maturity value is corrected through a regression model based on the initial moisture content and protein content of the ingredients. In a specific embodiment of the present specification, the taste preference label selected by the user is received through an interactive interface and parsed into a multi-dimensional intensity parameter. Based on a preset preference-maturity mapping table, the parameters of each dimension are weighted and fused to generate a nonlinear adjustment coefficient; the food database is synchronously called to obtain the initial moisture content detection data and protein spectral analysis value of the current batch of ingredients, which are input together with the adjustment coefficient into a dynamic compensation model constructed based on a random forest regression algorithm. The coupled effects of water evaporation rate and protein denaturation on maturity are captured through a feature cross layer, and a personalized calibrated target degree of maturity interval value is output. This value is loaded into the control center as a reference parameter to drive the subsequent temperature control and time control module.

[0084] S140: Matching a corresponding dynamic heating curve from a maturity-heating curve database according to the maturity difference, and converting the dynamic heating curve into a control instruction to drive the temperature control system to adjust the heating parameters until the maturity difference is not less than zero, thereby completing the heating of the dish.

[0085] In a specific implementation of the present specification, the Euclidean distance between the target degree of maturity and the current monitoring value is calculated in real time as a difference signal, triggering the heating strategy engine to access the time series database storing historical process data, and using a similarity matching algorithm based on dynamic time warping to screen out the heating curve template closest to the current difference pattern, and converting the temperature gradient, duration and other physical quantities in the curve into a PWM duty cycle sequence that can be parsed by the temperature control module; the control center sends the instruction set to the heating actuator through the Modbus protocol, and synchronously starts the incremental PID algorithm to perform closed-loop adjustment on the heating tube power, while continuously receiving the feedback data stream from the maturity analysis module, and activates the protection relay to cut off the heat source when it detects that the difference converges to a non-negative interval, completing precise closed-loop control of the heating stage.

[0086] Optionally, the method for generating the maturity-heating curve database includes:

[0087] Establish a baseline heating curve based on the heat conduction model and food type;

[0088] Through the reinforcement learning model, the deviation between the actual doneness and the target value in the historical cooking data is used to obtain the iterative optimization curve parameters, and a doneness-heating curve database is constructed.

[0089] In a specific implementation of the present specification, during the initialization phase, the corresponding thermal property parameter library is called according to the food classification label, a three-dimensional transient heat transfer simulation model is constructed based on the non-steady-state Fourier heat conduction equation, and the benchmark temperature-time relationship curves of food with different geometric shapes are generated by the finite element analysis method; when deploying the offline reinforcement learning framework, the heating power time series data and the doneness deviation value in the historical cooking records are used to construct a Markov decision process, and an optimizer based on the double-delay deep deterministic policy gradient algorithm is designed. The time position correction and temperature compensation value of the key control points in the heating curve are output through the action network, and the optimized curve parameter set is stored in the graph database after data cleaning, forming a knowledge graph with food hash codes as nodes and optimization curves as attributes, thereby realizing online retrieval and dynamic evolution of heating strategies.

[0090] Optionally, the adjustment method of the temperature control system includes:

[0091] For different types of composite food, the system can control the different zones and divide the heating areas into independent zones through thermal imaging data.

[0092] The weight ratio of microwave and heat conduction heating is dynamically allocated according to the density difference of ingredients.

[0093] In a specific embodiment of the present specification, a multispectral thermal imaging array is used to capture the surface temperature field distribution of food, and a semantic segmentation network based on a U-Net architecture is used to identify the thermal radiation feature areas corresponding to different food categories, and a masked independent temperature-controlled partition topology map is generated; the food physical property database is synchronously called to obtain the density ultrasonic detection value of the food in each partition, which is input into a fuzzy logic controller to calculate the dynamic ratio coefficient of microwave feeding efficiency and heat conduction rate, and energy directional delivery is achieved through the synergistic effect of the waveguide array and the infrared radiation plate, wherein the microwave transmitter uses adaptive phase modulation technology to focus on high-density areas according to the density gradient, and the heat conduction module proportionally adjusts the radiation flux according to the partition temperature difference. At the same time, an integrated thermocouple monitors the core temperature of each partition in real time, and the heating strategy is continuously optimized through an online learning algorithm until a cross-regional maturity balance is achieved.

[0094] Optionally, also include:

[0095] Determine whether the food image is partially carbonized. When the food image is partially carbonized, trigger an emergency cooling instruction of the temperature control system and push a manual intervention prompt.

[0096] In a specific embodiment of the present specification, a local area grid analysis is performed on the collected food images during the maturity monitoring period, and an improved local contrast enhancement algorithm is used to enhance the edge gradient difference between the carbonized area and the non-carbonized area. The texture entropy value and the chromaticity space abnormality offset of the carbonized patch are extracted through a pre-trained lightweight convolutional neural network. When it is detected that the local area entropy value exceeds the carbonization judgment threshold and the HSV brightness channel is continuously lower than the critical value, the priority interrupt signal is immediately triggered to call the underlying driver interface of the temperature control system, forcibly switching to the full-power operation state of the liquid nitrogen cooling module and locking the heating element power supply circuit. At the same time, an emergency alarm pop-up window containing a carbonized area coordinate heat map and a spectral analysis report is sent to the operation terminal through the MQTT protocol until the basic monitoring mode is restored after manual confirmation and reset.

[0097] In the present invention, a time-varying periodic image acquisition strategy is adopted to balance system resource consumption and real-time monitoring, and a dual-channel convolutional neural network combining hue evolution characteristics and morphological parameters is used to improve the robustness of doneness recognition; user preference mapping and food intrinsic property compensation mechanism are introduced to achieve personalized taste customization; through reinforcement learning optimized dynamic heating curve library and thermal field zoning control technology, the problem of uneven heating of complex ingredients is effectively solved; integrated carbonization real-time detection and emergency cooling protocol form a multi-layer safety protection system, ultimately achieving adaptive optimization of the cooking process and stable quality control.

[0098] Figure 2This is a schematic diagram of the principle of a heating device for dynamically controlling the degree of doneness of a dish provided in an embodiment of this specification. The device may include:

[0099] An acquisition module 10 is used to acquire food images at preset time intervals;

[0100] An analysis module 20 is configured to analyze the food image based on a maturity recognition model and output a real-time maturity value of the current food;

[0101] A comparison module 30 is used to compare the real-time maturity value with the preset NI target maturity value to obtain a maturity difference;

[0102] The heating module 40 is used to match the corresponding dynamic heating curve from the maturity-heating curve database according to the maturity difference, and convert the dynamic heating curve into a control instruction to drive the temperature control system to adjust the heating parameters until the maturity difference is not less than zero, thereby completing the heating of the dish.

[0103] Optionally, the acquisition module 10 includes:

[0104] acquiring food images at first preset time intervals;

[0105] It is determined whether the real-time maturity value of the current food meets a preset threshold value, and when the real-time maturity value of the current food meets the preset threshold value, the food image is obtained according to a second preset time interval.

[0106] Optionally, the parsing module 20 includes:

[0107] The hue change characteristics of the food surface are extracted through HSV color space conversion, and the Hu moment algorithm is used to quantify the food morphological expansion rate to obtain the food morphological characteristics;

[0108] The surface color change characteristics of the food and the morphological characteristics of the food are input into a convolutional neural network model to output a real-time degree of maturity value of the current food.

[0109] Optionally, the dynamic adjustment method of the NI target maturity value includes:

[0110] Get user taste preference parameters;

[0111] Mapping the user taste preference parameter to a target maturity value coefficient;

[0112] The NI target degree of maturity value is corrected through a regression model based on the initial moisture content and protein content of the ingredients.

[0113] Optionally, the method for generating the maturity-heating curve database includes:

[0114] Establish a baseline heating curve based on the heat conduction model and food type;

[0115] Through the reinforcement learning model, the deviation between the actual doneness and the target value in the historical cooking data is used to obtain the iterative optimization curve parameters, and a doneness-heating curve database is constructed.

[0116] Optionally, the adjustment method of the temperature control system includes:

[0117] For different types of composite food, the system can control the different zones and divide the heating areas into independent zones through thermal imaging data.

[0118] The weight ratio of microwave and heat conduction heating is dynamically allocated according to the density difference of ingredients.

[0119] Optionally, also include:

[0120] Determine whether the food image is partially carbonized. When the food image is partially carbonized, trigger an emergency cooling instruction of the temperature control system and push a manual intervention prompt.

[0121] The functions of the device in the embodiment of the present invention have been described in the above method embodiment. Therefore, for details not fully described in this embodiment, please refer to the relevant description in the above embodiment and will not be repeated here.

[0122] Based on the same inventive concept, an embodiment of this specification also provides an electronic device.

[0123] The following describes an electronic device embodiment of the present invention, which can be considered a specific physical implementation of the method and apparatus embodiments of the present invention described above. Details described in the electronic device embodiment of the present invention should be considered supplementary to the above-mentioned method or apparatus embodiments; details not disclosed in the electronic device embodiment of the present invention can be implemented with reference to the above-mentioned method or apparatus embodiments.

[0124] Figure 3 This is a schematic diagram of the structure of an electronic device provided in the embodiment of this specification. Figure 3 The electronic device 300 according to this embodiment of the present invention will be described. Figure 3 The electronic device 300 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0125] like Figure 3 As shown, electronic device 300 is implemented as a general-purpose computing device. Components of electronic device 300 may include, but are not limited to, at least one processing unit 310, at least one storage unit 320, a bus 330 connecting various system components (including storage unit 320 and processing unit 310), a display unit 340, and the like.

[0126] The storage unit stores program codes that can be executed by the processing unit 310, so that the processing unit 310 performs the steps according to various exemplary embodiments of the present invention described in the above processing method section of this specification. For example, the processing unit 310 can perform the following steps: Figure 1 Steps shown.

[0127] The storage unit 320 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 3201 and / or a cache memory unit 3202 , and may further include a read-only memory unit (ROM) 3203 .

[0128] The storage unit 320 may also include a program / utility 3204 having a set (at least one) of program modules 3205, such program modules 3205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include an implementation of a network environment.

[0129] Bus 330 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0130] The electronic device 300 may also communicate with one or more external devices 400 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable viewers to interact with the electronic device 300, and / or any device that enables the electronic device 300 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 350. Furthermore, the electronic device 300 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 360. The network adapter 360 may communicate with other modules of the electronic device 300 through the bus 330. It should be understood that although Figure 3 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 300, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0131] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the exemplary embodiments described in the present invention can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the above method according to the present invention. When the computer program is executed by a data processing device, the computer-readable medium is enabled to implement the above method of the present invention, that is: Figure 1 The method shown.

[0132] Figure 4 A schematic diagram of a computer-readable medium provided in accordance with an embodiment of this specification.

[0133] accomplish Figure 1 The computer program of the method shown can be stored on one or more computer-readable media. The computer-readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0134] The computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, wherein the readable program code is carried. The data signal propagated may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or component. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0135] The program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, and the like, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the viewer computing device, partially on the viewer device, as a stand-alone software package, partially on the viewer computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the viewer computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0136] In summary, the present invention can be implemented in hardware, or in a software module running on one or more processors, or in a combination thereof. It will be appreciated by those skilled in the art that general data processing equipment such as a microprocessor or a digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components in the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing a part or all of the methods described herein. Such a program for implementing the present invention can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0137] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the present invention is not inherently related to any specific computer, virtual device, or electronic device, and various general-purpose devices can also implement the present invention. The above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

[0138] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0139] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A heating method for dynamically controlling the degree of cooked food, characterized in that: include: Acquire food images at preset time intervals; Analyze the food image based on the maturity recognition model and output the real-time maturity value of the current food; Compare the real-time maturity value with the preset NI target maturity value to obtain the maturity difference; According to the degree of doneness difference, a corresponding dynamic heating curve is matched from the degree of doneness-heating curve database, and the dynamic heating curve is converted into a control instruction to drive the temperature control system to adjust the heating parameters until the degree of doneness difference is not less than zero, thereby completing the heating of the dish.

2. The method for dynamically controlling the degree of cooked food according to claim 1, wherein: The step of acquiring food images at preset time intervals includes: acquiring food images at first preset time intervals; It is determined whether the real-time maturity value of the current food meets a preset threshold value, and when the real-time maturity value of the current food meets the preset threshold value, the food image is obtained according to a second preset time interval.

3. The method for dynamically controlling the degree of cooked food as claimed in claim 2, characterized in that: The step of analyzing the food image based on the maturity recognition model and outputting a real-time maturity value of the current food includes: The hue change characteristics of the food surface are extracted through HSV color space conversion, and the Hu moment algorithm is used to quantify the food morphological expansion rate to obtain the food morphological characteristics; The surface color change characteristics of the food and the morphological characteristics of the food are input into a convolutional neural network model to output a real-time degree of maturity value of the current food.

4. The method for dynamically controlling the degree of cooked food according to claim 3, wherein: The dynamic adjustment method of the NI target maturity value includes: Get user taste preference parameters; Mapping the user taste preference parameter to a target maturity value coefficient; The NI target degree of maturity value is corrected through a regression model based on the initial moisture content and protein content of the ingredients.

5. The method for dynamically controlling the degree of cooked food according to claim 4, wherein: The method for generating the maturity-heating curve database includes: Establish a baseline heating curve based on the heat conduction model and food type; Through the reinforcement learning model, the deviation between the actual doneness and the target value in the historical cooking data is used to obtain the iterative optimization curve parameters, and a doneness-heating curve database is constructed.

6. The method for dynamically controlling the degree of cooked food according to claim 5, wherein: The adjustment method of the temperature control system includes: For different types of composite food, the system can control the different zones and divide the heating areas into independent zones through thermal imaging data. The weight ratio of microwave and heat conduction heating is dynamically allocated according to the density difference of ingredients.

7. The method for dynamically controlling the degree of cooked food according to claim 6, wherein: Also includes: Determine whether the food image is partially carbonized. When the food image is partially carbonized, trigger an emergency cooling instruction of the temperature control system and push a manual intervention prompt.

8. A heating device for dynamically controlling the degree of cooked food, characterized in that: include: An acquisition module, configured to acquire food images at preset time intervals; An analysis module is used to analyze the food image based on a maturity recognition model and output a real-time maturity value of the current food; A comparison module is used to compare the real-time maturity value with the preset NI target maturity value to obtain the maturity difference; The heating module is used to match the corresponding dynamic heating curve from the maturity-heating curve database according to the maturity difference, and convert the dynamic heating curve into a control instruction to drive the temperature control system to adjust the heating parameters until the maturity difference is not less than zero, thereby completing the heating of the dish.

9. An electronic device, wherein: The electronic device includes: processor; and, A memory storing computer executable instructions which, when executed, cause the processor to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, wherein: The computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, the method of any one of claims 1 to 7 is implemented.

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