An electric baking pan based on food material recognition and dynamic temperature control and a control method thereof

Abstract

The application relates to the technical field of intelligent kitchen appliances, and discloses an electric baking pan based on food material recognition and dynamic temperature control and a control method. The electric baking pan comprises a base and an upper cover body rotatably connected to the base through a hinge. The upper cover body and the base jointly enclose a cooking cavity for placing a cooking object in a closed state. The electric baking pan further comprises a first lock catch for connecting and fixing the upper cover body and the base in the closed state, and a second lock catch for unlocking the first lock catch. The upper cover body and the base are respectively provided with heating assemblies. The electric baking pan further comprises an image acquisition module which acquires a field of view covering the cooking cavity. Through the image acquisition module and an auxiliary light source, the type of food material can be recognized, and the thickness, size and other individualized physical characteristics of the food material can be accurately measured, so that stable and high-quality cooking effects can be achieved regardless of the number of uses and the size of the food material.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent kitchen appliances, in particular to an electric baking pan based on food material recognition and dynamic temperature control and a control method. BACKGROUND

[0002] As a popular household kitchen appliance, the electric baking pan can be used for grilling, baking and frying, and is particularly suitable for making pancakes, barbecued meat and fried dumplings.

[0003] The control mode of the currently marketed electric baking pan is relatively simple. Most products rely on a simple timer and a few fixed power levels (such as low, medium and high heat), and the user needs to manually set the heating time and heat according to personal experience. Even some advanced models equipped with preset menus for specific foods (such as steak mode and pancake mode) are essentially still executing a fixed, open-loop heating program.

[0004] The common defect of these prior art solutions is the lack of perception and adaptability to the physical state of the cooking object. Whether it is manual setting or preset menu, the heating strategy is fixed and completely ignores the specific differences in size, thickness, initial temperature, etc. of the food material in each cooking. For example, a fixed steak mode cannot distinguish between a 1 cm thick sirloin and a 3 cm thick fillet, resulting in unstable cooking results and often resulting in a burnt exterior and raw interior or a dry and overcooked whole. Therefore, the user often needs to repeatedly open the lid and subjectively judge the doneness by visual observation and manual pressing, which not only complicates the operation, disrupts the stable heating environment and prolongs the cooking time, but also greatly depends on the user's cooking skills and experience, and cannot guarantee the stability and consistency of the cooking quality, far from achieving true automation and intelligence.

[0005] In view of this, the present application aims to provide an electric baking pan based on food material recognition and dynamic temperature control and a control method to solve the problems existing in the prior art. SUMMARY

[0006] In view of the deficiencies of the prior art, the present application provides an electric baking pan based on food material recognition and dynamic temperature control and a control method, which solves the problem that the existing electric baking pan cannot adaptively adjust according to the specific physical characteristics such as the thickness and size of the food material due to the use of a fixed heating program, resulting in unstable cooking results and high dependence on user experience.

[0007] To achieve the above object, the present application is implemented by the following technical solutions: An electric baking pan based on food material recognition and dynamic temperature control, comprising a base and an upper cover body rotatably connected to the base through a hinge, the upper cover body and the base jointly form a cooking cavity for placing a cooking object in a closed state;

[0008] The electric baking pan further comprises a first lock for connecting and fixing the upper cover body and the base in the closed state, and a second lock for unlocking the first lock, the upper cover body and the base are respectively provided with a heating assembly;

[0009] The electric baking pan further comprises:

[0010] An image acquisition module for acquiring a field of view covering the cooking cavity;

[0011] A temperature sensor for monitoring the temperature in the cooking cavity;

[0012] A controller electrically connected to the heating assembly, the image acquisition module and the temperature sensor.

[0013] Preferably, the electric baking pan further comprises an auxiliary light source arranged in the cooking cavity for projecting a shadow of the cooking object in the cooking cavity, and the controller is used to determine the equivalent thickness of the cooking object.

[0014] A control method of an electric baking pan based on food material recognition and dynamic temperature control, comprising the following steps:

[0015] a) After the upper cover body and the base are closed through the hinge to form a cooking cavity, an initial image data and an initial temperature of a cooking object in the cooking cavity are acquired by an image acquisition module and a temperature sensor arranged inside the cooking cavity;

[0016] b) The controller determines at least one physical feature of the cooking object based on the initial image data by using an image recognition algorithm, the physical feature including size and equivalent thickness;

[0017] c) The controller reversely generates an initial heating power curve based on the physical feature and the initial temperature by solving a predictive process model with a target cooking state as a terminal constraint;

[0018] d) The controller controls the heating assembly arranged in the upper cover body and the base respectively to perform heating according to the initial heating power curve, and continuously acquires real-time image data of the cooking object in the cooking process by the image acquisition module;

[0019] e) said controller analyzes said real-time image data in real-time to monitor the actual progress of the surface color change of said cooking object, and re-plans the control strategy for said heating power profile when the actual progress of the surface color change deviates from the predicted progress by said predictive process model.

[0020] Preferably, in step b), the method of determining the equivalent thickness of said cooking object comprises:

[0021] A shadow of said cooking object is projected on the heating plate of said base by an auxiliary light source arranged in said cooking cavity, thereby constructing a triangular geometric model defined by the equivalent thickness of said cooking object, the length of the shadow, and the illumination path of said auxiliary light source;

[0022] Said controller analyzes said initial image data to identify and measure the characteristic length of said shadow, thereby obtaining a known side of said triangular geometric model;

[0023] Based on the obtained known side length and the preset illumination incident angle of said auxiliary light source as a known angle in said triangular geometric model, the equivalent thickness of said cooking object is calculated according to the inherent trigonometric function relationship of said triangular geometric model.

[0024] Preferably, in step c), said predictive process model comprises a coupled dynamic equation describing the changes of the core temperature and the surface color of the cooking object with the heating power and the time;

[0025] The process of reversely generating the initial heating power profile is an optimal control problem aiming to minimize the cooking time with the terminal state constraints of the target core temperature and the target surface color of said cooking object.

[0026] Preferably, said controller further defines a cooking progress index for quantifying the cooking completion degree of said cooking object in said cooking cavity;

[0027] In step e), the actual progress of the surface color change is monitored by continuously updating and correcting said cooking progress index by fusing the prediction of said predictive process model and the actual surface color of said cooking object in said cooking cavity analyzed based on said real-time image data.

[0028] Preferably, the step of updating and correcting said cooking progress index comprises:

[0029] According to the state at the previous time, a predicted cooking progress index increment is calculated by said predictive process model;

[0030] and calculate an observation residual between the actual surface color of the cooking object in the cooking cavity analyzed from the real-time image data and the surface color predicted by the predictive process model;

[0031] Then, the predicted cooking process index increment and the observation residual are weighted and fused to obtain a corrected cooking process index at the current time.

[0032] Preferably, in the step e), when a deviation between the corrected cooking process index and a planned process trajectory corresponding to the initial heating power curve exceeds a preset threshold, a control strategy re-planning for the upper cover body and the heating assembly in the base is triggered.

[0033] Preferably, when the cooking process index reaches a preset completion threshold, and the surface color of the cooking object in the cooking cavity monitored by the real-time image data enters a target range and is stable for a preset time length, the heating of the upper cover body and the heating assembly in the base is terminated.

[0034] Preferably, in the step b), the controller further constructs a type ID, a size, an equivalent thickness and the initial temperature of the cooking object in the cooking cavity into an initial state vector, and takes the initial state vector as an input of the predictive process model.

[0035] The application provides an electric baking pan based on food material recognition and dynamic temperature control and a control method.

[0036] 1. The application uses an image acquisition module and an auxiliary light source to not only identify the type of food material, but also accurately measure the individual physical characteristics such as thickness and size, and the controller customizes an optimal heating curve for each food material based on the real-time accurate data, instead of using a fixed preset program. This targeted control scheme fundamentally solves the problem of uneven cooking or different cooking times caused by the inability of traditional kitchen utensils to adapt to individual differences in food materials (such as the thickness of a steak or the size of a dumpling), ensuring stable and high-quality cooking results regardless of the number of uses or the size of the food material.

[0037] 2. The heating curve generation module of the application constructs the cooking process as an optimal control problem with the shortest time as the objective function. Through inverse solving based on the predictive process model, the controller can calculate a heating power path that meets all process constraints (such as core thorough cooking and proper surface charring) and has the shortest time. This method avoids the time redundancy and heat waste caused by traditional segmented control or constant power heating, realizes the most efficient energy transfer from the initial state to the target state, and thus saves the user's time and energy consumption to the greatest extent while ensuring the cooking quality.

[0038] 3. In this invention, users only need to put the ingredients into the electric griddle and close the lid. Without making any selections or settings, the device will automatically complete the entire process of ingredient recognition, status perception, strategy planning, process monitoring, and even dynamic adjustment. In particular, the dual termination condition judgment mechanism based on the cooking progress index and the actual surface color can intelligently determine whether the cooking is truly complete. This highly automated "put it in and go" approach lowers the barrier to entry and brings users an unprecedentedly convenient and easy cooking experience. Attached Figure Description

[0039] Figure 1 This is an overall structural diagram of the present invention;

[0040] Figure 2 This is a schematic diagram of the overall flow of the control method of the present invention;

[0041] Figure 3 This is a schematic diagram of the initial state acquisition process of the present invention;

[0042] Figure 4 This is a schematic diagram of the process for determining the physical characteristics of the present invention;

[0043] Figure 5 This is a schematic diagram illustrating the principle of calculating the equivalent thickness of food ingredients according to the present invention.

[0044] The components include: 1. Top cover; 2. Base; 3. Hinge; 4. Cooking cavity; 5. Second latch; 6. First latch. Detailed Implementation

[0045] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] See attached document Figure 1 This invention provides an electric griddle based on food identification and dynamic temperature control, including a base 2 and an upper cover 1 rotatably connected to the base 2 via a hinge 3. The upper cover 1 and the base 2 together form a cooking cavity 4 for placing the cooking object when closed.

[0047] The electric griddle also includes a first latch 6 for connecting and fixing the upper cover 1 in the closed state to the base 2, and a second latch 5 for unlocking the first latch 6. Heating components are respectively provided inside the upper cover 1 and the base 2.

[0048] Electric griddles also include:

[0049] an image acquisition module, which acquires a field of view covering the cooking cavity;

[0050] a temperature sensor, which monitors the temperature inside the cooking cavity;

[0051] a controller, which is electrically connected with the heating assembly, the image acquisition module and the temperature sensor;

[0052] The electric baking pan further comprises an auxiliary light source, which is arranged in the cooking cavity 4 and used to project a shadow of the cooking object in the cooking cavity 4, and the controller is used to determine the equivalent thickness of the cooking object.

[0053] Specifically, the electric baking pan provided by the present application comprises a base 2 and an upper cover body 1. The upper cover body 1 is rotatably connected with the base 2 through a hinge 3 arranged at the rear side of the upper cover body 1, so as to allow the upper cover body 1 to be opened and closed relative to the base 2. When the upper cover body 1 is in the closed position, it and the base 2 jointly enclose a cooking cavity 4 for accommodating and heating a cooking object. A lower heating disc is arranged on the upper surface of the base 2, and the cooking object is directly placed on the lower heating disc.

[0054] In order to reliably fix the upper cover body 1 and the base 2 in the closed state during the cooking process, the front end of the electric baking pan is further provided with a set of locking mechanisms. The mechanism comprises a first locking buckle 6 arranged on the upper cover body 1 and a clamping groove arranged on the base 2 and matched with the first locking buckle 6. A second locking buckle 5 for unlocking the first locking buckle 6 is arranged as a pressable button, which is mechanically linked with the first locking buckle 6. The user can make the first locking buckle 6 disengage from the clamping groove by pressing the second locking buckle 5, so as to open the upper cover body 1.

[0055] The heating function of the electric baking pan is realized by a heating assembly arranged inside the upper cover body 1 and the base 2 respectively. Specifically, an upper heating disc is arranged inside the upper cover body 1, and a lower heating disc is arranged inside the base 2, and the two heating discs jointly act to heat the cooking object in the cooking cavity 4 from the top and bottom.

[0056] In order to realize the intelligent perception and control of the cooking process, the electric baking pan also integrates an electronic system. The system includes an image acquisition module, a temperature sensor, and a controller 7. The image acquisition module, for example, a digital camera with a built-in wide-angle lens, is installed on the inner surface of the upper cover body 1, and its position and field of view are designed to ensure that its acquisition field of view can completely cover the lower heating disc area on the base 2. The temperature sensor, for example, an NTC thermistor or an infrared temperature sensor, is arranged in the cooking cavity 4 to monitor the temperature inside the cavity in real time. In an embodiment, the electric baking pan also includes an auxiliary light source, for example, an LED lamp bead, which is also arranged on the inner surface of the upper cover body 1, and its position has a fixed geometric angle relative to the image acquisition module. The role of the auxiliary light source is to illuminate under the instruction of the controller 7, so as to project a clear shadow on the cooking object, and the shadow image information is used by the controller 7 for subsequent thickness calculation.

[0057] The controller 7 is the core of the entire electronic system, and its physical entity is a printed circuit board assembly PCBA carrying a microprocessor MCU. The printed circuit board assembly PCBA also integrates a storage unit, such as a non-volatile flash memory and a random access memory RAM. The non-volatile flash memory is used to permanently store the control program of the present application, the preset predictive process model, the food material feature database, various parameter thresholds, etc.; the random access memory is used as a cache area for program running, temporarily storing the collected image data, intermediate calculation results, and generated heating power curve. The electric baking pan can also include a user interface module electrically connected to the controller 7 for receiving user input cooking preferences such as desired doneness or displaying cooking status to the user, which can include a display screen or status indicator lights.

[0058] The controller 7 is connected to various functional components through different electrical interfaces: it is connected to the upper and lower heating components through a drive circuit to control the output of the heating power; it is connected to the image acquisition module through a data bus, such as a MIPI or USB interface, to receive digital image data collected by the module; it is connected to the temperature sensor through an analog-to-digital conversion interface to read digitized temperature values; it is also connected to the auxiliary light source through an I / O port to control its opening and closing. The controller 7 coordinates the work of all these components by executing the internal fixed program algorithm, thereby realizing the complete automatic cooking method of the present application.

[0059] Referring to the accompanying Figure 2 , the present application provides a control method for an electric baking pan based on food material recognition and dynamic temperature control, which is executed in an electric baking pan including a controller 7. The controller 7 can include, but is not limited to: an initial state acquisition module, a physical feature determination module, a heating curve generation module, a closed-loop control module, and a cooking termination module.

[0060] The specific flow of the method is as follows:

[0061] After the user places the cooking object on the heating plate of the base 2 and closes the upper cover body 1, the initial state acquisition module in the controller 7 is activated. The module drives the image acquisition module arranged in the cooking cavity 4 to capture an initial image of the cooking object, and simultaneously reads the reading of the temperature sensor to obtain the initial temperature in the cooking cavity 4. The initial image data and the initial temperature are transmitted to the physical feature determination module. After receiving the initial image data, the physical feature determination module executes an image recognition algorithm. The module first calculates the size of the cooking object in the two-dimensional plane, such as length, width or area, through edge detection and contour analysis. Then, the module determines the equivalent thickness of the cooking object by using the shadow formed on the cooking object by the auxiliary light source. Specifically, the module identifies and measures the characteristic length of the shadow from the initial image data , and reads the preset light incidence angle of the auxiliary light source from the memory The equivalent thickness is calculated by the following trigonometric function relationship :

[0062] ;

[0063] wherein, is the equivalent thickness of the cooking object; is the measured characteristic length of the shadow; is the preset light incidence angle of the auxiliary light source.

[0064] After determining the physical features, the module also integrates the identified cooking object type ID, size, equivalent thickness H and obtained initial temperature into an initial state vector. Subsequently, the initial state vector is transmitted to the heating curve generation module. The module takes this vector as the initial condition of the predictive process model, and takes the preset cooking target, such as the target core temperature and the target surface color, as the terminal state constraint of the model, to solve an optimal control problem with the minimum cooking time as the objective function. The output of this solving process is an initial heating power curve, which defines the power value that the heating assembly should output at each time from the current time to the completion of the cooking. The initial heating power curve is sent to the closed-loop control module. The module controls the heating assembly arranged in the upper cover body 1 and the base 2 to start heating according to the curve. During the heating process, the closed-loop control module continuously obtains real-time cooking object image data from the image acquisition module, and monitors the cooking process in real time based on the real-time data. For this purpose, the closed-loop control module maintains a cooking process index , and continuously updates it by using the prediction correction mechanism. The updating process can be represented by the following formula:

[0065] ;

[0066] wherein, is the corrected cooking progress index at current time k; is the cooking progress index at previous time k-1, is the cooking progress index at previous time k-1, is the predicted progress index increment at current time k based on the state at previous time k-1; is the weighting factor at current time k; is the quantified value of actual surface color and glossiness derived from real-time image data analysis; is the quantified value of surface color and glossiness predicted by the model.

[0067] In one specific embodiment, may be a constant value set empirically and kept unchanged throughout the whole cooking process. In another preferred embodiment, may be a dynamic variable whose value is calculated according to Kalman filter theory as the Kalman gain. The size of the gain depends on the uncertainty of model prediction and the uncertainty of actual observation, thus achieving the optimal fusion of model prediction and actual observation.

[0068] The closed-loop control module also continuously compares the corrected cooking progress index with the theoretical progress trajectory planned by the initial heating power curve. When the deviation between the two exceeds a preset threshold, it indicates that the actual cooking progress deviates significantly from the plan, at which point the control strategy will be re-planned. The re-planning process is to take the current corrected cooking state, including , current temperature, etc. as new initial conditions, re-call the heating curve generation module, and calculate a new optimal heating power curve from the current state to the final target state to replace the remaining part of the original curve.

[0069] Finally, the cooking termination module monitors two termination conditions in parallel throughout the cooking process:

[0070] First, whether the corrected cooking progress index reaches the preset completion threshold;

[0071] Second, whether the surface color and glossiness of the cooking object monitored by real-time image data enter the target range and remain stable within the target range for a preset duration. Only when both conditions are met simultaneously, the cooking termination module will send a stop signal to the driving circuit of the heating assembly to terminate the heating of the heating assembly in the upper cover body 1 and the base 2, thus ending the entire cooking process.

[0072] Referring to the accompanying Figure 3The execution of the control method of the present invention begins with a clear physical trigger event. When the user closes the upper lid body 1 with the base 2 and locks it through the first lock 6, a position sensor, for example a micro switch or a Hall effect sensor, which is mechanically linked to the first lock 6, is triggered. The sensor sends a level signal to the controller 7 indicating that the cooking chamber is closed and locked. Upon receiving this signal, the controller 7 activates its internal initial state acquisition module and begins the initial state acquisition process.

[0073] The stable closure of the upper lid body 1 with the base 2 is ensured by the lock mechanism, the purpose of which is to create a repeatable, closed physical environment for subsequent image acquisition and temperature measurement, which is not disturbed by external light and air flow, which is a prerequisite for ensuring the accuracy of the initial state measurement.

[0074] The initial state acquisition module first activates the auxiliary light source provided in the cooking chamber 4 to a preset, constant luminous intensity through a control signal. This step aims to provide a stable and external environment light-affected lighting condition for image acquisition. After the brightness of the auxiliary light source is stable, the module immediately instructs the image acquisition module to capture a frame of static digital image covering the heating plate area in the cooking chamber 4. The static digital image, for example, in the form of a two-dimensional pixel matrix, exists as initial image data, which is transmitted by the image acquisition module to the internal storage of the controller 7 through the data interface.

[0075] At the same time or immediately after the image acquisition instruction is issued, the initial state acquisition module sends a data reading request to the temperature sensor provided in the cooking chamber 4. The temperature sensor is provided at a position that can reflect the initial environment temperature inside the cooking chamber 4, for example, the side wall of the chamber, and its measurement value can be used as a reference for the initial temperature of the cooking object. The temperature sensor converts the measured analog signal into a digital temperature value through an analog-to-digital conversion circuit ADC and transmits it to the controller 7.

[0076] Finally, the initial state acquisition module stores the received initial image data and digitized initial temperature value as a unified data set in a designated data buffer area in the controller 7. The successful generation and storage of the data set mark the completion of the initial state acquisition step and serve as the direct input for the subsequent physical feature determination module to process.

[0077] Referring to the accompanying drawings Figure 4 The process is executed by the physical feature determination module inside the controller 7, which receives the initial image data and initial temperature provided by the initial state acquisition module as input.

[0078] The physical feature determination module first pre-processes the received initial image data. This pre-processing step includes: converting the multi-channel color image to a single-channel grayscale image to simplify subsequent calculations; applying a de-noising algorithm such as Gaussian filtering to eliminate random noise generated during image acquisition; and enhancing the image contrast through histogram equalization to highlight the outline of the cooking object and its shadow.

[0079] After pre-processing, the physical feature determination module begins to determine the two-dimensional size of the cooking object. This process is achieved by performing a binarization operation on the pre-processed grayscale image, for example, using Otsu's method to automatically determine a threshold value, dividing the image into a pixel region representing the cooking object and a pixel region representing the background. Subsequently, the physical feature determination module performs a contour detection algorithm on the binarized image to extract a closed contour representing the outer boundary of the cooking object. Based on this contour, a series of two-dimensional geometric parameters can be calculated, such as the total number of pixels enclosed by the contour, the minimum circumscribed rectangle of the contour, and the centroid of the contour.

[0080] Next, the physical feature determination module performs the calculation of the equivalent thickness. Based on the fixed position of the auxiliary light source, a shadow will appear on the other side of the cooking object relative to the light source. The physical feature determination module analyzes the pixel intensity in the expected shadow area of the cooking object contour in the pre-processed image, and divides out the shadow area by setting a darker grayscale threshold. The physical feature determination module then calculates the characteristic length of the shadow , which can be the average of the distances from the cooking object boundary to the outer boundary of the shadow on multiple preset axes perpendicular to the illumination direction. The physical feature determination module reads a preset constant value from its non-volatile memory, which represents the angle between the auxiliary light source light and the heating plate surface of the base 2, i.e. the illumination incident angle . Then the equivalent thickness of the cooking object is calculated :

[0081] After obtaining the size and thickness information, the physical feature determination module can also perform type recognition of the cooking object. This process combines multiple features calculated in the previous steps, such as area, aspect ratio, circularity of the contour, and initial average chroma extracted from the initial color image, into a multi-dimensional feature vector. The physical feature determination module inputs this feature vector into a pre-trained and stored classification model in the controller 7, such as a support vector machine or a lightweight convolutional neural network. The classification model analyzes the input feature vector and outputs a type ID corresponding to a certain category in the predefined food library, such as dumplings, steak, pancakes, etc.

[0082] In another embodiment, type identification can be performed without using a machine learning model, but rather through a rule-based decision tree system. For example, the controller 7 can determine the type ID of the cooking object based on calculated features such as area, thickness, and aspect ratio, according to a set of preset logical rules. If the thickness is <3mm and the roundness is >0.9, then it is determined to be a pancake.

[0083] At the end of the process, the physical feature determination module will input all the key parameters obtained from this calculation and identification, including the determined type ID, the calculated two-dimensional dimensions, and the equivalent thickness. The initial temperature, received from the initial state acquisition module, is organized and constructed into a structured initial state vector. This vector is a complete digital description of the physical state of the cooking object before cooking begins, and it will be passed to the subsequent heating curve generation module as the final output of the physical feature determination module.

[0084] See attached document Figure 5 This process is executed by the heating curve generation module inside the controller 7, which receives the initial state vector output by the physical feature determination module as input.

[0085] The core of the heating curve generation module is solving an optimal control problem based on a predictive process model. This predictive process model exists as a set of coupled ordinary differential equations, used to describe the state evolution of the cooking object during the heating process. The model comprises at least two parts: a heat conduction sub-model and a surface color change sub-model. The heat conduction sub-model, based on the law of heat conduction, describes the changes in the internal and surface temperatures of the cooking object over time through one or more state variables, the rate of change being a function of the applied heating power and the current temperature distribution. Specifically, the specific parameters of the heat conduction sub-model are geometric parameters, such as area and equivalent thickness, determined by the physical characteristics determination module. Adjustments are made. For example, equivalent thickness. The surface area directly determines the time required for heat to transfer from the surface to the core, while the surface area affects the distribution of total heating power. By incorporating these real-time measured geometric parameters into the model, the model can more accurately reflect the physical reality of a specific cooking object. The surface color change sub-model, based on chemical reaction kinetics, describes the change of a quantified surface color index, the rate of change of which is a function of the surface temperature of the cooking object calculated by the heat conduction sub-model. Therefore, these two sub-models are coupled through surface temperature.

[0086] The state of the entire system can be represented by a state vector. To indicate, At least include core temperature and surface color index The heating curve generation module converts the initial state vector received from the physical characteristics determination module into initial conditions for the model while reading from its internal memory a target state vector corresponding to the type of cooking object This vector defines the target core temperature and target surface color index at the end of the cooking process. The goal of the heating curve generation module is to calculate a heating power curve i.e. a control variable, that drives the state of the cooking object from to in the shortest possible time . This problem is formulated as an optimal control problem with a target function and constraints including:

[0087] System dynamics equation: where represents the aforementioned coupled predictive process model.

[0088] Initial state constraint: equals the acquired initial state vector.

[0089] Terminal state constraint: .

[0090] Control variable constraint: where is the rated maximum power of the heating assembly.

[0091] Since the goal of this problem is to solve for the required control input in reverse from the known terminal state, this process is referred to as inverse generation. The heating curve generation module employs a numerical optimization algorithm to solve this optimal control problem. Specifically, the module discretizes this continuous-time problem into a large-scale nonlinear programming NLP problem. This conversion process parameterizes the total time to be optimized and the continuous control curve into a series of discrete decision variables. Subsequently, the processor within the controller 7 executes an iterative optimization solver to solve this NLP problem. The result of solving this NLP problem is a discrete time series that contains the optimal total cooking duration and a series of heating power setpoints in time sequence over this duration. This sequence of power values constitutes the final generated initial heating power curve. This curve is then output by the heating curve generation module to the closed-loop control module as a sequence of instructions for guiding the subsequent heating execution and monitoring process.

[0092] The process is executed by a closed-loop control module inside the controller 7, which receives as input the initial heating power profile generated by the heating profile generation module.

[0093] Upon receiving the initial heating power profile, the closed-loop control module starts applying control to the heating assembly arranged inside the upper lid body 1 and the base 2 through the driving circuit according to the power values and time sequence defined by the profile. At the same time, the closed-loop control module initiates a real-time monitoring and adjustment cycle with a fixed time period.

[0094] At the beginning of each monitoring cycle, the closed-loop control module first instructs the image acquisition module to capture a frame of current cooking object image. The closed-loop control module processes the real-time image to obtain a quantified actual surface color value . The process includes: locating one or more pre-defined surface regions as regions of interest ROl in the image according to the previously determined cooking object position;

[0095] calculating the average RGB color value of all pixels in the ROIs; and finally, converting the average RGB value into a single scalar value representing the actual surface color at the current time instant through a pre-defined mapping function.

[0096] At the same time, the closed-loop control module executes a prediction step. It invokes the same predictive process model as used by the heating profile generation module, inputs the state at the previous time instant and the heating power that has been executed, and calculates the predicted state at the current time instant. From the predicted state , the closed-loop control module extracts the predicted quantified surface color value and a predicted cooking progress index increment .

[0097] Next, the closed-loop control module executes a correction step to fuse the model prediction with the actual observation. It first calculates the observation residual between the actual observation and the model prediction, i.e. . Then, the closed-loop control module performs a weighted fusion of the predicted cooking progress index and the observation residual to calculate the corrected, more accurate cooking progress index at the current time instant.

[0098] After updating the cooking progress index , the closed-loop control module compares it with the value of the theoretical planned progress trajectory corresponding to the initial heating power profile at the current time instant , and calculates the absolute deviation When the deviation exceeds a preset deviation threshold stored in the memory of the controller 7, the control strategy is triggered to be re-planned.

[0099] After the re-planning is triggered, the closed-loop control module passes the current time's corrected complete state vector, containing the actual temperature and other state variables, to the heating curve generation module as the new initial condition. The heating curve generation module will execute the complete optimal control problem solving process again based on this new starting point to generate a new optimal heating power curve from the current state to the final target state. The closed-loop control module will discard the original old curve after receiving the new curve and start to continue the subsequent heating control according to the instructions of the new curve. If the deviation does not exceed the threshold, the current curve will continue to be used for heating. This loop continues until the cooking termination module issues a termination instruction.

[0100] The judgment process is performed in parallel by the cooking termination module inside the controller 7, which continuously receives the corrected cooking progress index and the analyzed actual surface color quantization value from the closed-loop control module.

[0101] The cooking termination module has two independent condition monitors built in to evaluate the two termination conditions simultaneously. The first condition monitor is responsible for evaluating the internal state. It compares the received real-time cooking progress index with a preset completion threshold stored in the non-volatile memory of the controller 7. This threshold is a value close to 1.0, such as 0.98. When the condition is met, the first condition monitor sets its internal state flag to true.

[0102] The second condition monitor is responsible for evaluating the external appearance state. It first compares the received actual surface color quantization value with a preset target color range. The target color range is read from a parameter library according to the type ID of the cooking object and can be defined as a region in a multi-dimensional color space, such as the CIELAB space, for example , , When the corresponding color coordinates fall within this region, the monitor starts an internal timer; if continues to fall within this region in subsequent monitoring periods, the timer continues to count;

[0103] Once ​When the region is exited, the timer is immediately cleared. When the count value of the timer reaches or exceeds a preset stabilization time, for example, 5 seconds, the second condition monitor sets its internal status flag to true. The cooking termination module performs a logical AND operation on the status flags of the two monitors at the end of each control cycle. In the case where both status flags are false or only one is true, no operation is performed and the heating process continues. The cooking termination module is triggered only when the status flag of the first condition monitor and the status flag of the second condition monitor are both true at the same time.

[0104] In addition, as a parallel safety measure, the cooking termination module also continuously monitors the real-time temperature value fed back by the temperature sensor. Once the temperature value exceeds a preset safety upper threshold value that is much higher than the normal cooking temperature, the module will be triggered immediately regardless of whether the two aforementioned cooking completion conditions are met, to prevent overheating or dry burning caused by accidental situations.

[0105] After being triggered, the cooking termination module immediately sends an explicit stop instruction signal, for example, sets the control pin to low, to the driving circuit of the heating assembly in the control upper cover body 1 and the base 2. The instruction makes the driving circuit cut off the power supply to the heating assembly, thereby terminating the entire cooking process. At the same time, the cooking termination module can send a completion signal to the user interface module to drive the buzzer to sound or light up an indicator light to prompt the user that the cooking has been completed.

[0106] Working principle: based on the food material recognition and dynamic temperature control electric baking pan, through the hinge 3 closing the upper cover body 1 and base 2 form cooking cavity 4, by the first lock 6 fixed, the second lock 5 unlocking, the upper cover body 1 and base 2 are respectively provided with heating assembly, at the same time, the image acquisition module covering the cooking cavity 4 is assembled, the temperature sensor monitoring the temperature in the cavity and the core controller are electrically connected with each component, the upper cover body 1 is also built-in auxiliary light source; when using, after closing the equipment, the image acquisition module first captures the initial image of food material, the temperature sensor synchronously obtains the initial temperature in the cavity, the controller adopts image recognition algorithm, combines the food material shadow projected by auxiliary light source to construct triangular geometric model, calculates the equivalent thickness of food material by shadow length and preset illumination incident angle, determines the size, equivalent thickness and type ID of food material and constructs initial state vector, inputs predictive process model containing core temperature and surface color and luster coupling dynamic equation, solves the optimal control problem with the minimum cooking time as the target and the target cooking state as the terminal constraint, reversely generates initial heating power curve, drives the upper and lower heating components to heat according to the curve; during heating, the image acquisition module continuously obtains real-time image of food material, the controller analyzes the image to monitor the surface color change, defines the cooking process index, fuses model prediction and actual color data to continuously correct the index, when the deviation of corrected index and planned trajectory exceeds the preset threshold, immediately re-plans the heating power curve; until the index reaches the completion threshold, and the surface color and luster of food material enter the target range and stabilize for a preset time length, the controller terminates heating, which not only solves the pain points of traditional electric baking pan relying on user experience, but also improves the cooking success rate and convenience, realizes intelligent and accurate cooking.

Claims

1. A control method of an electric baking pan based on food material recognition and dynamic temperature control, characterized by, The method comprises the following steps: a) After the upper cover (1) and the base (2) are closed by the hinge (3) to form a cooking cavity (4), the initial image data and the initial temperature of a cooking object in the cooking cavity (4) are obtained by the image acquisition module and the temperature sensor arranged inside the cooking cavity (4); b) The controller determines at least one physical characteristic of the cooking object based on the initial image data by using an image recognition algorithm, wherein the physical characteristic includes size and equivalent thickness; c) The controller reversely generates an initial heating power curve by solving a predictive process model with a target cooking state as a terminal constraint based on the physical characteristic and the initial temperature; d) The controller controls the heating components arranged in the upper cover (1) and the base (2) respectively to perform heating according to the initial heating power curve, and continuously obtains real-time image data of the cooking object in the cooking process by the image acquisition module; e) The controller analyzes the real-time image data in real time to monitor the surface color change of the cooking object, and performs control strategy re-planning on the heating power curve when the actual progress of the surface color change deviates from the progress predicted by the predictive process model; The electric baking pan further comprises an auxiliary light source arranged in the cooking cavity (4) for projecting a shadow of the cooking object in the cooking cavity (4), and the controller is used to determine the equivalent thickness of the cooking object; The method for determining the equivalent thickness of the cooking object comprises: The auxiliary light source arranged in the cooking cavity (4) is used to project a shadow of the cooking object on the heating disc of the base (2), so as to construct a triangular geometric model defined by the equivalent thickness of the cooking object, the length of the shadow and the illumination path; The controller analyzes the initial image data, identifies and measures the characteristic length of the shadow, so as to obtain a known side of the triangular geometric model; Based on the obtained known side length and the preset illumination incident angle of the auxiliary light source as a known angle in the triangular geometric model, the equivalent thickness of the cooking object is calculated according to the internal trigonometric function relationship of the triangular geometric model.

2. The control method of the electric baking pan based on food material recognition and dynamic temperature control according to claim 1, characterized in that, The base (2) and the upper cover (1) rotatably connected to the base (2) by the hinge (3), the upper cover (1) and the base (2) jointly enclose a cooking cavity (4) for placing a cooking object in the closed state; The electric baking pan further comprises a first lock (6) for connecting and fixing the upper cover (1) and the base (2) in the closed state, and a second lock (5) for unlocking the first lock (6), and the upper cover (1) and the base (2) are respectively provided with heating components; The electric baking pan further comprises: An image acquisition module which acquires a field of view covering the cooking cavity; A temperature sensor for monitoring the temperature in the cooking cavity; A controller which is electrically connected with the heating components, the image acquisition module and the temperature sensor.

3. The control method of the electric baking pan based on food material recognition and dynamic temperature control according to claim 1, characterized in that, In step c), the predictive process model comprises coupled dynamic equations for describing the changes of the core temperature and the surface color of the cooking object with the heating power and the time; The process of generating an initial heating power curve is to solve an optimal control problem aiming to minimize the cooking time, and the terminal state constraints are the target core temperature and the target surface color of the cooking object.

4. The control method of the electric baking pan based on food material recognition and dynamic temperature control according to claim 1, characterized in that, The controller also defines a cooking progress index for quantifying the cooking completion degree of the cooking object in the cooking cavity (4); In step e), the actual progress of the surface color change is monitored, specifically: by fusing the prediction of the predictive process model and the actual surface color of the cooking object in the cooking cavity (4) analyzed based on the real-time image data, the cooking progress index is continuously updated and corrected.

5. The control method of the electric baking pan based on food material recognition and dynamic temperature control according to claim 4, characterized in that, The step of updating and correcting the cooking progress index includes: According to the state of the previous moment, a predicted cooking progress index increment is calculated from the predictive process model; And calculate the observation residual between the actual surface color of the cooking object in the cooking cavity (4) analyzed based on the real-time image data and the surface color predicted by the predictive process model; Then the predicted cooking progress index increment and the observation residual are weighted and fused to obtain the corrected cooking progress index at the current moment.

6. The control method of the electric baking pan based on food material recognition and dynamic temperature control according to claim 5, characterized in that, When the deviation between the corrected cooking progress index and the planned progress trajectory corresponding to the initial heating power curve exceeds the preset threshold, the control strategy of the heating assembly in the upper cover body (1) and the bottom base (2) is re-planned.

7. The control method of the electric baking pan based on food material recognition and dynamic temperature control according to claim 6, characterized in that, When the cooking progress index reaches the preset completion threshold, and the surface color of the cooking object in the cooking cavity (4) monitored by the real-time image data enters the target range and stabilizes for a preset time, the heating of the heating assembly in the upper cover body (1) and the bottom base (2) is terminated.

8. The control method of the electric baking pan based on food material recognition and dynamic temperature control according to claim 1, characterized in that, In step b), the controller also constructs the type ID, size, equivalent thickness and initial temperature of the cooking object in the cooking cavity (4) into an initial state vector, and takes the initial state vector as the input of the predictive process model.

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