Full-automatic closed-loop control method of intelligent egg boiler

Abstract

The application provides a kind of full-automatic closed-loop control method of intelligent egg boiler, including based on cooking timing, the egg cooking process is divided into multi-stage heating, and according to the egg parameter to be cooked, egg boiler parameter and expected maturity, obtain each stage predicted heating curve;According to the predicted heating curve of current stage, execute dry state waiting / water adding / heating / cooling instruction;Obtain the cooking time of current stage and generate heating time signal, and according to water temperature signal, steam temperature signal, water level signal and heating time signal, heat accumulation calculation is carried out to obtain the heat accumulation value of current stage;Judge whether the heat accumulation value of current stage reaches the heat end point of its stage, until the predicted heating curve of all stages is executed to end.The application monitors heat accumulation value by multi-stage heating curve in stages during egg cooking process, achieves fine and high consistency temperature control, dynamically adjusts cooking strategy to resist interference full-automatic closed-loop control effect.

Description

Technical Field

[0001] This invention relates to the field of egg cooker technology, and more specifically to a fully automatic closed-loop control method for an intelligent egg cooker. Background Technology

[0002] Currently, most egg cookers on the market rely on simple temperature and time controls, using thermostats to cycle through heating and cooling. Some products use bimetallic thermostats that automatically cut off power after reaching a specific temperature. However, this approach cannot monitor the cooking status of the eggs. Improved designs use timers to control cooking time combined with temperature sensors to monitor the internal temperature of the cooker. However, due to numerous variations in initial temperature, water volume, water temperature, and overheating, inconsistent cooking results are common, failing to guarantee a stable egg texture. For example, egg whites begin to coagulate at 62℃, and yolks at 65℃. To cook soft-boiled eggs (soft-boiled eggs), the cooking temperature needs to be precisely controlled between 67-68℃. Existing egg cooker temperature control schemes typically have an error of ±5℃ or more and poor resistance to external interference, failing to achieve precise and consistent temperature control. This results in a high degree of randomness in the cooking of eggs with different textures, such as soft-boiled eggs, runny eggs, and hard-boiled eggs. In most cases, users need to judge the doneness of the eggs based on experience. Frequent opening of the lid not only greatly reduces the convenience of use, but also leads to heat loss. Even with experience in cooking time, the desired result may not be achieved.

[0003] Therefore, in order to meet consumers' refined and diversified demands for the taste of eggs, it is necessary to design a fully automatic closed-loop control method that can achieve refined and consistent temperature control and dynamically adjust the cooking strategy to resist interference. Summary of the Invention

[0004] In order to solve the common problems in the prior art, the purpose of this invention is to provide a fully automatic closed-loop control method for an intelligent egg cooker. This invention monitors the heat accumulation value in stages of the egg cooking process through multi-segment heating curves, so as to achieve a fully automatic closed-loop control effect with refined and highly consistent temperature control and dynamic adjustment of cooking strategy to resist interference.

[0005] The present invention achieves the above objectives through the following technical solutions:

[0006] A fully automatic closed-loop control method for an intelligent egg cooker includes:

[0007] S1: Based on the cooking time sequence, the egg cooking process is divided into multiple heating stages, and the predicted heating curves for each stage are obtained according to the parameters of the egg to be cooked, the parameters of the egg cooker, and the desired degree of doneness.

[0008] S2: Output the predicted heating curve in stages according to the cooking sequence. The multi-stage heating is divided into the following stages in sequence: dry state pre-heating stage, heating stage, temperature calibration stage, cooling stage, and heat preservation stage.

[0009] S3: Execute the dry waiting / water addition / heating / cooling commands according to the predicted heating curve of the current stage, and collect water temperature signal, steam temperature signal and water level signal in real time.

[0010] S4: Set the timing endpoint and heat endpoint for each stage, obtain the cooking time of the current stage and generate a heating time signal, and perform heat accumulation calculation based on the water temperature signal, steam temperature signal, water level signal and heating time signal to obtain the heat accumulation value of the current stage.

[0011] The system also includes a boiling threshold, with the time when the steam temperature reaches the boiling threshold as the starting point for the cooking time during the temperature calibration stage, based on the steam temperature signal.

[0012] S5: Determine whether the cooking time of the current stage has reached the end of the timing of the stage. If not, continue timing. If yes, determine whether the heat accumulation value of the current stage has reached the end of the heat of the stage. If it has, return to step S2 until the predicted heating curves of all stages are output. If it has not reached the end, return to step S3.

[0013] According to the fully automatic closed-loop control method for the intelligent egg cooker provided by the present invention, the method for obtaining the predicted heating curves at each stage in step S1 includes:

[0014] Establish a heat accumulation model and construct a temperature inversion model based on the parameters of the egg to be cooked, the parameters of the egg cooker, and the desired degree of doneness.

[0015] The current parameters of the egg to be cooked and the desired degree of doneness are input into the temperature inversion model, and the predicted heating curves for each stage are obtained through model calculation.

[0016] The parameters for the eggs to be cooked include the number of eggs, their total weight, and the initial temperature; the parameters for the egg cooker include the rated power of the heating plate and the heat capacity of the pot; and the desired degree of doneness is the yolk coagulation degree set according to the user's taste preference.

[0017] According to the fully automatic closed-loop control method for the intelligent egg cooker provided by the present invention, the process of constructing the temperature inversion model includes:

[0018] S11: Obtain the optimal water temperature, steam temperature, and heating time for each stage of the egg to be cooked to achieve the desired degree of doneness under the fixed egg cooker parameters for n groups of different parameters, and obtain the standard heating curve for each stage through the heat accumulation model.

[0019] S12: Construct a training dataset consisting of n sets of data. Each set of data includes: extracting the desired doneness of eggs under different parameters of the egg cooker under fixed egg cooker parameters, and the standard heating curves for each stage corresponding to the desired doneness.

[0020] S13: Construct a neural network model, including an input layer, hidden layers, and an output layer. Calculate a set of data layer by layer starting from the input layer and pass it forward until a predicted heating curve is output.

[0021] S14: The predicted heating curve is compared with the corresponding standard heating curve by the loss function to obtain the error curve. The error curve includes water temperature error, steam temperature error, water level error and heating time error. The error curve is passed back layer by layer from the output layer and the network parameter gradient is calculated.

[0022] S15: Update the network parameters according to the gradient of the network parameters, and repeat steps S13 to S15 to perform multiple rounds of iteration based on the training dataset to solve for the optimal network parameters.

[0023] S16: Calculate the predicted heating curves for each stage using the trained neural network model.

[0024] According to the fully automatic closed-loop control method for the intelligent egg cooker provided by the present invention, the process of obtaining the standard heating curve in step S11 includes:

[0025] S111: Measure the physical properties of the egg to be cooked at different temperatures and establish a heat conduction model. The physical properties include the thermal conductivity, specific heat capacity, and thermal diffusivity of the egg to be cooked.

[0026] S112: Measure the actual water temperature, steam temperature, and heating time at the current stage, and calculate the heat accumulation value at the current stage using the heat accumulation model.

[0027] S113: Input the current heat accumulation value into the heat conduction model, and predict the temperature field distribution inside the egg to be cooked through the model.

[0028] S114: Determine whether the egg to be cooked has reached the desired level of doneness at the current stage based on the temperature field distribution. If yes, proceed to the next step; otherwise, adjust the water temperature, steam temperature, and heating time, and return to step S112.

[0029] S115: Draw a standard heating curve for the current stage based on the water temperature value, steam temperature value, and heating time value.

[0030] S116: Repeat steps S112 to S115 until standard heating curves for all desired ripeness at each stage are obtained.

[0031] According to the fully automatic closed-loop control method for the intelligent egg cooker provided by the present invention, the current stage heat accumulation value is calculated through the heat accumulation model, and the process of establishing the heat accumulation model includes:

[0032] S41: In the dry state pre-cooking stage, a heat calculation benchmark is established based on the initial temperature of the eggs to be cooked.

[0033] S42: Obtain the target heat accumulation value required for the desired maturity during the heating stage, and preset the water consumption based on the heating time, and preset the target water temperature value and target steam temperature value based on the target heat accumulation value.

[0034] S43: Detect the water level signal and obtain the average value of the actual water volume during the heating stage, calculate the average value of the actual water volume, and calibrate the preset water consumption.

[0035] S44: Stabilize the water temperature and steam temperature values ​​at the target water temperature and target steam temperature values ​​respectively through dynamic temperature calibration.

[0036] S45: Set the cooling time and cooling temperature, heat preservation time and heat preservation temperature of the cooling stage according to the target heat accumulation value.

[0037] According to the fully automatic closed-loop control method for the intelligent egg cooker provided by the present invention, the cooling process in the cooling stage includes:

[0038] S51: Calculate the current total heat accumulation value through the heat accumulation model, and calculate the cooling water volume based on the set cooling time and cooling temperature.

[0039] S52: Determine whether the current total heat accumulation value has reached the target heat accumulation value. If so, start the cooling spray system and spray cooling water according to the cooling water volume to cool the egg.

[0040] According to the fully automatic closed-loop control method of the intelligent egg cooker provided by the present invention, the heat preservation process during the heat preservation stage includes:

[0041] S53: Calculate the current total heat accumulation value through the heat accumulation model, and calculate the heat accumulation value of insulation based on the set insulation time and insulation temperature.

[0042] S54: Determine whether the current total heat accumulation value has reached the heat preservation heat accumulation value. If yes, stop heating; otherwise, continue heating.

[0043] The fully automatic closed-loop control method for the intelligent egg cooker provided by the present invention further includes S6: determining whether the current stage is in the temperature calibration stage based on the heat endpoint; if so, obtaining the heating curve of the temperature calibration stage; calculating the PID parameters corresponding to the heating curve of the temperature calibration stage through a PID control algorithm, and performing PID calculation based on the water temperature signal and the steam temperature signal to dynamically adjust the PID parameters.

[0044] According to the fully automatic closed-loop control method for the intelligent egg cooker provided by the present invention, the process of establishing the PID control algorithm includes:

[0045] S61: Obtain the target water temperature value and target steam temperature value according to the heating curve of the temperature calibration stage, and collect the current water temperature signal and steam temperature signal.

[0046] S62: Calculate the temperature error and error rate of the current water temperature value, steam temperature value and the target water temperature value, steam temperature value.

[0047] S63: Perform PID calculations based on the temperature error and the rate of change of the error to obtain PID parameters including Kp, Ki, and Kd.

[0048] S64: Measure the changes in water temperature and steam temperature in real time and use them as input variables to perform fuzzy PID calculations to obtain the dynamic adjustment values ​​of the PID parameters.

[0049] According to the fully automatic closed-loop control method for the intelligent egg cooker provided by the present invention, the fuzzy PID calculation in step S64 includes:

[0050] S641: Divide the degree of change into levels based on the magnitude of the change in water temperature and the change in steam temperature, and construct a first fuzzy set and a second fuzzy set respectively.

[0051] S642: Establish a fuzzy rule base for the first fuzzy set and the second fuzzy set.

[0052] S643: Input the real-time water temperature change and steam temperature change into the fuzzy rule base to obtain the fuzzy adjustment values ​​of Kp / Ki / Kd.

[0053] S644: Defuzzify the fuzzy adjustment amounts of Kp / Ki / Kd to output the dynamic adjustment amounts of Kp, Ki, and Kd.

[0054] Therefore, compared with the prior art, the present invention has the following beneficial effects:

[0055] 1. This invention divides the egg-cooking process into stages, calculates the heat accumulation of each stage in real time, and determines whether the end point of the stage has been reached. This allows for precise control of the heating process of the egg to be cooked, thereby accurately achieving the user's desired level of doneness.

[0056] 2. This invention predicts the heating curves at each stage through a temperature inversion model. It can predict and output the optimal heating curves at each stage based on different parameters of the eggs to be cooked and the user's desired level of doneness. The corresponding cooking instructions are executed according to the predicted heating curves to achieve highly consistent temperature control and further improve the accuracy of the heat control of the eggs to be cooked.

[0057] 3. In the cooling stage, the present invention calculates and activates the spray system in a timely manner based on the heat accumulation value, which can prevent eggs that have reached the desired doneness from being overcooked due to residual heat and losing their texture; and in the heat preservation stage, it achieves precise heat preservation control by continuously monitoring the heat accumulation value, so that eggs at the desired doneness can be maintained at the best texture for a longer period of time.

[0058] 4. This invention introduces a PID control algorithm and combines it with fuzzy computing during the temperature calibration stage. It can dynamically adjust the PID parameters according to the changes in the real-time collected water temperature signal and steam temperature signal, thereby improving the anti-interference ability and ensuring that the water temperature and steam temperature can reach the target value quickly and stably.

[0059] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0060] Figure 1 This is a schematic diagram of a fully automatic closed-loop control method for an intelligent egg cooker according to the present invention. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0062] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0063] An embodiment of a fully automatic closed-loop control method for an intelligent egg cooker

[0064] See Figure 1 The present invention discloses a fully automatic closed-loop control method for an intelligent egg cooker, comprising:

[0065] S1: Based on the cooking time sequence, the egg cooking process is divided into multiple heating stages, and the predicted heating curves for each stage are obtained according to the parameters of the egg to be cooked, the parameters of the egg cooker, and the desired degree of doneness.

[0066] S2: Output the predicted heating curve in stages according to the cooking sequence. The multi-stage heating is divided into the following stages in sequence: dry state pre-heating stage, heating stage, temperature calibration stage, cooling stage, and heat preservation stage.

[0067] S3: Execute the dry waiting / water addition / heating / cooling commands according to the predicted heating curve of the current stage, and collect water temperature signal, steam temperature signal and water level signal in real time.

[0068] S4: Set the timing endpoint and heat endpoint for each stage, obtain the cooking time of the current stage and generate a heating time signal, and perform heat accumulation calculation based on the water temperature signal, steam temperature signal, water level signal and heating time signal to obtain the heat accumulation value of the current stage.

[0069] The system also includes a boiling threshold, with the time when the steam temperature reaches the boiling threshold as the starting point for the cooking time during the temperature calibration stage, based on the steam temperature signal.

[0070] S5: Determine whether the cooking time of the current stage has reached the end of the timing of the stage. If not, continue timing. If yes, determine whether the heat accumulation value of the current stage has reached the end of the heat of the stage. If it has, return to step S2 until the predicted heating curves of all stages are output. If it has not reached the end, return to step S3.

[0071] Specifically, the egg cooker described in this embodiment achieves dry-state pre-cooking through a spacer structure. The egg steaming rack is fixedly installed on the inner wall of the steaming cavity via its suspension structure, and the lowest point of the steaming rack is set at a height higher than the expected maximum working water level of the steaming cavity. This spacer structure creates a physical separation between the eggs to be cooked placed on the steaming rack and the water surface at the bottom of the steaming cavity, thereby achieving dry-state isolation during the pre-cooking period.

[0072] In this embodiment, the method for obtaining the predicted heating curves at each stage in step S1 includes:

[0073] Establish a heat accumulation model and construct a temperature inversion model based on the parameters of the egg to be cooked, the parameters of the egg cooker, and the desired degree of doneness.

[0074] The temperature inversion model is constructed using a neural network algorithm. It can predict and output the optimal heating curve for each stage based on different parameters of the eggs to be cooked and the user's desired level of doneness. By executing the corresponding heating commands according to the predicted heating curve, it can achieve highly consistent temperature control and further improve the accuracy of the heating control of the eggs to be cooked.

[0075] The current parameters of the egg to be cooked and the desired degree of doneness are input into the temperature inversion model, and the predicted heating curves for each stage are obtained through model calculation.

[0076] The parameters for the eggs to be cooked include the number of eggs, their total weight, and the initial temperature; the parameters for the egg cooker include the rated power of the heating plate and the heat capacity of the pot; and the desired degree of doneness is the yolk coagulation degree set according to the user's taste preference.

[0077] Specifically, in this embodiment, the eggs to be cooked are classified into soft-boiled eggs, yolk-filled eggs, semi-soft-boiled eggs, tender eggs, golden-sand eggs, hard-boiled eggs, and firm eggs according to the desired degree of doneness from low to high.

[0078] In this embodiment, the construction process of the temperature inversion model includes:

[0079] S11: Obtain the optimal water temperature, steam temperature, and heating time for each stage of the egg to be cooked to achieve the desired degree of doneness under the fixed egg cooker parameters for n groups of different parameters, and obtain the standard heating curve for each stage through the heat accumulation model.

[0080] S12: Construct a training dataset consisting of n sets of data. Each set of data includes: extracting the desired doneness of eggs under different parameters of the egg cooker under fixed egg cooker parameters, and the standard heating curves for each stage corresponding to the desired doneness.

[0081] S13: Construct a neural network model, including an input layer, hidden layers, and an output layer. Calculate a set of data layer by layer starting from the input layer and pass it forward until a predicted heating curve is output.

[0082] S14: The predicted heating curve is compared with the corresponding standard heating curve by the loss function to obtain the error curve. The error curve includes water temperature error, steam temperature error, water level error and heating time error. The error curve is passed back layer by layer from the output layer and the network parameter gradient is calculated.

[0083] S15: Update the network parameters according to the gradient of the network parameters, and repeat steps S13 to S15 to perform multiple rounds of iteration based on the training dataset to solve for the optimal network parameters.

[0084] S16: Calculate the predicted heating curves for each stage using the trained neural network model.

[0085] Specifically, in step S13 of this embodiment, the number of neurons in the input layer of the neural network model is equal to the number of different parameters of the eggs to be cooked that reach each desired degree of doneness; the number of neurons in each hidden layer is configured according to the desired degree of doneness, and an activation function is set to fit the nonlinear relationship; the output layer is used to output the predicted heating curve corresponding to one neuron.

[0086] The hidden layers can be one or more layers, and the number of neurons in each layer can be determined according to the dimension of the input features. Common configurations include 64, 128, or 256 neurons. The activation function of the hidden layers uses ReLU or a variant thereof, which can accelerate convergence while maintaining nonlinearity.

[0087] Specifically, in step S13 of this embodiment, the output calculation matrix of the l-th layer during forward propagation is:

[0088] a (l) =f(W (l) a (l-1) +b (l) (1)

[0089] Among them, a (l) The output vector calculated for all neurons in layer l, whose dimension is equal to the number of neurons in that layer; f() is the activation function; W (l) W is the weight matrix of all neurons in the l-th layer. (l) a (l-1) b is the weighted sum of all its inputs for the current layer; (l) is the bias vector for all neurons in the l-th layer.

[0090] Specifically, in step S14 of this embodiment, the mean squared error (MSE) is used to measure the temperature error / water level error / heating time error. The network parameters include the connection weights between neurons and bias compensation. The gradient calculation matrix of the loss function with respect to the weights of the l-th layer during backpropagation is as follows:

[0091]

[0092] in, Let L be the gradient matrix of the loss function L with respect to the l-th layer, connecting the weights of all neurons from the (l-1)-th layer to the l-th layer; a (l-1) The output vector calculated for all neurons in layer (l-1); Let be the error vector of the l-th layer, which measures the responsibility of each neuron in that layer for the final loss.

[0093] Specifically, in this embodiment, the loss function is calculated with respect to the bias gradient of the l-th layer as follows:

[0094]

[0095] Specifically, in step S15 of this embodiment, the weights are updated using gradient descent as the optimization strategy based on the weight gradient calculated according to formula (2). The calculation formula is as follows:

[0096]

[0097] Among them, W (l) new W is the updated weight matrix for the l-th layer; (l) old η is the weight matrix of the l-th layer before the update; η is the learning rate, which controls the step size of each parameter update.

[0098] Similarly, the bias gradient calculated according to formula (3) is updated using gradient descent as the optimization strategy. The calculation formula is as follows:

[0099]

[0100] Among them, b (l) new b is the updated weight matrix for the l-th layer; (l) old This is the weight matrix of the l-th layer before the update.

[0101] Specifically, this embodiment employs a decay strategy to control the learning rate step size. A fixed learning rate would cause the algorithm to oscillate around the minimum or even diverge, while a small learning rate would result in slow convergence. In practical applications, exponential decay is often used, with a large learning rate used initially to decrease rapidly, followed by a reduction in the learning rate to improve stability.

[0102] Specifically, in order to balance computational efficiency and stability, this embodiment adopts a mini-batch gradient descent strategy in practical applications, which divides the n sets of training data into multiple mini-batches. This batch training method is faster than batch gradient descent using all the data and more stable than stochastic gradient descent that updates data one by one.

[0103] Specifically, in step S16 of this embodiment, the current parameters of the egg to be cooked and the desired degree of doneness are input into the input layer of the trained neural network model. Through forward propagation calculation, the output layer outputs the predicted heating curves for each stage.

[0104] In this embodiment, the process of obtaining the standard heating curve in step S11 includes:

[0105] S111: Measure the physical properties of the egg to be cooked at different temperatures and establish a heat conduction model. The physical properties include the thermal conductivity, specific heat capacity, and thermal diffusivity of the egg to be cooked.

[0106] Specifically, in this embodiment, the thermal conductivity of the egg to be cooked is used to determine the rate at which heat is transferred from the external hot water to the interior of the egg, including the thermal conductivity of the egg white, yolk, and shell. The specific heat capacity of the egg reflects its ability to store heat; the higher the specific heat capacity, the more heat is required to raise the temperature by the same amount. Knowing the specific heat capacity of the egg is crucial for calculating the amount of heat needed to heat the yolk to the set coagulation level. The thermal diffusivity reflects the rate at which heat diffuses within the egg; the higher the thermal diffusivity, the faster the temperature inside and outside the egg reaches a uniform level.

[0107] S112: Measure the actual water temperature, steam temperature, and heating time at the current stage, and calculate the heat accumulation value at the current stage using the heat accumulation model.

[0108] S113: Input the current heat accumulation value into the heat conduction model, and predict the temperature field distribution inside the egg to be cooked through the model.

[0109] S114: Determine whether the egg to be cooked has reached the desired level of doneness at the current stage based on the temperature field distribution. If yes, proceed to the next step; otherwise, adjust the water temperature, steam temperature, and heating time, and return to step S112.

[0110] S115: Draw a standard heating curve for the current stage based on the water temperature value, steam temperature value, and heating time value.

[0111] S116: Repeat steps S112 to S115 until standard heating curves for all desired ripeness at each stage are obtained.

[0112] Specifically, in order to ensure the accuracy of the standard heating curve as a reference, the physical properties of the eggs to be cooked at different temperatures, the optimal water temperature, steam temperature, and heating time at each stage required to reach each desired level of doneness are all precisely measured using standard laboratory methods. At the same time, to reduce measurement errors, multiple measurements can be taken and the average value can be obtained.

[0113] In this embodiment, the current stage heat accumulation value is calculated using the heat accumulation model, and the process of establishing the heat accumulation model includes:

[0114] S41: In the dry state pre-cooking stage, a heat calculation benchmark is established based on the initial temperature of the eggs to be cooked.

[0115] S42: Obtain the target heat accumulation value required for the desired maturity during the heating stage, and preset the water consumption based on the heating time, and preset the target water temperature value and target steam temperature value based on the target heat accumulation value.

[0116] S43: Detect the water level signal and obtain the average value of the actual water volume during the heating stage, calculate the average value of the actual water volume, and calibrate the preset water consumption.

[0117] S44: Stabilize the water temperature and steam temperature values ​​at the target water temperature and target steam temperature values ​​respectively through dynamic temperature calibration.

[0118] S45: Set the cooling time and cooling temperature, heat preservation time and heat preservation temperature of the cooling stage according to the target heat accumulation value.

[0119] Specifically, in step S3 of this embodiment, the heating unit is controlled to heat the steaming water in the egg cooker, the steam temperature above the evaporation plate in the egg cooker is monitored by the first temperature sensor to generate a steam temperature signal, the water temperature of the steaming water is monitored by the second temperature sensor to generate a water temperature signal, and the water level of the steaming water is monitored by the liquid level detection sensor to generate a water level signal.

[0120] The process of acquiring the heating time signal includes: setting a boiling determination threshold T_boil and a target heating time T_cook for the current stage; determining whether the steam temperature has reached the boiling determination threshold T_boil and continuing for a set boiling time; if so, determining that the water is boiling and starting the heating time timing; otherwise, stopping the heating time timing and repeating the process until the heating time reaches the target heating time T_cook.

[0121] Specifically, taking the cooking of soft-boiled eggs as an example, the boiling threshold T_boil can be set to 99℃ or adjusted according to the local boiling point, and the boiling time can be set to 5 seconds, while the target heating time T_cook can be set to 6 minutes.

[0122] In this embodiment, the cooling process during the cooling phase includes:

[0123] S51: Calculate the current total heat accumulation value through the heat accumulation model, and calculate the cooling water volume based on the set cooling time and cooling temperature.

[0124] S52: Determine whether the current total heat accumulation value has reached the target heat accumulation value. If so, start the cooling spray system and spray cooling water according to the cooling water volume to cool the egg.

[0125] In another embodiment, a cooling command is generated when the heating timer reaches the target heating duration T_cook, and the heating unit is controlled to stop heating. A cooling actuator is activated within a preset delay time Δt to execute the cooling command, and the cooling time is T_cool. Taking the cooking of soft-boiled eggs as an example, the preset delay time Δt is set to an extremely short 100 milliseconds for electrical safety isolation; the cooling time T_cool is set to 90 seconds.

[0126] In this embodiment, the heat preservation process during the heat preservation stage includes:

[0127] S53: Calculate the current total heat accumulation value through the heat accumulation model, and calculate the heat accumulation value of insulation based on the set insulation time and insulation temperature.

[0128] S54: Determine whether the current total heat accumulation value has reached the heat preservation heat accumulation value. If yes, stop heating; otherwise, continue heating.

[0129] Specifically, in this embodiment, taking the cooking of soft-boiled eggs as an example, the heat preservation temperature can be set to 55℃ to 65℃.

[0130] In this embodiment, it further includes S6: determining whether the current stage is in the temperature calibration stage based on the heat endpoint; if so, obtaining the temperature calibration stage heating curve; calculating the PID parameters corresponding to the temperature calibration stage heating curve through a PID control algorithm, and performing PID calculations based on the water temperature signal and steam temperature signal to dynamically adjust the PID parameters.

[0131] In this embodiment, the process of establishing the PID control algorithm includes:

[0132] S61: Obtain the target water temperature value and target steam temperature value according to the heating curve of the temperature calibration stage, and collect the current water temperature signal and steam temperature signal.

[0133] S62: Calculate the temperature error and error rate of the current water temperature value, steam temperature value and the target water temperature value, steam temperature value.

[0134] S63: Perform PID calculations based on the temperature error and the rate of change of the error to obtain PID parameters including Kp, Ki, and Kd.

[0135] S64: Measure the changes in water temperature and steam temperature in real time and use them as input variables to perform fuzzy PID calculations to obtain the dynamic adjustment values ​​of the PID parameters.

[0136] Specifically, the calculation process of the initial PID parameters in this embodiment includes:

[0137] S631: A PID controller is used and set to pure proportional (P) control, that is, the integral time constant Ti = ∞ and the derivative time constant Td = 0.

[0138] S632: Gradually increase the proportional gain Kp from small to large until the system first exhibits sustained, constant-amplitude oscillations, and record the proportional gain Ku and oscillation period Tu at this point.

[0139] S633: Calculate the initial PID parameters based on the proportional gain Ku and the oscillation period Tu, where the proportional gain Kp = 0.6 * Ku; the integral coefficient Ki = Kp / Ti, the integral time constant Ti = 0.5 * Tu; the derivative coefficient Kd = Kp * Td, and the derivative time constant Td = 0.125 * Tu.

[0140] In this embodiment, the fuzzy PID calculation in step S64 includes:

[0141] S641: Divide the degree of change into levels based on the magnitude of the change in water temperature and the change in steam temperature, and construct a first fuzzy set and a second fuzzy set respectively.

[0142] S642: Establish a fuzzy rule base for the first fuzzy set and the second fuzzy set.

[0143] S643: Input the real-time water temperature change and steam temperature change into the fuzzy rule base to obtain the fuzzy adjustment values ​​of Kp / Ki / Kd.

[0144] S644: Defuzzify the fuzzy adjustment amounts of Kp / Ki / Kd to output the dynamic adjustment amounts of Kp, Ki, and Kd.

[0145] Specifically, the degree of change described in this embodiment, from smallest to largest, includes no change, slight, medium, and significant. The first fuzzy set and the second fuzzy set are set as: {no change, slight, medium, significant}.

[0146] The fuzzy rule base includes at least a first rule and a second rule. The first rule is: if the change in water temperature is significant and the change in steam temperature is not significant, then ΔKp is significant. At the same time, to avoid saturation caused by instantaneous deviation, ΔKi is small. Meanwhile, to limit the differential action, ΔKd is moderate.

[0147] The second rule is: if the change in water temperature is unchanged and the change in steam temperature is significant, then ΔKd is taken as moderate and ΔKi is taken as moderate. In this case, the system is sensitive to the differential signal, and the value of ΔKd has a greater impact on the system, so it is taken as small.

[0148] It also includes a third rule, which states that if the changes in water temperature and steam temperature are both small or non-existent, the system has good steady-state performance. In this case, ΔKp is taken as small and ΔKi is taken as medium. At the same time, in order to avoid the system from oscillating near the set value, ΔKd is taken as medium.

[0149] Specifically, in step S644 of this embodiment, the centroid method is used for defuzzification. The membership degree of the water temperature change is set as follows: no change, small, medium, and significant, corresponding to 0.3, 0.4, 0.5, and 0.7, respectively; the membership degree of the steam temperature change is set as follows: no change, small, medium, and significant, corresponding to 0.2, 0.4, 0.6, and 0.8, respectively.

[0150] The formulas for calculating the dynamic adjustment amounts of Kp, Ki, and Kd are as follows:

[0151]

[0152] Where, μ i Z represents the membership degree, indicating the weight of the output quantity ΔK in the fuzzy set. i is the universe of discourse value, representing the actual PID value corresponding to the i-th point in the fuzzy set {no change, small, medium, significant}; n is the number of points considered in the output universe of discourse.

[0153] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0154] The above embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of protection of the present invention. Any non-substantial changes and substitutions made by those skilled in the art based on the present invention shall fall within the scope of protection claimed by the present invention.

Claims

1. A fully automatic closed-loop control method for an intelligent egg cooker, characterized in that, include: S1: Based on the cooking time sequence, the egg cooking process is divided into multiple heating stages, and the predicted heating curves for each stage are obtained according to the parameters of the egg to be cooked, the parameters of the egg cooker, and the desired degree of doneness. S2: Output the predicted heating curve in stages according to the cooking sequence; wherein, the multi-stage heating is divided into the dry state pre-heating stage, the heating stage, the temperature calibration stage, the cooling stage, and the heat preservation stage in sequence. S3: Execute the dry waiting / water addition / heating / cooling commands according to the predicted heating curve of the current stage, and collect water temperature signal, steam temperature signal and water level signal in real time; S4: Set the timing endpoint and heat endpoint for each stage, obtain the cooking time of the current stage and generate a heating time signal, and perform heat accumulation calculation based on the water temperature signal, steam temperature signal, water level signal and heating time signal to obtain the heat accumulation value of the current stage. It also includes a boiling threshold, and the time when the steam temperature reaches the boiling threshold is used as the starting point for the cooking time of the temperature calibration stage based on the steam temperature signal. S5: Determine whether the cooking time of the current stage has reached the end of the timing of the stage. If not, continue timing. If yes, determine whether the heat accumulation value of the current stage has reached the end of the heat of the stage. If it has, return to step S2 until the predicted heating curves of all stages are output. If it has not reached the end, return to step S3.

2. The fully automatic closed-loop control method for the intelligent egg cooker according to claim 1, characterized in that, The methods for obtaining the predicted heating curves in each stage of step S1 include: Establish a heat accumulation model and construct a temperature inversion model based on the parameters of the egg to be cooked, the parameters of the egg cooker, and the desired degree of doneness; Input the current parameters of the egg to be cooked and the desired degree of doneness into the temperature inversion model, and obtain the predicted heating curves for each stage through model calculation; The parameters for the eggs to be cooked include the number of eggs, their total weight, and the initial temperature; the parameters for the egg cooker include the rated power of the heating plate and the heat capacity of the pot; and the desired degree of doneness is the yolk coagulation degree set according to the user's taste preference.

3. The fully automatic closed-loop control method for the intelligent egg cooker according to claim 2, characterized in that, The construction process of the temperature inversion model includes: S11: Obtain the optimal water temperature, steam temperature, and heating time for each stage of the eggs to be cooked to the desired degree of doneness under the fixed parameters of the egg cooker, and obtain the standard heating curve for each stage through the heat accumulation model. S12: Construct a training dataset including n sets of data. Each set of data includes: extracting the desired doneness of eggs under different parameters of the egg cooker under fixed egg cooker parameters, and the standard heating curves for each stage corresponding to the desired doneness. S13: Construct a neural network model, including an input layer, a hidden layer and an output layer. Calculate a set of data layer by layer starting from the input layer and pass it forward until a predicted heating curve is output. S14: The predicted heating curve is compared with the corresponding standard heating curve by the loss function to obtain the error curve. The error curve includes water temperature error, steam temperature error, water level error and heating time error. The error curve is passed back layer by layer from the output layer and the gradient of network parameters is calculated. S15: Update the network parameters according to the network parameter gradient, and repeat steps S13 to S15 to perform multiple rounds of iteration based on the training dataset to solve for the optimal network parameters. S16: Calculate the predicted heating curves for each stage using the trained neural network model.

4. The fully automatic closed-loop control method for the intelligent egg cooker according to claim 3, characterized in that, The process of obtaining the standard heating curve in step S11 includes: S111: Measure the physical properties of the egg to be cooked at different temperatures and establish a heat conduction model. The physical properties include the thermal conductivity, specific heat capacity, and thermal diffusivity of the egg to be cooked. S112: Measure the actual water temperature, steam temperature and heating time at the current stage, and calculate the heat accumulation value at the current stage using the heat accumulation model; S113: Input the current heat accumulation value into the heat conduction model, and predict the current temperature field distribution inside the egg to be cooked through the model; S114: Determine whether the egg to be cooked has reached the desired degree of doneness at the current stage based on the temperature field distribution. If yes, proceed to the next step; otherwise, adjust the water temperature, steam temperature, and heating time and return to step S112. S115: Draw a standard heating curve for the current stage based on the water temperature value, steam temperature value, and heating time value; S116: Repeat steps S112 to S115 until standard heating curves for all desired ripeness at each stage are obtained.

5. The fully automatic closed-loop control method for the intelligent egg cooker according to claim 4, characterized in that: The current stage heat accumulation value is calculated using the heat accumulation model, and the process of establishing the heat accumulation model includes: S41: In the dry state pre-cooking stage, a heat calculation benchmark is established based on the initial temperature of the eggs to be cooked; S42: Obtain the target heat accumulation value required for the desired maturity during the heating stage, and preset the water consumption according to the heating time, and preset the target water temperature value and target steam temperature value according to the target heat accumulation value; S43: Detect the water level signal and obtain the average value of the actual water volume during the heating stage, calculate the average value of the actual water volume and calibrate the preset water consumption; S44: Stabilize the water temperature and steam temperature values ​​at the target water temperature and target steam temperature values ​​respectively through dynamic temperature calibration; S45: Set the cooling time and cooling temperature, heat preservation time and heat preservation temperature of the cooling stage according to the target heat accumulation value.

6. The fully automatic closed-loop control method for the intelligent egg cooker according to claim 5, characterized in that, The cooling process during the cooling phase includes: S51: Calculate the current total heat accumulation value through the heat accumulation model, and calculate the cooling water volume based on the set cooling time and cooling temperature; S52: Determine whether the current total heat accumulation value has reached the target heat accumulation value. If so, start the cooling spray system and spray cooling water according to the cooling water volume to cool the egg.

7. The fully automatic closed-loop control method for the intelligent egg cooker according to claim 6, characterized in that, The insulation process during the insulation stage includes: S53: Calculate the current total heat accumulation value through the heat accumulation model, and calculate the heat accumulation value of insulation based on the set insulation time and insulation temperature; S54: Determine whether the current total heat accumulation value has reached the heat preservation heat accumulation value. If yes, stop heating; otherwise, continue heating.

8. The fully automatic closed-loop control method for the intelligent egg cooker according to claim 1, characterized in that, Also includes: S6: Determine whether the current stage is in the temperature calibration stage based on the current heat accumulation value. If so, obtain the heating curve for the temperature calibration stage. Calculate the PID parameters corresponding to the heating curve for the temperature calibration stage using a PID control algorithm, and perform PID calculations based on the water temperature signal and steam temperature signal to dynamically adjust the PID parameters.

9. The fully automatic closed-loop control method for the intelligent egg cooker according to claim 8, characterized in that, The process of establishing the PID control algorithm includes: S61: Obtain the target water temperature value and target steam temperature value according to the heating curve of the temperature calibration stage, and collect the current water temperature signal and steam temperature signal; S62: Calculate the temperature error and error rate of the current water temperature value, steam temperature value and the target water temperature value, steam temperature value; S63: Perform PID calculations based on the temperature error and the rate of change of error to obtain PID parameters including Kp, Ki, and Kd; S64: Measure the changes in water temperature and steam temperature in real time and use them as input variables to perform fuzzy PID calculations to obtain the dynamic adjustment values ​​of the PID parameters.

10. The fully automatic closed-loop control method for the intelligent egg cooker according to claim 9, characterized in that, The fuzzy PID calculation in step S64 includes: S641: Classify the degree of change according to the magnitude of the change in water temperature and the change in steam temperature, and construct a first fuzzy set and a second fuzzy set respectively; S642: Establish a fuzzy rule base for the first fuzzy set and the second fuzzy set; S643: Input the real-time water temperature change and steam temperature change into the fuzzy rule base to obtain the fuzzy adjustment values ​​of Kp / Ki / Kd; S644: Defuzzify the fuzzy adjustment amounts of Kp / Ki / Kd to output the dynamic adjustment amounts of Kp, Ki, and Kd.

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