A control system and circuit for precise temperature adjustment of an electromagnetic oven

Abstract

The application discloses a kind of control system and circuit of electromagnetic oven temperature precision regulation, including main control module, multi-source temperature detection module, temperature data fusion processing module, temperature prediction analysis module, power regulation module and feedback correction module, the main control module is electrically connected with multi-source temperature detection module, temperature data fusion processing module, temperature prediction analysis module, power regulation module and feedback correction module respectively, the cooperation of the present application through multi-source temperature detection module, temperature data fusion processing module, temperature prediction analysis module, power regulation module and feedback correction module, reach multi-source detection elimination lag, fusion identification distinguish residual heat, prediction regulation early intervention, feedback correction optimization precision whole-link cooperation, temperature is controlled in ±2 ℃, effectively reduce overshoot amplitude, shorten delay time, realize the precision control of electromagnetic oven temperature.

Description

Technical Field

[0001] This invention belongs to the field of induction cooker technology, specifically relating to a control system and circuit for precise temperature adjustment of an induction cooker. Background Technology

[0002] Temperature control in an induction cooker typically relies on the coordinated operation of a temperature detection module and a power regulation module. The temperature detection module is usually installed below the induction cooker panel or near the coil, indirectly sensing the temperature of the bottom of the cookware through contact with the panel. The power regulation module adjusts the heating power based on the difference between the set temperature and the actual temperature from the temperature detection module using pulse width modulation.

[0003] However, the temperature detection module's sensor, which indirectly senses temperature through the panel, suffers from a lag. For example, if the cookware is locally overheated due to excessive heat, the sensor needs to conduct heat through the panel before it can send a signal. At this point, the power adjustment module is already lagging, which can easily lead to temperature overshoot, i.e., exceeding the set value. After the heat is turned off, the residual heat of the panel can cause the sensor to misjudge that it is still at a high temperature, resulting in a delay in restarting the heating process. This affects the accuracy of the induction cooker's temperature regulation. Therefore, we need to propose a control system and circuit for precise temperature regulation of induction cookers to solve the above-mentioned problems and effectively improve the temperature regulation accuracy of induction cookers. Summary of the Invention

[0004] The purpose of this invention is to provide a control system and circuit for precise temperature regulation of an induction cooker, which can effectively improve the temperature regulation accuracy of the induction cooker and solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A control system for precise temperature regulation of an induction cooker, comprising:

[0007] Multi-source temperature detection module: Simultaneously collects multi-dimensional temperature data from the actual temperature of the cookware, the panel temperature, and the ambient temperature, providing raw data for accurate temperature measurement;

[0008] Temperature data fusion processing module: used to process multi-dimensional temperature data, fuse the actual temperature of the cookware, and identify the current system status;

[0009] Temperature prediction and analysis module: Based on the current temperature trend, it predicts future temperatures in advance through a prediction model, calculates the prediction deviation, and provides advance allowance for power regulation;

[0010] Power regulation module: dynamically adjusts heating power based on prediction deviation and real-time temperature;

[0011] Feedback correction module: used to correct prediction model errors and ensure the stability of long-term adjustment;

[0012] Main control module: used to coordinate data interaction and timing control of various modules. The main control module is electrically connected to the multi-source temperature detection module, temperature data fusion processing module, temperature prediction and analysis module, power regulation module and feedback correction module respectively.

[0013] Preferably, the multi-source temperature detection module includes a sensing unit and a data preprocessing unit connected to the sensing unit. The sensing unit includes a contact sensor, an infrared sensor, a panel sensor, and an environmental sensor. The contact sensor uses a PT1000 platinum resistance thermometer, which, together with a high-precision operational amplifier, forms a bridge temperature measurement circuit to suppress common-mode interference. The infrared sensor uses an MLX90614, which directly outputs a digital temperature signal through an I²C interface without additional conditioning. The environmental sensor uses an NTC thermistor, which, together with an RC filter circuit and an LM358 operational amplifier, forms a voltage follower to filter out high-frequency noise.

[0014] Preferably, the data preprocessing unit performs the following preprocessing steps:

[0015] A1. After receiving the acquisition command from the main control module, it synchronously starts acquiring data from contact sensors, infrared sensors, panel sensors, and environmental sensors.

[0016] A2. Outliers are removed using the 3σ criterion, and then the infrared sensor data is filtered by moving average.

[0017] A3. Pack the pre-processed multi-channel temperature data and send it to the main control module and the temperature data fusion processing module.

[0018] Preferably, the temperature data fusion processing module includes a multi-source data fusion unit and a state recognition unit. When the multi-source data fusion unit performs multi-source fusion, it makes predictions by calculating prior estimates and updates them by combining observations to calculate posterior estimates, and outputs the fused true temperature of the cookware.

[0019] Preferably, the status recognition unit first extracts the features of the temperature difference between the cookware and the panel, the temperature difference between the cookware and the environment, and the temperature change rate, and then makes a decision tree judgment based on the features. Finally, it transmits and sends the fused temperature and status label.

[0020] Preferably, the process for handling prediction deviations in the temperature prediction and analysis module is as follows:

[0021] B1. Input the fused cookware temperature and current status label, extract the rate of change of the temperature sequence, cumulative temperature rise value, and status label, and construct the input vector X;

[0022] B2. Based on the input vector X, a prediction model is used to predict and output the predicted temperature of the cookware in the next 3-5 seconds. The prediction model uses a 3-layer LSTM network.

[0023] B3. Calculate the prediction deviation and real-time deviation based on the temperature prediction value;

[0024] B4. Transmit the predicted temperature, predicted deviation, and real-time deviation to the main control module and the power regulation module.

[0025] Preferably, the process of dynamic heating power adjustment by the power adjustment module is as follows:

[0026] C1. Dynamically allocate weights based on prediction deviation, real-time deviation, and current status label;

[0027] C2. Preset 10 power levels, and determine the power adjustment direction and amplitude based on the total deviation;

[0028] C3. Convert the power level to PWM duty cycle;

[0029] C4. The PWM signal is output through the TIM timer of the main control module to control the conduction time of the IGBT switching transistor and adjust the power of the heating coil.

[0030] C5. Send the actual output PWM duty cycle to the main control module and the feedback correction module.

[0031] Preferably, the error correction process of the feedback correction module is as follows:

[0032] D1. Calculate the prediction error and control error based on the predicted temperature, actual fusion temperature, and set temperature;

[0033] D2. Calculate the PID correction value based on the prediction error and control error;

[0034] D3. Error correction is performed using Kalman filtering variance;

[0035] D4. Send the corrected LSTM weights and Kalman filter covariance parameters to the main control module to update the model parameters of the prediction and fusion modules.

[0036] Preferably, the main control module performs the following control steps:

[0037] E1. After powering on, initialize each module, set the timing cycle, and load the preset temperature.

[0038] E2. Self-check the communication status of each module; if an abnormality is detected, an alarm will be triggered.

[0039] E3. Send a data acquisition command to the multi-source temperature detection module every 10ms and synchronously receive cookware temperature, panel temperature and ambient temperature data.

[0040] E4. Forward the raw temperature data to the temperature data fusion module; receive the fused real cookware temperature and forward it to the prediction module and feedback module;

[0041] E5. Receive the prediction deviation and real-time temperature deviation from the prediction module, and send a power adjustment command to the power adjustment module; receive the correction parameters from the feedback module, and update the parameters of the prediction model and fusion algorithm.

[0042] Based on the above description of a control system for precise temperature adjustment of an induction cooker, the present invention also provides a control circuit for precise temperature adjustment of an induction cooker, comprising: a main control circuit, wherein the main control circuit includes a main control chip of model STM32F103C8T6, and the main control chip includes a crystal oscillator circuit and a reset circuit;

[0043] A multi-source temperature detection circuit is provided, which uses a DS18B20 temperature sensor and is installed below the induction cooker panel or near the coil. The sensor is connected to the GPIO pin of the main control chip via a single bus.

[0044] The power regulation circuit uses the main control chip to output a PWM signal through a timer to control the conduction and cutoff of the MOSFET, thereby adjusting the heating power of the induction cooker coil.

[0045] Human-computer interaction circuits use OLED or LCD modules via I 2 Communication interfaces such as C or SPI are connected to the main control chip to display information such as the current temperature and the set temperature.

[0046] The auxiliary circuit uses an AC-DC power module to convert the mains power into a suitable DC voltage, and then uses an LDO voltage regulator chip to provide a stable voltage to the main control chip.

[0047] The main control circuit is electrically connected to the multi-source temperature detection circuit, the power regulation circuit, the human-machine interaction circuit, and the auxiliary circuit.

[0048] The present invention provides a control system and circuit for precise temperature adjustment of an induction cooker, which has the following advantages compared with the prior art:

[0049] 1. This invention achieves full-link synergy through the cooperation of a multi-source temperature detection module, a temperature data fusion processing module, a temperature prediction and analysis module, a power adjustment module, and a feedback correction module. This enables multi-source detection to eliminate lag, fusion identification to distinguish residual heat, prediction and adjustment to intervene in advance, and feedback correction to optimize accuracy. The temperature is controlled within ±2℃, effectively reducing overshoot and shortening delay time, thus achieving precise temperature control of the induction cooker.

[0050] 2. This invention combines contact and non-contact sensors to simultaneously acquire the actual temperature of the cookware, the panel temperature, and the ambient temperature, avoiding the lag or misjudgment of a single sensor.

[0051] 3. This invention distinguishes between the actual heating state of the cookware and the residual heat state of the panel by comparing the difference and rate of change between the cookware temperature, panel temperature and ambient temperature based on a decision tree. If it is detected that the cookware temperature has dropped to near the ambient temperature, but the panel temperature is still high, it is determined to be the residual heat period, thus avoiding false triggering and restarting of the sensor due to the high panel temperature.

[0052] 4. This invention uses Kalman filtering to fuse contact and non-contact data, eliminating the hysteresis of a single sensor. Decision trees distinguish between waste heat and actual heating, solving the problem of misjudgment. LSTM predicts future temperature, and PID calculates power adjustment in advance, avoiding the overshoot problem of traditional adjustment only when the temperature is reached. Attached Figure Description

[0053] Figure 1 A system block diagram of the control system according to an embodiment of the present invention is shown;

[0054] Figure 2 A flowchart of the preprocessing process of the data preprocessing unit according to an embodiment of the present invention is shown;

[0055] Figure 3 A flowchart illustrating dynamic heating power adjustment by the power adjustment module according to an embodiment of the present invention is shown;

[0056] Figure 4 A circuit diagram of the main control circuit according to an embodiment of the present invention is shown. Detailed Implementation

[0057] The technical solutions of 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. The specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention. 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.

[0058] This invention provides, for example Figure 1-3 The control system for precise temperature adjustment of an induction cooker shown includes a main control module, a multi-source temperature detection module, a temperature data fusion processing module, a temperature prediction and analysis module, a power adjustment module, and a feedback correction module. The main control module is electrically connected to the multi-source temperature detection module, the temperature data fusion processing module, the temperature prediction and analysis module, the power adjustment module, and the feedback correction module, respectively.

[0059] The multi-source temperature detection module simultaneously collects multi-dimensional temperature data of the actual temperature of the cookware, the panel temperature, and the ambient temperature, providing the original basis for accurate temperature measurement.

[0060] The multi-source temperature detection module includes a sensing unit and a data preprocessing unit connected to the sensing unit. The sensing unit includes a contact sensor, an infrared sensor, a panel sensor, and an environmental sensor. The contact sensor uses a PT1000 platinum resistance thermometer (accuracy ±0.1℃, range -50~200℃), and is paired with a high-precision operational amplifier (such as INA2128) to form a bridge temperature measurement circuit to suppress common-mode interference. The infrared sensor uses an MLX90614 (response time <0.1 seconds, accuracy ±0.5℃), which directly outputs a digital temperature signal through an I²C interface without additional conditioning. The environmental sensor uses an NTC thermistor (B value 3950), and is paired with an RC filter circuit (10kΩ resistor + 100nF capacitor) and an LM358 operational amplifier to form a voltage follower to filter out high-frequency noise.

[0061] The data preprocessing unit performs the following preprocessing steps:

[0062] A1. After receiving the acquisition command from the main control module, it synchronously starts acquiring data from contact sensors, infrared sensors, panel sensors, and environmental sensors.

[0063] A2. Outlier removal is performed using the 3σ criterion, and then the infrared sensor data is filtered by moving average (e.g., taking the average of the three most recent data) to reduce fluctuations caused by oil fumes and light interference. The 3σ criterion is determined as follows: if the data exceeds the mean ± 3 times the standard deviation, it is judged as an anomaly and replaced with the valid value of the previous moment.

[0064] A3. Pack the preprocessed multi-channel temperature data and send it to the main control module and the temperature data fusion processing module;

[0065] By combining contact and non-contact sensors, the actual temperature of the cookware, the panel temperature, and the ambient temperature can be obtained simultaneously, avoiding the lag or misjudgment of a single sensor.

[0066] The temperature data fusion processing module is used to process multi-dimensional temperature data, fuse the actual temperature of the cookware, and identify the current system status.

[0067] The temperature data fusion processing module includes a multi-source data fusion unit and a state recognition unit. When the multi-source data fusion unit performs multi-source fusion, it makes a prediction by calculating prior estimates and updates by combining observation values ​​to calculate posterior estimates, and outputs the fused true temperature of the cookware.

[0068] For example, assume that the temperature change of the cookware follows a linear model. ,in Let k be the temperature at time k. The sampling interval is 10ms. For the rate of temperature change, This is process noise (Gaussian distribution);

[0069] ,in The observed value is the actual temperature. For contact cookware temperature, Infrared cookware temperature, H represents the panel temperature, and H represents the observation matrix (set according to sensor weights). To observe noise;

[0070] The status recognition unit first extracts the features of the temperature difference between the cookware and the panel, the temperature difference between the cookware and the environment, and the temperature change rate, and then makes a decision tree judgment based on the features. Finally, it transmits and sends the fused temperature and status labels.

[0071] The formula for calculating the temperature difference between the cookware and the cooktop is: ,in, This refers to the actual temperature of the cookware. For panel temperature, This refers to the temperature difference between the cookware and the cooktop.

[0072] The formula for calculating the temperature difference between the cookware and the environment is: ,in, This refers to the temperature difference between the cookware and the environment. This refers to the actual temperature of the cookware. Ambient temperature;

[0073] The formula for calculating the rate of temperature change is: ,in, For the rate of temperature change, Let k be the temperature of the cookware at time k. Let k be the temperature of the cookware at time k-1;

[0074] The following situations exist when making decisions using a decision tree:

[0075] like When the temperature is greater than 2℃ and r is greater than 0.5℃ / s, it is determined to be "heating", that is, the temperature of the cookware is higher than the panel and continues to rise;

[0076] like When the temperature is less than -2℃ and r is less than -0.5℃ / s, it is determined to be the "residual heat period" (the temperature of the cookware is lower than that of the panel and continues to drop, with residual heat from the panel dominating).

[0077] like When the temperature is less than 3℃ and r is less than 0.2℃ / s, it is considered to be in the "cooling period" (the temperature of the cookware approaches the ambient temperature and tends to stabilize).

[0078] By comparing the difference and rate of change between the cookware temperature, panel temperature and ambient temperature based on a decision tree, the actual heating state of the cookware and the residual heat state of the panel are distinguished. If it is detected that the cookware temperature has dropped to near the ambient temperature, but the panel temperature is still high, it is determined to be the residual heat period, thus avoiding false triggering and restarting of the sensor due to the high panel temperature.

[0079] The temperature prediction and analysis module predicts future temperatures in advance based on the current temperature trend and calculates the prediction deviation to provide advance allowance for power regulation.

[0080] The process for predicting deviation in the temperature prediction and analysis module is as follows:

[0081] B1. Input the fused cookware temperature and current status label, extract the rate of change of the temperature sequence, cumulative temperature rise value, and status label, and construct the input vector X;

[0082] B2. Based on the input vector X, a prediction model is used to predict and output the predicted temperature of the cookware in the next 3-5 seconds. The prediction model uses a 3-layer LSTM network (500-dimensional input layer, 64 neurons in the hidden layer, and 3-dimensional output layer, corresponding to the predicted temperature value in the next 3 seconds). The predicted value after 3 seconds is taken as the main reference because heating inertia is most significant in this range.

[0083] B3. Calculate the prediction deviation and real-time deviation based on the predicted temperature values. The formula for calculating the prediction deviation is:

[0084] ,in, Set the temperature for the user. To predict temperature, For prediction bias;

[0085] like When the temperature is above 2°C, the predicted temperature will exceed the set value, and the power needs to be reduced in advance.

[0086] like When the temperature is below -2℃, the predicted temperature will be lower than the set value, and the power needs to be increased in advance;

[0087] The formula for calculating real-time deviation is:

[0088] ,in, Set the temperature for the user. The actual current temperature. For real-time deviation;

[0089] B4. Transmit the predicted temperature, prediction deviation, and real-time deviation to the main control module and power regulation module;

[0090] The power adjustment module dynamically adjusts the heating power based on the predicted deviation and real-time temperature;

[0091] The process of dynamic heating power adjustment by the power adjustment module is as follows:

[0092] C1. Dynamically assign weights based on prediction bias, real-time bias, and current status label. The weight assignment formula is as follows:

[0093] ,

[0094] in, Weighting coefficients, heating in progress =0.7, residual heat period =0.3, with a higher weighting for prediction deviation during heating, requiring earlier intervention; and a higher weighting for real-time deviation during the residual heat period. The total deviation after assigning weight values;

[0095] C2. Preset 10 power levels (100W-2200W, 200W per level), based on the total deviation. The power adjustment direction and amplitude are determined as follows:

[0096] like If the temperature exceeds 5°C, the power will be reduced by 2 levels (e.g., from 1000W to 600W).

[0097] If 2℃ < If the temperature is below 5℃, the power will decrease by one level.

[0098] If -5℃ <-2℃, power increases by 1 level;

[0099] like At temperatures below -5℃, power increases by 2 levels;

[0100] like 2℃, maintain current power;

[0101] C3. Convert the power level to PWM duty cycle (e.g., 2200W corresponds to a 90% duty cycle, 100W corresponds to a 5% duty cycle). The duty cycle calculation formula is as follows:

[0102] ,in, For PWM duty cycle, This is the current duty cycle. For each duty cycle adjustment, This refers to the change in power level;

[0103] C4. The PWM signal is output through the TIM timer of the main control module to control the conduction time of the IGBT switching transistor and adjust the power of the heating coil.

[0104] C5. Send the actual output PWM duty cycle (corresponding power) to the main control module and the feedback correction module.

[0105] The feedback correction module is used to correct prediction model errors and ensure the stability of long-term adjustment.

[0106] The error correction process of the feedback correction module is as follows:

[0107] D1. Calculate the prediction error and control error based on the predicted temperature, actual fusion temperature, and set temperature;

[0108] The formula for calculating prediction error is: ,in, For the prediction error value, To predict temperature, This refers to the actual fusion temperature;

[0109] The formula for calculating control error is: ,in, To control error, This is the actual fusion temperature. To set the temperature;

[0110] D2. Calculate the PID correction value based on the prediction error and control error;

[0111] The formula for calculating the PID correction value is:

[0112] ,

[0113] ,

[0114] + ,

[0115] in, This is the total correction amount. This is the proportionality coefficient. The integral coefficient is... These are the differential coefficients. Let be the prediction error at time t. To predict the correction amount, To control the correction amount, The control error at time t;

[0116] D3. Error correction is performed using Kalman filtering variance;

[0117] The error correction formula is:

[0118] ,

[0119] in, To correct the weights of the output layer of the LSTM model, To correct the weights of the output layer of the previous LSTM model, The learning rate of the LSTM model is used. If the prediction error is greater than 3℃ for five consecutive times, the process noise covariance and observation noise covariance of the Kalman filter are dynamically adjusted to enhance the model's adaptability to temperature changes.

[0120] D4. Send the corrected LSTM weights and Kalman filter covariance parameters to the main control module to update the model parameters of the prediction module and the fusion module.

[0121] By fusing contact and non-contact data using Kalman filtering to eliminate the hysteresis of a single sensor, and using decision trees to distinguish between waste heat and actual heating to solve the problem of misjudgment, LSTM is used to predict future temperature, and combined with PID to calculate power adjustment in advance, avoiding the overshoot problem of traditional adjustment only when the temperature is reached.

[0122] The main control module is used to coordinate data interaction and timing control among the modules;

[0123] The main control module performs the following control steps:

[0124] E1. After power-on, initialize all modules (sensors, MCU peripherals, algorithm model parameters), set the timing period (e.g., 10ms / time), and load the preset temperature (user-defined value T). set ).

[0125] E2. Self-check the communication status of each module (such as whether the sensor is connected properly and whether the power module is responding). If there is an abnormality, trigger an alarm (such as a buzzer).

[0126] E3. Sends a data acquisition command to the multi-source temperature detection module every 10ms, and synchronously receives the cookware temperature (T). pot ), panel temperature (T) pan ), ambient temperature (T) )data.

[0127] E4. Forward the raw temperature data to the temperature data fusion module; receive the fused actual cookware temperature (T). real ), and forward it to the prediction module and the feedback module.

[0128] E5. Prediction bias (ΔT) of the receiving prediction module pre ) and real-time temperature deviation (ΔT) real It sends power adjustment commands to the power adjustment module; receives correction parameters from the feedback module, and updates the parameters of the prediction model and fusion algorithm.

[0129] E6. Repeat the process from E3 to E5 until the user shuts down the system or an abnormal shutdown occurs;

[0130] By coordinating the multi-source temperature detection module, temperature data fusion processing module, temperature prediction and analysis module, power adjustment module, and feedback correction module, the entire chain of multi-source detection is coordinated to eliminate lag, fusion identification distinguishes residual heat, prediction adjustment intervenes in advance, and feedback correction optimizes accuracy. This keeps the temperature within ±2℃, effectively reduces overshoot, shortens delay time, and achieves precise temperature control of the induction cooker.

[0131] Based on the above description of a control system for precise temperature adjustment of an induction cooker, this invention also provides a control circuit for precise temperature adjustment of an induction cooker, including a main control circuit, a multi-source temperature detection circuit, a power adjustment circuit, a human-machine interaction circuit, and an auxiliary circuit. The main control circuit is electrically connected to the multi-source temperature detection circuit, the power adjustment circuit, the human-machine interaction circuit, and the auxiliary circuit, respectively.

[0132] like Figure 4 As shown, the main control circuit includes an STM32F103C8T6 main control chip. The main control chip includes a crystal oscillator circuit and a reset circuit. The crystal oscillator circuit includes crystal oscillator Y1 connected between pins 3 and 4 of the main control chip and crystal oscillator Y2 connected between pins 5 and 6 of the main control chip. Capacitors C1 and C2 are connected between the two ends of crystal oscillator Y1. A resistor R4 is connected in parallel on one side of crystal oscillator Y2, and capacitors C3 and C4 are connected on the other side of crystal oscillator Y2. Crystal oscillators Y1 and Y2 typically use a 12MHz crystal oscillator to provide the main clock for the system, and a 32.768Hz crystal oscillator is used for the RTC (Real-Time Clock) function. The reset circuit includes a switch S1 connected to pin 7 of the main control chip. One end of switch S1 is connected to connector P2. Connector P2 and the other end of switch S1 are connected to resistor R5. The connection end of resistor R5 and switch S1 is connected to a capacitor C6 to ground for manual or power-on reset of the microcontroller.

[0133] The multi-source temperature detection circuit uses the DS18B20 temperature sensor, which features high precision and single-bus communication. It can directly output digital signals without additional analog-to-digital conversion. The sensor is installed below the induction cooker panel or near the coil and connected to the GPIO pins of the main control chip via a single bus. If an NTC thermistor is used, a voltage divider circuit and a low-pass filter circuit are required. The thermistor is connected in series with a precision resistor. The voltage between the two is collected, and the ADC module of the main control chip converts the voltage value into a digital value. The current temperature value is then calculated based on the temperature-resistance characteristics of the thermistor.

[0134] The power regulation circuit utilizes the main control chip to output a PWM signal via a timer, controlling the on / off state of the MOSFET (such as IRF540N), thereby regulating the heating power of the induction cooker coil. To protect the MOSFET, a pull-down resistor can be added to its gate. For coils with inductive loads, a freewheeling diode needs to be added. Alternatively, a solid-state relay (SSR) can be used as the actuator. The PWM signal output by the STM32 is optocoupled and then controlled to switch the SSR on and off, thus regulating the heating power.

[0135] The human-computer interaction circuit uses an OLED or LCD module via I 2 The communication interface, such as C or SPI, is connected to the main control chip to display information such as the current temperature and the set temperature. The button circuit can be composed of several tactile switches for setting the target temperature, selecting the working mode, etc. The buttons can be debouncing through parallel capacitors and series resistors and are connected to the GPIO pins of the main control chip.

[0136] The auxiliary circuit uses an AC-DC power module to convert the mains power into a suitable DC voltage, and then uses an LDO (such as AMS117) or other voltage regulator chip to provide stable voltages of 3.3V and 5V to the main control chip and other circuits. To avoid ground noise interference between the MCU and the power circuit, optocouplers or DC-DC modules can be used for power isolation.

[0137] By using the DS18B20 sensor (accuracy ±0.5℃, resolution 0.0625℃) in conjunction with the main control chip's 12-bit ADC (conversion error <±1LSB), accurate temperature data near the cookware can be directly obtained, avoiding the nonlinear error of traditional single NTC sensors. Through software calibration of the main control chip, the temperature measurement error can be controlled within ±1℃, providing a reliable basis for precise adjustment.

[0138] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A control system for precise temperature adjustment of an induction cooker, characterized in that: include: Multi-source temperature detection module: Simultaneously collects multi-dimensional temperature data from the actual temperature of the cookware, the panel temperature, and the ambient temperature, providing raw data for accurate temperature measurement; Temperature data fusion processing module: used to process multi-dimensional temperature data, fuse the actual temperature of the cookware, and identify the current system status; The temperature data fusion processing module includes a multi-source data fusion unit and a state recognition unit. When the multi-source data fusion unit performs multi-source fusion, it makes a prediction by calculating prior estimates and updates by combining observation values ​​to calculate posterior estimates, and outputs the fused true temperature of the cookware. The status recognition unit first extracts the features of the temperature difference between the cookware and the panel, the temperature difference between the cookware and the environment, and the temperature change rate, and then makes a decision tree judgment based on the features. Finally, it transmits and sends the fused temperature and status labels. Temperature prediction and analysis module: Based on the current temperature trend, it predicts future temperatures in advance through a prediction model, calculates the prediction deviation, and provides advance allowance for power regulation; The process for predicting deviation in the temperature prediction and analysis module is as follows: B1. Input the fused cookware temperature and current status label, extract the rate of change of the temperature sequence, cumulative temperature rise value, and status label, and construct the input vector X; B2. Based on the input vector X, a prediction model is used to predict and output the predicted temperature of the cookware in the next 3-5 seconds. The prediction model uses a 3-layer LSTM network. B3. Calculate the prediction deviation and real-time deviation based on the temperature prediction value; B4. Transmit the predicted temperature, prediction deviation, and real-time deviation to the main control module and power regulation module; Power regulation module: dynamically adjusts heating power based on prediction deviation and real-time temperature; Feedback correction module: used to correct prediction model errors and ensure the stability of long-term adjustment; Main control module: used to coordinate data interaction and timing control of various modules. The main control module is electrically connected to the multi-source temperature detection module, temperature data fusion processing module, temperature prediction and analysis module, power regulation module and feedback correction module respectively.

2. The control system for precise temperature adjustment of an induction cooker according to claim 1, characterized in that: The multi-source temperature detection module includes a sensing unit and a data preprocessing unit connected to the sensing unit. The sensing unit includes a contact sensor, an infrared sensor, a panel sensor, and an environmental sensor. The contact sensor uses a PT1000 platinum resistance thermometer, which, together with a high-precision operational amplifier, forms a bridge temperature measurement circuit to suppress common-mode interference. The infrared sensor uses an MLX90614, which directly outputs a digital temperature signal through an I²C interface without additional conditioning. The environmental sensor uses an NTC thermistor, which, together with an RC filter circuit and an LM358 operational amplifier, forms a voltage follower to filter out high-frequency noise.

3. The control system for precise temperature adjustment of an induction cooker according to claim 2, characterized in that: The data preprocessing unit performs the following preprocessing steps: A1. After receiving the acquisition command from the main control module, it synchronously starts acquiring data from contact sensors, infrared sensors, panel sensors, and environmental sensors. A2. Outliers are removed using the 3σ criterion, and then the infrared sensor data is filtered by moving average. A3. Pack the pre-processed multi-channel temperature data and send it to the main control module and the temperature data fusion processing module.

4. The control system for precise temperature adjustment of an induction cooker according to claim 3, characterized in that: The process of dynamic heating power adjustment by the power adjustment module is as follows: C1. Dynamically allocate weights based on prediction deviation, real-time deviation, and current status label; C2. Preset 10 power levels, and determine the power adjustment direction and amplitude based on the total deviation; C3. Convert the power level to PWM duty cycle; C4. The main control module outputs a PWM signal through its timer to control the conduction time of the IGBT switching transistor and adjust the power of the heating coil. C5. Send the actual output PWM duty cycle to the main control module and the feedback correction module.

5. The control system for precise temperature adjustment of an induction cooker according to claim 4, characterized in that: The error correction process of the feedback correction module is as follows: D1. Calculate the prediction error and control error based on the predicted temperature, actual fusion temperature, and set temperature; D2. Calculate the PID correction value based on the prediction error and control error; D3. Error correction is performed using Kalman filtering variance; D4. Send the corrected LSTM weights and Kalman filter covariance parameters to the main control module to update the model parameters of the prediction and fusion modules.

6. The control system for precise temperature adjustment of an induction cooker according to claim 5, characterized in that: The main control module performs the following control steps: E1. After powering on, initialize each module, set the timing cycle, and load the preset temperature. E2. Self-check the communication status of each module; if an abnormality is detected, an alarm will be triggered. E3. Send a data acquisition command to the multi-source temperature detection module every 10ms and synchronously receive cookware temperature, panel temperature and ambient temperature data. E4. Forward the raw temperature data to the temperature data fusion module; receive the fused real cookware temperature and forward it to the prediction module and feedback module; E5. Receive the prediction deviation and real-time temperature deviation from the prediction module, and send a power adjustment command to the power adjustment module; receive the correction parameters from the feedback module, and update the parameters of the prediction model and fusion algorithm.

7. A control circuit for precise temperature adjustment of an induction cooker, based on the control system for precise temperature adjustment of an induction cooker as described in any one of claims 1-6, characterized in that: include: The main control circuit includes an STM32F103C8T6 main control chip, which includes a crystal oscillator circuit and a reset circuit. A multi-source temperature detection circuit is provided, which uses a DS18B20 temperature sensor and is installed below the induction cooker panel or near the coil. The sensor is connected to the GPIO pin of the main control chip via a single bus. The power regulation circuit uses the main control chip to output a PWM signal through a timer to control the conduction and cutoff of the MOSFET, thereby adjusting the heating power of the induction cooker coil. Human-computer interaction circuits use OLED or LCD modules via I 2 Communication interfaces such as C or SPI are connected to the main control chip to display information such as the current temperature and the set temperature. The auxiliary circuit uses an AC-DC power module to convert the mains power into a suitable DC voltage, and then uses an LDO voltage regulator chip to provide a stable voltage to the main control chip. The main control circuit is electrically connected to the multi-source temperature detection circuit, the power regulation circuit, the human-machine interaction circuit, and the auxiliary circuit.

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