Intelligent temperature control method and system for heating process of kitchen appliances

Abstract

The application relates to an intelligent temperature control method and system for a heating process of a kitchen electrical appliance, which comprises the following steps: acquiring a current temperature, a historical temperature and a target temperature; calculating a current temperature deviation and a change rate; determining a current heating stage according to a preset interval in which the deviation and the change rate are located; acquiring a corresponding thermal inertia coefficient, calculating an expected residual heat temperature rise in combination with the change rate, and correcting the current temperature deviation to obtain an equivalent control deviation; inputting the equivalent control deviation and the change rate into a fuzzy reasoning model to calculate current proportional, integral and differential parameters; combining historical control parameters of a previous period to calculate a target heating power of a current period; outputting a power control signal to an electric heating execution device according to the target heating power, and updating the current temperature, the equivalent control deviation and the target heating power as historical control parameters of a next period. The application can improve the temperature control precision and response speed of the kitchen electrical appliance.

Description

Technical Field

[0001] This application relates to the field of intelligent home appliance control technology, and in particular to an intelligent temperature control method and system for the heating process of kitchen appliances. Background Technology

[0002] With the rapid development of smart home appliance technology and the continuous improvement of people's quality of life, the automation and intelligence of kitchen appliances are increasing. Among various kitchen appliances such as smart stir-fry machines, electric ovens, and induction cookers, the heating process is the most crucial functional element, and the precision of heating temperature control directly determines the taste, nutrient retention, and safety of the cooked food. Traditional kitchen appliances typically use simple mechanical temperature switches or basic on-off control algorithms, controlling the heating element's on / off state by setting upper and lower temperature thresholds. Due to the diversity of cooked foods and the complexity of the heating environment, this coarse-grained control method is insufficient for the demands of refined cooking. In recent years, to improve temperature control, some kitchen appliances have begun to introduce classic control algorithms such as proportional-integral-derivative (PID) algorithms, attempting to achieve closed-loop control of the heating temperature through continuous power adjustment.

[0003] However, the objects heated by kitchen appliances typically possess high thermal inertia and strong nonlinear physical characteristics. Even after the power is cut off or the power is reduced, the heat stored in the heating element continues to be transferred to the load, causing the load temperature to continue to rise in a short period of time, resulting in significant temperature overshoot. Simultaneously, adding cold ingredients or opening the lid during cooking causes sudden temperature drops, and traditional fixed-parameter control algorithms struggle to adapt to these dynamically changing conditions, often exhibiting slow response, long temperature recovery times, or even system oscillations.

[0004] Furthermore, control logic that passively responds based solely on the current temperature deviation lacks the ability to predict the future thermodynamic trends of the system. When the temperature approaches the target value, it cannot effectively suppress overshoot caused by thermal inertia in advance, resulting in technical problems such as insufficient temperature control accuracy, excessively long stabilization time, and weak robustness against thermal shocks during the heating process. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides an intelligent temperature control method and system for the heating process of kitchen appliances.

[0006] Firstly, this application provides an intelligent temperature control method for the heating process of a kitchen appliance, employing the following technical solution: A smart temperature control method for the heating process of a kitchen appliance is applied to a kitchen appliance including a temperature acquisition component and a power-adjustable electric heating actuator. The smart temperature control method includes: The current temperature, the historical temperature of the previous sampling period, and the preset target temperature are obtained by the kitchen appliance through the temperature acquisition component. The current temperature deviation is calculated based on the preset target temperature and the current temperature, and the current temperature change rate is calculated based on the current temperature and the historical temperature. The current heating stage is determined based on the preset range of the current temperature deviation and the current temperature change rate. The corresponding thermal inertia coefficient is obtained based on the current heating stage. The expected waste heat temperature rise is calculated based on the thermal inertia coefficient and the current temperature change rate. The current temperature deviation is corrected based on the expected waste heat temperature rise to obtain the equivalent control deviation. The equivalent control deviation and the current temperature change rate are input into a preset fuzzy inference model to calculate the current control parameters, including the current proportional parameter, the current integral parameter and the current derivative parameter; The historical control parameters of the previous sampling period are obtained, and the target heating power of the current period is calculated based on the equivalent control deviation, the current control parameters, and the historical control parameters; wherein, the historical control parameters include the historical equivalent control deviation and the historical heating power. Based on the target heating power, a corresponding power control signal is generated and output to the electric heating actuator of the kitchen appliance, and the current temperature, the equivalent control deviation, and the target heating power are updated to the historical control parameters of the next sampling period.

[0007] By adopting the above technical solution, the problems of large overshoot and long stabilization time in conventional temperature control of kitchen appliances with high inertia and strong nonlinear heating are effectively solved. By introducing interval division of temperature change rate and temperature deviation, refined state identification of complex heating processes is achieved. Based on dynamically acquiring the thermal inertia coefficient at different heating stages and calculating the expected residual heat temperature rise, an equivalent control deviation with forward-looking prediction function is reconstructed, realizing a shift from passive response to proactive prevention. Combined with a preset fuzzy inference model, control parameters are adaptively tuned online, and an incremental algorithm is used to fuse historical control parameters to output precise target heating power. This not only enhances the system's robustness to nonlinear disturbances such as thermal shocks but also ensures the smoothness of control output. This technical solution can improve the temperature control accuracy and response speed of kitchen appliances without increasing hardware costs.

[0008] Secondly, this application provides an intelligent temperature control system for the heating process of a kitchen appliance, employing the following technical solution: A smart temperature control system for the heating process of a kitchen appliance is applied to a kitchen appliance including a temperature acquisition component and a power-adjustable electric heating actuator. The smart temperature control system includes: The temperature data acquisition module is used to acquire the current temperature, the historical temperature of the previous sampling period, and the preset target temperature collected by the kitchen appliance through the temperature acquisition component; The state feature calculation module is used to calculate the current temperature deviation based on the preset target temperature and the current temperature, and to calculate the current temperature change rate based on the current temperature and the historical temperature. The heating stage determination module is used to determine the current heating stage based on the preset range of the current temperature deviation and the current temperature change rate. The equivalent deviation calculation module is used to obtain the corresponding thermal inertia coefficient according to the current heating stage, calculate the expected waste heat temperature rise according to the thermal inertia coefficient and the current temperature change rate, and correct the current temperature deviation according to the expected waste heat temperature rise to obtain the equivalent control deviation. The fuzzy parameter tuning module is used to input the equivalent control deviation and the current temperature change rate into a preset fuzzy inference model to calculate the current control parameters, including the current proportional parameter, the current integral parameter and the current derivative parameter; The target power calculation module is used to obtain the historical control parameters of the previous sampling period, and calculate the target heating power of the current period based on the equivalent control deviation, the current control parameters, and the historical control parameters; wherein, the historical control parameters include the historical equivalent control deviation and the historical heating power. The control output and update module is used to generate a corresponding power control signal based on the target heating power and output it to the electric heating actuator of the kitchen appliance, and update the current temperature, the equivalent control deviation and the target heating power to the historical control parameters of the next sampling period.

[0009] Thirdly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium storing a computer program that can be loaded by a processor and executed as in any of the methods in the first aspect.

[0010] In summary, this application includes at least one of the following beneficial technical effects: By combining staged thermal inertia prediction correction with fuzzy PID adaptive control, this application effectively overcomes the problems of large thermal inertia and nonlinear lag in the heating process of kitchen appliances, avoids temperature overshoot, achieves precise and stable constant temperature control, and improves the temperature control accuracy and response speed of kitchen appliances. Attached Figure Description

[0011] Figure 1 This is a first flowchart illustrating an intelligent temperature control method for the heating process of a kitchen appliance, according to one embodiment of this application.

[0012] Figure 2This is a second flowchart illustrating an intelligent temperature control method for the heating process of a kitchen appliance, according to one embodiment of this application.

[0013] Figure 3 This is a schematic diagram of the third process of an intelligent temperature control method for the heating process of a kitchen appliance, according to one embodiment of this application.

[0014] Figure 4 This is a schematic diagram of the fourth process of an intelligent temperature control method for the heating process of a kitchen appliance, according to one embodiment of this application.

[0015] Figure 5 This is a fifth process diagram of an intelligent temperature control method for the heating process of a kitchen appliance, according to one embodiment of this application.

[0016] Figure 6 This is a sixth process diagram of an intelligent temperature control method for the heating process of a kitchen appliance, according to one embodiment of this application.

[0017] Figure 7 This is a schematic diagram of the seventh process of an intelligent temperature control method for the heating process of a kitchen appliance, according to one embodiment of this application.

[0018] Figure 8 This is the eighth process diagram of the intelligent temperature control method for the heating process of kitchen appliances according to one embodiment of this application. Detailed Implementation

[0019] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figures 1-8 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0020] This application discloses an intelligent temperature control method for the heating process of kitchen appliances.

[0021] Reference Figure 1 A smart temperature control method for the heating process of kitchen appliances, applied to kitchen appliances including temperature acquisition components and power-adjustable electric heating actuators, the smart temperature control method specifically includes: Step S101: Obtain the current temperature, the historical temperature of the previous sampling period, and the preset target temperature collected by the kitchen appliance through the temperature acquisition component; Specifically, kitchen appliances typically collect data at fixed sampling intervals using built-in temperature sensors. At the beginning of each control cycle, the system reads the sensor signal and converts it into a digital temperature value, i.e., the current temperature. Simultaneously, the microcontroller in the system retrieves the temperature data saved from the previous control cycle as historical temperatures from its storage area. The preset target temperature is the desired temperature value set by the user through an interactive interface or automatically configured by a specific cooking program.

[0022] It should be noted that the kitchen appliances referred to in the embodiments of this application are devices with real-time temperature detection function and electric heating power adjustment function (such as PWM power adjustment), such as, but not limited to, smart rice cookers, electric ovens, induction cookers, smart cooking machines, etc.

[0023] For example, in the application scenario of water temperature control in a smart cooking machine, the user sets a preset target temperature of 100 degrees Celsius. The microcontroller reads the water temperature sensor data every 500 milliseconds. If the current temperature read is 60 degrees Celsius, and the previously recorded historical temperature is 58 degrees Celsius, the system obtains these three key temperature parameters, preparing data for subsequent judgment of the rate of water temperature rise and the current heating stage.

[0024] Step S102: Calculate the current temperature deviation based on the preset target temperature and the current temperature, and calculate the current temperature change rate based on the current temperature and historical temperatures; The current temperature deviation measures the absolute distance between the current state and the target state, determining whether the system should continue to output energy or suppress energy output. However, the magnitude of the deviation alone cannot determine the system's motion trend; for example, a deviation of 5 degrees Celsius could indicate a rapid temperature increase or a stagnant state. The current temperature change rate compensates for this deficiency, reflecting the dynamic trend of temperature change over time and serving as an important quantitative indicator of the system's thermal inertia.

[0025] In this embodiment, the current temperature deviation is obtained by calculating the difference between the preset target temperature and the current temperature. When the difference is positive, it indicates that the target temperature has not yet been reached; when it is negative, it indicates that overshoot has occurred. The current temperature change rate is obtained by calculating the difference between the current temperature and the historical temperature, combined with the time span of the sampling period, and represents the magnitude of temperature rise or fall per unit time.

[0026] For example, if the current temperature is 80 degrees Celsius and the preset target temperature is 100 degrees Celsius, then the current temperature deviation is 20 degrees Celsius. If the historical temperature is 75 degrees Celsius and the sampling period is 0.5 seconds, then the current temperature change rate is 5 degrees Celsius every 0.5 seconds. This rapid rate of increase indicates that the object being heated is in a rapid heat absorption phase, and the system must be wary of the risk of subsequent overshoot due to thermal inertia.

[0027] Step S103: Determine the current heating stage based on the preset range of the current temperature deviation and the current temperature change rate; The heating process is not linear and uniform, and the load thermodynamic characteristics differ significantly at different stages. Therefore, it is necessary to divide the heating process into stages based on state variables.

[0028] Specifically, the system internally presets multiple threshold ranges based on deviation and rate of change. When the current temperature deviation is greater than a large set threshold and the current rate of change is greater than zero, it indicates that the target is still far away and the temperature is rising. The system determines that it is in the rapid heating phase, at which point the output power needs to be maximized. When the deviation shrinks to a medium range and the rate of change remains at a high level, the system determines that it has entered the temperature approach phase, which is a critical period for preventing overshoot. When the absolute value of the deviation enters the minimum range and the rate of change also approaches zero, the system determines that it has entered the constant temperature stabilization phase, and only fine-tuning power is needed to compensate for heat dissipation.

[0029] For example, in an oven baking scenario, the target temperature is set to 200 degrees Celsius. Initially, the temperature deviation is 150 degrees Celsius, with a rate of change of 2 degrees Celsius per second. The system determines it is currently in a rapid heating phase and operates at full power. When the temperature reaches 190 degrees Celsius, the deviation decreases to 10 degrees Celsius, but the rate of change remains at 1 degree Celsius per second. The system then determines it has entered the temperature approach phase. If the temperature stabilizes between 199 and 201 degrees Celsius, and the rate of change is almost zero, the system determines it is in a constant temperature stabilization phase and switches to low-power maintenance mode.

[0030] Step S104: Obtain the corresponding thermal inertia coefficient according to the current heating stage, calculate the expected waste heat temperature rise according to the thermal inertia coefficient and the current temperature change rate, and correct the current temperature deviation according to the expected waste heat temperature rise to obtain the equivalent control deviation. Among these challenges, thermal inertia is a core difficulty for kitchen appliance heating systems. This manifests as the load temperature continuing to rise even after the heat source is cut off due to the delayed heat transfer between the heating element and the load. To overcome this hysteresis effect, this application introduces the anticipated residual heat temperature rise as a forward-looking predictive variable. Instead of passively responding based solely on the current temperature deviation, the system estimates in advance, based on the current thermodynamic stage and temperature trend, the additional temperature rise that the system would experience due to thermal inertia if heating were stopped at that moment. This predicted value is used to reduce and correct the current temperature deviation, resulting in an equivalent control deviation that more accurately reflects future temperature control requirements.

[0031] In this embodiment, different current heating stages correspond to different thermal inertia coefficients. This is because during the rapid heating stage, the heat storage capacity of the heating element is large, and the thermal inertia coefficient is set relatively high; while during the constant temperature stabilization stage, heat transfer tends to be balanced, and the thermal inertia coefficient decreases accordingly. The system calculates the expected residual heat temperature rise by calling the thermal inertia coefficient matched to the current heating stage and calculating it with the current temperature change rate. Subsequently, the current temperature deviation is subtracted from this expected residual heat temperature rise to generate an equivalent control deviation.

[0032] It should be noted that if the current temperature is stable and there is no need for significant advance prediction, the expected residual heat temperature rise will be set to zero, and the original current temperature deviation will be used directly as the equivalent control deviation to avoid excessive correction that could lead to a sluggish system response.

[0033] For example, the current temperature is 95 degrees Celsius, the preset target temperature is 100 degrees Celsius, and the current temperature deviation is 5 degrees Celsius. The system determines that it is currently in the temperature approach phase, obtains the corresponding thermal inertia coefficient as 0.5, and if the current temperature change rate is 2 degrees Celsius per second, then the expected residual heat temperature rise is calculated to be 1 degree Celsius. The system subtracts the expected residual heat temperature rise of 1 degree Celsius from the current temperature deviation of 5 degrees Celsius, obtaining an equivalent control deviation of 4 degrees Celsius. This means that although the actual current temperature is still 5 degrees Celsius below the target temperature, the system predicts that the thermal inertia will rise by an additional 1 degree Celsius in the future. Therefore, the system calculates the control quantity based on the equivalent deviation of 4 degrees Celsius, thereby reducing the power output in advance and effectively avoiding overshoot caused by the temperature eventually exceeding 100 degrees Celsius.

[0034] Step S105: Input the equivalent control deviation and the current temperature change rate into the preset fuzzy inference model to calculate the current control parameters, including the current proportional parameter, the current integral parameter and the current derivative parameter; The heating process of kitchen appliances is highly nonlinear, and fixed control parameters cannot adapt to dynamic changes in load characteristics. The role of the preset fuzzy inference model is to dynamically self-tune the control parameters online based on the real-time operating status of the system. The equivalent control deviation obtained after the aforementioned steps has incorporated thermal inertia prediction information. Using this deviation, along with the current temperature change rate reflecting the trend, as input to fuzzy inference, the model can simultaneously consider both distance and velocity dimensions when evaluating the system state, thereby outputting the proportional, integral, and derivative parameters most suitable for the current complex operating conditions.

[0035] Specifically, the pre-defined fuzzy inference model establishes a fuzzy rule base based on empirical data. The system first fuzzifies the input equivalent control deviation and current temperature change rate, converting them into corresponding fuzzy linguistic variables. Then, it performs inference and judgment based on the set fuzzy rules. When the equivalent control deviation is large and the temperature change rate is small, it indicates that the system is struggling to heat up; the model outputs larger proportional and integral parameters to strengthen the heating effect. When the equivalent control deviation is small but the temperature change rate is large, it indicates that the system is rapidly approaching the target and has a risk of overshoot; the model outputs smaller proportional parameters and larger derivative parameters to provide strong damping. Finally, after defuzzification, the system outputs precise current proportional, integral, and derivative parameters.

[0036] For example, if cold ingredients are suddenly added during cooking, the sudden drop in temperature causes a rapid increase in the equivalent control deviation, while the current temperature change rate increases negatively. Upon receiving this input, the pre-defined fuzzy inference model determines that the system has encountered a thermal shock disturbance based on its internal rules. It immediately calculates and outputs a larger current proportional parameter to quickly restore the temperature. As the temperature gradually rises and approaches the target value, the equivalent control deviation decreases and the current temperature change rate increases positively. The model then calculates and outputs a larger current differential parameter to suppress further temperature increases, achieving rapid and stable adaptive adjustment to nonlinear disturbances.

[0037] Step S106: Obtain the historical control parameters of the previous sampling period, and calculate the target heating power of the current period based on the equivalent control deviation, the current control parameters, and the historical control parameters; wherein, the historical control parameters include the historical equivalent control deviation and the historical heating power. This step calculates the increment of the control quantity using the current state variables and historical state variables. The historical control parameters from the previous sampling period contain the system's deviation state and output power state at the previous moment, and these data are indispensable benchmarks for calculating the increment of the current period. Combining the dynamically tuned current control parameters and the equivalent control deviation corrected for thermal inertia, the system can calculate the target heating power required for the current period. This power value reflects both the response to the current deviation and the inheritance of historical states and the prediction of future trends.

[0038] In this embodiment, the system retrieves historical control parameters from the memory, including the historical equivalent control deviations of the previous few cycles and the historical heating power of the previous cycle. Using the current proportional parameter, current integral parameter, and current derivative parameter from the current control parameters, a weighted calculation is performed on the current equivalent control deviation and its difference from the historical equivalent control deviation to generate a proportional control component, an integral control component, and a derivative control component. These three components are then superimposed to form the current control increment.

[0039] It should be noted that, in order to prevent the calculation results from exceeding the physical limits of the device, the system adds the current control increment to the historical heating power and then performs a limiting process to ensure that the final output target heating power is within a safe and executable range.

[0040] For example, the system calculates a current control increment of 10% of the duty cycle increment, while the historical heating power from the previous sampling period is 50% of the duty cycle. Adding these two together yields an initial target heating power of 60%. If the system's maximum power limit is set to 100%, since 60% is within the limit, it is used as the final target heating power output. This process ensures that each power adjustment is a smooth progression based on the previous state, avoiding the impact of drastic changes in the control signal on the heating device.

[0041] Step S107: Generate a corresponding power control signal based on the target heating power and output it to the electric heating actuator of the kitchen appliance. Update the current temperature, equivalent control deviation and target heating power to the historical control parameters of the next sampling period.

[0042] The generation of power control signals is a crucial step in mapping digital-level calculations to analog-level energy output. Simultaneously, the control system operates as a continuously running, cyclical system, requiring all critical state data from the current cycle to be properly stored as historical data for the next sampling cycle. This data replacement mechanism ensures the continuity and consistency of the algorithm over time.

[0043] Specifically, the microcontroller adjusts the comparison register value of its internal timer based on the target heating power, thereby generating a pulse width modulation signal with a corresponding duty cycle. This signal is amplified by an isolated drive circuit and output to the electric heating actuator. By controlling the on-time ratio of the switching device, the actual output power of the heating tube or heating coil is precisely adjusted. Simultaneously, the system writes the current temperature collected in this cycle, the calculated equivalent control deviation, and the final target heating power output into a designated area of ​​memory, overwriting the original historical data and completing the recursive update of the state variables, preparing for the start of the next control cycle.

[0044] For example, if the target heating power is 40% of the rated power, the microcontroller generates a pulse width modulation signal with a 40% duty cycle. This signal controls the bidirectional thyristor inside the electric heating actuator to conduct for 40% of the time in each carrier cycle, allowing the heating element to output heat energy of corresponding intensity according to this duty cycle ratio. Simultaneously, the system stores the current temperature, equivalent control deviation, and target heating power in a register. At the next sampling cycle, this data will be read and used as historical temperature and historical control parameters in the next round of calculations, ensuring uninterrupted temperature control and logical consistency.

[0045] It should be noted that, in order to support the subsequent model correction function, after the above steps of updating historical control parameters, the system can further store the expected waste heat temperature rise, thermal inertia coefficient and current heating stage calculated in this cycle as historical state parameters, so as to perform data backtracking and comparison in subsequent sampling cycles.

[0046] The above implementation effectively solves the problems of large overshoot and long stabilization time in conventional temperature control for kitchen appliances with high inertia and strong nonlinear heating. By introducing interval division of temperature change rate and temperature deviation, refined state identification of complex heating processes is achieved. Based on the dynamic acquisition of thermal inertia coefficients at different heating stages and the calculation of expected residual heat temperature rise, an equivalent control deviation with forward-looking prediction function is reconstructed, realizing the transformation from passive response to proactive prevention. Combined with a preset fuzzy inference model, the control parameters are adaptively tuned online, and an incremental algorithm is used to fuse historical control parameters to output accurate target heating power. This not only enhances the robustness of the system to nonlinear disturbances such as thermal shocks, but also ensures the smoothness of control output. This technical solution can improve the temperature control accuracy and response speed of kitchen appliances without increasing hardware costs.

[0047] Reference Figure 2 As a further implementation of the intelligent temperature control method for the heating process of kitchen appliances, before the first sampling cycle at the initial stage of the kitchen appliance startup, the intelligent temperature control method also includes: Step S201: Obtain the rated heating power and initial load temperature of the electric heating actuator; In the initial stage of starting up kitchen appliances, the control system has not yet accumulated enough operating data. At this time, if fixed interval parameters are directly used to divide the heating stages, it is often difficult to adapt to the thermodynamic differences caused by different ingredients or different amounts of water. Rated heating power represents the constant thermal energy benchmark output by the system, while the initial load temperature is the initial thermodynamic state of the heated object before heat absorption.

[0048] Specifically, after receiving the cooking start command, the microcontroller first accesses the memory to read the factory calibration parameters of the current kitchen appliance, namely the rated heating power of the electric heating actuator. Simultaneously, before the device officially enters the closed-loop temperature control logic, it collects the current ambient or material temperature data through temperature sensors placed inside the heating cavity or on the load contact surface, using this data as the initial load temperature. This data is temporarily stored in memory, providing a comparison benchmark for subsequent actual temperature rise detection.

[0049] For example, in a smart cooking machine, the microcontroller reads the rated heating power of 2100 watts from its memory. After the user adds cold water and ingredients to the pot and closes the lid, the system reads the current temperature using a sensor at the bottom before starting heating, measuring the initial load temperature as 20 degrees Celsius. The system extracts these two values, preparing data for subsequent temperature rise monitoring.

[0050] Step S202: Within the preset initial heating time, heat the kitchen appliance according to the rated heating power, and collect the first actual load temperature at the end of heating. Understandably, since different heating loads exhibit drastically different temperature rises when absorbing the same amount of heat, the system must actually apply a constant heat source to determine the true heat absorption characteristics of the load. The preset initial heating duration is a fixed time window for probing, during which heating is performed at full power, and the subsequently measured temperature accurately reflects the current heat capacity of the load.

[0051] In this embodiment, after the device is started, the microcontroller controls the electric heating actuator to operate at full power with the rated heating power, and simultaneously starts an internal timer. When the timer accumulates to the preset initial heating duration, the microcontroller controls the temperature sensor to collect the current temperature again to obtain the first actual load temperature.

[0052] For example, the system is set to a preset initial heating time of 10 seconds. After the microcontroller controls the heating element to heat at full power of 2100 watts for 10 seconds, the first actual load temperature measured by the temperature sensor is read as 25 degrees Celsius. This temperature data reflects the actual thermodynamic response of the load after absorbing a fixed amount of heat.

[0053] Step S203: Calculate the initial temperature rise value based on the difference between the first actual load temperature and the initial load temperature; The absolute temperature difference directly reflects the efficiency of heat transfer to the load. The larger the difference, the smaller the load mass or the lower the specific heat capacity, resulting in rapid temperature rise. The smaller the difference, the larger the load heat capacity, indicating that it has absorbed a large amount of heat but the temperature rises slowly.

[0054] Specifically, after obtaining the first actual load temperature and the initial load temperature, the microcontroller performs a subtraction operation, using the first actual load temperature as the minuend and the initial load temperature as the subtrahend. The result of the subtraction is the initial temperature rise value, which represents the temperature jump amplitude brought about by the heating process.

[0055] Step S204: Obtain multiple preset temperature rise threshold ranges, compare the initial temperature rise value with the multiple preset temperature rise threshold ranges, and determine the heat capacity level of the current heating load. Since the parameter configuration of the control system is usually managed based on discrete categories, directly using continuous temperature rise values ​​for parameter interpolation calculations is computationally intensive and prone to introducing errors. Multiple preset temperature rise threshold ranges are set based on engineering experience, allowing the system to quickly determine the category of the current load through comparison.

[0056] Specifically, the microcontroller's storage medium contains multiple preset temperature rise threshold ranges, such as heavy load, medium load, and light load ranges, each defined by an upper and lower threshold. The microcontroller extracts the currently calculated initial temperature rise value, iterates through these preset ranges, and determines which range's boundary the value falls into. Once a match is found, the system extracts the corresponding level label for that range and identifies it as the heat capacity level of the current heating load.

[0057] For example, the system has preset temperature rise threshold ranges including: 0 to 3 degrees Celsius for heavy load, 3 to 7 degrees Celsius for medium load, and 7 to 15 degrees Celsius for light load. The currently calculated initial temperature rise is 5 degrees Celsius. The microcontroller compares this value with each of the above ranges and finds that 5 degrees Celsius falls within the 3 to 7 degree Celsius range. The system then successfully matches the value and determines that the current heating load's heat capacity level is medium load.

[0058] Step S205: Obtain the corresponding interval boundary adjustment coefficient according to the heat capacity level; The rate of temperature change varies significantly depending on the load's heat capacity. For example, the temperature change of a heavy load is naturally slower. If a uniform default interval boundary is used, the system is prone to misjudging the heating stage. The interval boundary adjustment coefficient is a weighting factor used to amplify or reduce the default parameters in subsequent steps.

[0059] In this embodiment, a mapping relationship between heat capacity levels and interval boundary adjustment coefficients is established in the microcontroller's storage area. After determining the heat capacity level of the current heating load, the microcontroller reads the coefficient data uniquely corresponding to that level using this level as an index. This coefficient is typically a value greater than or less than 1, designed to enable the control logic to adapt to the physical characteristics of the current load and avoid control strategy mismatch caused by rigid thresholds.

[0060] For example, the system determines the current heat capacity level to be medium load. The microcontroller queries the mapping relationship based on the "medium load" label and obtains the interval boundary adjustment factor corresponding to medium load as 1.5. This means that the system needs to appropriately expand the default interval determination boundary to adapt to the slower heating characteristics of medium heat capacity loads.

[0061] Step S206: Obtain the default set of deviation boundary values ​​and the set of rate of change boundary values; The default boundary value set consists of standard operating parameters set at the factory for the control system. It is usually calibrated based on the heating characteristics of a normal load and is used to provide basic state identification capabilities when load adaptation is not performed.

[0062] Specifically, after acquiring the adjustment coefficients, the microcontroller accesses the default configuration file in the system parameter storage area. From this file, it reads the set of upper and lower limit values ​​used to divide the temperature deviation range, i.e., the default deviation boundary value set; at the same time, it reads the set of upper and lower limit values ​​used to divide the temperature change rate range, i.e., the default change rate boundary value set.

[0063] For example, the system reads the default configuration file and obtains the default set of deviation boundary values ​​as 5 degrees Celsius, 10 degrees Celsius, and 15 degrees Celsius, and the default set of rate of change boundary values ​​as 0.5 degrees Celsius, 1 degree Celsius, and 2 degrees Celsius per second. These two sets constitute the standard stage judgment thresholds for the system when no load identification is performed, and the system will use these as the basis for the next step of coefficient adjustment.

[0064] Step S207: Multiply the interval boundary adjustment coefficient with each boundary value in the default deviation boundary value set and each boundary value in the default rate of change boundary value set to generate a target deviation boundary value set and a target rate of change boundary value set that are adapted to the current load. Through multiplication, the system scales the default boundary value set proportionally according to the actual heat capacity characteristics of the current load. The resulting target boundary value set can truly match the physical characteristics of the current heating object, making the timing of stage switching more reasonable.

[0065] Specifically, the microcontroller uses the acquired interval boundary adjustment coefficients as multipliers and each value in the default deviation boundary value set and the default rate of change boundary value set as multiplicands, performing multiple independent multiplication operations. The resulting two sets of new values ​​constitute new deviation boundaries and rate of change boundaries, respectively. The system then recombines these new boundary values ​​to construct a target deviation boundary value set and a target rate of change boundary value set adapted to the current load.

[0066] For example, the system obtains an interval boundary adjustment coefficient of 1.5, the default deviation boundary value set is 5 degrees Celsius, 10 degrees Celsius, and 15 degrees Celsius, and the default rate of change boundary value set is 0.5 degrees Celsius, 1 degree Celsius, and 2 degrees Celsius per second. The microcontroller performs multiplication operations, multiplying 1.5 by 5, 10, and 15 respectively to generate a new target deviation boundary value set of 7.5 degrees Celsius, 15 degrees Celsius, and 22.5 degrees Celsius, and multiplying 1.5 by 0.5, 1, and 2 respectively to generate a new target rate of change boundary value set of 0.75 degrees Celsius, 1.5 degrees Celsius, and 3 degrees Celsius per second. Based on this, the system generates a new boundary value set adapted to the current medium load.

[0067] Step S208: Use the target deviation boundary value set and the target rate of change boundary value set as the data source for subsequently obtaining the preset deviation boundary value set and the preset rate of change boundary value set.

[0068] Specifically, after generating the target deviation boundary value set and the target rate of change boundary value set adapted to the current load, the microcontroller updates the data to the system's parameter storage area or directly passes it to the subsequent judgment logic unit. Subsequently, when the system executes the step of obtaining the preset deviation boundary value set and rate of change boundary value set, it will directly read these two target boundary value sets as the current preset parameters for comparison, thereby outputting a heating stage that conforms to the current load characteristics.

[0069] For example, the microcontroller stores the calculated target deviation boundary value set and target rate of change boundary value set as data sources. In subsequent temperature control cycles, when the system needs to obtain the preset deviation boundary value set and preset rate of change boundary value set, it will directly call these two target boundary value sets for interval judgment, thereby ensuring that the stage division is fully adapted to the actual temperature rise characteristics of the medium load.

[0070] In the above embodiments, a load characteristic adaptive identification and boundary parameter dynamic reconstruction mechanism based on initial thermodynamic response is constructed, effectively solving the problems of misjudgment of heating stages and mismatch of temperature control strategies caused by fixed interval division when kitchen appliances face unknown loads. The system calculates the initial temperature rise value to accurately quantify the heat capacity characteristics of the current heating load; by comparing the initial temperature rise value with the preset temperature rise threshold interval, the heat capacity level is determined and the corresponding interval boundary adjustment coefficient is obtained. Then, the boundary values ​​in the default deviation boundary value set and the rate of change boundary value set are multiplied and scaled to generate a target deviation boundary value set and a target rate of change boundary value set that are highly adapted to the current load characteristics. This process can automatically adjust the boundary conditions of stage switching according to the actual load of each cooking task, improving the accuracy and flexibility of heating stage identification, and laying a solid data foundation for subsequent precise anti-overshooting and constant temperature control.

[0071] Reference Figure 3 As one implementation of step S103, the step of determining the current heating stage based on the preset range of the current temperature deviation and the current temperature change rate includes: Step S301: Obtain the preset set of deviation boundary values ​​and the set of rate of change boundary values; The deviation boundary value set and the rate of change boundary value set are essentially a set of threshold reference standards used to divide state intervals. By acquiring these pre-configured boundary value sets, the system provides a comparison benchmark for subsequently mapping continuous numerical changes to discrete interval states, thereby enabling the microcontroller to judge complex heating conditions based on clear data boundaries.

[0072] Specifically, the microcontroller's storage medium pre-stores boundary value data set for different temperatures and the characteristics of different heated objects. The deviation boundary value set typically contains multiple temperature deviation critical points arranged from smallest to largest, used to define the magnitude of the deviation; the rate of change boundary value set contains multiple temperature change rate critical points, used to define the severity of temperature rise or fall. When the system needs to perform state recognition in each control cycle, it directly reads these two sets of data from the storage area, preparing the data source for the subsequent interval judgment operation.

[0073] For example, in the temperature control system of a kitchen appliance, the preset deviation boundary value set may include two critical values: 5 degrees and 2 degrees. The rate of change boundary value set may include two critical values: 2 degrees per second and 0.5 degrees per second. When the system starts the status recognition logic, it first retrieves these two boundary value sets from the internal register so that the real-time measured temperature deviation and rate of change data can be compared with these critical values.

[0074] Step S302: Compare the current temperature deviation with the deviation boundary value set to determine the current deviation interval in which the current temperature deviation is located; compare the current temperature change rate with the change rate boundary value set to determine the current change rate interval in which the current temperature change rate is located. Since directly using specific temperature deviation and rate of change values ​​in the control logic is easily affected by sensor noise and is difficult to use directly for rule reasoning, the system compares these two real-time calculated dynamic feature values ​​with the corresponding boundary value sets to determine which specific numerical range the current deviation and rate of change fall into, thereby filtering out state jitter caused by small numerical fluctuations.

[0075] In this embodiment, the microcontroller compares the real-time acquired current temperature deviation with each critical value in the deviation boundary value set to determine whether the deviation is greater than the maximum boundary value, between the two boundary values, or less than the minimum boundary value, thereby locking the corresponding current deviation interval. Similarly, the system performs the same comparison logic on the current temperature change rate to determine its corresponding current change rate interval.

[0076] For example, if the system reads a current temperature deviation of 4 degrees, it compares this with the deviation boundary value set (including 5 degrees and 2 degrees), finding that 4 degrees falls between 2 and 5 degrees, thus determining the current deviation range as a medium deviation range. Simultaneously, if the current temperature change rate is 3 degrees per second, it compares this with the change rate boundary value set (including 2 degrees per second and 0.5 degrees per second), finding that it is greater than 2 degrees per second, thus determining the current change rate range as a high-speed change range.

[0077] Step S303: Combine the current deviation interval and the current rate of change interval to generate a current interval combination identifier; In this regard, neither a single deviation range nor a rate of change range can accurately describe the complete thermodynamic state of the object being heated. For example, the same medium deviation range, if combined with a high-speed change range, indicates that the system is rapidly approaching the target temperature and there is a risk of overshoot; if combined with a low-speed change range, it indicates that the heating is weak.

[0078] Therefore, after determining the current deviation range and the current rate of change range, the microcontroller can integrate these two ranges into a unified data tag using data combination methods such as string concatenation, bitwise operations, or table lookup encoding. This data tag is the current range combination identifier, which encapsulates comprehensive information such as the system's current distance from the target and the speed of approach in a compact data format, thus preparing the data structure for subsequent rapid state mapping.

[0079] For example, the system identifies the current deviation interval as "deviation B" and the current rate of change interval as "rate of change A". The microcontroller concatenates these two identifiers according to preset rules to generate the string "deviation B - rate of change A", which serves as the current interval combination identifier for the current cycle. This identifier summarizes that the heated object is currently in a complex state of medium distance deviation accompanied by rapid temperature rise.

[0080] Step S304: Obtain a preset stage mapping relationship table. The stage mapping relationship table contains the correspondence between multiple preset interval combination identifiers and preset heating stage identifiers. Among them, the preset heating stages include a rapid heating stage, a temperature approach stage, and a constant temperature stabilization stage. In this embodiment, the developers pre-wrote a stage mapping table in the system's non-volatile memory. This table stores a large number of preset interval combination identifiers and their unique corresponding preset heating stage identifiers in the form of key-value pairs. These correspondences are derived based on thermodynamic principles and a large amount of experimental data from kitchen appliance heating, covering various typical operating conditions throughout the heating process.

[0081] For example, as one implementation of the stage mapping table, the deviation boundary value set divides the temperature deviation into three intervals: large deviation, medium deviation, and small deviation; the change rate boundary value set divides the temperature change rate into three intervals: high change rate, medium change rate, and low change rate. Through combination, a total of nine current interval combination identifiers are generated. The mapping rules of the stage mapping table are as follows: When the deviation is large and the rate of change is any rate of change (high, medium, or low) (i.e., the system is still far from the target), it is mapped to a rapid heating stage. When the deviation is medium and the rate of change is high or medium (i.e., the system is approaching the target and has a certain velocity), it is mapped to the temperature approach stage. When the deviation is small and the rate of change is any rate of change (high, medium, or low) (i.e., it is close to the target), it is mapped to the isothermal stability stage. Furthermore, when the deviation is medium and the rate of change is low (weak heating), it is also mapped to a rapid heating stage to enhance the heating intensity.

[0082] Step S305: Match the current interval combination identifier with the preset interval combination identifier in the stage mapping table, and extract the preset heating stage identifier that has been successfully matched as the current heating stage.

[0083] Specifically, the system uses the currently generated interval combination identifier as the search term and compares it one by one in the preset interval combination identifier field of the stage mapping relationship table. When the same identifier value is found, the system immediately locks the data row, reads the content of the corresponding preset heating stage identifier field and assigns it to the current heating stage variable. This allows the system to clearly know whether it is in a rapid heating, temperature approach, or constant temperature stability stage in the following control cycle, thus providing a decision basis for calling the corresponding thermal inertia coefficient and control parameters.

[0084] In the above implementation, a set of deviation boundary values ​​and a set of rate of change boundary values ​​are introduced to divide and combine real-time temperature deviation and temperature rate of change into intervals. A stage mapping table is then used for rapid matching and querying, transforming complex and continuous thermodynamic dynamic data into clear and discrete identifiers of the current heating stage. This interval-based state recognition mechanism not only effectively filters out state jumps caused by minute fluctuations in sensor values, enhancing the system's anti-interference capability, but also reduces the computational load on the microcontroller for judging complex operating conditions. This allows the system to accurately lock onto the current heating stage within a very short control cycle, providing a reliable and efficient state decision-making basis for subsequent differentiated thermal inertia prediction compensation and parameter adaptive tuning. This improves the response speed and control accuracy of the kitchen appliance temperature control system when dealing with large inertia and nonlinear objects.

[0085] Reference Figure 4 As one implementation of step S104, the steps of obtaining the corresponding thermal inertia coefficient based on the current heating stage, calculating the expected waste heat temperature rise based on the thermal inertia coefficient and the current temperature change rate, and correcting the current temperature deviation based on the expected waste heat temperature rise to obtain the equivalent control deviation include: Step S401: Based on the preset stage coefficient mapping table, match and query the corresponding thermal inertia coefficient according to the current heating stage; The thermal inertia coefficient is a physical quantity that characterizes the degree of lag in temperature change of a heated object after a change in the state of the heat source. Because the heat transfer state between the heating element and the load differs significantly in different heating stages of kitchen appliances, such as rapid heating, temperature approach, and constant temperature stabilization, the overall system's thermal inertia also changes dynamically.

[0086] Specifically, the microcontroller's storage medium pre-stores a stage coefficient mapping table, which records the empirical values ​​corresponding to each preset heating stage identifier. After the system determines the current heating stage through the aforementioned steps, it uses this stage identifier as a search term to traverse and search the mapping table. Once a matching entry is found, the system reads the value stored in that entry and extracts it as the thermal inertia coefficient used in the current cycle, providing a crucial scaling factor for subsequent waste heat temperature rise calculations.

[0087] For example, in the water temperature control scenario of a smart cooking machine, if the system determines that the current heating stage is a rapid heating stage, it will retrieve the corresponding thermal inertia coefficient of 0.8 by querying the stage coefficient mapping table. This large value reflects that the heating element has accumulated a lot of heat during the high-power heating stage. If the system determines that it has entered the constant temperature and stable stage, the thermal inertia coefficient retrieved from the mapping table may be only 0.1, reflecting that the heat transfer tends to be in equilibrium and the system's heat storage capacity is greatly reduced.

[0088] Step S402: Multiply the thermal inertia coefficient by the current rate of temperature change to calculate the initial residual heat temperature rise; Understandably, the current rate of temperature change reflects the instantaneous speed at which the temperature rises or falls, while the thermal inertia coefficient represents how long this speed can maintain its inertial effect after the heat source is cut off or decays. Multiplying the two essentially estimates how much the load temperature will continue to rise if heating is stopped immediately, as the heat already accumulated inside the system continues to be transferred to the load.

[0089] In this embodiment, after obtaining the thermal inertia coefficient matching the current heating stage, the microcontroller retrieves the current temperature change rate calculated in the aforementioned steps and performs a multiplication operation. By combining the inertia coefficient, which characterizes physical properties, with the rate of change, which characterizes dynamic trends, a quantified temperature prediction value, i.e., the initial residual heat temperature rise, is output. This value represents the temperature increment that the system can achieve solely through its own thermal inertia without further strong intervention.

[0090] For example, the system currently extracts a thermal inertia coefficient of 0.5, while the current temperature change rate is 2 degrees Celsius per second. Multiplying these two values ​​together, the calculated initial residual heat temperature rise is 1 degree Celsius. This means that even if the heating power is immediately cut off, the load temperature is expected to continue to rise by 1 degree Celsius due to the residual heat transfer from the pot and heating plate.

[0091] Step S403: Calculate the absolute value of the current temperature deviation; The absolute value of the current temperature deviation represents the actual physical distance between the current temperature and the preset target temperature. If the predicted initial residual heat temperature rise is greater than or equal to this distance, it means that the temperature can be exceeded by inertia alone, and the predicted value must be limited. If it is less than this distance, it means that the residual heat is insufficient to directly reach the target temperature, and the predicted value can be used safely.

[0092] Specifically, the microcontroller first takes the absolute value of the current temperature deviation calculated above to eliminate the influence of the positive and negative signs on the numerical comparison, ensuring that the comparison process is based on pure numerical calculations of distance.

[0093] Step S404: Determine whether the initial residual heat temperature rise is greater than or equal to the absolute value of the current temperature deviation; if yes, proceed to step S405; if no, proceed to step S406. The system compares the calculated initial waste heat temperature rise with the absolute value of the deviation to determine which correction strategy to adopt to generate the final expected waste heat temperature rise. This avoids the temperature control system from prematurely shutting off the heating power due to over-reliance on the predicted value, which could lead to under-adjustment where the temperature remains below the target value.

[0094] For example, if the current preset target temperature is 100 degrees Celsius and the current temperature is 98 degrees Celsius, the calculated current temperature deviation is 2 degrees Celsius, with an absolute value of 2 degrees Celsius. If the calculated initial residual heat temperature rise is 3 degrees Celsius, the system compares it with the absolute value of 2 degrees Celsius and finds that the predicted residual heat is already greater than the remaining temperature rise distance. The system then marks the need to implement a limiting strategy.

[0095] Step S405: Calculate the difference between the absolute value of the current temperature deviation and the preset safety margin, and use it as the expected residual heat temperature rise. The preset safety margin is a small temperature margin reserved by the developers to ensure that the system will not completely cut off the control output under extreme inertial prediction, thus maintaining the closed-loop activity of the control algorithm. It is usually set between 0.1℃ and 0.5℃ depending on the temperature control accuracy requirements. By limiting the expected residual heat temperature rise to the level of the absolute value of the deviation minus the safety margin, the system both acknowledges the fact that inertia will dominate the temperature approaching the target and retains a very small error margin for the controller to fine-tune.

[0096] In this embodiment, when the system determines that the initial waste heat temperature rise is greater than or equal to the absolute value of the current temperature deviation, the microcontroller immediately calls the pre-stored preset safety margin parameter. Subsequently, the system performs a subtraction operation, subtracting the preset safety margin from the absolute value of the current temperature deviation, and forcibly overwrites the resulting difference as the final expected waste heat temperature rise. This avoids the problem of subsequent equivalent control deviations becoming negative or zero due to excessively large predicted waste heat, thus preventing control logic confusion.

[0097] For example, if the absolute value of the current temperature deviation is 2 degrees Celsius and the initial waste heat temperature rise is 3 degrees Celsius, the system determines that the condition of being greater than or equal to is met. The preset safety margin is configured as 0.2 degrees Celsius. The system calculates the difference between the absolute value of 2 degrees Celsius and the safety margin of 0.2 degrees Celsius, obtaining 1.8 degrees Celsius, and uses this 1.8 degrees Celsius as the final expected waste heat temperature rise. This reflects both the prediction that waste heat will cause a significant temperature rise and the retention of a 0.2-degree Celsius margin to maintain the system's base power output.

[0098] Step S406: Determine the initial waste heat temperature rise as the expected waste heat temperature rise; If the initial residual heat temperature rise is less than the absolute value of the current temperature deviation, the inertial residual heat predicted by the system is insufficient to directly fill the gap between the current temperature and the target temperature. This indicates that the system still needs to actively output a larger heating power to drive the temperature up. In this case, there is no need to intervene or limit the predicted value; directly using the original calculated value can most realistically reflect the future thermodynamic change trend of the system.

[0099] Specifically, when the comparison result shows that the initial waste heat temperature rise is strictly less than the absolute value of the current temperature deviation, the microcontroller skips the limiting calculation logic and directly executes the data transmission operation. The system directly assigns the initial waste heat temperature rise obtained by multiplying the thermal inertia coefficient by the current temperature change rate to the expected waste heat temperature rise variable, ensuring that in stages where the distance from the target is far or the heating is slow, the predictive compensation mechanism can intervene in the control correction with real data, without over-intervention or lacking foresight.

[0100] For example, the absolute value of the current temperature deviation is 5 degrees Celsius, while the calculated initial waste heat temperature rise is only 1 degree Celsius. The system determines that 1 degree Celsius is less than 5 degrees Celsius and directly recognizes this 1-degree Celsius initial waste heat temperature rise as the expected waste heat temperature rise. The system believes that although there is a 1-degree Celsius expected waste heat, there is still a 4-degree Celsius difference from the total 5-degree Celsius difference that needs to be compensated by the current heating power; therefore, the predicted value can be safely used.

[0101] Step S407: Calculate the difference between the current temperature deviation and the expected waste heat temperature rise, and use the difference as the equivalent control deviation.

[0102] It is understandable that the equivalent control deviation is not the actual temperature difference measured by the sensor, but a comprehensive error signal that integrates the current instantaneous deviation and the future inertial prediction trend. By subtracting the expected waste heat temperature rise from the current temperature deviation, the system senses future temperature changes in advance at the data level, enabling the controller to perform calculations based on this reduced equivalent deviation, thereby reducing the heating power in advance before the physical temperature actually reaches the target value.

[0103] In this embodiment of the application, after the microcontroller determines the final expected waste heat temperature rise, it retrieves the current temperature deviation data generated in the aforementioned steps. The system performs a subtraction operation with the current temperature deviation as the minuend and the expected waste heat temperature rise as the subtrahend. The resulting difference is the equivalent control deviation sent to the control algorithm in this cycle.

[0104] For example, the system calculates the current temperature deviation to be 5 degrees Celsius, while the expected residual heat temperature rise, determined after judgment and correction, is 1 degree Celsius. The system subtracts 1 degree Celsius from 5 degrees Celsius, resulting in an equivalent control deviation of 4 degrees Celsius. This means that although the actual distance from the target is still 5 degrees Celsius, the system believes that 1 degree Celsius can be reached by inertia. Therefore, the control algorithm only needs to calculate the heating power for the equivalent difference of 4 degrees Celsius, thus achieving the feedforward compensation effect to prevent overshoot.

[0105] In the above embodiments, the system can accurately match the thermal inertia coefficient according to the thermodynamic characteristics of different heating stages, enabling the prediction model to have adaptive capabilities under operating conditions. By combining the thermal inertia coefficient with the rate of temperature change to calculate the initial waste heat temperature rise, and using the absolute value of the current temperature deviation for amplitude comparison and safety margin correction, the system effectively avoids the temperature control stagnation problem caused by overcompensation of predicted waste heat under extreme operating conditions. Finally, the equivalent control deviation is reconstructed by calculating the difference between the current temperature deviation and the expected waste heat temperature rise, feeding back possible future temperature changes to the current control cycle in advance. This technical solution improves the system's prediction accuracy and anti-interference capability for complex thermal inertial objects, achieving a smooth and overshoot-free precise temperature control effect.

[0106] Reference Figure 5 As one implementation of step S105, the step of inputting the equivalent control deviation and the current temperature change rate into a preset fuzzy inference model to calculate the current control parameters includes: Step S501: Based on the membership function in the preset fuzzy inference model, the equivalent control deviation and the current temperature change rate are fuzzified respectively to generate the corresponding deviation fuzzy variable and change rate fuzzy variable. In the heating process of kitchen appliances, the equivalent control deviation after thermal inertia correction and the current temperature change rate reflecting the trend are both specific numerical values. However, the fuzzy inference system does not directly perform function calculations based on numerical values, but rather makes decisions based on linguistic rules derived from human experience. The role of fuzzification is to evaluate the degree to which these precise numerical values ​​belong to various preset fuzzy sets through membership functions, thereby mapping individual numerical values ​​into fuzzy variables with membership weights, making subsequent rule matching possible.

[0107] Specifically, the pre-configured fuzzy inference model includes multiple membership functions for deviation and rate of change. These functions define the distribution range of fuzzy linguistic sets such as negative large, negative medium, zero, positive medium, and positive large. After acquiring the equivalent control deviation and the current temperature change rate, the microcontroller substitutes these specific values ​​into the corresponding membership functions to calculate the membership degree of each value to different fuzzy sets. These linguistic variables with membership information are the deviation fuzzy variables and the rate of change fuzzy variables, which express comprehensive information about the fuzzy context in which the current temperature state exists.

[0108] For example, the system calculates an equivalent control deviation of 4 degrees Celsius, and the current temperature change rate is 2 degrees Celsius per second. After fuzzification, the equivalent control deviation may have a membership degree of 0.8 belonging to the central fuzzy set and 0.2 belonging to the large fuzzy set, thus generating a deviation fuzzy variable containing this membership degree information. Similarly, the current temperature change rate is also processed in the same way to generate a change rate fuzzy variable, providing a standardized input format for subsequent rule-based reasoning.

[0109] Step S502: Input the deviation fuzzy variable and the rate of change fuzzy variable into the fuzzy rule base in the preset fuzzy inference model for condition matching, and generate parameter adjustment fuzzy variables; The fuzzy rule base stores a large number of conditional statements based on thermodynamic control experience. These statements associate different combinations of temperature deviation states and temperature change trends with corresponding control parameter adjustment strategies.

[0110] In this embodiment, the fuzzy rule base exists in the microcontroller in the form of a two-dimensional lookup table or logical judgment. Each rule's antecedent consists of a deviation fuzzy variable and a rate of change fuzzy variable, while the consequent is a parameter adjustment fuzzy variable. The microcontroller cross-matches the currently generated deviation and rate of change fuzzy variables with the antecedents of each rule in the rule base to calculate the rule's activation strength. Subsequently, the system synthesizes the consequents of all activated rules according to their activation strength, outputting a comprehensive parameter adjustment fuzzy variable. This variable, in the form of a fuzzy set, specifies how the proportional, integral, and derivative parameters should be adjusted.

[0111] For example, when the deviation fuzzy variable indicates that the deviation is in a neutral state, and the rate of change fuzzy variable indicates that the rate of change is in a large state, the system matches the corresponding rule in the fuzzy rule base. This rule indicates that the system is rapidly approaching the target and there is a risk of overshoot. Its consequent indicates that the proportional parameter needs to be appropriately reduced and the derivative parameter increased. After integrating the consequents of all matching rules, the system generates a parameter adjustment fuzzy variable that indicates reducing the proportional parameter and increasing the derivative parameter, which serves as the data source for subsequent defuzzification operations.

[0112] Step S503: Based on the defuzzification rules in the preset fuzzy inference model, perform precise calculations on the fuzzy variables for parameter adjustment to obtain the corresponding parameter adjustment amounts; Understandably, the fuzzy variables for parameter adjustment generated through rule-based reasoning are still expressed in a fuzzy language and cannot be directly used for arithmetic operations by the microcontroller. The role of defuzzification rules is to perform mathematical analysis on this fuzzy information, extract the most representative precise values, and thus transform the expert's experience judgment into a quantitative adjustment signal that the microcontroller can recognize and execute.

[0113] Specifically, the pre-defined fuzzy inference model incorporates defuzzification algorithms such as the centroid method and the maximum membership method. After the microcontroller obtains the fuzzy variables for parameter adjustment, it calculates the geometric characteristic values ​​of the membership function graph represented by the fuzzy variable according to the selected defuzzification rule. Through integration or weighted averaging, the system compresses the fuzzy parameter adjustment indication into a specific numerical value, which is the parameter adjustment amount. This value clearly indicates the specific amount by which the proportional, integral, and derivative parameters need to be increased or decreased in the current period.

[0114] For example, the fuzzy variable for parameter adjustment generated by the system inference indicates that the proportional parameter should be adjusted negatively. After the corresponding fuzzy set is defuzzified using the centroid method, the specific value is 0.5. This 0.5 is the parameter adjustment amount of the proportional parameter, meaning that the system needs to subtract 0.5 from the original proportional parameter. The fuzzy logical instruction is thus transformed into a precise mathematical increment.

[0115] Step S504: Obtain the preset basic control parameters, superimpose the parameter adjustment amount with the basic control parameters, and calculate the current proportional parameter, current integral parameter and current derivative parameter, which are used as the current control parameters.

[0116] Among them, the basic control parameters are the conventional state parameters set by the system in the initial calibration stage, which can ensure the basic stable operation of the system when there are no special disturbances. They are usually calibrated according to the rated power of the object being heated. The proportional parameter range is generally 1.0-5.0, the integral parameter range is 0.1-1.0, and the derivative parameter range is 0.01-0.5.

[0117] In this embodiment, the microcontroller's storage area pre-stores a set of basic control parameters calibrated for a specific kitchen appliance. After completing the defuzzification calculation to obtain the adjustment amounts of each parameter, the system adds the proportional parameter adjustment amount to the basic proportional parameter, the integral parameter adjustment amount to the basic integral parameter, and the derivative parameter adjustment amount to the basic derivative parameter. If the superimposed result exceeds the preset safety parameter range, the system will also perform amplitude limiting processing. The final generated current proportional parameter, current integral parameter, and current derivative parameter together constitute the current control parameters for this cycle, which directly participate in the calculation of the target heating power.

[0118] For example, the system's preset base proportional parameter is 2.0, base integral parameter is 0.5, and base derivative parameter is 0.1. Of the parameter adjustments calculated through the aforementioned steps, the proportional parameter adjustment is -0.5, and the derivative parameter adjustment is +0.2. The system adds these two together, calculating the current proportional parameter as 1.5, the current derivative parameter as 0.3, and the integral parameter remaining at 0.5. This set of current control parameters is then sent to the power calculation module, allowing for more reasonable control of the power output to address the current temperature approach process.

[0119] The above implementation effectively solves the problem that traditional fixed-parameter temperature control algorithms are ill-suited to complex operating conditions such as strong nonlinearity and high thermal inertia in kitchen appliance heating. By fuzzifying the equivalent control deviation and temperature change rate using membership functions, the system can accurately capture the dynamic characteristics of the heating process. With the help of conditional matching reasoning from a fuzzy rule base, the system transforms expert control experience into intelligent decisions on parameter adjustment direction. Then, by defuzzifying the rules, the decisions are quantified into precise parameter adjustment amounts and superimposed on the basic control parameters, achieving online dynamic optimization of control parameters.

[0120] In practical applications, this technical solution enables the proportional, integral, and derivative parameters to be adaptively adjusted according to the temperature deviation and trend of each cycle. It can maintain the best control strength when facing thermal shock or large inertial residual heat, thus improving the response speed, stability, and anti-interference ability of the temperature control system.

[0121] Reference Figure 6 As one implementation of step S106, the step of obtaining historical control parameters from the previous sampling period and calculating the target heating power for the current period based on the equivalent control deviation, current control parameters, and historical control parameters includes: Step S601: Obtain the historical control parameters of the previous sampling period, including historical equivalent control deviation and historical heating power; Among them, the historical equivalent control deviation reflects the temperature difference after thermal inertia correction in the previous sampling period, while the historical heating power records the actual energy output level sent to the electric heating actuator in the previous period. These two parameters together constitute the initial reference system for the power calculation of the current period.

[0122] Specifically, the microcontroller allocates a dedicated register or memory area in the storage medium for historical control parameters. At the end of each control cycle, the system overwrites the equivalent control deviation and target heating power of the current cycle into this area as historical data for the next cycle. When entering a new control cycle, the microcontroller first accesses this storage area to read the historical equivalent control deviation and historical heating power saved in the previous sampling cycle. This allows the microcontroller to use these preceding state variables for differential calculation and power superposition in subsequent steps, thereby ensuring the continuity and smoothness of the control output.

[0123] For example, in the temperature control cycle of a smart rice cooker, the system saves the current equivalent control deviation and the calculated target heating power at the end of each sampling cycle. When a new sampling cycle begins and the power calculation stage is executed, the microcontroller retrieves the historical equivalent control deviation and historical heating power from the storage area. These two values ​​are compared and calculated with the parameters collected and processed in real time in the current cycle to ensure that the power adjustment is based on the gradual change of the state in the previous cycle.

[0124] Step S602: Calculate the difference between the equivalent control deviation and the historical equivalent control deviation to obtain the current deviation change. Understandably, although the equivalent control deviation has undergone thermal inertia prediction compensation, it only reflects the static distance from the target temperature at the current moment. The change in the current deviation, however, characterizes the rate at which this distance contracts or expands within two adjacent sampling periods. This differential information of the deviation plays an irreplaceable role in the control system's ability to predict temperature trends in advance and suppress temperature oscillations.

[0125] In this embodiment, after acquiring the equivalent control deviation and the historical equivalent control deviation, the microcontroller performs a subtraction operation, using the equivalent control deviation as the minuend and the historical equivalent control deviation as the subtrahend. The difference between the two is the current deviation change. This change is a signed value, and its sign and magnitude directly reflect the rate at which the system eliminates the temperature deviation, providing the necessary input data source for subsequent differential control.

[0126] For example, the historical equivalent control deviation of the system in the previous sampling period was 5 degrees Celsius, and after thermal inertia correction, the equivalent control deviation in the current period is 3 degrees Celsius. The microcontroller calculates the difference between the two and obtains the current deviation change as -2 degrees Celsius. This change of -2 degrees Celsius indicates that the temperature deviation is decreasing at a rate of 2 degrees Celsius per period, and the system is effectively approaching the preset target temperature.

[0127] Step S603: Based on the current proportional parameter, current integral parameter, and current derivative parameter, perform a weighted fusion calculation on the equivalent control deviation and the current deviation change to generate the current power adjustment increment; The current control parameters, dynamically tuned by the fuzzy inference model, are dynamic weights adapted to different heating stages. The system uses these dynamic weights to comprehensively assess the equivalent control deviation and the change in the current deviation, calculating the power adjustment that should be made compared to the previous cycle, thereby achieving precise proportional, integral, and derivative control.

[0128] Specifically, the microcontroller multiplies the current proportional parameter by the equivalent control deviation to obtain the proportional control component, multiplies the current integral parameter by the equivalent control deviation to obtain the integral control component, and multiplies the current derivative parameter by the current deviation change to obtain the derivative control component. The proportional, integral, and derivative control components are then added together to obtain the current power adjustment increment. This increment is a relative adjustment amount representing the direction and magnitude of power increase or decrease, reflecting the incremental algorithm's fine-grained response to minute changes in the system state.

[0129] For example, the fuzzy inference model outputs the current proportional parameter, current integral parameter, and current derivative parameter based on the current operating condition. The microcontroller multiplies the current proportional parameter by the equivalent control deviation, multiplies the current derivative parameter by the current deviation change, and incorporates the result of the integral term calculation. Assuming that after fusion calculation, the system determines that the current power adjustment increment for the current cycle is -100 watts, this means that compared to the previous cycle, the system determines that the power output needs to be reduced by 100 watts in the current cycle to prevent temperature overshoot.

[0130] Step S604: The current power adjustment increment is superimposed with the historical heating power to generate the current preliminary heating power; Understandably, the characteristic of incremental control is that it always makes fine adjustments based on the state of the previous moment. Therefore, by directly adding the calculated current power adjustment increment to the historical heating power of the previous cycle, the system can smoothly transition to the power level of the current cycle, avoiding the output jump that may be caused by absolute calculation, thus ensuring the stability of the operation of the electric heating actuator.

[0131] In this embodiment, after obtaining the current power adjustment increment, the microcontroller retrieves the historical heating power obtained in the aforementioned steps and performs an addition operation. If the current power adjustment increment is positive, the current preliminary heating power after addition will be higher than the historical heating power, indicating that the system needs to strengthen heating; if the increment is negative, the power after addition will be reduced.

[0132] For example, if the historical heating power of the system in the previous sampling period was 800 watts, and the current power adjustment increment obtained through dynamic weighting calculation is -100 watts, the microcontroller will add the -100 watt increment to the historical power of 800 watts, generating a current initial heating power of 700 watts. This indicates that the system, based on the fact that the current temperature deviation is decreasing, decides to moderately reduce the output power compared to the previous period to smoothly enter the isothermal stage.

[0133] Step S605: Obtain the preset power constraint threshold, compare the current initial heating power with the preset power constraint threshold, limit the current initial heating power according to the comparison result, and output the target heating power for the current cycle.

[0134] In actual heating processes, the system's output power has an insurmountable upper limit due to the physical limitations of the heating element or the power supply's carrying capacity; simultaneously, a lower limit constraint exists to prevent control failure or flameout caused by excessively low power. The preset power constraint threshold defines this safe output range, and the system must ensure that the final issued commands are within the physically permissible range.

[0135] Specifically, the microcontroller's storage medium contains preset power constraint thresholds, including the maximum allowable power and the minimum sustaining power. After generating the current initial heating power, the microcontroller compares it with these thresholds. If the current initial heating power exceeds the maximum threshold, the system forcibly truncates it to the maximum threshold; if it is below the minimum threshold, it is raised to the minimum threshold; if it is within the threshold range, it remains unchanged. The value after this limiting process is the target heating power for the current cycle, ensuring the physical executability of the control commands.

[0136] For example, the preset power constraint threshold for kitchen appliances ranges from a minimum of 100 watts to a maximum of 2100 watts. If the algorithm calculates the current initial heating power to be 2300 watts, and after comparison, it is found that the power exceeds the upper limit, the system will limit it and output 2100 watts as the target heating power for the current cycle. If the initial power calculation is zero watts, the system will limit it to 100 watts to maintain the basic heating state, thereby ensuring both temperature control accuracy and equipment safety.

[0137] The above implementation effectively solves the problems of sudden power output changes and hardware overload in kitchen appliances under complex thermodynamic conditions. By introducing historical equivalent control deviations and historical heating power, and calculating the current deviation change, the system can accurately capture the dynamic evolution trend of temperature error. By combining the current control parameters output by the fuzzy inference model with weighted fusion calculations, a refined current power adjustment increment is generated, enabling the control intensity to be adaptively adjusted according to the operating conditions. By superimposing the increment with historical power and performing strict threshold limiting processing, not only is a smooth transition of power output guaranteed and temperature oscillations avoided, but the operation of electric heating actuators within a safe range is also fundamentally guaranteed, improving the robustness and reliability of the intelligent temperature control system.

[0138] Reference Figure 7 As one implementation of step S107, the step of generating a corresponding power control signal based on the target heating power includes: Step S701: Obtain the rated heating power of the electric heating actuator, calculate the ratio of the target heating power to the rated heating power, and obtain the target duty cycle; The target heating power is the energy required for the current cycle calculated by the aforementioned control algorithm based on temperature deviation and dynamic parameters. However, the electric heating actuators in kitchen appliances typically only operate in a fully on or fully off state and cannot directly receive and execute continuous absolute power values. Therefore, it is necessary to introduce the rated heating power as a reference benchmark. By calculating the ratio, the absolute power demand is mapped to a proportionality coefficient between 0 and 1, i.e., the target duty cycle, thereby providing basic data for subsequent on / off time allocation.

[0139] Specifically, the microcontroller's storage medium pre-writes the rated heating power of the electric heating actuator (such as a heating element or heating plate in a kitchen appliance). This value represents the maximum power output capability of the electric heating actuator when continuously conducting at full voltage. Within each control cycle, the microcontroller acquires the target heating power after incremental calculation and limiting, uses it as the numerator, and performs a division operation with the rated heating power as the denominator. The resulting quotient is the target duty cycle, which, in percentage or decimal form, represents the proportion of time the heating device should be in the conducting state during the current cycle.

[0140] For example, a microwave oven has a rated heating power of 1000 watts, while the target heating power calculated by the fuzzy control algorithm for the current cycle is 500 watts. The microcontroller performs a ratio calculation to obtain a target duty cycle of 0.5. This means that within the current control cycle, the heating element needs to be in the on state for 50% of the time to output an average power of 500 watts to meet the temperature control requirements.

[0141] Step S702: Obtain the preset carrier cycle parameters, and calculate the on-time parameters and off-time parameters of the current cycle based on the carrier cycle parameters and the target duty cycle. The carrier period parameter is the length of a complete on / off cycle set by the system. It determines the frequency of the heating device's switching action and is generally set between 1 and 10 seconds based on the heating device's thermal inertia. The target duty cycle is only a proportional coefficient and cannot directly drive the hardware. It must be combined with the specific carrier period to decompose the high-level duration and low-level duration required by the hardware timer, thereby achieving precise time control of the heating device.

[0142] In this embodiment, the microcontroller pre-stores a carrier cycle parameter, which is typically calibrated based on the thermal response characteristics of the heating device and the lifespan of the relay or thyristor. After obtaining the target duty cycle, the microcontroller multiplies it by the carrier cycle parameter to obtain the duration for which the electric heating actuator should remain on in the current cycle, i.e., the on-time parameter. Subsequently, the system subtracts the on-time parameter from the carrier cycle parameter, and the remaining time is the off-time parameter. These two parameters appear in pairs, completely describing the on-off timing allocation within a carrier cycle.

[0143] For example, the system's preset carrier period parameter is 2 seconds, and the currently calculated target duty cycle is 0.5. The microcontroller multiplies 2 seconds by 0.5 to obtain a 1-second on-time parameter. Then, it subtracts the 1-second on-time from the 2-second carrier period to obtain a 1-second off-time parameter. The macroscopic duty cycle is thus decomposed into specific 1-second on-time and 1-second off-time parameters, providing precise time dimension parameters for subsequent waveform generation.

[0144] Step S703: Generate a pulse width modulation control command based on the on-time parameter and the off-time parameter; Microcontrollers typically integrate dedicated hardware timers or pulse width modulation (PWM) modules. These peripherals rely on counter values ​​and comparison match values ​​in registers to generate square wave signals. Generating PWM control instructions involves mapping physical time parameters to configuration code in hardware registers, enabling the hardware peripherals to automatically toggle level states according to the set timing sequence.

[0145] Specifically, after calculating the on-time and off-time parameters for the current cycle, the microcontroller converts these physical time values ​​into timer count values ​​based on the system clock frequency. Subsequently, the system packages these count values, along with relevant port configuration information and timer operating modes, into a set of pulse width modulation (PWM) control instructions. This set of instructions includes the reload value of the compare register required by the hardware timer and the upper limit of the counting cycle. Once written to the peripheral register, it can control the microcontroller pins to output precise square wave signals.

[0146] For example, the microcontroller obtains an on-time parameter of 2 seconds and an off-time parameter of 1 second. Based on its internal 10 MHz clock frequency, the system converts 2 seconds into 20 million count values ​​and 1 second into 10 million count values. Subsequently, the microcontroller combines these two values ​​with configuration information such as the forward counting mode to generate a pulse width modulation (PWM) control instruction. This instruction is then prepared to be written into the timer's compare register and period register to control the pin output of a periodic waveform consisting of 2 seconds of high level followed by 1 second of low level.

[0147] Step S704: The pulse width modulation control command is determined as a power control signal.

[0148] In this embodiment, after the microcontroller's control core completes instruction encapsulation, it transmits the instruction to the timer peripheral via the internal bus and triggers startup. At this time, the pulse width modulation control instruction is formally recognized as a power control signal at the system output level. The microcontroller's output pins drive isolation circuits, relay coils, or thyristor gates according to the level changes of this signal, thereby controlling the on / off state of the high-voltage circuit, enabling the physical heating device to operate according to the energy output expected by the algorithm.

[0149] For example, after the microcontroller writes the generated pulse width modulation control instruction into the hardware timer and starts the output, the instruction is converted into a power control signal. The microcontroller's pins output continuous high and low level transitions based on this signal. This transition signal is transmitted to the input of the optocoupler isolator, driving the bidirectional thyristor in the back end to turn on and off, so that the 2-kilowatt heating tube operates at a rhythm of 2 seconds on and 1 second off, ultimately converting the target heating power calculated by the algorithm into actual heat energy output.

[0150] The above implementation effectively solves the matching problem between the output value of the intelligent temperature control algorithm and the execution mode of the physical heating device. By calculating the ratio of the target heating power to the rated heating power, the target duty cycle is obtained, and the system normalizes the absolute energy demand into a universal proportional coefficient. Combining the carrier period parameter, the duty cycle is decomposed into on-time and off-time parameters, further concretizing the proportional coefficient into a time dimension. By generating and issuing pulse width modulation control commands as power control signals, it is ensured that the decisions of the control algorithm can be accurately executed by the hardware timer.

[0151] In practical applications, this technical solution achieves continuous and smooth control of heating power without changing the basic heating hardware structure. This avoids damage to mechanical components such as relays due to frequent operation and ensures that kitchen appliances can accurately output energy according to the target power output by the aforementioned complex control logic, thereby improving the practicality and reliability of the overall intelligent temperature control system.

[0152] Reference Figure 8 As a further implementation of the intelligent temperature control method for the heating process of kitchen appliances, after updating the current temperature, equivalent control deviation, and target heating power to the historical control parameters of the next sampling period, it also includes: Step S801: Store the expected waste heat temperature rise, thermal inertia coefficient, and current heating stage as historical state parameters; Specifically, after completing the routine historical control parameter updates, in order to support the subsequent model self-calibration function based on actual thermodynamic feedback, the microcontroller needs to additionally extract and properly store the key prediction data generated in the current cycle. The microcontroller retrieves the expected waste heat temperature rise generated after correction for this cycle, the thermal inertia coefficient used to calculate the temperature rise, and the identified current heating stage identifier from memory, and writes these three data items as a data structure into a dedicated historical state cache, making it a benchmark reference for error comparison and coefficient iteration in the next sampling cycle.

[0153] For example, in a certain control cycle of the intelligent cooking machine, the system calculates that the expected residual heat temperature rise is 1 degree Celsius, the extracted thermal inertia coefficient is 0.5, and the current heating stage is in the temperature approximation stage. The microcontroller then packages and stores these three values ​​in the register. When the next cycle arrives, the system can directly read these data marked as historical state parameters, thereby ensuring that the subsequent temperature rise prediction deviation calculation has complete data source support.

[0154] Step S802: Obtain the historical expected residual heat temperature rise, historical thermal inertia coefficient, historical heating stage, and historical heating power of the previous sampling period, and calculate the actual temperature rise value based on the current temperature and historical temperature. In this embodiment, the microcontroller reads the historical expected residual heat temperature rise, historical thermal inertia coefficient, historical heating stage, and historical heating power stored in the previous sampling period from the storage area. These parameters represent the system's estimation of thermal inertia effects and the operating environment in the previous period. Simultaneously, the microcontroller retrieves the current temperature of the current period and the historical temperature of the previous period, subtracts the two, and the difference is the actual temperature rise. This actual temperature rise objectively reflects the actual temperature jump of the heated object after experiencing the target heating power of the previous period.

[0155] For example, in the temperature control process of a smart rice cooker, the system reads the historical expected residual heat temperature rise from the previous sampling period as 2 degrees Celsius, the historical thermal inertia coefficient as 0.5, and the historical heating stage as the temperature approximation stage. If the current temperature is 95 degrees Celsius and the historical temperature is 93 degrees Celsius, the microcontroller subtracts 93 from 95 to calculate the actual temperature rise as 2 degrees Celsius. This actual temperature rise will serve as a benchmark for evaluating the accuracy of the previous thermal inertia prediction.

[0156] Step S803: Calculate the difference between the actual temperature rise and the historical expected total temperature rise to generate the temperature rise prediction deviation. Among them, the historical expected total temperature rise is the sum of the historical expected waste heat temperature rise and the expected active heating temperature rise, and the expected active heating temperature rise is the product of the historical heating power and the preset power temperature rise conversion coefficient.

[0157] Understandably, in an ideal thermodynamic prediction model, the actual temperature rise should match the predicted total temperature rise. However, in practical applications, due to changes in the heat absorption characteristics of food or changes in environmental heat dissipation conditions, there are often differences between the two.

[0158] Specifically, after acquiring the actual temperature rise, the historical expected waste heat temperature rise, and the historical heating power, the microcontroller accesses the memory to read the preset power-temperature rise conversion coefficient. It multiplies the historical heating power by this coefficient to obtain the active heating expected temperature rise, and then adds this to the historical expected waste heat temperature rise to synthesize the total historical expected temperature rise. Subsequently, the microcontroller performs a subtraction operation, using the actual temperature rise as the minuend and the total historical expected temperature rise as the subtrahend. The result is the temperature rise prediction deviation. This deviation is a value with positive and negative polarities; its absolute value represents the extent to which the prediction deviates from reality, while its polarity indicates whether the prediction is too high or too low, providing a direct mathematical basis for subsequent parameter adjustments.

[0159] For example, if the system output historical heating power of 800 watts in the previous sampling period, and the preset power-to-temperature-rise conversion factor is 0.001 degrees Celsius / watt, then the calculated expected active heating temperature rise is 0.8 degrees Celsius. If the historical expected residual heat temperature rise predicted in the previous period was 2 degrees Celsius, then the synthesized historical expected total temperature rise is 2.8 degrees Celsius. If the actual temperature rise calculated based on the current temperature and historical temperature in the current period is only 2.5 degrees Celsius, the generated temperature rise prediction deviation is -0.3 degrees Celsius. This deviation of -0.3 degrees Celsius indicates that the actual temperature rise due to thermal inertia is 0.3 degrees Celsius less than the system prediction, suggesting that the currently used historical thermal inertia factor is too large, overestimating the heat storage effect, and a correction command to reduce the thermal inertia factor needs to be triggered.

[0160] Step S804: Obtain the preset deviation tolerance range. If the temperature rise prediction deviation exceeds the preset deviation tolerance range, generate a coefficient correction command. Understandably, in practical engineering, sensor noise or minor airflow disturbances can cause slight temperature fluctuations. If the system adjusts its parameters for even the smallest prediction deviation, it will lead to control logic oscillations. The preset deviation tolerance range defines an allowable error band. Only when the prediction deviation substantially exceeds this error band will the system consider the thermal inertia model to have failed and trigger the correction mechanism.

[0161] In this embodiment, the microcontroller's storage medium has a preset deviation tolerance range, which is typically set to a positive or negative value range based on the temperature control accuracy requirements. The microcontroller extracts the generated temperature rise prediction deviation and compares its absolute value with the boundary value of the preset deviation tolerance range. If the temperature rise prediction deviation is within this range, the system determines that the prediction error is within a controllable range and does not perform any operation; if the temperature rise prediction deviation exceeds this range, the system immediately generates a coefficient correction command, forcibly triggering the subsequent thermal inertia coefficient adjustment process.

[0162] For example, the system's preset deviation tolerance range is ±0.5 degrees Celsius. The currently calculated temperature rise prediction deviation is ±1 degree Celsius. The microcontroller compares this to the range boundary and finds that ±1 degree Celsius exceeds the upper limit of ±0.5 degrees Celsius. The system determines that the prediction error has exceeded the tolerance limit and immediately generates a coefficient correction command, preparing to intervene in the thermal inertia parameters.

[0163] Step S805: Based on the coefficient correction command and the sign polarity of the temperature rise prediction deviation, the historical thermal inertia coefficient is iteratively adjusted to generate the corrected thermal inertia coefficient. Understandably, since the thermal inertia coefficient characterizes the delayed effect of heat storage on load temperature, a positive prediction deviation indicates that the actual temperature rise is greater than the predicted temperature rise, meaning the thermal inertia effect is underestimated, and the thermal inertia coefficient needs to be increased; conversely, it needs to be decreased. The iterative adjustment method ensures that the parameters change gradually, preventing abrupt parameter changes from causing drastic jumps in the control output.

[0164] Specifically, after receiving the coefficient correction command, the microcontroller first identifies the sign polarity of the temperature rise prediction deviation. If the polarity is positive, the microcontroller increases the historical thermal inertia coefficient by one step unit according to the preset adjustment step size; if the polarity is negative, it decreases the historical thermal inertia coefficient by one step unit. The final output of this step-based iterative adjustment calculation is the corrected thermal inertia coefficient, which is closer to the actual thermodynamic characteristics of the object being heated.

[0165] For example, if the temperature rise prediction deviation is +1 degree Celsius (positive polarity), the microcontroller obtains a corresponding historical thermal inertia coefficient of 0.5. Based on a preset adjustment step of 0.05, the microcontroller increases this step by 0.05, generating a corrected thermal inertia coefficient of 0.55. This adjustment allows the system to assign greater weight to the rate of temperature change when subsequently calculating the expected waste heat temperature rise, thus predicting a higher waste heat temperature rise.

[0166] Step S806: Associate and bind the corrected thermal inertia coefficient with the historical heating stage to generate a thermal inertia coefficient mapping relationship and update the stage coefficient mapping table.

[0167] In this embodiment, after generating the corrected thermal inertia coefficient, the microcontroller retrieves the previously acquired historical heating stage identifier. The system uses the historical heating stage identifier as a key and the corrected thermal inertia coefficient as the corresponding parameter value to perform a mapping and binding operation. This operation overwrites or adds parameter records under that heating stage, ultimately generating an updated thermal inertia coefficient mapping relationship containing the latest correction data.

[0168] For example, if the current operating condition triggering the correction is in the temperature approach phase, the microcontroller binds the calculated corrected thermal inertia coefficient of 0.55 to the identifier of the temperature approach phase. The system internally generates an updated thermal inertia coefficient mapping record, explicitly indicating that a thermal inertia coefficient of 0.55 should be used in the temperature approach phase, thereby achieving fine-grained classification and management of parameters.

[0169] Subsequently, the microcontroller writes the updated thermal inertia coefficient mapping relationship into the stage coefficient mapping table for updating. In subsequent sampling cycles, when the system executes the step of obtaining the corresponding thermal inertia coefficient based on the current heating stage, it will directly access the latest stage coefficient mapping table and match the thermal inertia coefficient corresponding to the current stage for the calculation of the expected waste heat temperature rise.

[0170] The above implementation addresses the problem of inaccurate thermal inertia prediction models in kitchen appliances due to changes in load conditions or environmental disturbances, leading to overshoot or slow response. The system generates a temperature rise prediction deviation by comparing the actual temperature rise with the historical expected total temperature rise. When the deviation exceeds a preset tolerance range, the system iteratively adjusts the historical thermal inertia coefficient based on the sign polarity of the deviation, generating a corrected thermal inertia coefficient and updating it in the mapping relationship. This process transforms the thermal inertia coefficient from a fixed empirical value into a dynamic parameter that continuously optimizes with actual thermodynamic feedback. This improves the environmental adaptability and prediction compensation accuracy of the control system under complex operating conditions, ensuring the stability and robustness of long-term temperature control.

[0171] This application also discloses an intelligent temperature control system for the heating process of kitchen appliances.

[0172] An intelligent temperature control system for the heating process of kitchen appliances, applied to kitchen appliances including temperature acquisition components and power-adjustable electric heating actuators, the intelligent temperature control system specifically includes: The temperature data acquisition module is used to acquire the current temperature, the historical temperature of the previous sampling period, and the preset target temperature collected by the kitchen appliance through the temperature acquisition component. The state characteristic calculation module is used to calculate the current temperature deviation based on the preset target temperature and the current temperature, and to calculate the current temperature change rate based on the current temperature and historical temperatures. The heating stage determination module is used to determine the current heating stage based on the preset range of the current temperature deviation and the current temperature change rate. The equivalent deviation calculation module is used to obtain the corresponding thermal inertia coefficient based on the current heating stage, calculate the expected waste heat temperature rise based on the thermal inertia coefficient and the current temperature change rate, and correct the current temperature deviation based on the expected waste heat temperature rise to obtain the equivalent control deviation. The fuzzy parameter tuning module is used to input the equivalent control deviation and the current temperature change rate into the preset fuzzy inference model to calculate the current control parameters, including the current proportional parameter, the current integral parameter and the current derivative parameter; The target power calculation module is used to obtain the historical control parameters of the previous sampling period, and calculate the target heating power of the current period based on the equivalent control deviation, the current control parameters, and the historical control parameters; wherein, the historical control parameters include the historical equivalent control deviation and the historical heating power. The control output and update module is used to generate a corresponding power control signal based on the target heating power and output it to the electric heating actuator of the kitchen appliance. It also updates the current temperature, equivalent control deviation, and target heating power to the historical control parameters for the next sampling period.

[0173] An intelligent temperature control system for the heating process of kitchen appliances according to an embodiment of this application can implement any of the above methods, and the specific working process of each module in the system can refer to the corresponding process in the above method embodiments.

[0174] In the several embodiments provided in this application, it should be understood that the provided methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for example, the division of a certain module is merely a logical functional division, and in actual implementation there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0175] This application also discloses a computer-readable storage medium.

[0176] A computer-readable storage medium storing a computer program that can be loaded by a processor and executed by any of the intelligent temperature control methods for a kitchen appliance heating process as described above.

[0177] The computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device; the program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0178] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A method for intelligent temperature control during the heating process of a kitchen appliance, characterized in that, The intelligent temperature control method, applied to kitchen appliances including temperature acquisition components and power-adjustable electric heating actuators, includes: The current temperature, the historical temperature of the previous sampling period, and the preset target temperature are obtained by the kitchen appliance through the temperature acquisition component. The current temperature deviation is calculated based on the preset target temperature and the current temperature, and the current temperature change rate is calculated based on the current temperature and the historical temperature. The current heating stage is determined based on the preset range of the current temperature deviation and the current temperature change rate. The corresponding thermal inertia coefficient is obtained based on the current heating stage. The expected waste heat temperature rise is calculated based on the thermal inertia coefficient and the current temperature change rate. The current temperature deviation is corrected based on the expected waste heat temperature rise to obtain the equivalent control deviation. The equivalent control deviation and the current temperature change rate are input into a preset fuzzy inference model to calculate the current control parameters, including the current proportional parameter, the current integral parameter and the current derivative parameter; The historical control parameters of the previous sampling period are obtained, and the target heating power of the current period is calculated based on the equivalent control deviation, the current control parameters, and the historical control parameters; wherein, the historical control parameters include the historical equivalent control deviation and the historical heating power. The corresponding power control signal is generated based on the target heating power and output to the electric heating actuator of the kitchen appliance. The current temperature, the equivalent control deviation, and the target heating power are then updated to the historical control parameters for the next sampling period.

2. The intelligent temperature control method for the heating process of a kitchen appliance according to claim 1, characterized in that, Before the first sampling cycle at the initial stage of the kitchen appliance startup, the intelligent temperature control method further includes: Obtain the rated heating power and initial load temperature of the electric heating actuator; Within the preset initial heating time, the kitchen appliance is heated according to the rated heating power, and the first actual load temperature at the end of heating is collected; The initial temperature rise is calculated based on the difference between the first actual load temperature and the initial load temperature. Obtain multiple preset temperature rise threshold ranges, compare the initial temperature rise value with the multiple preset temperature rise threshold ranges, and determine the heat capacity level of the current heating load; Obtain the corresponding interval boundary adjustment coefficient based on the heat capacity level; Retrieve the default set of deviation boundary values ​​and the set of rate of change boundary values; The interval boundary adjustment coefficient is multiplied by each boundary value in the default deviation boundary value set and each boundary value in the default rate of change boundary value set to generate a target deviation boundary value set and a target rate of change boundary value set adapted to the current load. The target deviation boundary value set and the target rate of change boundary value set are used as the data source for subsequently obtaining the preset deviation boundary value set and the preset rate of change boundary value set.

3. The intelligent temperature control method for the heating process of a kitchen appliance according to claim 2, characterized in that, The steps for determining the current heating stage based on the preset range of the current temperature deviation and the current temperature change rate include: Obtain the preset set of deviation boundary values ​​and the set of rate of change boundary values; The current temperature deviation is compared with the deviation boundary value set to determine the current deviation interval in which the current temperature deviation is located; the current temperature change rate is compared with the change rate boundary value set to determine the current change rate interval in which the current temperature change rate is located. The current deviation interval and the current rate of change interval are combined to generate a current interval combination identifier; Obtain a preset stage mapping table, which contains the correspondence between multiple preset interval combination identifiers and preset heating stage identifiers; wherein, the preset heating stages include a rapid heating stage, a temperature approach stage, and a constant temperature stabilization stage; The current interval combination identifier is matched with the preset interval combination identifier in the stage mapping table, and the preset heating stage identifier that is successfully matched is extracted as the current heating stage.

4. The intelligent temperature control method for the heating process of a kitchen appliance according to claim 1, characterized in that, The steps of obtaining the corresponding thermal inertia coefficient based on the current heating stage, calculating the expected waste heat temperature rise based on the thermal inertia coefficient and the current temperature change rate, and correcting the current temperature deviation based on the expected waste heat temperature rise to obtain the equivalent control deviation include: Based on a preset stage coefficient mapping table, the corresponding thermal inertia coefficient is matched and queried according to the current heating stage; The initial residual heat temperature rise is calculated by multiplying the thermal inertia coefficient by the current temperature change rate. Calculate the absolute value of the current temperature deviation; Determine whether the initial residual heat temperature rise is greater than or equal to the absolute value of the current temperature deviation; If yes, then calculate the difference between the absolute value of the current temperature deviation and the preset safety margin and use it as the expected waste heat temperature rise; if no, then determine the initial waste heat temperature rise as the expected waste heat temperature rise. Calculate the difference between the current temperature deviation and the expected waste heat temperature rise, and use the difference as the equivalent control deviation.

5. The intelligent temperature control method for the heating process of a kitchen appliance according to claim 4, characterized in that, The steps of inputting the equivalent control deviation and the current temperature change rate into a preset fuzzy inference model to calculate the current control parameters include: Based on the membership function in the preset fuzzy inference model, the equivalent control deviation and the current temperature change rate are fuzzified respectively to generate corresponding deviation fuzzy variables and change rate fuzzy variables. The deviation fuzzy variable and the rate of change fuzzy variable are input into the fuzzy rule base in the preset fuzzy inference model for condition matching, and parameter adjustment fuzzy variables are generated. Based on the defuzzification rules in the preset fuzzy inference model, the parameter adjustment fuzzy variables are precisely calculated to obtain the corresponding parameter adjustment amount; Obtain the preset basic control parameters, and superimpose the parameter adjustment amount with the basic control parameters to calculate the current proportional parameter, current integral parameter, and current derivative parameter, which are used as the current control parameters.

6. The intelligent temperature control method for the heating process of a kitchen appliance according to claim 5, characterized in that, The steps of obtaining historical control parameters from the previous sampling period and calculating the target heating power for the current period based on the equivalent control deviation, the current control parameters, and the historical control parameters include: Obtain historical control parameters from the previous sampling period, including historical equivalent control deviation and historical heating power; Calculate the difference between the equivalent control deviation and the historical equivalent control deviation to obtain the current deviation change. Based on the current proportional parameter, the current integral parameter, and the current derivative parameter, the equivalent control deviation and the change in the current deviation are weighted and fused to generate the current power adjustment increment; The current power adjustment increment is superimposed with the historical heating power to generate the current preliminary heating power; Obtain a preset power constraint threshold, compare the current initial heating power with the preset power constraint threshold, limit the current initial heating power according to the comparison result, and output the target heating power for the current period.

7. The intelligent temperature control method for the heating process of a kitchen appliance according to claim 1, characterized in that, The step of generating a corresponding power control signal based on the target heating power includes: Obtain the rated heating power of the electric heating actuator, calculate the ratio of the target heating power to the rated heating power, and obtain the target duty cycle; Obtain the preset carrier cycle parameters, and calculate the on-time parameters and off-time parameters of the current cycle based on the carrier cycle parameters and the target duty cycle. Based on the on-time parameter and the off-time parameter, a pulse width modulation control command is generated; The pulse width modulation control command is determined as the power control signal.

8. The intelligent temperature control method for the heating process of a kitchen appliance according to claim 1, characterized in that, After updating the current temperature, the equivalent control deviation, and the target heating power to the historical control parameters of the next sampling period, the method further includes: The expected waste heat temperature rise, the thermal inertia coefficient, and the current heating stage are stored as historical state parameters. The historical expected residual heat temperature rise, historical thermal inertia coefficient, historical heating stage, and historical heating power of the previous sampling period are obtained, and the actual temperature rise value is calculated based on the current temperature and the historical temperature. The difference between the actual temperature rise and the historical expected total temperature rise is calculated to generate the temperature rise prediction deviation; wherein, the historical expected total temperature rise is determined based on the historical expected waste heat temperature rise and the historical heating power of the previous sampling period; Obtain a preset deviation tolerance range; if the temperature rise prediction deviation exceeds the preset deviation tolerance range, generate a coefficient correction command. Based on the coefficient correction command and the sign polarity of the temperature rise prediction deviation, the historical thermal inertia coefficient is iteratively adjusted to generate the corrected thermal inertia coefficient. The corrected thermal inertia coefficient is associated with the historical heating stage to generate a thermal inertia coefficient mapping relationship and the stage coefficient mapping table is updated.

9. An intelligent temperature control system for the heating process of a kitchen appliance, characterized in that, The intelligent temperature control system, applied to kitchen appliances including temperature acquisition components and power-adjustable electric heating actuators, comprises: The temperature data acquisition module is used to acquire the current temperature, the historical temperature of the previous sampling period, and the preset target temperature collected by the kitchen appliance through the temperature acquisition component; The state feature calculation module is used to calculate the current temperature deviation based on the preset target temperature and the current temperature, and to calculate the current temperature change rate based on the current temperature and the historical temperature. The heating stage determination module is used to determine the current heating stage based on the preset range of the current temperature deviation and the current temperature change rate. The equivalent deviation calculation module is used to obtain the corresponding thermal inertia coefficient according to the current heating stage, calculate the expected waste heat temperature rise according to the thermal inertia coefficient and the current temperature change rate, and correct the current temperature deviation according to the expected waste heat temperature rise to obtain the equivalent control deviation. The fuzzy parameter tuning module is used to input the equivalent control deviation and the current temperature change rate into a preset fuzzy inference model to calculate the current control parameters, including the current proportional parameter, the current integral parameter and the current derivative parameter; The target power calculation module is used to obtain the historical control parameters of the previous sampling period, and calculate the target heating power of the current period based on the equivalent control deviation, the current control parameters, and the historical control parameters; wherein, the historical control parameters include the historical equivalent control deviation and the historical heating power. The control output and update module is used to generate a corresponding power control signal based on the target heating power and output it to the electric heating actuator of the kitchen appliance, and update the current temperature, the equivalent control deviation and the target heating power to the historical control parameters of the next sampling period.

10. A computer-readable storage medium, characterized in that: The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1 to 8.

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