A method for precise temperature management using a cooking appliance controller

Abstract

The application discloses a kind of methods for realizing accurate temperature management using cooking equipment controller, belong to cooking equipment adjusting system technical field, comprising: respectively in the bottom of pot and pot wall set up multi-point sensor array acquisition temperature signal, obtain temperature distribution data;According to the temperature distribution data, the heat conduction difference of bottom and wall of pot is calculated, the gradient analysis method is used to quantify the degree of heat unevenness, and the unevenness ratio is determined;If the unevenness ratio exceeds threshold value, generate component adjustment instruction by adjusting heat source component operation distribution;According to the component adjustment instruction, the power adjustment amount of each operating heat source component is determined using proportional-integral-derivative control algorithm;According to component adjustment instruction and power adjustment amount, the corresponding heat source component is driven to execute adjustment.The method for realizing accurate temperature management using cooking equipment controller solves the problem that the current cooking equipment still has poor temperature management accuracy.

Description

Technical Field

[0001] This invention relates to the field of cooking equipment control systems, and in particular to a method for achieving precise temperature management using a cooking equipment controller. Background Technology

[0002] For pot-type cooking equipment, such as steaming and boiling equipment, temperature management directly affects the quality and taste of food. With the increasing demand for healthy eating and convenient cooking, achieving precise temperature control has become a key element in enhancing the steaming and boiling experience. This control not only ensures that ingredients are heated evenly but also adapts to the needs of diverse dishes.

[0003] However, existing pot-type cooking equipment often relies on simple on / off heating mechanisms for temperature control. This approach is prone to temperature instability when handling complex cooking processes, especially in multi-stage heating scenarios. It cannot respond promptly to differences in heat distribution within the pot, leading to localized overheating or underheating and affecting overall cooking consistency. Furthermore, real-time temperature monitoring within the pot is limited by the sensor's ability to capture heat distribution. Because the heat conduction paths differ between the pot bottom and walls, the collected data cannot fully reflect the actual heating state. This incompleteness directly creates blind spots in the regulation mechanism, meaning the control system, based on inaccurate temperature feedback, cannot achieve precise heating. For example, when simmering soup, if the temperature detection deviation exceeds 0.5 degrees Celsius during the transition from boiling to heat preservation, it will cause localized heat accumulation, resulting in overflowing soup or scorching at the bottom. Therefore, current pot-type cooking equipment still suffers from poor temperature management accuracy. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention provides a method for achieving precise temperature management using a cooking equipment controller, thereby solving the aforementioned problems.

[0005] The technical solution adopted by this invention to solve its technical problem is: a method for achieving precise temperature management using a cooking equipment controller, comprising the following steps: S1: Set up a multi-point sensor array on the bottom and wall of the pot to collect temperature signals and obtain temperature distribution data; S2: Calculate the difference in heat conduction between the bottom of the pot and the pot wall based on the temperature distribution data, use gradient analysis to quantify the degree of heat unevenness, and determine the degree of unevenness ratio; S3: If the unevenness ratio exceeds the threshold, generate a component adjustment command by adjusting the operating distribution of the heat source components; S4: Based on the component adjustment instructions, the power adjustment amount of each operating heat source component is determined using a proportional-integral-derivative control algorithm; S5: Drive the corresponding heat source component to perform adjustment according to the component adjustment command and power adjustment amount.

[0006] Preferably, in step S1, the Butterworth low-pass filter from the SciPy library of Python is applied to the temperature distribution data, with a cutoff frequency of 5Hz, to remove high-frequency noise interference and obtain smoothed temperature distribution data; in step S2, the difference in heat conduction between the bottom of the pot and the pot wall is calculated based on the smoothed temperature distribution data.

[0007] Optionally, in step S2, temperature distribution data of the bottom of the pot and temperature distribution data of the pot wall are obtained from the temperature distribution data. The gradient analysis method includes: The temperature distribution difference between the bottom temperature distribution data and the wall temperature distribution data is obtained by calculating the rate of temperature change between two adjacent temperature distribution data at the interface between the bottom and the wall of the pot using the NumPy library. If the temperature distribution difference exceeds the preset threshold, the distribution deviation threshold is obtained using Matlab's quantization function based on the temperature distribution difference. The unevenness ratio is calculated based on the distribution deviation threshold.

[0008] Specifically, in step S3, if the unevenness ratio exceeds a preset threshold, the current operating distribution data is obtained from the heat source component, and the difference between the unevenness ratio and the preset threshold is used to obtain the corresponding heat source component balanced standard operating distribution data according to the difference-heat source component balanced standard operating distribution data mapping table. The component adjustment instruction is obtained by comparing the difference between the current operating distribution data and the balanced standard distribution data.

[0009] It is worth noting that in step S4, the current operating power of each operating heat source component after adjustment according to the component adjustment instruction is obtained; For each operating heat source component, obtain the deviation value e between the set power value and the current operating power, where the deviation value e is the difference between the set power value and the current operating power; A proportional-integral-derivative (PID) control algorithm is adopted. For each operating heat source component, a proportional term is calculated, which is the proportional coefficient Kp multiplied by the deviation value e; an integral term is calculated for each operating heat source component, which is the cumulative sum of the integral coefficient Ki multiplied by the deviation value e; and a derivative term is calculated for each operating heat source component, which is the rate of change of the derivative coefficient Kd multiplied by the deviation value e. The power adjustment amount u is calculated based on the proportional term, the integral term, and the derivative term, where the power adjustment amount u is the sum of the proportional term, the integral term, and the derivative term; Based on the power adjustment amount u, the power output of each operating heat source component is adjusted to obtain the adjusted power output value.

[0010] Preferably, in step S5, after the corresponding heat source component is driven to perform adjustment according to the component adjustment command and power adjustment amount, the temperature distribution data after the adjustment is performed is obtained, the average value of the temperature distribution data is calculated as the temperature feedback data, the timestamp information is obtained from the temperature feedback data, and the temperature feedback data is arranged based on the timestamp to obtain the dynamic heating sequence.

[0011] Optionally, after step S5, a step S6 is also included, which includes: determining whether the dynamic heating sequence exceeds the temperature fluctuation threshold; if the dynamic heating sequence exceeds the temperature fluctuation threshold, obtaining the target temperature point of the current stage according to the current cooking stage of the cooking device, updating the power adjustment amount through an optimization process based on the dynamic heating sequence and the target temperature point, and driving the corresponding heat source component to perform adjustment according to the updated power adjustment amount.

[0012] Specifically, in step S6, the maximum temperature value and the minimum temperature value are extracted from the dynamic heating sequence, the difference between the maximum temperature value and the minimum temperature value is calculated to obtain the temperature fluctuation value, and then it is determined whether the temperature fluctuation value exceeds the preset temperature fluctuation threshold to obtain the judgment result. If the judgment result is that the temperature fluctuation threshold is exceeded, then the target temperature point of the current cooking stage is obtained according to the current cooking stage of the cooking equipment. Based on the target temperature point, the Adam optimizer is used to process the dynamic heating sequence. The Adam optimizer takes the dynamic heating sequence as input and iteratively minimizes the loss function. The loss function is the sum of the squared differences between the points in the dynamic heating sequence and the target temperature point. The gradient direction is determined by calculating the derivative of the loss function with respect to the current operating power, and the power adjustment amount is output.

[0013] The beneficial effects of this invention are as follows: In the method for achieving precise temperature management using a cooking equipment controller, addressing the problem of uneven heat distribution caused by differences in heat conduction between the bottom and walls of the pot during cooking, a multi-point sensor array is deployed on the bottom and walls of the pot to collect temperature distribution data in real time. Gradient analysis is used to quantify the degree of heat unevenness, and the unevenness ratio is calculated. When the ratio exceeds a threshold, a control algorithm generates power adjustment commands for the heat source components, dynamically adjusting the operating distribution of each heat source component. Furthermore, a proportional-integral-derivative (PID) control algorithm is used to adjust the power adjustment amount of each operating heat source component, achieving precise heat control. This invention significantly improves the accuracy of temperature management in cooking equipment and optimizes cooking results by integrating multi-point sensing, dynamic compensation, and optimized adjustment, thereby reducing energy consumption. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating a method for achieving precise temperature management using a cooking equipment controller, as described in one embodiment of the present invention. Figure 2 This is a flowchart of step S5 in one embodiment of the present invention; Figure 3 This is a flowchart of step S6 in one embodiment of the present invention. Detailed Implementation

[0015] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. It should be noted that these descriptions are for the purpose of aiding understanding the present invention, but do not constitute a limitation thereof. Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0016] like Figure 1-3 As shown, a method for achieving precise temperature management using a cooking equipment controller includes the following steps: S1: Set up a multi-point sensor array on the bottom and wall of the pot to collect temperature signals and obtain temperature distribution data; S2: Calculate the difference in heat conduction between the bottom of the pot and the pot wall based on the temperature distribution data, use gradient analysis to quantify the degree of heat unevenness, and determine the degree of unevenness ratio; S3: If the unevenness ratio exceeds the threshold, generate a component adjustment command by adjusting the operating distribution of the heat source components; S4: Based on the component adjustment instructions, the power adjustment amount of each operating heat source component is determined using a proportional-integral-derivative control algorithm; S5: Drive the corresponding heat source component to perform adjustment according to the component adjustment command and power adjustment amount.

[0017] In the method for achieving precise temperature management using a cooking equipment controller, addressing the uneven heat distribution caused by differences in heat conduction between the pot bottom and pot walls during cooking, a multi-point sensor array is deployed on the pot bottom and pot walls to collect temperature distribution data in real time. Gradient analysis is used to quantify the degree of heat unevenness, and the unevenness ratio is calculated. When the ratio exceeds a threshold, a control algorithm generates power adjustment commands for the heat source components, dynamically adjusting the operating distribution of each heat source component. A proportional-integral-derivative (PID) control algorithm is then used to adjust the power adjustment amount of each operating heat source component, achieving precise heat control. This invention significantly improves the accuracy of temperature management in cooking equipment and optimizes cooking results by integrating multi-point sensing, dynamic compensation, and optimized adjustment, thereby reducing energy consumption.

[0018] Preferably, in step S1, the Butterworth low-pass filter from the SciPy library of Python is applied to the temperature distribution data, with a cutoff frequency of 5Hz, to remove high-frequency noise interference and obtain smoothed temperature distribution data; in step S2, the difference in heat conduction between the bottom of the pot and the pot wall is calculated based on the smoothed temperature distribution data.

[0019] In this embodiment, the sensor array is a thermocouple array. Temperature signals are collected at the bottom and sides of the pot using the thermocouple array, sampling once per second to obtain real-time temperature distribution data. Specifically, the thermocouple array is positioned at key locations on the bottom and sides of the pot, typically including the center of the bottom, the edge of the bottom, and the upper-middle part of the side wall, to capture subtle changes in heat distribution. A thermocouple is a temperature sensor based on the Seebeck effect. It measures temperature by generating a voltage signal through the junction of two different metals. When the pot is heated, these arrays sample once per second, converting the voltage signal into a digital temperature value, thus forming a real-time temperature distribution matrix. For example, the temperature at the center of the bottom of the pot might be 80 degrees Celsius, while the temperature at the upper part of the side wall might be 70 degrees Celsius. This data acquisition process ensures uniform monitoring of heat during cooking, preventing localized overheating that could lead to food burning.

[0020] In one possible implementation, the acquired real-time temperature distribution data often contains high-frequency noise, which may originate from electromagnetic interference or mechanical vibrations. For example, slight shaking of a cookware during heating can introduce instantaneous temperature fluctuations. To process this data, the Butterworth low-pass filter from Python's SciPy library can be used. The Butterworth filter is an infinite impulse response filter characterized by a frequency response curve that is as flat as possible in the passband and sharply attenuates in the stopband without ripple, making it particularly suitable for smoothing temperature signals without introducing additional distortion. In implementation, the SciPy signal module needs to be imported first, and then the filter order and cutoff frequency need to be designed. Here, setting it to 5Hz means that frequencies below 5Hz are allowed to pass, while noise above this is attenuated. For example, for a temperature sequence sampled per second, the filter calculates the smoothing value for each point, ensuring phase integrity through forward and backward filtering, ultimately obtaining a temperature distribution data with reduced noise.

[0021] Optionally, in step S2, temperature distribution data of the bottom of the pot and temperature distribution data of the pot wall are obtained from the temperature distribution data. The gradient analysis method includes: The temperature distribution difference between the bottom temperature distribution data and the wall temperature distribution data is obtained by calculating the rate of temperature change between two adjacent temperature distribution data at the interface between the bottom and the wall of the pot using the NumPy library. If the temperature distribution difference exceeds the preset threshold, the distribution deviation threshold is obtained using Matlab's quantization function based on the temperature distribution difference. The unevenness ratio is calculated based on the distribution deviation threshold.

[0022] In one possible implementation, the process of obtaining the temperature distribution data of the pot bottom and the pot wall from the temperature distribution data can be achieved by first encoding each sensor. This encoding allows the system to distinguish whether the temperature distribution data belongs to the pot bottom or the pot wall. Since the sensors are encoded, the temperature distribution data corresponding to the encoded sensor at the interface between the pot bottom and the pot wall can be extracted to calculate the rate of change.

[0023] In one possible implementation, the NumPy library is used to calculate the temperature difference between the bottom and wall temperature distribution data by calculating the rate of temperature change between two adjacent temperature distribution data points at the interface between the bottom and wall of the pot. Here, the rate of temperature change refers to the temperature difference between adjacent points divided by the distance between them. For example, in NumPy, the `diff` function can be used to calculate the difference between adjacent points, and then divided by a preset sensor distance interval, such as 1 cm, to obtain the rate of change. This difference in the rate of change between the bottom and wall endpoints is then used as an indicator of the overall distribution difference. This method relies on the array operation efficiency of NumPy and is suitable for real-time processing of large amounts of data. In this embodiment, sensor pairs consisting of the encoded sensors at the interface between the bottom and wall are pre-defined, and the temperature rate of change corresponding to each sensor pair is calculated. The average of these temperature rate of change is then calculated to obtain the temperature difference between the bottom and wall temperature distribution data.

[0024] In one possible implementation, if the temperature distribution difference exceeds a preset threshold, the distribution deviation threshold is obtained using a quantize function in Matlab based on the temperature distribution difference. This quantize function maps continuous differences to discrete levels. For example, if the preset threshold is 4 degrees Celsius per centimeter, and the calculated distribution difference is 6 degrees Celsius per centimeter, exceeding the threshold, the difference is input into the quantize function. Ten quantization levels are set, each representing a different degree of deviation, thus outputting a quantized threshold. A high deviation level indicates the need for further attention. This function, based on the quantization principle of signal processing, converts analog signals into digital representations for easier subsequent classification. For instance, in the temperature control module of an industrial-grade cooking equipment, if the distribution difference is 6 degrees Celsius per centimeter, exceeding the threshold of 4 degrees Celsius per centimeter, the quantize function is called in the Matlab environment. The difference and quantization boundary are input, such as a range of 0 to 10 divided into five levels. The function maps 6 to level 3 using linear interpolation, outputting a distribution deviation threshold of 3. This reflects the severity of the deviation and provides a quantizable reference value for the system.

[0025] In one possible implementation, the unevenness ratio is calculated based on the distribution deviation threshold to obtain the heat distribution deviation value. The unevenness ratio can be defined as the deviation threshold divided by the benchmark value of the ideal uniform distribution. For example, if the benchmark value of the ideal uniform distribution is preset to be 1, then the unevenness ratio of the distribution deviation threshold 3 is 3.

[0026] Specifically, in step S3, if the unevenness ratio exceeds a preset threshold, the current operating distribution data is obtained from the heat source component, and the difference between the unevenness ratio and the preset threshold is used to obtain the corresponding heat source component balanced standard operating distribution data according to the difference-heat source component balanced standard operating distribution data mapping table. The component adjustment instruction is obtained by comparing the difference between the current operating distribution data and the balanced standard distribution data.

[0027] In one possible implementation, when the unevenness ratio exceeds a preset threshold, it is first necessary to obtain the current operating distribution data from the heat source components. Here, the heat source components usually refer to the heating elements in the cooking equipment, such as heating wires, and the current operating distribution data is which of these heating elements is currently operating.

[0028] In one possible implementation, the difference between the unevenness ratio and a preset threshold is used to obtain the corresponding balanced standard operating distribution data of the heat source components according to the difference-heat source component balanced standard operating distribution data mapping table. The difference calculation is simple, that is, the unevenness ratio minus the threshold. The mapping table is a predefined lookup table that maps different difference ranges to the ideal balanced operating distribution mode. For example, the difference in the range of 0.5 to 1 corresponds to the standard distribution A, and the standard distribution A records the operating status of each heat source component.

[0029] In one possible implementation, component adjustment instructions are obtained by comparing the differences between the current operating distribution data and the equilibrium standard distribution data. This comparison involves comparing the current operating distribution data (where heat source components are running or not running) with the equilibrium standard distribution data (where heat source components need to run or not run). Heat source components that need to run in the equilibrium standard distribution data but are not running in the current operating distribution data are activated; heat source components that do not need to run in the equilibrium standard distribution data but are running in the current operating distribution data are deactivated. The states of heat source components that need to run in the equilibrium standard distribution data and are running in the current operating distribution data, and those that do not need to run in the equilibrium standard distribution data and are not running in the current operating distribution data, remain unchanged. These logics generate component adjustment instructions to control the actions of the corresponding heat source components.

[0030] It is worth noting that in step S4, the current operating power of each operating heat source component after adjustment according to the component adjustment instruction is obtained; For each operating heat source component, the deviation value e between the set power value and the current operating power is obtained. The deviation value e is the difference between the set power value and the current operating power. When calculating the deviation value e between the set power value and the initial value for each operating heat source component, this deviation value is essentially the difference between the set power and the initial power. This calculation process is simple and direct. By comparing the ideal setting with the actual initial value, it helps to identify the degree of power deviation.

[0031] A proportional-integral-derivative (PID) control algorithm is employed. For each operating heat source component, a proportional term is calculated, which is the proportional coefficient Kp multiplied by the deviation value e. For each operating heat source component, an integral term is calculated, which is the cumulative sum of the integral coefficient Ki multiplied by the deviation value e. For each operating heat source component, a derivative term is calculated, which is the derivative coefficient Kd multiplied by the rate of change of the deviation value e. In one possible implementation, when using the PID control algorithm to handle these deviations, the proportional, integral, and derivative terms of the PID control algorithm are calculated separately. The proportional term, i.e., the proportional response part of the proportional coefficient Kp multiplied by the deviation value e, serves to quickly respond to the current deviation, providing immediate correction and pushing the power closer to the setpoint. The integral term, i.e., the integral response of the cumulative sum of the integral coefficient Ki multiplied by the deviation value e (i.e., the integral of the deviation value e over time), takes into account historical deviations, aiming to eliminate steady-state errors and ensure long-term power stability. The differential term is calculated as the differential response of the rate of change of the deviation value e multiplied by the differential coefficient Kd (that is, the derivative of the deviation value e with respect to time). This term focuses on the rate of change of the deviation, suppresses overshoot, and helps the system transition smoothly.

[0032] The power adjustment amount u is calculated based on the proportional term, the integral term, and the derivative term, where the power adjustment amount u is the sum of the proportional term, the integral term, and the derivative term; Based on the power adjustment amount u, the power output of each operating heat source component is adjusted to obtain the adjusted power output value.

[0033] For example, the power adjustment u is obtained by summing the proportional, integral, and derivative terms. For instance, if the proportional term is 25 watts, the integral term is 20 watts, and the derivative term is 2 watts, then the power adjustment u is 47 watts. This instruction synthesis process integrates the three responses to form a comprehensive control signal that guides the fine adjustment of power.

[0034] Preferred, such as Figure 2As shown, in step S5, after the corresponding heat source component is driven to perform adjustment according to the component adjustment command and power adjustment amount, the temperature distribution data after the adjustment is performed is obtained, the average value of the temperature distribution data is calculated as the temperature feedback data, the timestamp information is obtained from the temperature feedback data, and the temperature feedback data is arranged based on the timestamp to obtain the dynamic heating sequence.

[0035] In this embodiment, the component adjustment command is used to control the corresponding heat source component to start, and the power adjustment amount is used to control the corresponding heat source component to operate at the power adjustment amount.

[0036] The process of obtaining timestamp information from temperature feedback data requires first parsing the timestamps in the data structure. These timestamps are typically recorded in a standard format such as Unix timestamps. For example, in cooking equipment, temperature distribution data might contain temperature samples taken once per second, with each sample point having a timestamp accurate to milliseconds. Therefore, the timestamps of the calculated temperature feedback data will also be consistent with those of the temperature distribution data. After the system extracts the timestamps from the temperature distribution data using a data processing script, it assigns the timestamps to the temperature feedback data. Then, it sorts the temperature values ​​of the temperature feedback data based on the order of the timestamps, forming a time-increasing sequence, i.e., a dynamic heating sequence.

[0037] Optionally, after step S5, a step S6 is also included, which includes: determining whether the dynamic heating sequence exceeds the temperature fluctuation threshold; if the dynamic heating sequence exceeds the temperature fluctuation threshold, obtaining the target temperature point of the current stage according to the current cooking stage of the cooking device, updating the power adjustment amount through an optimization process based on the dynamic heating sequence and the target temperature point, and driving the corresponding heat source component to perform adjustment according to the updated power adjustment amount.

[0038] Specifically, such as Figure 3As shown, in step S6, the maximum and minimum temperature values ​​are extracted from the dynamic heating sequence. The difference between the maximum and minimum temperature values ​​is calculated to obtain the temperature fluctuation value. Then, it is determined whether the temperature fluctuation value exceeds a preset temperature fluctuation threshold, and a judgment result is obtained. In one possible implementation, the process of extracting the maximum and minimum temperature values ​​from the dynamic heating sequence involves traversing and comparing data points. For example, in the steaming system of a cooking device, the dynamic heating sequence consists of a series of temperature readings at a certain time point. By cyclically scanning all points, the maximum and minimum values ​​are initialized as the first temperature value of the sequence. Subsequently, subsequent points are compared one by one. If the current point is higher than the maximum value, the maximum value is updated; if it is lower than the minimum value, the minimum value is updated. This process is repeated until the entire sequence is traversed, ultimately obtaining a maximum temperature value such as 95 degrees Celsius and a minimum temperature value such as 75 degrees Celsius. When calculating the temperature fluctuation value by subtracting the difference between the maximum and minimum temperature values, the temperature fluctuation value reflects the temperature change amplitude during the heating process.

[0039] If the judgment result is that the temperature fluctuation threshold is exceeded, then the target temperature point of the current cooking stage is obtained according to the current cooking stage of the cooking device. Obtaining the target temperature point of the current cooking stage involves querying a preset stage table. For example, in the porridge cooking stage, if the current stage is boiling, the target temperature point may be set to 92 degrees Celsius. The system reads the stage information from the device status and matches the corresponding temperature target.

[0040] Based on the target temperature point, the Adam optimizer is used to process the dynamic heating sequence. The Adam optimizer takes the dynamic heating sequence as input and iteratively minimizes the loss function. The loss function is the sum of the squared differences between the points in the dynamic heating sequence and the target temperature point. The gradient direction is determined by calculating the derivative of the loss function with respect to the current operating power, and the power adjustment amount is output.

[0041] The steps for using the Adam optimizer to process dynamic heating sequences based on target temperature points first require understanding that the Adam optimizer is an adaptive learning rate optimization algorithm that combines momentum and RMSProp methods to efficiently minimize the loss function. In the temperature management of cooking equipment, the Adam optimizer uses the dynamic heating sequence as input data and iteratively adjusts parameters to optimize the heating strategy. After initializing the learning rate and momentum parameters, the optimizer calculates the gradient for each iteration step and adaptively adjusts the step size based on historical gradient information, thereby quickly converging to the point of minimum loss.

[0042] The Adam optimizer iteratively minimizes the loss function using a dynamic heating sequence as input, involving multiple loop calculations. For example, in the simmering stage of a cooking appliance such as a rice cooker, the input dynamic heating sequence contains multiple temperature points. In each iteration, the optimizer updates the parameters, estimates the first and second moments, then corrects these moment values ​​with bias, calculates the effective learning rate, and finally updates the model parameters based on these values, so that the loss function gradually decreases.

[0043] In one possible implementation, the loss function is specifically calculated as the sum of the squared differences between the points in the dynamically heated sequence and the target temperature point. This involves squaring the difference between each point in the sequence and the target point and then summing the results. For example, in the quick cooking mode of a cooking device such as a rice cooker, if the target temperature is 90 degrees Celsius and the sequence has 10 points, the total loss is obtained by summing the squared differences between each point and 90. This function quantifies the degree to which the sequence deviates from the target and helps the optimizer find the best adjustment path.

[0044] In the Adam optimizer, the gradient direction is obtained by calculating the derivative of the loss function with respect to the current operating power. Then, an adjustment amount is generated based on the gradient descent principle. In the heating control of cooking equipment, if the gradient points to an increase in power, a power adjustment amount is output to replace the original power adjustment amount, so as to update the power adjustment amount and guide the actual adjustment of the heat source components to achieve precise and stable temperature.

[0045] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and these variations still fall within the protection scope of the present invention.

Claims

1. A method for achieving precise temperature management using a cooking equipment controller, characterized in that, Includes the following steps: S1: Set up a multi-point sensor array on the bottom and wall of the pot to collect temperature signals and obtain temperature distribution data; S2: Calculate the difference in heat conduction between the bottom of the pot and the pot wall based on the temperature distribution data, use gradient analysis to quantify the degree of heat unevenness, and determine the degree of unevenness ratio; In step S2, temperature distribution data of the bottom of the pot and temperature distribution data of the pot wall are obtained from the temperature distribution data. The gradient analysis method includes: The temperature distribution difference between the bottom temperature distribution data and the wall temperature distribution data is obtained by calculating the rate of temperature change between two adjacent temperature distribution data at the interface between the bottom and the wall of the pot using the NumPy library. If the temperature distribution difference exceeds the preset threshold, the distribution deviation threshold is obtained using Matlab's quantization function based on the temperature distribution difference. Calculate the unevenness ratio based on the distribution deviation threshold; S3: If the unevenness ratio exceeds the threshold, generate a component adjustment command by adjusting the operating distribution of the heat source components; In step S3, if the unevenness ratio exceeds a preset threshold, the current operating distribution data is obtained from the heat source component, and the difference between the unevenness ratio and the preset threshold is used to obtain the corresponding heat source component balanced standard operating distribution data according to the difference-heat source component balanced standard operating distribution data mapping table. The component adjustment instruction is obtained by comparing the difference between the current operating distribution data and the balanced standard distribution data. S4: Based on the component adjustment instructions, the power adjustment amount of each operating heat source component is determined using a proportional-integral-derivative control algorithm; S5: Drives the corresponding heat source component to perform adjustment according to the component adjustment command and power adjustment amount; In step S5, after the corresponding heat source component is driven to perform adjustment according to the component adjustment command and power adjustment amount, the temperature distribution data after the adjustment is performed is obtained, the average value of the temperature distribution data is calculated as the temperature feedback data, the timestamp information is obtained from the temperature feedback data, and the temperature feedback data is arranged based on the timestamp to obtain the dynamic heating sequence. S6: Determine whether the dynamic heating sequence exceeds the temperature fluctuation threshold; if the dynamic heating sequence exceeds the temperature fluctuation threshold, obtain the target temperature point of the current stage according to the current cooking stage of the cooking device, update the power adjustment amount through the optimization process based on the dynamic heating sequence and the target temperature point, and drive the corresponding heat source component to perform adjustment according to the updated power adjustment amount; In step S6, the maximum and minimum temperature values ​​are extracted from the dynamic heating sequence, the difference between the maximum and minimum temperature values ​​is calculated to obtain the temperature fluctuation value, and then it is determined whether the temperature fluctuation value exceeds the preset temperature fluctuation threshold to obtain the judgment result. If the judgment result is that the temperature fluctuation threshold is exceeded, then the target temperature point of the current cooking stage is obtained according to the current cooking stage of the cooking equipment. Based on the target temperature point, the Adam optimizer is used to process the dynamic heating sequence. The Adam optimizer takes the dynamic heating sequence as input and iteratively minimizes the loss function. The loss function is the sum of the squared differences between the points in the dynamic heating sequence and the target temperature point. The gradient direction is determined by calculating the derivative of the loss function with respect to the current operating power, and the power adjustment amount is output.

2. The method for achieving precise temperature management using a cooking equipment controller according to claim 1, characterized in that: In step S1, the Butterworth low-pass filter from the SciPy library in Python is applied to the temperature distribution data, with a cutoff frequency of 5Hz, to remove high-frequency noise interference and obtain smoothed temperature distribution data; in step S2, the difference in heat conduction between the bottom of the pot and the pot wall is calculated based on the smoothed temperature distribution data.

3. The method for achieving precise temperature management using a cooking equipment controller according to claim 1, characterized in that: In step S4, the current operating power of each operating heat source component after adjustment according to the component adjustment instruction is obtained; For each operating heat source component, obtain the deviation value e between the set power value and the current operating power, where the deviation value e is the difference between the set power value and the current operating power; A proportional-integral-derivative (PID) control algorithm is used to calculate a proportional term for each operating heat source component. The proportional term is the proportional coefficient Kp multiplied by the deviation value e. For each operating heat source component, an integral term is calculated, which is the cumulative sum of integral coefficients Ki multiplied by deviation value e; for each operating heat source component, a differential term is calculated, which is the rate of change of differential coefficient Kd multiplied by deviation value e. The power adjustment amount u is calculated based on the proportional term, the integral term, and the derivative term, where the power adjustment amount u is the sum of the proportional term, the integral term, and the derivative term; Based on the power adjustment amount u, the power output of each operating heat source component is adjusted to obtain the adjusted power output value.

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