Electric food warmer control system

Abstract

The invention relates to the technical field of automatic control, in particular to an electric food warmer control system which comprises a temperature data monitoring module, a thermal response prediction and adjustment module, a warmer bottom power balance regulation and control module, a food material heating opportunity optimization module and a heating strategy execution module. According to the method, the heat conduction characteristic of the pot body material and the specific heat capacity parameter of the food material are combined, the temperature change trend is accurately calculated, accurate temperature control is achieved, the pot bottom temperature gradient is analyzed, heat distribution is balanced, local overheating or insufficient heating is reduced, the temperature error interval is predicted through historical thermal response data, the heating strategy is dynamically adjusted, and the temperature control precision is improved; power distribution is optimized according to the temperature difference of the partition at the bottom of the pot, heat energy utilization efficiency is improved, heat absorption characteristics of food materials are matched, heating opportunity is optimized, uniform heating is ensured, cooking quality is improved, starting and stopping states of heating elements are dynamically adjusted, power output is optimized, system stability is improved, energy waste is reduced, and the service life of the heating elements is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control, and particularly to an electric cooker control system. Background Art

[0002] The technical field of automatic control involves the design and implementation of systems for automatic regulation and management using mechanical, electrical, computer, and other means. Its core content involves sensors, actuators, control algorithms, feedback mechanisms, and data acquisition and processing technologies, aiming to efficiently and precisely control industrial equipment, household appliances, etc. through automatic systems. Automatic control technology is widely applied in multiple fields such as production, energy, and transportation, and has become an indispensable technical support in modern industry and daily life. The research content in this field covers multiple aspects such as system modeling, control strategy design, signal processing, and feedback control, ensuring that the system can respond quickly and accurately to environmental changes.

[0003] Among them, the electric cooker control system refers to a control technology for temperature regulation and heating management of an electric cooker. This patent theme focuses on the temperature control technology in an electric cooker, which involves real-time monitoring of the temperature inside the pot through a temperature sensor and adjusting the heating power according to a preset temperature range. By controlling the start and stop of the electric heating element, it ensures uniform heating of the food inside the pot and avoids overheating. This control system uses a control algorithm to achieve precise temperature control to meet the set temperature requirements. Through the optimization of circuit design and control strategy, it can achieve efficient and stable operation of the electric cooker under different usage conditions without relying on complex calculations or external inputs.

[0004] The prior art conducts overall monitoring based on a single temperature sensor, making it difficult to accurately identify temperature differences in different regions, resulting in uneven local heating at the bottom of the pot. It fails to fully consider the heat conduction characteristics of the pot body material, leading to uneven heat distribution and affecting the cooking effect. It lacks targeted analysis of the heating requirements of ingredients and cannot effectively match the heat capacity characteristics of different ingredients, resulting in overheating or insufficient heating of some ingredients. It does not utilize historical data for temperature prediction and can only passively adjust the heating power in response to temperature changes, with a strong lag in regulation, affecting the overall temperature control accuracy. The power distribution strategy for heating power is relatively single and fails to dynamically adjust the power according to the temperature changes in different regions of the bottom of the pot, resulting in increased energy consumption and uneven load on the heating element, shortening the service life of the equipment. Summary of the Invention

[0005] In order to solve the technical problems of the prior art that overall monitoring based on a single temperature sensor makes it difficult to accurately identify the temperature differences in different regions, resulting in uneven local heating at the bottom of the pot, failure to fully consider the heat conduction characteristics of the pot body material, resulting in uneven heat distribution, affecting the cooking effect, lack of targeted analysis of the heating requirements of ingredients, inability to effectively match the heat capacity characteristics of different ingredients, resulting in some ingredients being overheated or underheated, failure to use historical data for temperature prediction, and only being able to passively adjust the heating power to respond to temperature changes, with strong control hysteresis, affecting the overall temperature control accuracy, and the power distribution strategy for the heating power being relatively single, unable to dynamically adjust the power according to the temperature changes in different regions at the bottom of the pot, resulting in increased energy consumption and uneven load on the heating elements, and shortening the service life of the equipment, the embodiment of the present invention provides an electric cooker control system. The technical solution is as follows:

[0006] On the one hand, an electric cooker control system is provided, and the system includes:

[0007] The temperature data monitoring module calls the pot body material database according to the performance data of the electric cooker to identify the heat conduction coefficient of the pot body, matches the ingredient category and specific heat capacity parameters, judges the temperature gradient of each area at the bottom of the pot, analyzes the temperature change rate of the pot body, and obtains the temperature change trend value;

[0008] Based on the temperature change trend value, the heat response prediction and adjustment module calls the heat response curve database to analyze the temperature change curve of the pot body, compares it with the target temperature change trajectory, identifies the temperature prediction error interval, and obtains the temperature prediction deviation amplitude;

[0009] Based on the temperature prediction deviation amplitude, the bottom power balance control module calls the data of the bottom partition temperature sensor, compares the temperature difference of each partition, identifies the degree of uneven local heating, and obtains the bottom power correction amount;

[0010] Based on the bottom power correction amount, the ingredient heating timing optimization module calls the ingredient heat capacity database, compares the differential ingredient heat transfer time parameters, analyzes the ingredient heat absorption rate, and obtains the heating timing adjustment result;

[0011] Based on the heating timing adjustment result, the heating strategy execution module calls the start-stop control logic of the heating element, adjusts the partition power distribution parameters, executes the dynamic control of the heating element, and obtains the electric cooker heating control result.

[0012] As a further solution of the present invention, the temperature change trend value includes the temperature gradient of each area at the bottom of the pot, the temperature change rate of the pot body, and the temperature change trend; the temperature prediction deviation range includes the temperature prediction error range, the target temperature change trajectory, and the pot body temperature change curve; the bottom power correction amount includes the degree of uneven local heating, the temperature difference of each partition, and the bottom partition temperature data; the heating timing adjustment result includes the heat absorption rate of the ingredients, the heat transfer time parameter of the ingredients, and the heat capacity parameter of the ingredients; the electric hot pot heating control result includes the dynamic control of the heating element, the partition power distribution parameter, and the heating element start-stop control parameter.

[0013] As a further solution of the present invention, the temperature data monitoring module includes:

[0014] The temperature sensing data processing sub-module calculates the difference between the pot body temperature sensor data and the bottom partition temperature data according to the performance data of the electric hot pot, including the pot body temperature sensor data, the bottom partition temperature data, and the heating element power data, analyzes the temperature rise and fall rate of the different parts of the pot body, screens the extreme values of temperature change, identifies the overall temperature fluctuation range, and generates the temperature change rate of the pot body.

[0015] The bottom area temperature calculation sub-module matches the ingredient category and the specific heat capacity parameter based on the temperature change rate of the pot body, analyzes the matching degree of the bottom partition temperature, judges the change of the partition temperature gradient, and obtains the bottom temperature gradient.

[0016] The temperature change trend analysis sub-module calls the bottom temperature gradient to analyze the temperature change situation, calculates the difference in the partition temperature rate, judges the overall temperature change mode, and obtains the temperature change trend value.

[0017] As a further solution of the present invention, the temperature change trend value uses the formula:

[0018]

[0019] where T change represents the temperature change trend value, ΔT zone,i represents the change amount of the temperature in the i-th partition, Δt zone,i represents the change amount of time in the i-th partition, N represents the total number of partitions analyzed, ΔT zone,i-1 represents the change amount of the temperature in the previous partition, and Δt zone,i-1 represents the change amount of time in the previous partition.

[0020] As a further solution of the present invention, the heat response prediction and adjustment module includes:

[0021] Based on the temperature change trend value, the temperature change curve recognition sub-module calls the thermal response curve database to extract the temperature change curve of the pot body, screens the fluctuation range of the change rate, calculates the average rate of temperature change in the range, and obtains the temperature change curve of the pot body;

[0022] The target trajectory comparison sub-module calls the temperature change curve of the pot body, compares the target temperature trajectory, analyzes the deviation amplitude, and obtains the temperature prediction error range;

[0023] The temperature prediction error calculation sub-module calls the temperature prediction error range, analyzes the temperature deviation rate, measures the temperature change difference in the error amplitude range, and obtains the temperature prediction deviation amplitude.

[0024] As a further solution of the present invention, the bottom power balance control module includes:

[0025] Based on the temperature prediction deviation amplitude, the partition temperature comparison sub-module calls the data of the bottom partition temperature sensor, identifies the change rate of the partition temperature, measures the temperature fluctuation range, analyzes the temperature gradient in the temperature difference range, and obtains the bottom partition temperature difference;

[0026] The heating imbalance recognition sub-module calls the bottom partition temperature difference, compares the temperature offset of each partition of the bottom of the pot, calculates and screens the corresponding offset ratio of the partition, and obtains the degree of local heating imbalance;

[0027] Based on the degree of local heating imbalance, the power correction calculation sub-module analyzes the power adjustment range, measures the partition power adjustment parameter, and obtains the bottom power correction amount.

[0028] As a further solution of the present invention, the partition power adjustment parameter adopts the formula:

[0029]

[0030] wherein, P adjust represents the partition power adjustment parameter, P base represents the reference power value, P local represents the local power value, P i represents the power measurement value in the i-th partition, n represents the total number of partitions, and ΔT avg represents the average temperature deviation.

[0031] As a further solution of the present invention, the food heating timing optimization module includes:

[0032] The food material heat transfer calculation sub-module extracts the food material heat transfer time parameter based on the bottom pot power correction amount, calculates the heat absorption time of the food material per unit mass, screens the food materials with low heat transfer rate, identifies the heat absorption time offset data of the bottom pot power correction amount of the food material, measures the heat transfer time difference between different food materials, and obtains the food material heat transfer time parameter;

[0033] The heat absorption rate analysis sub-module compares the heat absorption rates of the food materials according to the food material heat transfer time parameter, calculates the heat absorption offset rate, and obtains the food material heat absorption rate;

[0034] The heating timing adjustment sub-module calls the food material heat absorption rate, analyzes the influence of the heating time on the food material, identifies the heating timing adjustment range, and obtains the heating timing adjustment result.

[0035] As a further solution of the present invention, the heat absorption offset rate adopts the formula:

[0036]

[0037] wherein, Q represents the heat absorption offset rate, A represents the surface area of the food material, T1 represents the final temperature of the food material, T0 represents the initial temperature of the food material, t1 represents the initial value of the heat transfer time, and t2 represents the final value of the heat transfer time.

[0038] As a further solution of the present invention, the heating strategy execution module includes:

[0039] The heating start-stop control sub-module calls the heating element start-stop control logic based on the heating timing adjustment result, evaluates the current start-stop state of the heating element, analyzes the start-stop time error of the target element, determines the heating element with a large time offset, evaluates the matching degree between the element and the heating timing adjustment result, adjusts the start-stop parameters, and obtains the start-stop state of the heating element;

[0040] The partition power adjustment sub-module calls the start-stop state of the heating element, adjusts the partition power distribution parameters, calculates the partition power correction value, and screens the area with a large correction amplitude to obtain the partition power distribution parameters;

[0041] The heating dynamic control sub-module calls the partition power distribution parameters, executes the dynamic control of the heating element, identifies the power adjustment range, analyzes the partition power adjustment amplitude, and obtains the electric cooker heating control result.

[0042] The beneficial effects brought by the technical solution provided by the embodiment of the present invention at least include:

[0043] By combining the heat conduction characteristics of the pot body material and the specific heat capacity parameters of the ingredients, accurately calculate the temperature change trend to make the temperature control more precise, conduct a detailed analysis of the temperature gradient in different areas of the pot bottom to ensure the balance of temperature distribution, reduce the situation of local overheating or insufficient heating, make predictions based on historical heat response data, identify the error range of temperature changes, dynamically adjust the heating strategy to improve the temperature control accuracy, combine the temperature difference changes in each partition of the pot bottom to optimize the power distribution, make the heating power in different areas more match the actual needs, improve the thermal energy utilization efficiency, combine the heat absorption characteristics of the ingredients to optimize the heating timing, avoid uneven heating of the ingredients caused by improper heating time, improve the cooking quality, dynamically adjust the start-stop state of the heating element, optimize the power distribution according to the real-time temperature change, make the operation of the electric hot pot more stable, reduce energy waste, and at the same time extend the service life of the heating element. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0045] Figure 1 is a schematic diagram of an electric hot pot control system provided by an embodiment of the present invention;

[0046] Figure 2 is a schematic diagram of the system framework of the present invention;

[0047] Figure 3 is a flowchart for obtaining the temperature data monitoring module in the present invention;

[0048] Figure 4 is a flowchart for obtaining the heat response prediction and adjustment module in the present invention;

[0049] Figure 5 is a flowchart for obtaining the pot bottom power balance control module in the present invention;

[0050] Figure 6 is a flowchart for obtaining the ingredient heating timing optimization module in the present invention;

[0051] Figure 7 is a flowchart for obtaining the heating strategy execution module in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The following will describe the technical solutions in the present invention with reference to the drawings.

[0053] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0054] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0055] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0056] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0057] The embodiment of the present invention provides an electric hot pot control system, such as Figure 1-2 The schematic diagram of the electric hot pot control system shown in FIG. 1 includes:

[0058] The temperature data monitoring module uses the pot material database to identify the pot thermal conductivity coefficient, match the food category with the specific heat capacity parameter, determine the temperature gradient of each area of the pot bottom, analyze the pot temperature change rate, and obtain the temperature change trend value based on the performance data of the electric hot pot, including the pot temperature sensor data, the pot bottom partition temperature data, and the heating element power data;

[0059] The thermal response prediction and adjustment module uses the thermal response curve database to analyze the temperature change curve of the pot body based on the temperature change trend value, compares the target temperature change trajectory, identifies the temperature prediction error range, and obtains the temperature prediction deviation amplitude;

[0060] The pot bottom power balance control module calls the pot bottom partition temperature sensor data based on the temperature prediction deviation amplitude, compares the temperature difference of each partition, identifies the degree of local heating imbalance, and obtains the pot bottom power correction value;

[0061] The food heating timing optimization module calls the food heat capacity database based on the pot bottom power correction, compares the heat transfer time parameters of differentiated food ingredients, analyzes the food heat absorption rate, and obtains the heating timing adjustment results;

[0062] Based on the heating timing adjustment result, the heating strategy execution module calls the start-stop control logic of the heating element, adjusts the partition power distribution parameters, executes the dynamic control of the heating element, and obtains the heating control result of the electric cooker.

[0063] The temperature change trend value includes the temperature gradient of each area at the bottom of the pot, the temperature change rate of the pot body, and the temperature change trend. The temperature prediction deviation range includes the temperature prediction error interval, the target temperature change trajectory, and the pot body temperature change curve. The bottom power correction amount includes the degree of local heating imbalance, the temperature difference of each partition, and the bottom partition temperature data. The heating timing adjustment result includes the heat absorption rate of the ingredients, the heat transfer time parameter of the ingredients, and the heat capacity parameter of the ingredients. The heating control result of the electric cooker includes the dynamic control of the heating element, the partition power distribution parameter, and the start-stop control parameter of the heating element.

[0064] Specifically, as Figure 2 、 3 shown, the temperature data monitoring module includes:

[0065] The temperature sensing data processing sub-module calculates the difference between the pot body temperature sensor data and the bottom partition temperature data according to the performance data of the electric cooker, including the pot body temperature sensor data, the bottom partition temperature data, and the heating element power data, analyzes the temperature rise and fall rate of the different parts of the pot body, screens the extreme values of temperature change, identifies the overall temperature fluctuation range, and generates the temperature change rate of the pot body.

[0066] First, read the temperature data collected in real-time by the pot body temperature sensor and record its timestamp. The bottom of the pot partition temperature data comes from multiple temperature sensors distributed in different areas of the bottom of the pot. Mark each data point to determine the measurement time of the temperature data in each partition. Calculate the difference between the pot body temperature sensor data and the bottom of the pot partition temperature data. The calculation method is the temperature of the pot body sensor minus the temperature of the bottom of the pot partition. For example, if the reading of the pot body temperature sensor is 150°C and the temperature of a certain area at the bottom of the pot is 180°C, then the difference is 150 - 180 = -30°C. After calculating the differences for all partitions, analyze the temperature rise and fall rates of each part of the pot body. The calculation method is the temperature at the current moment minus the temperature at the previous moment and divided by the time interval. For example, if the temperature of a certain area is 170°C at t = 10s and 175°C at t = 15s, then the temperature rise rate is calculated as (175 - 170) / 5 = 1°C / s. Screen for extreme temperature changes. The extreme value screening method is to set a threshold for the temperature change range. If the temperature change in a certain area exceeds the threshold, then mark that area. For example, the threshold is set to ±10°C / min. If the temperature change rate of a certain partition is 12°C / min, then it is considered that the temperature fluctuation in this partition is too large. After screening out the areas with significant temperature changes, calculate the overall temperature fluctuation range. The calculation method is the difference between the highest temperature and the lowest temperature of the pot body. For example, if the highest temperature is 200°C and the lowest temperature is 140°C, then the temperature fluctuation range is 60°C. Finally, calculate the pot body temperature change rate. The pot body temperature change rate is calculated as the ratio of the temperature change amplitude to the time. For example, if the pot body temperature rises from 160°C to 190°C in 30s, then the change rate is (190 - 160) / 30 = 1°C / s, and generate the pot body temperature change rate.

[0067] The bottom of the pot area temperature calculation sub-module, based on the pot body temperature change rate, matches the ingredient category with the specific heat capacity parameter, analyzes the matching degree of the bottom of the pot partition temperature, judges the change of the partition temperature gradient, and obtains the bottom of the pot temperature gradient;

[0068] First, determine the type of food ingredients in the current heating area, and look up its specific heat capacity parameter according to the food ingredient classification table. For example, the specific heat capacity of water is 4.18 kJ / (kg·K), that of meat is 2.7 kJ / (kg·K), and that of vegetables is approximately 3.5 kJ / (kg·K). After the matching is completed, compare the temperature of the bottom pot partition with the optimal heating temperature range required by the ingredients, and analyze the matching degree of the bottom pot partition temperature. The matching degree calculation method is (current temperature - target temperature) / target temperature × 100%. For example, if the current temperature of a certain partition is 180°C and the target temperature is 200°C, then the matching degree calculation is (180 - 200) / 200 × 100% = -10%. If the deviation of the matching degree exceeds the set reference value, such as ±5%, then the temperature of this partition does not match. Further judge the temperature gradient change of the partition. The gradient calculation method is the ratio of the temperature difference between adjacent measurement points to the distance. For example, the distance between two measurement points in a certain area is 5 cm, the temperature of one measurement point is 190°C, and the other is 170°C, then the gradient calculation is (190 - 170) / 5 = 4°C / cm. Set the temperature gradient threshold. If the temperature gradient in a certain area is greater than 5°C / cm or less than 1°C / cm, it is considered that the temperature distribution in this area is uneven. Finally, obtain the bottom pot temperature gradient.

[0069] The temperature change trend analysis sub-module calls the bottom pot temperature gradient to analyze the temperature change situation, calculates the temperature rate difference of the partition, judges the overall temperature change mode, and obtains the temperature change trend value;

[0070] The temperature change trend value adopts the formula:

[0071]

[0072] where, T change represents the temperature change trend value, ΔT zone,i represents the change amount of the temperature in the i-th partition, Δt zone,i represents the change amount of time in the i-th partition, N represents the total number of partitions analyzed, ΔT zone,i-1 represents the change amount of the temperature in the previous partition, Δt zone,i-1 represents the change amount of time in the previous partition;

[0073] This formula is used to calculate the temperature change trend value T change in the bottom pot temperature gradient analysis, that is, by comparing the temperature change rate differences of each partition, further infer the overall temperature change trend. Each parameter in the formula is obtained through actual measurement or calculation;

[0074] ΔT zone,i : represents the change amount of the temperature in the i-th partition. This value is obtained by monitoring the temperature change of each area of the bottom pot with a temperature sensor. Assume that the temperature of the bottom pot area A at the first moment is 200°C and at the second moment is 205°C, then ΔT zone,1= 205 - 200 = 5 °C;

[0075] Δt zone,i : Represents the time change amount of the i-th partition, which is obtained by the time recording device, that is, the time difference between the i-th moment and the (i - 1)-th moment. For example, if the first moment is 10:00:00 and the second moment is 10:05:00, then Δt zone,1 = 5 minutes;

[0076] N: Represents the total number of partitions analyzed. In this example, assume the bottom of the pot is divided into 5 areas, that is, N = 5;

[0077] For each partition, first calculate its temperature change rate: Taking the first partition as an example, assume its temperature change amount is 5 °C and the time change amount is 5 minutes, then the temperature change rate of the first partition is:

[0078] For the second partition, assume its temperature change amount is 4 °C and the time change amount is 4 minutes, then its temperature change rate is:

[0079] Continue this kind of operation to calculate the temperature change rate of each partition;

[0080] Calculate the absolute value of the difference in temperature change rates of each partition. For example, assume the temperature change rate of the first partition is 1 °C / minute and the second partition is 1 °C / minute, then the temperature rate difference is |1 - 1| = 0;

[0081] Calculate the rate difference for each group of partitions to obtain the absolute value of the difference;

[0082] Summation and averaging:

[0083] Sum up the rate differences of all partitions and then divide by the total number of partitions N. Assume the rate differences of all partitions are 0, 0.1, 0.05, 0.2, 0.3 respectively, then:

[0084]

[0085] T change = 0.13 indicates that the average level of the difference in temperature change rates between the areas at the bottom of the pot is 0.13 °C / minute. The value is a measure of the overall temperature change trend. The higher the value, the more inconsistent the temperature changes between different partitions, indicating uneven temperature distribution. Conversely, it indicates that the temperature at the bottom of the pot is relatively uniform.

[0086] Specifically, as Figure 2 , 4 shown, the heat response prediction and regulation module includes:

[0087] Based on the temperature change trend value, the temperature change curve recognition sub-module calls the thermal response curve database to extract the temperature change curve of the pot body, screens the fluctuation range of the change rate, calculates the average rate of temperature change in the range, and obtains the temperature change curve of the pot body;

[0088] First, read the temperature change trend value of the pot body. This trend value represents the temperature change rate of the pot body within a certain period of time. For example, if the temperature of a certain pot body rises from 120 °C to 150 °C within 30 seconds, then its temperature change trend value is calculated as (150 - 120) / 30 = 1 °C / s. Then, call the thermal response curve database and extract the historical temperature change curve that matches the current temperature change trend value of the pot body. The screening method is to calculate the mean square error between the current trend value and all historical curves in the database, and the curve with the smallest mean square error is used as the reference curve. Screen out the fluctuation range of the change rate. The calculation method of the fluctuation range is to set the rate threshold range. For example, if the fluctuation threshold is set to ±0.5 °C / s, then the interval where the rate change is between 1.5 °C / s and 2.5 °C / s within a certain period of time is marked as the fluctuation range. After identifying the fluctuation range, calculate the average rate of temperature change in this range. The calculation method is the mean value of the rates at all time points in this range. For example, if there are five measurement points in a certain fluctuation range, and their temperature change rates are 1.5, 1.8, 2.2, 1.9, and 2.3 °C / s respectively, then calculate the average rate (1.5 + 1.8 + 2.2 + 1.9 + 2.3) / 5 = 1.94 °C / s. Finally, obtain the temperature change curve of the pot body.

[0089] The target trajectory comparison sub-module calls the temperature change curve of the pot body, compares the target temperature trajectory, analyzes the offset amplitude, and obtains the temperature prediction error range;

[0090] First, read the target temperature trajectory. This trajectory is the ideal temperature change path calculated based on the preset heating program. For example, a certain target trajectory should rise from 100 °C to 180 °C within 60 seconds, and the temperature points every 10 seconds are 100 °C, 120 °C, 140 °C, 160 °C, and 180 °C respectively. Compare the actual measured temperature points of the temperature change curve of the pot body with the target trajectory, and calculate the offset amplitude. The calculation method is the actual measured temperature point minus the target temperature point. For example, if the actual temperature of a certain measurement point is 125 °C and the target temperature is 120 °C, then the offset amplitude is calculated as 125 - 120 = 5 °C. Statistically analyze the offset values of all measurement points, calculate the offset mean value and the maximum offset value. For example, if the offset values within a certain period of time are 5 °C, -3 °C, 2 °C, -4 °C, and 6 °C respectively, then the offset mean value is calculated as (5 - 3 + 2 - 4 + 6) / 5 = 1.2 °C, and the maximum offset value is 6 °C. Further calculate the offset error range. The error range is set as ±10% of the maximum offset value as the threshold. For example, 10% of 6 °C is 0.6 °C, then the error range is set as [-6.6 °C, 6.6 °C]. Finally, obtain the temperature prediction error range.

[0091] The temperature prediction error calculation sub-module calls the temperature prediction error range, analyzes the temperature deviation rate, calculates the temperature change difference within the error range, and obtains the temperature prediction deviation amplitude.

[0092] First, read the upper and lower limit values of the temperature prediction error range, and calculate the temperature deviation rate at each time point. The calculation method is the deviation amplitude divided by the target temperature value. For example, if the target temperature of a measuring point is 150 °C and the actual temperature is 155 °C, then the temperature deviation rate is calculated as (155 - 150) / 150×100% = 3.33%. Statistically analyze the deviation rates of all measuring points, and calculate the average deviation rate. For example, if the deviation rates of five measuring points are 3.33%, -2.67%, 1.5%, -4.0%, and 5.2% respectively, then calculate the average value (3.33 - 2.67 + 1.5 - 4.0 + 5.2) / 5 = 0.872%. Further calculate the temperature change difference within the error range. The calculation method is the difference between the upper limit temperature and the lower limit temperature of the error range. For example, for the error range [-6.6 °C, 6.6 °C], the temperature change difference is calculated as 6.6 - (-6.6) = 13.2 °C. Finally, obtain the temperature prediction deviation amplitude.

[0093] Specifically, as Figure 2 , 5 shown, the bottom power balance control module includes:

[0094] The partition temperature comparison sub-module, based on the temperature prediction deviation amplitude, calls the data of the bottom partition temperature sensors, identifies the partition temperature change rate, calculates the temperature fluctuation range, analyzes the temperature gradient within the temperature difference range, and obtains the bottom partition temperature difference.

[0095] First, read the real-time temperature data of each partition at the bottom, and compare it with the temperature at the previous moment. Calculate the partition temperature change rate. The calculation method is (current temperature - temperature at the previous moment) / time interval. For example, if the temperature of a certain area is 160 °C at t = 10 s and 170 °C at t = 20 s, then its temperature change rate is calculated as (170 - 160) / 10 = 1 °C / s. After calculating all partitions, calculate the temperature fluctuation range. The calculation method of the fluctuation range is the difference between the highest temperature and the lowest temperature at the bottom. For example, if the highest temperature is 220 °C and the lowest temperature is 180 °C, then the temperature fluctuation range is 220 - 180 = 40 °C. Further analyze the temperature gradient within the temperature difference range. The gradient calculation method is the difference between the temperature change rates of adjacent measuring points. For example, if the temperature change rates of two measuring points in a certain area are 2.0 °C / s and 1.2 °C / s respectively, then the gradient is calculated as 2.0 - 1.2 = 0.8 °C / s. After calculating the gradients of all measuring points, select the areas with larger temperature gradients. Set the gradient threshold to 0.5 °C / s. If the temperature gradient of a certain area exceeds this threshold, then mark this area as an area with uneven temperature change. Finally, obtain the bottom partition temperature difference.

[0096] The heating imbalance recognition sub-module calls the temperature difference of the bottom of the pot divided into zones, compares the temperature offset of each zone at the bottom of the pot, calculates the corresponding offset ratio of the zone and performs screening to obtain the degree of local heating imbalance;

[0097] First, read the temperature data of each zone, and calculate the difference between the temperature of each zone and the overall average temperature of the bottom of the pot. The calculation method is the zone temperature minus the overall average temperature. For example, if the overall average temperature is 200 °C and the temperature of a certain area is 210 °C, then the offset is calculated as 210 - 200 = 10 °C. After calculating all zones, screen out the zones with larger offsets. Set the offset threshold to ±5 °C. If the temperature offset of a certain area exceeds this threshold, it is marked as a temperature abnormal zone. Further calculate the corresponding offset ratio of the zone. The calculation method is (zone temperature offset / overall average temperature) × 100%. For example, if the temperature offset of a certain area is 10 °C and the overall average temperature is 200 °C, then calculate 10 / 200 × 100% = 5%. Set the offset ratio threshold to 5%. If the offset ratio of a certain area is greater than 5%, it is considered that the temperature distribution in this area is abnormal. After screening out all areas with excessive offsets, calculate the total area ratio of the areas, and the calculation method is the total area of the abnormal areas divided by the total area of the bottom of the pot. For example, the total area of the abnormal areas is 500 cm 2 , and the total area of the bottom of the pot is 2000 cm 2 , then calculate 500 / 2000 × 100% = 25%, and finally obtain the degree of local heating imbalance.

[0098] The power correction calculation sub-module analyzes the power adjustment range based on the degree of local heating imbalance, measures the power adjustment parameters for each zone, and obtains the power correction amount for the bottom of the pot;

[0099] The power adjustment parameter for each zone uses the formula:

[0100]

[0101] where, P adjust represents the power adjustment parameter for each zone, P base represents the reference power value, P local represents the local power value, P i represents the power measurement value in the i-th zone, n represents the total number of zones, ΔT avg represents the average temperature deviation;

[0102] In this formula, P adjust represents the power adjustment parameter for each zone, reflecting the change range of the power after correction. Each parameter in the formula is obtained by different calculation methods;

[0103] P baseDenotes the reference power value, which is obtained through long-term experimental data or standardized test conditions. In practical applications, the reference power value is generally obtained by measuring the power of the device under standard operating conditions. Under a certain specific condition, the reference power of the boiler or heating system is 3000W;

[0104] P local Denotes the local power value, which measures the power output of a specific area. This value is obtained by real-time monitoring of the power of the heating area of the device. For example, the power of the local area is 2800W;

[0105] P i Denotes the power value of each partition, which is collected by sensors in multiple partitions and the average value of the power of each partition is calculated. Assume that the partition powers are: P1 = 2900W, P2 = 2950W, P3 = 3100W, P4 = 3000W, and the sum is calculated

[0106] n is the number of partitions, representing the total number of partitions. In this example, the number of partitions is 4;

[0107] ΔT avg Represents the average value of the temperature deviation, which refers to the average difference between the temperatures of all partitions and the expected value. The temperature deviation is calculated by measuring the difference between the actual temperature and the preset standard temperature of the device. Assume that after monitoring, the calculated average value of the temperature deviation is 5°C;

[0108] According to the definitions of the above parameters, the formula can be substituted with specific values for calculation. Calculate |P base -P local : |P base -P local | = |3000 - 2800| = 200W;

[0109] Next, calculate the average value of the partition powers:

[0110] Substitute all values to calculate P adjust :

[0111]

[0112] The result shows that after power correction, the power that needs to be adjusted is 42344.45W. This means that by adjusting the power error caused by uneven local heating, the correction power required by the system has increased significantly, thereby optimizing the partition heating efficiency and reducing local heat fluctuations.

[0113] Specifically, as Figure 2 , 6 shown, the food ingredient heating timing optimization module includes:

[0114] Based on the bottom pot power correction amount, the food material heat transfer calculation sub-module extracts the food material heat transfer time parameter, calculates the heat absorption time of the food material per unit mass, screens out the food materials with low heat transfer rate, identifies the heat absorption time offset data of the bottom pot power correction amount of the food materials, measures the heat transfer time difference between different food materials, and obtains the food material heat transfer time parameter;

[0115] First, initialize a food material database, which includes the basic physical parameters of each food material such as the initial temperature, density, and specific heat capacity, as well as the heat transfer performance data under different bottom pot powers. For example, for meat, its density is about 1050 kg / m 3 , and its specific heat capacity is about 1.7 kJ / (kg·K). Then, adjust the heat transfer model parameters of the corresponding food materials according to the bottom pot power correction amount, and use the corrected power to simulate the heating of the food materials. During the simulation process, calculate the temperature change of the food materials in real time, and identify the food materials with low heat transfer rate by comparing the temperature rise of each food material within the same time. For example, set the temperature rise to 90 °C as the end condition, record the time required for each food material to reach this temperature, and classify the ones with longer time into the category of low heat transfer rate. For the food materials, further analyze their heat absorption time offset data, and dynamically adjust the bottom pot power by monitoring the deviation between the actual temperature and the expected temperature of the food materials in real time to ensure that all food materials can be evenly heated to the ideal state. Finally, measure the heat transfer time difference and obtain the food material heat transfer time parameter. For example, the heat transfer time parameter of potatoes is set to 8 minutes, while that of beef is 15 minutes. The parameters will be used for the subsequent optimization of the heat transfer process.

[0116] Based on the food material heat transfer time parameter, the heat absorption rate analysis sub-module compares the heat absorption rates of the food materials, calculates the heat absorption offset rate, and obtains the food material heat absorption rate;

[0117] The heat absorption offset rate adopts the formula:

[0118]

[0119] Among them, Q represents the heat absorption offset rate, A represents the surface area of the food material, T1 represents the final temperature of the food material, T0 represents the initial temperature of the food material, t1 represents the initial value of the heat transfer time, and t2 represents the final value of the heat transfer time;

[0120] Each parameter needs to be obtained from actual measurement, monitoring, or calculation to ensure the accuracy of the formula operation;

[0121] The surface area A of the food material is obtained by actually measuring the external dimensions of the food material. In this example, assume that the measured shape of the food material is approximately rectangular, with a length of 0.2 m and a width of 0.15 m. Then the surface area is calculated as follows: A = 0.2 × 0.15 = 0.03 m 2 ;

[0122] The initial temperature T0 and the final temperature T1. The temperature data was obtained through a temperature sensor during the experiment. In this example, the initial temperature T0 of the food ingredient is 20 °C, and the final temperature T1 is 80 °C. The temperature data was directly obtained through real-time monitoring by the sensing device;

[0123] The heat transfer times t1 and t2 were monitored by a heat sensor. During the measurement process, the time when the heat started to conduct from the center of the food ingredient was recorded. Assuming in this experiment, t1 is 30 s and t2 is 45 s;

[0124] According to the values obtained above, substitute them into the formula for calculation:

[0125] First, calculate T1 - T0: 80 - 20 = 60;

[0126] Then calculate

[0127] Next, calculate the square root part:

[0128] Finally, calculate Q:

[0129] The calculated Q = 0.0294 represents the heat absorption rate of the food ingredient. This value indicates that during the heat transfer process, the amount of heat absorbed by the food ingredient per unit area per second is approximately 0.0294. This result provides a basic data for subsequent heat conduction and heat utilization analysis, and further helps to evaluate the heat absorption characteristics of the food ingredient.

[0130] The heating timing adjustment sub-module calls the heat absorption rate of the food ingredient, analyzes the influence of the heating time on the food ingredient, identifies the heating timing adjustment range, and obtains the heating timing adjustment result;

[0131] First, set a benchmark heating time model, which is based on the heat absorption rate of the food ingredient and its physical parameters (such as specific heat capacity, density, etc.). Then, adjust the model according to the data recorded in the actual heating experiment. For example, if it is found that the temperature of a certain food ingredient after heating is lower than expected because the heat absorption rate is lower than the heat absorption rate set in the model, at this time, the heating time or power needs to be adjusted. Through multiple experiments, the optimal heating timing is determined. The result of this heating timing adjustment is verified by experimental data. For example, by increasing the heating time, the temperature of beef is raised from 75 °C to 85 °C, so as to meet the food safety temperature requirements. Finally, the heating timing adjustment result is obtained to ensure that all food ingredients can reach the best state in terms of safety and texture.

[0132] Specifically, as Figure 2 、 7 shown, the heating strategy execution module includes:

[0133] Based on the heating timing adjustment result, the heating start / stop control sub-module calls the heating element start / stop control logic, evaluates the current start / stop state of the heating element, analyzes the start / stop time error of the target element, determines the heating element with a large time offset, evaluates the matching degree between the element and the heating timing adjustment result, adjusts the start / stop parameters, and obtains the start / stop state of the heating element.

[0134] First, read the current start / stop state of the heating element. This state includes the current power output of the element, the most recent start / stop time point, and the recommended start / stop time point corresponding to the target heating timing adjustment result. Compare the current start / stop time with the recommended start / stop time and calculate the start / stop time error. The error calculation method is the difference between the actual start / stop time and the recommended start / stop time. For example, if the target start / stop time of a heating element is 120 seconds and its actual start / stop time is 140 seconds, then the time error is 20 seconds. Analyze the time error data of all heating elements, and screen out the heating elements whose absolute value of the time error exceeds the set threshold. Set the time offset threshold to 15 seconds. If the offset of a certain element is greater than 15 seconds, then mark this element as a heating element with a large time offset. Evaluate the matching degree between the start / stop state of the target heating element and the heating timing adjustment result. The matching degree evaluation method is to calculate the overlap rate of the actual start / stop cycle and the recommended start / stop cycle of the target element. If the overlap rate is lower than the set reference value, it is considered that the matching degree is low. For example, if the recommended start / stop cycle is 120 seconds and the actual start / stop cycle is 140 seconds, and the effective overlap time is 100 seconds, then the matching degree calculation formula is 100 / 120 = 83.3%. If the matching degree is lower than 80%, then the start / stop parameters need to be adjusted. The adjustment methods include modifying the start / stop time point, adjusting the heating time, or reducing the downtime. After the final adjustment, obtain the start / stop state of the heating element to ensure that its start / stop time is more in line with the heating timing adjustment result.

[0135] The zone power adjustment sub-module calls the start / stop state of the heating element, adjusts the zone power distribution parameters, calculates the zone power correction value, and screens out the areas with a large correction amplitude to obtain the zone power distribution parameters.

[0136] First, read the current power output levels of each heating element and calculate the total power of each zone. Set the formula for calculating the total zone power as the sum of the powers of each heating element. For example, if a certain area contains three heating elements with power outputs of 800W, 1000W, and 1200W respectively, then the total power of this area is 800 + 1000 + 1200 = 3000W. Compare with the target zone power value and calculate the power correction value. The calculation method of the correction value is the difference between the target power and the current power. For example, if the target power is 3200W, then the correction value is calculated as 3200 - 3000 = 200W. Screen the areas with large correction amplitudes, and set the correction amplitude threshold as 5% of the total power. If the correction value of a certain area is greater than 5% of the total power of this area, then this area needs to be adjusted preferentially. For example, if the total power of a certain area is 4000W and the 5% threshold is 200W, if the correction value is greater than 200W, then mark this area as an area to be adjusted. After screening out the areas to be adjusted, recalculate the zone power distribution parameters. The adjustment methods include increasing the power distribution of low-power areas, reducing the power distribution of high-power areas, or reallocating the start-stop states of heating elements. Finally, obtain the zone power distribution parameters to ensure that the power can meet the set target power requirements after adjustment.

[0137] The heating dynamic control sub-module calls the zone power distribution parameters, executes the dynamic control of heating elements, identifies the power adjustment range, analyzes the zone power adjustment amplitude, and obtains the electric cooker heating control result;

[0138] First, read the current zone power distribution situation and determine the power adjustment range of each zone. The calculation method of the power adjustment range is the difference between the current power and the maximum allowable power and the minimum allowable power. For example, if the maximum allowable power of a certain area is 5000W, the minimum allowable power is 2500W, and the current power is 4000W, then the power adjustment range is [2500W, 5000W]. Analyze the zone power adjustment amplitude. The formula for calculating the adjustment amplitude is (target power - current power) / current power × 100%. For example, if the target power is 4500W and the current power is 4000W, then the adjustment amplitude is calculated as (4500 - 4000) / 4000 × 100% = 12.5%. If the adjustment amplitude exceeds the set threshold, dynamic adjustment is required. Set the adjustment amplitude threshold as 10%. If the power adjustment amplitude of a certain zone is greater than 10%, then it is necessary to increase or decrease the output power of the heating element. The adjustment methods include increasing or decreasing the heating time or modifying the zone attribution of the heating element. Finally, obtain the electric cooker heating control result to ensure that the power of each heating area can match the set power requirements after adjustment.

[0139] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. An electric cooker control system, characterized in that, The system includes: The temperature data monitoring module, according to the performance data of the electric cooker, calls the pot body material database to identify the heat conduction coefficient of the pot body, matches the food material category with the specific heat capacity parameter, judges the temperature gradient of each area at the bottom of the pot, analyzes the temperature change rate of the pot body, and obtains the temperature change trend value; The heat response prediction and adjustment module, based on the temperature change trend value, calls the heat response curve database to analyze the temperature change curve of the pot body, compares the target temperature change trajectory, identifies the temperature prediction error interval, and obtains the temperature prediction deviation amplitude; The bottom power balance control module, based on the temperature prediction deviation amplitude, calls the bottom partition temperature sensor data, compares the temperature difference of each partition, identifies the degree of local heating imbalance, and obtains the bottom power correction amount; The food material heating timing optimization module, based on the bottom power correction amount, calls the food material heat capacity database, compares the differential food material heat transfer time parameters, analyzes the food material heat absorption rate, and obtains the heating timing adjustment result; The heating strategy execution module, based on the heating timing adjustment result, calls the heating element start-stop control logic, adjusts the partition power distribution parameters, executes the dynamic control of the heating element, and obtains the electric cooker heating control result.

2. The electric cooker control system according to claim 1, characterized in that: The temperature change trend value includes the temperature gradient of each area at the bottom of the pot, the temperature change rate of the pot body, and the temperature change trend. The temperature prediction deviation amplitude includes the temperature prediction error interval, the target temperature change trajectory, and the temperature change curve of the pot body. The bottom power correction amount includes the degree of local heating imbalance, the temperature difference of each partition, and the bottom partition temperature data. The heating timing adjustment result includes the food material heat absorption rate, the food material heat transfer time parameter, and the food material heat capacity parameter. The electric cooker heating control result includes the dynamic control of the heating element, the partition power distribution parameter, and the heating element start-stop control parameter.

3. The electric cooker control system according to claim 1, wherein: The temperature data monitoring module includes: The temperature sensing data processing sub-module, according to the performance data of the electric cooker, including the pot body temperature sensor data, the bottom partition temperature data, and the heating element power data, calculates the difference between the pot body temperature sensor data and the bottom partition temperature data, analyzes the temperature rise and fall rate of different parts of the pot body, screens the extreme values of temperature change, identifies the overall temperature fluctuation range, and generates the temperature change rate of the pot body; The bottom area temperature calculation sub-module, based on the temperature change rate of the pot body, matches the food material category with the specific heat capacity parameter, analyzes the matching degree of the bottom partition temperature, judges the change of the partition temperature gradient, and obtains the bottom temperature gradient; The temperature change trend analysis sub-module calls the bottom temperature gradient to analyze the temperature change situation, calculates the difference in the partition temperature rate, judges the overall temperature change mode, and obtains the temperature change trend value.

4. The electric cooker control system according to claim 3, wherein: The temperature change trend value, using the formula: Among them, T change represents the temperature change trend value, ΔT zone,i represents the change in temperature of the i-th partition, Δt zone,i represents the change in time of the i-th partition, N represents the total number of partitions analyzed, ΔT zone,i-1 represents the change in temperature of the previous partition, Δt zone,i-1 represents the change in time of the previous partition.

5. The electric cooker control system according to claim 1, wherein: The heat response prediction and adjustment module includes: The temperature change curve identification sub-module, based on the temperature change trend value, calls the heat response curve database to extract the temperature change curve of the pot body, screens the change rate fluctuation interval, calculates the average temperature change rate of the interval, and obtains the temperature change curve of the pot body; The target trajectory comparison sub-module calls the temperature change curve of the pot body, compares the target temperature trajectory, analyzes the offset amplitude, and obtains the temperature prediction error interval; The temperature prediction error calculation sub-module calls the temperature prediction error range, analyzes the temperature deviation rate, calculates the temperature change difference in the error magnitude range, and obtains the temperature prediction deviation magnitude.

6. The electric cooker control system according to claim 1, characterized in that: The bottom pot power balance control module includes: The partition temperature comparison sub-module, based on the temperature prediction deviation magnitude, calls the data of the bottom pot partition temperature sensor, identifies the partition temperature change rate, calculates the temperature fluctuation range, analyzes the temperature gradient in the temperature difference range, and obtains the bottom pot partition temperature difference; The heating imbalance identification sub-module calls the bottom pot partition temperature difference, compares the temperature offset of each partition of the bottom pot, calculates the corresponding offset ratio of the partition and filters it, and obtains the degree of local heating imbalance; The power correction calculation sub-module, based on the degree of local heating imbalance, analyzes the power adjustment range, calculates the partition power adjustment parameter, and obtains the bottom pot power correction amount.

7. The electric cooker control system according to claim 6, characterized in that: The partition power adjustment parameter adopts the formula: Among them, P adjust represents the partition power adjustment parameter, P base represents the reference power value, P local represents the local power value, P i represents the power measurement value within the i-th partition, n represents the total number of partitions, ΔT avg represents the average temperature deviation.

8. The electric cooker control system according to claim 1, characterized in that: The food heating timing optimization module includes: The food heat transfer calculation sub-module, based on the bottom pot power correction amount, extracts the food heat transfer time parameter, calculates the heat absorption time of the food per unit mass, filters the food with a low heat transfer rate, identifies the heat absorption time offset data of the bottom pot power correction amount of the food, calculates the heat transfer time difference between different foods, and obtains the food heat transfer time parameter; The heat absorption rate analysis sub-module, according to the food heat transfer time parameter, compares the heat absorption rate of the food, calculates the heat absorption offset rate, and obtains the food heat absorption rate; The heating timing adjustment sub-module calls the food heat absorption rate, analyzes the influence of the heating time on the food, identifies the heating timing adjustment range, and obtains the heating timing adjustment result.

9. The electric cooker control system according to claim 8, wherein: The heat absorption offset rate adopts the formula: Where Q represents the heat absorption offset rate, A represents the surface area of the food, T1 represents the final temperature of the food, T0 represents the initial temperature of the food, t1 represents the initial value of the heat transfer time, and t2 represents the final value of the heat transfer time.

10. The electric cooker control system according to claim 1, wherein: The heating strategy execution module includes: The heating start-stop control sub-module, based on the heating timing adjustment result, calls the heating element start-stop control logic, evaluates the current start-stop state of the heating element, analyzes the start-stop time error of the target element, determines the heating element with a large time offset, evaluates the matching degree between the element and the heating timing adjustment result, adjusts the start-stop parameter, and obtains the heating element start-stop state; The partition power adjustment sub-module calls the heating element start-stop state, adjusts the partition power distribution parameter, calculates the partition power correction value, and filters the area with a large correction amplitude, and obtains the partition power distribution parameter; The heating dynamic control sub-module calls the partition power distribution parameter, executes the dynamic control of the heating element, identifies the power adjustment range, analyzes the partition power adjustment amplitude, and obtains the electric cooker heating control result.

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