An electric food warmer control system

Abstract

The application relates to the technical field of automation control, in particular to an electric heating pot control system, which comprises a temperature data monitoring module, a thermal response prediction and adjustment module, a pot bottom power balance regulation module, a food material heating time optimization module and a heating strategy execution module.In the application, the temperature change trend is accurately calculated by combining the heat conduction characteristics of the pot body material and the specific heat capacity parameters of the food material, precise temperature control is realized, the pot bottom temperature gradient is analyzed, the heat distribution is balanced, local overheating or insufficient heating is reduced, the temperature error interval is predicted by using historical thermal response data, the heating strategy is dynamically adjusted, the temperature control precision is improved, the power distribution is optimized according to the pot bottom partition temperature difference, the thermal energy utilization efficiency is improved, the food material heat absorption characteristics are matched, the heating time is optimized, uniform heating is ensured, the cooking quality is improved, the heating element start-stop state is dynamically adjusted, the power output is optimized, the system stability is improved, energy waste is reduced, and the service life of the heating element is prolonged.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of automation control technology, in particular to an electric food warmer control system. BACKGROUND

[0002] The field of automation control technology involves the design and implementation of systems that utilize mechanical, electrical, and computer-based means for automatic regulation and management. The core content of this field includes sensors, actuators, control algorithms, feedback mechanisms, and data acquisition and processing techniques. The goal of automation control technology is to efficiently and accurately control industrial equipment, household appliances, and other devices through automated systems. This technology is widely used in various fields such as production, energy, and transportation. By optimizing work processes, improving efficiency, and ensuring operational safety, automation control technology has become an indispensable technical support in modern industry and daily life. Research in this field covers system modeling, control strategy design, signal processing, and feedback control, ensuring that systems can respond quickly and accurately to environmental changes.

[0003] Among them, the electric food warmer control system refers to a control technology for temperature regulation and heating management of an electric food warmer. This patent subject focuses on temperature control technology in electric food warmers, involving real-time monitoring of the temperature inside the pot through temperature sensors and adjusting the heating power according to the preset temperature range. By controlling the start and stop of the electric heating element, it ensures uniform heating of food in the pot and avoids overheating. This control system uses control algorithms to accurately regulate temperature to meet the set temperature requirements. Through circuit design and optimization of control strategies, this system can achieve efficient and stable operation of the electric food warmer under different use conditions without relying on complex calculations or external inputs.

[0004] Existing technologies based on a single temperature sensor for overall monitoring cannot accurately identify temperature differences in different areas, leading to uneven heating of the pot bottom. The heat conduction characteristics of the pot material are not fully considered, resulting in uneven heat distribution and affecting cooking results. The heating needs of food materials lack targeted analysis, and the heat capacity characteristics of different food materials cannot be effectively matched, leading to over-heating or under-heating of some food materials. Historical data cannot be used for temperature prediction, and only passive adjustment of heating power can respond to temperature changes, with strong regulation lag, affecting overall temperature control accuracy. The power distribution strategy is relatively simple, and the power cannot be dynamically adjusted according to the temperature changes in different areas of the pot bottom, leading to increased energy consumption and uneven load on heating elements, shortening the service life of the equipment. SUMMARY

[0005] In order to solve the technical problems of the prior art that the overall monitoring based on a single temperature sensor cannot accurately identify the temperature difference of different areas, leading to uneven local heating of the pot bottom, the heat conduction characteristics of the pot body material are not fully considered, resulting in uneven heat distribution, affecting the cooking effect, lacking targeted analysis of the heating needs of food materials, and being unable to effectively match the heat capacity characteristics of different food materials, leading to over-heating or under-heating of some food materials, and being unable to utilize historical data for temperature prediction, being able to only passively adjust the heating power to respond to temperature changes, having strong lag in regulation and control, affecting the overall temperature control precision, having a single allocation strategy for heating power, being unable to dynamically adjust the power according to the temperature changes of different areas of the pot bottom, leading to increased energy consumption and uneven load of the heating element, and shortening the service life of the equipment, the embodiments of the present application provide an electric kettle control system. The technical solution is as follows:

[0006] In one aspect, an electric kettle control system is provided, which comprises:

[0007] The temperature data monitoring module identifies the heat conduction coefficient of the pot body according to the performance data of the electric kettle, matches the food material category and the specific heat capacity parameter, judges the temperature gradient of each area of the pot bottom, analyzes the temperature change rate of the pot body, and obtains the temperature change trend value;

[0008] The heat response prediction and adjustment module analyzes the temperature change curve of the pot body based on the temperature change trend value, compares the target temperature change track, identifies the temperature prediction error interval, and obtains the temperature prediction deviation amplitude;

[0009] The pot bottom power balance regulation module compares the temperature difference value of each partition based on the temperature prediction deviation amplitude, identifies the degree of local uneven heating, and obtains the pot bottom power correction amount;

[0010] The food material heating timing optimization module compares the differential food material heat transfer time parameter based on the pot bottom power correction amount, analyzes the food material heat absorption rate, and obtains the heating timing adjustment result;

[0011] The heating strategy execution module adjusts the partition power allocation parameter based on the heating timing adjustment result, executes dynamic control of the heating element, and obtains the electric kettle heating control result.

[0012] As a further scheme of the present application, the temperature change trend value comprises a temperature gradient of each region of the pot bottom, a temperature change rate of the pot body, a temperature change trend, the temperature prediction deviation amplitude comprises a temperature prediction error interval, a target temperature change track, a temperature change curve of the pot body, the pot bottom power correction amount comprises a local heating imbalance degree, a temperature difference value of each partition, pot bottom partition temperature data, the heating timing adjustment result comprises a food material heat absorption rate, a food material heat transfer time parameter, a food material heat capacity parameter, and the electric kettle heating control result comprises a heating element dynamic control, a partition power distribution parameter, and a heating element start-stop control parameter.

[0013] As a further scheme of the present application, the temperature data monitoring module comprises:

[0014] The temperature sensing data processing submodule calculates the difference value of the pot body temperature sensor data and the pot bottom partition temperature data according to the performance data of the electric kettle, including the pot body temperature sensor data, the pot bottom partition temperature data and the heating element power data, analyzes the temperature rise and fall rate of the differentiated parts of the pot body, screens the temperature change extreme value, identifies the overall temperature fluctuation range, and generates the temperature change rate of the pot body.

[0015] The pot bottom region temperature calculation submodule matches the food material category and the specific heat capacity parameter based on the temperature change rate of the pot body, analyzes the matching degree of the pot bottom partition temperature, judges the change of the partition temperature gradient, and obtains the temperature gradient of the pot bottom.

[0016] The temperature change trend analysis submodule analyzes the temperature variation condition by calling the temperature gradient of the pot bottom, calculates the partition temperature rate difference value, judges the overall temperature change mode, and obtains the temperature change trend value.

[0017] As a further scheme of the present application, the temperature change trend value adopts the formula:

[0018]

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

[0020] As a further scheme of the present application, the heat response prediction and adjustment module comprises:

[0021] The temperature change curve identification submodule calls a thermal response curve database based on the temperature change trend value to extract a pot body temperature change curve, screens a change rate fluctuation interval, calculates an interval temperature change average rate, and obtains the pot body temperature change curve;

[0022] The target trajectory comparison submodule calls the pot body temperature change curve, compares a target temperature trajectory, analyzes a deviation amplitude, and obtains a temperature prediction error interval;

[0023] The temperature prediction error calculation submodule calls the temperature prediction error interval, analyzes a temperature deviation rate, measures a temperature change difference of an error amplitude interval, and obtains a temperature prediction deviation amplitude.

[0024] As a further scheme of the present application, the pot bottom power balance control module comprises:

[0025] The partition temperature comparison submodule calls pot bottom partition temperature sensor data based on the temperature prediction deviation amplitude, identifies a partition temperature change rate, measures a temperature fluctuation range, analyzes a temperature gradient of a temperature difference interval, and obtains a pot bottom partition temperature difference value;

[0026] The heating imbalance identification submodule calls the pot bottom partition temperature difference value, compares a pot bottom temperature deviation of each partition, calculates and screens a partition corresponding deviation ratio, and obtains a local heating imbalance degree;

[0027] The power correction calculation submodule analyzes a power adjustment range based on the local heating imbalance degree, measures a partition power adjustment parameter, and obtains a pot bottom power correction amount.

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

[0029]

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

[0031] As a further scheme of the present application, the food material heating time optimization module comprises:

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

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

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

[0035] As a further scheme of the present application, the heat absorption offset rate adopts a 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 scheme of the present application, the heating strategy execution module comprises:

[0039] The heating start-stop control sub-module calls heating element start-stop control logic based on the heating time adjustment result, evaluates the current start-stop state of the heating element, analyzes the target element start-stop time error, determines the heating element with large time offset, evaluates the matching degree of the element and the heating time adjustment result, adjusts the start-stop parameter, 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 parameter, calculates a partition power correction value, and screens regions with large correction amplitude, and obtains the partition power distribution parameter.

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

[0042] The technical scheme provided by the embodiment of the present application has at least the following beneficial effects:

[0043] By combining the heat conduction characteristics of the pot body material and the specific heat capacity parameters of the food material, the temperature change trend is accurately calculated, the temperature regulation is more accurate, the temperature gradient of different areas of the pot bottom is analyzed in detail, the balance of temperature distribution is ensured, the situation of local overheating or insufficient heating is reduced, the historical thermal response data is predicted, the error interval of temperature change is identified, the heating strategy is dynamically adjusted, the temperature control accuracy is improved, the temperature difference change of each partition of the pot bottom is combined, the power distribution is optimized, the heating power of different areas is more matched with the actual demand, the thermal energy utilization efficiency is improved, the heating time is optimized according to the heat absorption characteristics of the food material, the uneven heating of the food material caused by improper heating time is avoided, the cooking quality is improved, the start and stop state of the heating element is dynamically adjusted, the power distribution is optimized according to the real-time temperature change, the operation of the electric kettle is more stable, the energy waste is reduced, and the service life of the heating element is prolonged. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0045] Figure 1 It is a schematic diagram of an electric kettle control system provided by the embodiments of the present application.

[0046] Figure 2 It is a system framework schematic diagram of the present application.

[0047] Figure 3 It is a flowchart of the temperature data monitoring module in the present application.

[0048] Figure 4 It is a flowchart of the thermal response prediction and adjustment module in the present application.

[0049] Figure 5 It is a flowchart of the pot bottom power balance regulation module in the present application.

[0050] Figure 6 It is a flowchart of the food material heating time optimization module in the present application.

[0051] Figure 7 It is a flowchart of the heating strategy execution module in the present application. DETAILED DESCRIPTION

[0052] The technical solutions in the present application will be described below in combination with the drawings.

[0053] In the embodiments of the present application, the words such as "exemplary", "for example", etc. are used to represent an example, illustration, or description. Any embodiment or design scheme described as "exemplary" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "exemplary" is intended to present the concept in a specific manner. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0054] In the embodiments of the present application, "image" and "picture" can be used interchangeably at times, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized. "Of", "corresponding" and "relevant" can be used interchangeably at times, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized.

[0055] In the embodiments of the present application, subscripts such as W1 may be written in non-subscript form such as W1 at times, and the meanings expressed are consistent when the distinction is not emphasized.

[0056] In order to make the technical problems, technical schemes and advantages to be solved by the present application more clear, the following will be described in detail in conjunction with the drawings and specific embodiments.

[0057] The embodiments of the present application provide an electric food warmer control system, as shown in Figures 1-2 The electric food warmer control system schematic diagram, the system comprises:

[0058] The temperature data monitoring module calls the pot body material database to identify the pot body thermal conductivity coefficient according to the performance data of the electric food warmer, including the pot body temperature sensor data, the pot bottom partition temperature data and the heating element power data, matches the food material category and the specific heat capacity parameter, judges the temperature gradient of each region of the pot bottom, analyzes the pot body temperature change rate, and obtains the temperature change trend value;

[0059] The thermal response prediction and adjustment module calls the thermal response curve database to analyze the pot body temperature change curve based on the temperature change trend value, compares the target temperature change track, identifies the temperature prediction error interval, 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 value of each partition, identifies the local heating imbalance degree, and obtains the pot bottom power correction amount;

[0061] The food material heating timing optimization module calls the food material heat capacity database based on the pot bottom power correction amount, compares the differential food material heat transfer time parameter, analyzes the food material heat absorption rate, and obtains the heating timing adjustment result;

[0062] The heating strategy execution module calls heating element start-stop control logic based on the heating timing adjustment result, adjusts the partition power distribution parameter, executes dynamic control of the heating element, and obtains the electric frying pan heating control result.

[0063] The temperature change trend value includes the temperature gradient of each region of the pot bottom, 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 pot body temperature change curve. The pot bottom power correction amount includes the local heating imbalance degree, the temperature difference value of each partition, and the pot 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 frying pan heating control result includes the dynamic control of the heating element, the partition power distribution parameter, and the heating element start-stop control parameter.

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

[0065] The temperature sensing data processing submodule calculates the difference between the pot body temperature sensor data and the pot bottom partition temperature data according to the performance data of the electric frying pan, including the pot body temperature sensor data, the pot bottom partition temperature data, and the heating element power data, analyzes the temperature rise and fall rate of the differentiated parts of the pot body, filters the temperature change extreme value, identifies the overall temperature fluctuation range, and generates the pot body temperature change rate.

[0066] Firstly, the temperature data collected by the pot body temperature sensor in real time is read, and the time stamp is recorded. The pot bottom partition temperature data comes from multiple temperature sensors distributed in different areas of the pot bottom. Each data point is labeled to determine the measurement time of each partition temperature data. The difference between the pot body temperature sensor data and the pot bottom partition temperature data is calculated. The calculation method is to subtract the pot bottom partition temperature from the pot body sensor temperature. For example, if the pot body temperature sensor reading is 150°C and the temperature of a certain area of the pot bottom is 180°C, the difference is 150-180=-30°C. After calculating the difference for all partitions, the temperature rise rate of each part of the pot body is analyzed. The calculation method is to subtract the temperature at the last time from the temperature at the current time and divide by the time interval. For example, the temperature of a certain area at t=10s is 170°C, and the temperature at t=15s is 175°C. The temperature rise rate is calculated as (175-170) / 5=1°C / s. The extreme value of temperature change is screened. The extreme value screening method is to set a temperature change range threshold. If the temperature change of a certain area exceeds the threshold, mark the area. For example, if the threshold is set to ±10°C / min, and the temperature change rate of a certain partition is 12°C / min, it is considered that the temperature fluctuation of the partition is too large. After screening the areas with significant temperature changes, the overall temperature fluctuation range is calculated. The calculation method is to subtract the lowest temperature from the highest temperature. For example, if the highest temperature is 200°C and the lowest temperature is 140°C, the temperature fluctuation range is 60°C. Finally, the pot body temperature change rate is calculated. The pot body temperature change rate is calculated as the ratio of temperature change amplitude to time. For example, if the pot body temperature rises from 160°C to 190°C in 30s, the change rate is (190-160) / 30=1°C / s. The pot body temperature change rate is generated.

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

[0068] First, determine the current heating area of food category, according to the specific heat capacity parameters of the classification table, for example, the specific heat capacity of water is 4.18kJ / (kg·K), meat specific heat capacity is 2.7kJ / (kg·K), the specific heat capacity of vegetables is about 3.5kJ / (kg·K), matching is completed, compared with the bottom of the pan partition temperature and the best heating temperature range required by food, analysis of the bottom of the pan partition temperature matching degree, matching degree calculation method is (current temperature-target temperature) / target temperature x 100%, for example, a certain partition current temperature is 180℃, the target temperature is 200℃, then the matching degree is calculated as (180-200) / 200x100%=-10%, if the matching degree deviation exceeds the set reference value, such as ±5%, then the partition temperature is not matched, further judge the partition temperature gradient change, gradient calculation method is the temperature difference between adjacent measuring points and distance ratio, for example, the distance between two measuring points in a certain area is 5cm, one measuring point temperature is 190℃, the other is 170℃, then the gradient is calculated as (190-170) / 5=4℃ / cm, set the temperature gradient threshold, if the temperature gradient of a certain area is greater than 5℃ / cm or less than 1℃ / cm, it is considered that the temperature distribution of this area is not uniform, and finally the temperature gradient of the bottom of the pan is obtained.

[0069] Temperature variation trend analysis submodule calls the bottom of the pan temperature gradient analysis temperature variation, calculates the partition temperature rate difference, judges the overall temperature change mode, and obtains the temperature variation trend value;

[0070] Temperature variation trend value, using formula:

[0071]

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

[0073] This formula is used to calculate the temperature variation trend value T change in the bottom of the pan temperature gradient analysis, that is, by comparing the temperature change rate difference of each partition, further deducing the overall temperature variation trend, each parameter in the formula is obtained by actual measurement or calculation;

[0074] ΔT zone,i : represents the temperature change of the i-th partition, this value is obtained by monitoring the temperature change of each area of the bottom of the pan by temperature sensor, assuming that the temperature of the bottom of the pan area A is 200℃ at the first time, and 205℃ at the second time, then ΔT zone,1= 205 - 200 = 5°C;

[0075] Δt zone,i represents the time change of the ith zone, which is obtained by a time recording device, i.e. the time difference between the ith time and the i-1th time. For example, if the 1th time is 10:00:00 and the 2th time is 10:05:00, then Δt zone,1 = 5 minutes;

[0076] N: represents the total number of zones analyzed, in this case, it is assumed that the bottom of the pot is divided into 5 zones, i.e. N = 5;

[0077] For each zone, first calculate its temperature change rate: Take the 1th zone as an example, assuming that its temperature change is 5°C and the time change is 5 minutes, then the temperature change rate of the 1th zone is:

[0078] For the 2th zone, assuming that its temperature change is 4°C and the time change is 4 minutes, then its temperature change rate is:

[0079] Continue to perform such operations to calculate the temperature change rate of each zone;

[0080] Calculate the absolute value of the difference between the temperature change rates of each zone, for example, assuming that the temperature change rate of the 1th zone is 1°C / min and that of the 2th zone is 1°C / min, then the temperature rate difference is |1-1| = 0;

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

[0082] Summation and averaging:

[0083] Sum all the rate differences of the zones and divide by the total number of zones N, assuming that the rate differences of all zones are 0, 0.1, 0.05, 0.2, and 0.3, respectively, then:

[0084]

[0085] T change = 0.13 represents the average level of the temperature change rate difference between the zones of the pot bottom, which is a measure of the overall temperature change trend, and a higher value indicates that the temperature changes are inconsistent between different zones, indicating that the temperature distribution is uneven, and vice versa, indicating that the temperature of the pot bottom is relatively uniform.

[0086] Specifically, as shown in Figure 2 , 4 the thermal response prediction and adjustment module comprises:

[0087] The temperature change curve identification submodule calls the thermal response curve database to extract the pot body temperature change curve based on the temperature change trend value, filters the change rate fluctuation interval, calculates the interval temperature change average rate, and obtains the pot body temperature change curve;

[0088] First, the pot body temperature change trend value is read, which represents the temperature change rate of the pot body within a certain time, for example, the temperature of a certain pot body rises from 120°C to 150°C in 30 seconds, and the temperature change trend value is calculated as (150-120) / 30=1°C / s. Then, the thermal response curve database is called to extract the historical temperature change curve matching the current pot body temperature change trend value. The filtering method is to calculate the mean square error of the current trend value and all historical curves in the database, and the one with the smallest mean square error is taken as the reference curve. The fluctuation interval of the change rate is filtered by setting a rate threshold range, for example, setting the fluctuation threshold to ±0.5°C / s. The interval with a rate change between 1.5°C / s and 2.5°C / s in a certain time period is marked as the fluctuation interval. After identifying the fluctuation interval, the average rate of temperature change in the interval is calculated, which is the average of the rate at all time points in the interval. For example, a fluctuation interval has five measurement points with temperature change rates of 1.5, 1.8, 2.2, 1.9, and 2.3°C / s, respectively. The average rate is calculated as (1.5+1.8+2.2+1.9+2.3) / 5=1.94°C / s. Finally, the pot body temperature change curve is obtained.

[0089] The target trajectory comparison submodule calls the pot body temperature change curve and compares the target temperature trajectory to analyze the offset amplitude and obtain the temperature prediction error interval.

[0090] First, the target temperature trajectory is read, which 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 in 60 seconds, with temperature points every 10 seconds at 100°C, 120°C, 140°C, 160°C, and 180°C. The actual measured temperature points of the pot body temperature change curve are compared with the target trajectory to calculate the offset amplitude, which is calculated by subtracting the target temperature point from the actual measured temperature point. For example, the actual temperature of a certain measurement point is 125°C, and the target temperature is 120°C, so the offset amplitude is calculated as 125-120=5°C. The offset values of all measurement points are counted to calculate the offset mean and the offset maximum. For example, the offset values in a certain time period are 5°C, -3°C, 2°C, -4°C, and 6°C, respectively. The offset mean is calculated as (5-3+2-4+6) / 5=1.2°C, and the offset maximum is 6°C. The offset error range is further calculated, with the error range set as ±10% of the offset maximum as the threshold. For example, 10% of 6°C is 0.6°C, so the error range is set as [-6.6°C, 6.6°C]. Finally, the temperature prediction error interval is obtained.

[0091] The temperature prediction error calculation submodule calls the temperature prediction error interval, analyzes the temperature deviation rate, calculates the temperature change difference of the error amplitude interval, and obtains the temperature prediction deviation amplitude;

[0092] First, the upper and lower limit values of the temperature prediction error interval are read, and the temperature deviation rate at each time point is calculated. The calculation method is to divide the deviation amplitude by the target temperature value. For example, if the target temperature of a certain measurement point is 150°C and the actual temperature is 155°C, the temperature deviation rate is calculated as (155-150) / 150*100%=3.33%, the deviation rates of all measurement points are counted, and the average deviation rate is calculated. For example, the deviation rates of five measurement points are 3.33%, -2.67%, 1.5%, -4.0%, and 5.2%, respectively. The average value is calculated as (3.33-2.67+1.5-4.0+5.2) / 5=0.872%, the temperature change difference of the error amplitude interval is further calculated, and the calculation method is to subtract the lower limit temperature from the upper limit temperature of the error interval. For example, the error interval is [-6.6°C, 6.6°C], and the temperature change difference is calculated as 6.6-(-6.6)=13.2°C. Finally, the temperature prediction deviation amplitude is obtained.

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

[0094] The partition temperature comparison submodule calls the bottom partition temperature sensor data based on the temperature prediction deviation amplitude, identifies the partition temperature change rate, calculates the temperature fluctuation range, analyzes the temperature gradient of the temperature difference interval, and obtains the bottom partition temperature difference value.

[0095] First, the real-time temperature data of each partition of the bottom is read and compared with the temperature at the previous time, the partition temperature change rate is calculated, and the calculation method is (current temperature-last time temperature) / time interval. For example, the temperature of a certain area is 160°C at t=10s and 170°C at t=20s, and the temperature change rate is calculated as (170-160) / 10=1°C / s. After calculating all the partitions, the temperature fluctuation range is calculated, and the calculation method is to subtract the minimum temperature from the maximum temperature of the bottom. For example, the maximum temperature is 220°C and the minimum temperature is 180°C, and the temperature fluctuation range is 220-180=40°C. Further analyze the temperature gradient of the temperature difference interval. The gradient calculation method is the temperature change rate difference between adjacent measurement points. For example, the temperature change rates of two measurement points in a certain area are 2.0°C / s and 1.2°C / s, respectively, and the gradient is calculated as 2.0-1.2=0.8°C / s. After calculating the gradient of all measurement points, the area with a larger temperature gradient is selected, and the gradient threshold is set to 0.5°C / s. If the temperature gradient of a certain area exceeds the threshold, mark the area as a temperature change uneven area, and finally obtain the bottom partition temperature difference value.

[0096] The heating imbalance identification submodule calls the temperature difference of the pot bottom partition, compares the temperature offset of each partition of the pot bottom, calculates the corresponding offset ratio of the partition, and screens it to obtain the degree of local heating imbalance;

[0097] First, read the temperature data of each partition and calculate the difference between the temperature of each partition and the overall average temperature of the pot bottom. The calculation method is to subtract the overall average temperature from the partition temperature. For example, if the overall average temperature is 200℃ and the temperature of a certain area is 210℃, the offset is calculated as 210-200=10℃. After calculating all partitions, filter out the partitions with larger offsets and set the offset threshold to ±5℃. If the temperature offset of a certain area exceeds the threshold, it is marked as a temperature abnormal partition. Further calculate the corresponding offset ratio of the partition. The calculation method is (partition temperature offset / overall average temperature)×100%. For example, if the temperature offset of a certain area is 10℃ and the overall average temperature is 200℃, the calculation method is 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 of this area is abnormal. After filtering out all areas with excessive offsets, the total area ratio of the area is calculated. The calculation method is to divide the total area of ​​the abnormal area by the total area of ​​the pot bottom. For example, the total area of ​​the abnormal area is 500cm 2 The total area of ​​the pot bottom is 2000cm 2 , then calculate 500 / 2000×100%=25%, and finally obtain the degree of local heating imbalance.

[0098] The power correction calculation submodule analyzes the power adjustment range based on the degree of local heating imbalance, calculates the partition power adjustment parameters, and obtains the bottom pot power correction value;

[0099] The partition power adjustment parameter uses the formula:

[0100]

[0101] 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 in the i-th partition, n represents the total number of partitions, ΔT avg represents the mean temperature deviation;

[0102] In this formula, P adjust Represents the partition power adjustment parameter, reflecting the change in power after correction. Each parameter in the formula is calculated in a different way;

[0103] P baseIndicates the benchmark power value, which is obtained through long-term experimental data or standardized test conditions. In actual application, the benchmark power value is generally obtained by measuring the power of the equipment under standard operating conditions. Under certain specific conditions, the benchmark power of the boiler or heating system is 3000W;

[0104] P local Indicates 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 Indicates the power value of each partition, which is collected by sensors in multiple partitions and the average power of each partition is calculated. Assuming that the partition powers are: P1 = 2900W, P2 = 2950W, P3 = 3100W, P4 = 3000W, the total is calculated

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

[0107] ΔT avg The average value of temperature deviation refers to the average difference between the temperature of all zones and the expected value. The temperature deviation is calculated by measuring the difference between the actual temperature of the equipment and the preset standard temperature. Assume that the average temperature deviation calculated after monitoring is 5°C.

[0108] According to the definition of the above parameters, the formula can be substituted into the specific values ​​for calculation to calculate |P base -P local :|P base -P local |=|3000-2800|=200W;

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

[0110] Substitute all values ​​to calculate P adjust :

[0111]

[0112] The results show that after power correction, the power that needs to be adjusted is 42344.45W, which means that by adjusting the power error caused by local heating imbalance, the correction power required by the system is greatly increased, thereby optimizing the zone heating efficiency and reducing local heat fluctuations.

[0113] Specifically, if Figure 2 、 6 As shown, the food heating timing optimization module includes:

[0114] The food material heat transfer calculation sub-module extracts the food material heat transfer time parameter based on the wok bottom power correction amount, calculates the heat absorption time of unit mass food material, screens food materials with low heat transfer rate, identifies the heat absorption time deviation data of the food material wok bottom power correction amount, 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 initial temperature, density, specific heat capacity and other basic physical parameters of each food material, as well as its heat transfer performance data under different wok bottom power. For example, for meat, its density is about 1050 kg / m 3 , and the specific heat capacity is about 1.7 kJ / (kg·K). Then, adjust the heat transfer model parameters of the corresponding food material according to the wok bottom power correction amount, and use the corrected power to simulate the heating of the food material. During the simulation process, the temperature change of the food material is calculated in real time, and the temperature rise of each food material at the same time is compared to identify the food material with low heat transfer rate. For example, set the temperature rise to 90℃ as the end condition, record the time required for each food material to reach this temperature, and classify the food material with longer time as the category with low heat transfer rate. Further analyze the heat absorption time deviation data of the food material, dynamically adjust the wok bottom power by monitoring the deviation between the actual temperature and the expected temperature of the food material, and ensure that all food materials can be uniformly heated to the ideal state. Finally, measure the heat transfer time difference to obtain the food material heat transfer time parameter. For example, the heat transfer time parameter of potato is set to 8 minutes, and that of beef is set to 15 minutes. The parameter will be used for subsequent heat transfer process optimization.

[0116] The heat absorption rate analysis sub-module compares the heat absorption rate of the food material based on the food material heat transfer time parameter, calculates the heat absorption deviation rate, and obtains the heat absorption rate of the food material;

[0117] The heat absorption deviation rate is calculated using the formula:

[0118]

[0119] Where Q represents the heat absorption deviation 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, it is assumed that the shape of the food material is approximately rectangular, with a length of 0.2 m and a width of 0.15 m. The surface area is calculated as follows: A = 0.2 x 0.15 = 0.03 m 2 ;

[0122] The initial temperature T0 and the final temperature T1 are obtained by a temperature sensor in the experiment, and in this case, the initial temperature T0 of the food material is 20°C, and the final temperature T1 is 80°C, and the temperature data is obtained directly through real-time monitoring of the induction device;

[0123] The heat transfer times t1 and t2 are obtained by a heat sensor, and the time for heat conduction from the center of the food material is recorded during the measurement process. It is assumed that in this experiment, t1 is 30s, and t2 is 45s;

[0124] According to the above obtained values, the formula is substituted 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 material, which indicates that during the heat transfer process, the food material absorbs about 0.0294 per unit area per second. 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 material.

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

[0131] First, set a baseline heating time model based on the heat absorption rate and physical parameters (such as specific heat capacity, density, etc.) of the food material, and 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 food material after heating is lower than expected, it is 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, and the result of the 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, thereby meeting the food safety temperature requirement. Finally, the heating timing adjustment result is obtained to ensure that all food materials can achieve the best state in terms of safety and quality.

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

[0133] The heating start-stop control submodule calls 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 target element start-stop time error, determines the heating element with large time offset, evaluates the matching degree of 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, the current start-stop state of the heating element is read, including the current power output of the element, the last start-stop time point, and the recommended start-stop time point corresponding to the target heating timing adjustment result. The current start-stop time is compared with the recommended start-stop time, the start-stop time error is calculated, the error calculation method is the difference between the actual start-stop time and the recommended start-stop time, for example, the target start-stop time of a certain heating element is 120 seconds, and the actual start-stop time is 140 seconds, then the time error is 20 seconds. Analyze the time error data of all heating elements, and filter out the heating elements with time error absolute value exceeding the set threshold. The time offset threshold is set to 15 seconds. If the offset of a certain element is greater than 15 seconds, the element is marked as a heating element with large time offset. Evaluate the matching degree of 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 period of the target element and the recommended start-stop period. If the overlap rate is lower than the set reference value, it is considered that the matching degree is low. For example, the recommended start-stop period is 120 seconds, and the actual start-stop period is 140 seconds, and the effective overlap time is 100 seconds. The matching degree calculation formula is 100 / 120=83.3%. If the matching degree is lower than 80%, the start-stop parameters need to be adjusted. The adjustment method includes modifying the start-stop time point, adjusting the heating time or reducing the downtime. Finally, the start-stop state of the heating element is obtained to ensure that the start-stop time is more in line with the heating timing adjustment result.

[0135] The partition power adjustment submodule calls the start-stop state of the heating element, adjusts the partition power distribution parameters, calculates the partition power correction value, and filters out the regions with large correction amplitude to obtain the partition power distribution parameters.

[0136] Firstly, the current power output level of each heating element is read, and the total power of each partition is calculated. The total power calculation formula of the partition is set as the sum of the power of each heating element. For example, a certain area contains three heating elements, which output power is 800W, 1000W and 1200W respectively, and the total power of the area is 800+1000+1200=3000W. Compared with the target power value of the partition, the power correction value is calculated. The correction value is calculated as the difference between the target power and the current power. For example, the target power is 3200W, and the correction value is calculated as 3200-3000=200W. The area with large correction range is selected, and the correction range threshold is set as 5% of the total power. If the correction value of a certain area is greater than 5% of the total power of the area, the area needs to be adjusted first. For example, the total power of a certain area is 4000W, and the 5% threshold is 200W. If the correction value is greater than 200W, the area is marked as an area that needs to be adjusted. After selecting the area that needs to be adjusted, the partition power distribution parameters are recalculated, and the adjustment method includes increasing the power distribution of low-power areas, reducing the power distribution of high-power areas, or reassigning the start-stop state of heating elements. Finally, the partition power distribution parameters are obtained to ensure that the power adjustment can meet the target power requirement.

[0137] The heating dynamic control submodule calls the partition power distribution parameters, executes the dynamic control of the heating element, identifies the power adjustment range, analyzes the partition power adjustment range, and obtains the electric kettle heating control result.

[0138] Firstly, the current partition power distribution is read, and the power adjustment range of each partition is determined. The power adjustment range is calculated as the difference between the current power and the maximum and minimum allowed power. For example, the maximum allowed power of a certain area is 5000W, the minimum allowed power is 2500W, and the current power is 4000W. The power adjustment range is [2500W, 5000W]. The partition power adjustment range is analyzed, and the adjustment range formula is calculated as (target power-current power) / current power*100%. For example, the target power is 4500W, and the current power is 4000W. The adjustment range is calculated as (4500-4000) / 4000*100%=12.5%. If the adjustment range exceeds the set threshold, dynamic adjustment is needed. The adjustment range threshold is set as 10%. If the power adjustment range of a certain partition is greater than 10%, the output power of the heating element needs to be increased or decreased. The adjustment method includes increasing or decreasing the heating time or modifying the partition attribution of the heating element. Finally, the electric kettle heating control result is obtained to ensure that the power adjustment of each heating area can match the set power requirement.

[0139] The above merely illustrates the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An electric hot pot control system, characterized in that: The system comprises: The temperature data monitoring module uses the performance data of the electric hot pot to call the pot material database to identify the pot's thermal conductivity coefficient, match the food type with the specific heat capacity parameter, determine the temperature gradient of each area on the pot bottom, analyze the pot temperature change rate, and obtain the temperature change trend value; The thermal response prediction and adjustment module calls 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 interval, and obtains the temperature prediction deviation amplitude; 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; The food heating timing optimization module calls the food heat capacity database based on the pot bottom power correction value, compares the heat transfer time parameters of the differentiated food, analyzes the heat absorption rate of the food, and obtains the heating timing adjustment result; The heating strategy execution module calls the heating element start and stop control logic based on the heating timing adjustment result, adjusts the partition power allocation parameters, performs dynamic control of the heating element, and obtains the electric hot pot heating control result.

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

3. The electric hot pot control system according to claim 1, characterized in that: The temperature data monitoring module includes: The temperature sensor data processing submodule calculates the difference between the pot body temperature sensor data and the pot bottom partition temperature data based on the performance data of the electric hot pot, including the pot body temperature sensor data, the pot bottom partition temperature data, and the heating element power data. It analyzes the temperature rise and fall rates of different parts of the pot body, screens the temperature change extremes, identifies the overall temperature fluctuation range, and generates the pot body temperature change rate. The pot bottom area temperature calculation submodule matches the food category and specific heat capacity parameters based on the pot body temperature change rate, analyzes the temperature matching degree of the pot bottom partitions, determines the temperature gradient change of the partitions, and obtains the pot bottom temperature gradient; The temperature change trend analysis submodule calls the pot bottom temperature gradient to analyze the temperature change, calculates the temperature rate difference of the partition, determines the overall temperature change mode, and obtains the temperature change trend value.

4. The electric hot pot control system according to claim 3, characterized in that: The temperature change trend value adopts the formula: Among them, T change Indicates 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 Indicates the change in temperature of the previous partition, Δt zone,i-1 Indicates the change in the previous partition time.

5. The electric hot pot control system according to claim 1, characterized in that: The thermal response prediction and adjustment module includes: The temperature change curve identification submodule extracts the pot body temperature change curve based on the temperature change trend value by calling the thermal response curve database, screens the change rate fluctuation interval, calculates the average temperature change rate of the interval, and obtains the pot body temperature change curve; The target trajectory comparison submodule calls the pot body temperature change curve, compares the target temperature trajectory, analyzes the offset amplitude, and obtains the temperature prediction error range; The temperature prediction error calculation submodule calls the temperature prediction error interval, analyzes the temperature offset rate, measures the temperature change difference in the error amplitude interval, and obtains the temperature prediction deviation amplitude.

6. The electric hot pot control system according to claim 1, characterized in that: The pot bottom power balancing control module includes: The partition temperature comparison submodule calls the pot bottom partition temperature sensor data based on the temperature prediction deviation amplitude, identifies the partition temperature change rate, measures the temperature fluctuation range, analyzes the temperature gradient of the temperature difference interval, and obtains the pot bottom partition temperature difference; The heating imbalance identification submodule calls the temperature difference of the pot bottom partition, compares the temperature offset of each partition of the pot bottom, calculates the corresponding offset ratio of the partition and screens it to obtain the degree of local heating imbalance; The power correction calculation submodule analyzes the power adjustment range based on the degree of local heating imbalance, calculates the partition power adjustment parameters, and obtains the pot bottom power correction amount.

7. The electric hot pot 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 in the i-th partition, n represents the total number of partitions, ΔT avg Represents the mean temperature deviation.

8. The electric hot pot control system according to claim 1, characterized in that: The food heating timing optimization module includes: The food heat transfer calculation submodule extracts food heat transfer time parameters based on the pot bottom power correction value, calculates the heat absorption time of the food per unit mass, screens out food with low heat transfer rate, identifies the heat absorption time offset data of the food pot bottom power correction value, measures the heat transfer time difference between differentiated food ingredients, and obtains the food heat transfer time parameters; The heat absorption rate analysis submodule compares the heat absorption rate of the food according to the heat transfer time parameter of the food, calculates the heat absorption offset rate, and obtains the heat absorption rate of the food; The heating timing adjustment submodule calls the heat absorption rate of the food, analyzes the impact of the heating time on the food, identifies the heating timing adjustment range, and obtains the heating timing adjustment result.

9. The electric hot pot control system according to claim 8, characterized in that: The heat absorption offset rate is calculated using the formula: Among them, 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 hot pot control system according to claim 1, characterized in that: The heating strategy execution module includes: The heating start-stop control submodule calls the heating element start-stop control logic based on the heating timing adjustment result, evaluates the current start-stop status 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 status of the heating element; The partition power adjustment submodule calls the start and stop status of the heating element, adjusts the partition power allocation parameters, calculates the partition power correction value, and selects the area with a large correction amplitude to obtain the partition power allocation parameters; The heating dynamic control submodule calls the partition power allocation parameters, performs dynamic control of the heating element, identifies the power adjustment range, analyzes the partition power adjustment amplitude, and obtains the heating control result of the electric hot pot.

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