A method and system for analyzing and processing heating temperature data of a pancake machine

Abstract

The present invention discloses a method and system for analyzing and processing heating temperature data of a pancake machine, which relates to the technical field of data analysis. In step S2, a temperature distribution model is fitted using a temperature rise data group, and the statistical characteristics of the temperature in each monitoring period are accurately reflected by calculating the optimal estimated parameter. Through probability density analysis and dividing multiple groups of temperature intervals in an equal probability manner, high-density areas in the temperature distribution can be effectively identified. This division method based on probability distribution ensures that the cumulative probability of each temperature interval is equal. Step S3 sets the target temperature and dynamically optimizes the heating power based on the divided temperature intervals. This process can monitor and adjust the heating power in real time to keep the heating process of the pancake machine within the target temperature range, reducing the uneven heating phenomenon caused by temperature fluctuations.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a method and system for analyzing and processing heating temperature data of a pancake machine. Background Art

[0002] With the development of automation and intelligent technology, food processing equipment is gradually transitioning from traditional mechanical operation to data-driven fine control. Among food processing equipment, a pancake machine is a typical device used to make high-quality pancakes, and the key link is temperature control during the heating process. During the pancake making process, the pancake machine needs to monitor and accurately adjust the temperature of the heating area in real time to ensure that the pancakes are heated evenly, while improving production efficiency and reducing energy waste. Therefore, real-time analysis and optimization of heating temperature has become the core link to improve the quality of pancake processing.

[0003] At present, pancake machines on the market generally adopt simple heating control strategies, usually based on fixed power or simple feedback control to adjust the heating temperature. However, this method has many shortcomings. For example, the temperature data of the heating area is not fully utilized for precise control, resulting in uneven heating of the pancake. In addition, the existing methods lack real-time analysis and optimization mechanisms for heating temperature, especially in the pancake making process, the dynamic characteristics of temperature changes in different monitoring periods have not been fully explored. The lack of scientific temperature interval division and target temperature setting mechanism often leads to a lag in the regulation of heating power, making it difficult to meet the needs of efficient and stable heating. When the heating power is difficult to dynamically optimize according to the real-time temperature distribution, a variety of abnormal effects will occur, such as local temperature is too high, causing the pancake to burn, or local temperature is too low, causing the pancake to be not fully cooked. In addition, unstable temperature control will also lead to increased energy consumption, shortened equipment life, and even reduced product production efficiency. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method and system for analyzing and processing heating temperature data of a pancake machine, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for analyzing and processing heating temperature data of a pancake machine, comprising the following steps:

[0006] S1. During the heating process of the pancake machine, the temperature change data of the pancake spreading area in the pancake machine is monitored in real time, and after data preprocessing, a relevant temperature rise data group is generated;

[0007] S2. Based on the relevant temperature rise data set obtained in S1, analyze and fit the temperature distribution to estimate the optimal estimated parameters, and analyze the probability density of the temperature in each monitoring period based on the optimal estimated parameters. , divide multiple groups of temperature intervals according to equal probability;

[0008] S3. Set the target temperature according to multiple temperature ranges , optimize the heating power P of the pancake machine, and after the optimization, monitor the temperature change state of the pancake spreading area in the pancake machine in real time to obtain the optimized temperature data set;

[0009] S4, based on the optimized temperature data set and combined with the target temperature , analyze the temperature stability of the pancake machine during heating to obtain the temperature deviation index Wcb, and adjust the target temperature according to the value of the temperature deviation index Wcb And determine whether it is necessary to optimize the heating power P of the pancake machine again.

[0010] Preferably, the specific steps of S1 include:

[0011] S11, pre-installing a thermocouple in the pancake machine to monitor the temperature change data of the pancake spreading area in the pancake machine in real time, wherein the temperature change data includes the original temperature value Wdz in each monitoring period;

[0012] S12: Remove noise from the temperature change data obtained in S11 to obtain smoothed temperature values ​​in each monitoring period. The specific acquisition method is as follows:

[0013] ;

[0014] In the formula, Indicates the window size; i indicates the time point at which the smoothed temperature value is being calculated; j indicates the index of all data points within the window range; represents the original temperature value at the jth moment;

[0015] S13, smoothing the temperature values ​​in each monitoring period obtained in S12 , and re-statistics are performed to obtain the relevant warming data set.

[0016] Preferably, the specific steps of S2 include:

[0017] S21, based on the relevant temperature rise data group obtained in S1, analyze and fit the temperature distribution to estimate the optimal estimated parameters, wherein the optimal estimated parameters include the optimal temperature mean and the standard deviation of the optimal temperature , which is obtained by the following formula:

[0018] ;

[0019] In the formula, represents the logarithmic probability function; represents the smoothed temperature value in the i-th monitoring period; n represents the monitoring period; i=1, 2, 3, ..., n; Represents a mathematical constant; represents the logarithmic function;

[0020] S22, by calculating the optimal temperature average in the formula involved in S21 and the standard deviation of the optimal temperature Take the derivative and set it to zero to find the log probability function The optimal estimate parameter to maximize.

[0021] Preferably, the specific step S2 also includes:

[0022] S23. Analyze the probability density of temperature in each monitoring period based on the optimal estimated parameters , which can be obtained by:

[0023] ;

[0024] In the formula, represents the natural exponential function.

[0025] Preferably, the specific step S2 also includes:

[0026] S24. According to the probability density of temperature in each monitoring period , the temperature of each monitoring period is divided into four groups of temperature intervals, and the total probability in each group of temperature intervals is made the same according to the equal probability method, that is, the probability of each group of temperature intervals is 25%, then the target cumulative probability of the first temperature interval is determined to be =0.25, the target cumulative probability in the second temperature interval is =0.50, the target cumulative probability in the third temperature interval is = 0.75 and the target cumulative probability for the fourth temperature interval is =1.0, where , , and are respectively a temperature boundary value of the first temperature interval, a temperature boundary value of the second temperature interval, a temperature boundary value of the third temperature interval, and a temperature boundary value of the fourth temperature interval;

[0027] S25. Target cumulative probability using corresponding temperature range The inverse function of , combined with the standard normal distribution table, is used to find the target cumulative probability of the corresponding temperature range. The corresponding quantile z is calculated based on the quantile z to find the temperature boundary value in each temperature range, specifically: Where x represents the temperature boundary value of the corresponding temperature interval, Indicates the target cumulative probability for the corresponding temperature range The corresponding quantile point, represents the optimal temperature mean, represents the standard deviation of the optimal temperature.

[0028] Preferably, the specific steps of S3 include:

[0029] S31, based on the four groups of temperature intervals obtained in S25, identify and extract the peak area in the four groups of temperature intervals as the target area, and take the median in the target area as the target temperature ; The peak area refers to the difference between the two boundary values ​​of the temperature range;

[0030] S32, according to the relevant temperature rise data group and the target temperature , adjust and optimize the heating power P of the pancake machine, which can be obtained according to the following formula:

[0031] ;

[0032] In the formula, is the maximum power, is the smoothed temperature value, is the target temperature.

[0033] Preferably, the specific step S3 also includes:

[0034] S33. Based on the content of S32, after optimization, the temperature change state of the pancake spreading area in the pancake machine is monitored in real time to obtain an optimized temperature data set, wherein the optimized temperature data set includes the temperatures in each monitoring period after optimization.

[0035] Preferably, the specific steps of S4 include:

[0036] S41, based on the optimized temperature data set and combined with the target temperature , analyze the temperature stability of the pancake machine during heating to obtain the temperature deviation index Wcb, which is obtained by the following formula:

[0037] ;

[0038] In the formula, represents the temperature in the mth monitoring period after optimization, M represents the optimized monitoring period, and m=1, 2, 3, ..., M.

[0039] Preferably, the specific step S4 also includes:

[0040] S42, pre-set the deviation threshold Q, and compare and evaluate it with the temperature deviation index Wcb to determine whether the heating power of the pancake machine needs to be optimized again. The specific comparison content is as follows:

[0041] If the temperature deviation index Wcb exceeds the deviation threshold Q, it means that the heating process in the pancake machine is not in a stable state after the current optimized heating power P. At this time, the target area will be set according to the target temperature. To adjust the center, spread out to the surroundings to adjust the target temperature The value is then iterated through steps S32, S33 and S4 until the heating process in the pancake machine is in a stable state, and the iterative analysis is stopped;

[0042] If the temperature deviation indicator Wcb does not exceed the deviation threshold Q, it means that the heating process in the pancake maker is not in a stable state at the currently optimized heating power P, and the heating temperature data in the pancake maker will be continuously monitored.

[0043] A pancake machine heating temperature data analysis and processing system, comprising a data acquisition module, a data analysis module, an optimization module and an iterative optimization module;

[0044] The data acquisition module is used to monitor the temperature change data of the pancake spreading area in the pancake machine in real time during the heating process of the pancake machine, and generate a relevant temperature rise data group after data preprocessing;

[0045] The data analysis module is used to analyze and fit the temperature distribution based on the relevant temperature rise data group to estimate the optimal estimation parameters, and analyze the probability density of the temperature in each monitoring period based on the optimal estimation parameters. , divide multiple groups of temperature intervals according to equal probability;

[0046] The optimization module is used to set the target temperature according to multiple groups of temperature intervals. , optimize the heating power P of the pancake machine, and after the optimization, monitor the temperature change state of the pancake spreading area in the pancake machine in real time to obtain the optimized temperature data set;

[0047] The iterative optimization module is used to optimize the temperature data set based on the optimized temperature data set and the target temperature , analyze the temperature stability of the pancake machine during heating to obtain the temperature deviation index Wcb, and adjust the target temperature according to the value of the temperature deviation index Wcb And determine whether it is necessary to optimize the heating power P of the pancake machine again.

[0048] The present invention provides a method and system for analyzing and processing heating temperature data of a pancake machine, which has the following beneficial effects:

[0049] (1) Through the real-time monitoring and data preprocessing of step S1, the noise interference in the original temperature data is effectively removed, and a smooth temperature rise data set is generated. This process ensures the accuracy and reliability of the temperature data, laying a data foundation for the subsequent temperature distribution analysis and heating optimization. In step S2, the temperature distribution model is fitted using the temperature rise data set, and the statistical characteristics of the temperature in each monitoring period are accurately reflected by calculating the optimal estimated parameters. Through probability density analysis and dividing multiple groups of temperature intervals in an equal probability manner, the high-density area in the temperature distribution can be effectively identified. This probability distribution-based division method ensures that the cumulative probability of each temperature interval is equal, thereby more accurately guiding the setting of the target temperature. Step S3 sets the target temperature and dynamically optimizes the heating power based on the divided temperature intervals. This process can monitor and adjust the heating power in real time to keep the heating process of the pancake machine within the target temperature range, reduce the uneven heating phenomenon caused by temperature fluctuations, and improve the efficiency of the heating process and the quality of the pancake product. In step S4, by analyzing the optimized temperature data set and calculating the temperature deviation index in combination with the target temperature, the stability of the heating process can be quantified. In short, this method forms a closed loop in the heating process of the pancake machine from data collection, distribution analysis to dynamic optimization and stability control, which further improves the temperature control accuracy, ensures that the pancakes are evenly heated, reduces the phenomenon of burning or undercookedness caused by improper temperature control, and reduces energy consumption by dynamically adjusting power, thereby improving equipment operation efficiency.

[0050] (2) By using probability density The temperature intervals are divided by the width of the intervals, and the width of the intervals is determined by the probability of the temperature data appearing in each interval. The core idea of ​​this method is: if the temperature has a higher probability of appearing in certain intervals, then the width of these intervals can be appropriately reduced to improve the accuracy of temperature control; while in areas with a lower probability of temperature appearing, the interval width can be increased to reduce the energy waste caused by excessive control. Through this unequal width interval division method, it is further ensured that most of the data is concentrated in certain temperature intervals, thereby optimizing the temperature control strategy.

[0051] (3) By calculating the temperature probability density function and combining the target cumulative probability, the temperature data is divided into four groups of intervals in an equal probability manner, and the cumulative probability of each group of intervals is ensured to be the same. This probability density-based division method can clearly reflect the distribution law of temperature data and make the division of temperature intervals more scientific and reasonable. The target cumulative probability of each interval ensures that the temperature coverage in different intervals is balanced, avoiding the interval division errors caused by uneven data distribution in traditional temperature control strategies, thereby improving the accuracy and efficiency of the heating process. The divided temperature intervals clarify the cumulative probability and density characteristics of each temperature range, providing a scientific basis for setting the target temperature and optimizing the heating power during the heating process. By accurately adjusting the target temperature and optimizing the heating power, the temperature of the heating interval is made more uniform, reducing the deviation caused by temperature fluctuations. On this basis, the temperature control during the heating process is more stable, avoiding overheating or overcooling, and ultimately improving the processing quality of the pancakes while significantly reducing energy consumption.

[0052] (4) By adjusting the heating power, the temperature is always maintained within the target range, further avoiding energy waste caused by overheating. Through precise temperature control, the pancake machine can operate more efficiently and reduce energy consumption. Accurate temperature control not only improves product quality and consistency, but also reduces quality problems caused by uneven heating, thereby improving production efficiency. By analyzing the high-density areas in the temperature range, the target temperature is optimized to ensure uniformity and stability during the pancake heating process. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A schematic diagram of a method for analyzing and processing heating temperature data of a pancake machine according to the present invention;

[0054] Figure 2 The present invention is a block diagram of a pancake machine heating temperature data analysis and processing system. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] Example 1

[0057] See also Figure 1 The present invention provides a method for analyzing and processing heating temperature data of a pancake machine, comprising the following steps:

[0058] S1. During the heating process of the pancake machine, the temperature change data of the pancake spreading area in the pancake machine is monitored in real time, and after data preprocessing, a relevant temperature rise data group is generated;

[0059] S2. Based on the relevant temperature rise data set obtained in S1, analyze and fit the temperature distribution to estimate the optimal estimated parameters, and analyze the probability density of the temperature in each monitoring period based on the optimal estimated parameters. , divide multiple groups of temperature intervals according to equal probability;

[0060] S3. Set the target temperature according to multiple temperature ranges , optimize the heating power P of the pancake machine, and after the optimization, monitor the temperature change state of the pancake spreading area in the pancake machine in real time to obtain the optimized temperature data set;

[0061] S4, based on the optimized temperature data set and combined with the target temperature , analyze the temperature stability of the pancake machine during heating to obtain the temperature deviation index Wcb, and adjust the target temperature according to the value of the temperature deviation index Wcb And determine whether it is necessary to optimize the heating power P of the pancake machine again.

[0062] In this embodiment, the method monitors the temperature change of the pancake spreading area in the pancake machine in real time, and pre-processes the temperature data, eliminates the interference of noise and outliers, and generates a high-quality temperature rise data group. This pre-processing step provides a reliable basis for subsequent temperature distribution analysis and optimization, and effectively improves the temperature control accuracy. By fitting the temperature data distribution, the optimal estimation parameter is accurately estimated, and multiple groups of temperature intervals are further divided according to the temperature probability density in an equal probability manner to ensure that the total probability in each temperature interval is the same. This temperature control analysis based on probability distribution can identify the high-density temperature area (hot spot) in the heating area, and provides a scientific basis for the accurate setting of the target temperature; on the basis of multiple groups of temperature intervals, the target temperature is dynamically set, and the heating power is optimized through the optimized power adjustment formula. During the optimization process, the temperature change of the pancake spreading area in the pancake machine is monitored and fed back in real time to generate an optimized temperature data group. This optimization strategy not only improves the heating efficiency, but also significantly reduces energy consumption and extends the service life of the equipment. By analyzing the optimized temperature data and combining the target temperature, the temperature deviation index of the heating process is calculated. According to the temperature deviation index, it is judged whether it is necessary to adjust the target temperature or further optimize the heating power. This method realizes the closed-loop feedback and iterative optimization of the temperature control system, ensures the temperature stability of the pancake machine heating process, reduces temperature fluctuations, and improves the cooking quality of the pancake. Through precise temperature interval division and dynamic target temperature adjustment, it further effectively avoids the situation that the pancake is burnt due to overheating or underheated due to insufficient heating, and improves the consistency and quality of the product. In addition, the optimized heating control strategy reduces the complexity of operation and debugging and improves the production efficiency of the pancake machine. In summary, this method not only further improves the accuracy and stability of temperature control, but also effectively reduces energy consumption and improves the overall heating effect and production efficiency of the pancake machine through the multi-step coordination of real-time monitoring, data analysis, probability density distribution division, dynamic optimization of target temperature and closed-loop feedback. It has important practical value and promotion significance for the food processing industry.

[0063] Example 2

[0064] Please refer to Figure 1 , specifically: S1 specific steps include:

[0065] S11, pre-installing a thermocouple in the pancake machine to monitor the temperature change data of the pancake spreading area in the pancake machine in real time, wherein the temperature change data includes the original temperature value Wdz in each monitoring period;

[0066] S12: Remove noise from the temperature change data obtained in S11 to obtain smoothed temperature values ​​in each monitoring period. The specific acquisition method is as follows:

[0067] ;

[0068] In the formula, represents the window size, that is, the number of data points within a data range used for smoothing; i represents the time point at which the smoothed temperature value is being calculated; j represents the index of all data points within the window range, that is, the index of each data point involved in the calculation within the smoothing window at the current time i; represents the original temperature value at the jth moment; It means summing the data in the window, that is, the sum of the data values ​​from time i-k / 2 to i+k / 2. This summation range defines the contribution of the temperature data around the current time i to the smoothing value.

[0069] For example, if =3, indicating that the smoothing window is the data point before, the current, and the next data point at the current time i (a total of 3);

[0070] The window size determines the number of data points included in each smoothing calculation and also affects the characteristics of the smoothed data (the larger the window, the more obvious the smoothing effect, but it may cause lag or loss of detail).

[0071] S13, smoothing the temperature values ​​in each monitoring period obtained in S12 , and re-statistics are performed to obtain the relevant warming data set.

[0072] In this embodiment, by pre-installing thermocouples in the pancake spreading area in the pancake machine in step S11, the temperature change can be monitored in real time and the original temperature value in each monitoring period can be obtained. This design ensures the real-time and comprehensiveness of the temperature data in the heating area of ​​the pancake machine, and provides accurate data support for reflecting the real temperature dynamics of the pancake spreading area. The sensitive capture of subtle changes in the temperature acquisition process can help identify potential problems of uneven heating. In step S12, the original temperature value is de-noised by the weighted moving average method to eliminate random noise caused by environmental interference or equipment fluctuations during the monitoring process, further improving the data quality. The window size of the smoothing process can be flexibly set according to the specific scenario, so that the smoothing result can not only retain the key temperature change trend, but also reduce the interference of short-term fluctuations on data analysis. For example, when k=3, the smoothing window can effectively cover the current time i and its previous and subsequent data points, thereby reflecting the real average change trend of the temperature at the current time. Through step S13, the smoothed temperature data is re-counted to generate a more representative temperature rise data group, which can fully reflect the temperature change law of the heating process and provide a reliable basis for subsequent temperature distribution analysis and heating power optimization. The smoothed data set was further structured and regularized in the statistical process, which helped to identify possible temperature anomaly areas during the heating process of the pancake machine.

[0073] Example 3

[0074] Please refer to Figure 1 , specifically: S2 specific steps include:

[0075] S21, based on the relevant temperature rise data group obtained in S1, analyze and fit the temperature distribution to estimate the optimal estimated parameters, wherein the optimal estimated parameters include the optimal temperature mean and the standard deviation of the optimal temperature , which is obtained by the following formula:

[0076] ;

[0077] In the formula, represents the logarithmic probability function, and the goal is to find a set of parameters, temperature mean and temperature standard deviation, so that the observed temperature data is most likely (with the highest probability) to come from the fitted normal distribution; represents the smoothed temperature value in the i-th monitoring period; n represents the monitoring period; i=1, 2, 3, ..., n; Represents a mathematical constant, approximately equal to 3.14159; represents the logarithmic function; It represents the contribution of the standard deviation of the optimal temperature to the logarithmic probability function. The larger the standard deviation, the more dispersed the distribution, and the greater the negative impact of the logarithmic probability function (making its value smaller); represents a constant term, which does not change with the optimal temperature mean and the standard deviation of the optimal temperature , and only produces a fixed offset to the calculation result; Represents each temperature data point squared deviation from the mean (reflects the distance of a data point from the center of the distribution); represents the sum of the squared deviations of all data points; Indicates the contribution of the square of the data deviation to the log probability function. The larger the square of the deviation, the smaller the log probability function (the worse the distribution fitting effect);

[0078] S22, by calculating the optimal temperature average in the formula involved in S21 and the standard deviation of the optimal temperature Take the derivative and set it to zero to find the log probability function The optimal estimate parameter to maximize.

[0079] The specific steps of S2 also include:

[0080] S23. Analyze the probability density of temperature in each monitoring period based on the optimal estimated parameters , which can be obtained by:

[0081] ;

[0082] In the formula, represents the natural exponential function.

[0083] In this embodiment, in S21, the optimal temperature mean and the optimal temperature standard deviation are calculated by fitting the normal distribution of the temperature rise data group of each monitoring period of the pancake machine, accurately describing the central trend and distribution form of the temperature data, optimizing the temperature distribution parameters by logarithmic probability function, effectively reflecting the temperature distribution characteristics of the pancake machine during heating, and providing an accurate data basis for the optimization of temperature control strategy. The calculation of the probability density function accurately divides the temperature distribution into different areas, quantifies the possibility of each temperature value, especially the high-density area (the area with relatively concentrated temperature), which provides a basis for the temperature interval division and target temperature setting. The analysis results can help identify the temperature fluctuation law during the heating process of the pancake machine, and finely guide the dynamic adjustment of the heating power to ensure uniform heating of the pancake spreading area. The calculation of the optimal temperature mean and standard deviation can accurately set the target temperature range of the heating process, and identify the high-density area and abnormal temperature fluctuation through probability density analysis. This process combines the probability density function and data distribution characteristics, can dynamically optimize the target temperature, reduce the possibility of overheating or underheating, and greatly improve the heating accuracy of the pancake machine. Through normal distribution fitting, it is possible to dynamically adapt to the temperature change characteristics of different monitoring periods, avoiding the low adaptability of fixed temperature control strategies to actual complex distributions. In short, through the steps of S2, the system not only completes the accurate modeling of the temperature distribution of the heating process, but also provides high-quality data support for the temperature control strategy. The calculation of the optimal estimated parameters and probability density functions makes the heating process more intelligent, significantly improving the temperature control accuracy, heating efficiency and quality of the pancake machine. This data-driven method realizes the transformation of temperature control from empirical regulation to scientific optimization, and has important application value for the food processing industry.

[0084] Example 4

[0085] Please refer to Figure 1 Specifically: S2 includes the following specific steps:

[0086] S24. According to the probability density of temperature in each monitoring period , the temperature of each monitoring period is divided into four groups of temperature intervals, and the total probability in each group of temperature intervals is made the same according to the equal probability method, that is, the probability of each group of temperature intervals is 25%, then the target cumulative probability of the first temperature interval is determined to be =0.25, the target cumulative probability in the second temperature interval is =0.50, the target cumulative probability in the third temperature interval is = 0.75 and the target cumulative probability for the fourth temperature interval is =1.0, where , , and are respectively a temperature boundary value of the first temperature interval, a temperature boundary value of the second temperature interval, a temperature boundary value of the third temperature interval, and a temperature boundary value of the fourth temperature interval;

[0087] S25. Target cumulative probability using corresponding temperature range The inverse function of , combined with the standard normal distribution table, is used to find the target cumulative probability of the corresponding temperature range. The corresponding quantile z is calculated based on the quantile z to find the temperature boundary value in each temperature range, specifically: ; where x represents the temperature boundary value of the corresponding temperature interval, Indicates the target cumulative probability for the corresponding temperature range The corresponding quantile point, represents the optimal temperature mean, represents the standard deviation of the optimal temperature.

[0088] Among them, the target cumulative probability in the first temperature interval is The expression is: ;in, represents a very small increment of the integral variable t; and so on, the target cumulative probability of the second temperature interval The expression is: ; Target cumulative probability in the third temperature interval The expression is: ; Target cumulative probability of the fourth temperature interval The expression is: ;

[0089] According to the temperature data distribution, the system can optimize the heating range to stabilize the temperature during the heating process, reduce temperature deviation, and improve the heating effect.

[0090] In this embodiment, in S24, by analyzing the temperature probability density of each monitoring period, the temperature data is divided into four groups of equal probability intervals, and the probability of each interval is 25%. This division method is based on probability, which ensures comprehensive coverage of temperature distribution and further avoids the deviation caused by the simple division based on temperature value. The target cumulative probability of each interval can effectively locate the boundary value of different temperature ranges, clarify the high-density area and boundary area, and provide a scientific basis for the subsequent target temperature setting. In S25, the inverse function of the target cumulative probability is combined with the standard normal distribution table to accurately find the quantile of each group of temperature intervals, and calculate the boundary value of each interval through the inverse standardization formula. This temperature boundary value calculation based on the normal distribution parameters (mean and standard deviation) can accurately reflect the actual characteristics of temperature distribution, so that the interval division is more in line with the probability characteristics of the data, thereby avoiding the problem of excessive or insufficient temperature control. The interval division result can be used to optimize the heating strategy, and by focusing on controlling the target temperature of the high probability density area, the heating process of the pancake machine can be ensured to be more uniform and stable. By reasonably dividing and optimizing the heating interval, the system can significantly reduce the deviation problem caused by temperature fluctuations, thereby improving the heating effect and product quality. In summary, this method ensures the scientificity and comprehensiveness of the temperature control range by dividing the intervals based on the probability density of temperature data, and significantly improves the accuracy and reliability of temperature control by calculating the interval boundary values ​​through the normal distribution table and the denormalization formula. By optimizing the heating interval, the system can effectively reduce the temperature deviation during the temperature control process, ensure uniform heating of the pancakes, improve product quality, and reduce energy consumption.

[0091] Example 5

[0092] Please refer to Figure 1 , specifically: S3 specific steps include:

[0093] S31, based on the four groups of temperature intervals obtained in S25, identify and extract the peak area (high density area) in the four groups of temperature intervals as the target area, and take the median in the target area as the target temperature ; The peak area refers to the difference between the two boundary values ​​of the temperature interval, which indicates the temperature range covered by the corresponding temperature interval, that is, the interval width. If the interval width is small, but the interval still contains 25% of the cumulative probability, it means that the temperature data is very concentrated (high density) in the interval;

[0094] S32, according to the relevant temperature rise data group and the target temperature , adjust and optimize the heating power P of the pancake machine, which can be obtained according to the following formula:

[0095] ;

[0096] In the formula, is the maximum power, is the smoothed temperature value, is the target temperature.

[0097] The maximum power of the above Refers to the maximum heating power that the pancake maker heating system can provide.

[0098] In this embodiment, in S31, based on the temperature intervals divided in S25, by identifying the peak areas in each group of temperature intervals, the concentration trend of the temperature distribution can be accurately located. This high-density area is the range with the largest probability density of temperature data. Its interval width is small but the cumulative probability of coverage is the same, indicating that the temperature data is highly concentrated in this interval. By extracting the median in the high-density area as the target temperature, it can be more in line with the actual temperature distribution characteristics, further avoiding the deviation that may be caused by setting the target temperature with the traditional mean or empirical value, and ensuring that the target temperature is more representative and applicable. By dynamically optimizing the heating power, the system can significantly improve the efficiency of temperature control in the actual heating process of the pancake machine, and further avoid energy waste caused by excessive power or long-term heating. At the same time, precise power adjustment avoids overheating or overcooling, prolongs the service life of the equipment and improves product quality. This method sets the target temperature by the median of the peak area, combined with real-time power adjustment, to ensure that the pancake is evenly heated in the entire pancake spreading area, avoiding the phenomenon of burnt or undercooked pancakes caused by temperature fluctuations. The combination of dynamic power regulation and precise temperature control makes the heating process smoother, and the color, taste and doneness of the final product can all reach a relatively good state. In short, through high-density area recognition and median extraction, the target temperature is closer to the actual distribution characteristics. Adjust the heating power in real time to quickly return the temperature to the target value, improve heating efficiency and reduce energy consumption. The optimized power adjustment ensures that temperature fluctuations are minimized, improves the quality of the pancakes and the stability of equipment operation. This method provides a new technical means for the intelligent temperature control of the pancake machine, which not only improves production efficiency, but also significantly improves the quality and uniformity of the finished pancakes.

[0099] Example 6

[0100] Please refer to Figure 1 , specifically: S3 specific steps also include:

[0101] S33. Based on the content of S32, after optimization, the temperature change state of the pancake spreading area in the pancake machine is monitored in real time to obtain an optimized temperature data group, wherein the optimized temperature data group includes the temperatures in each monitoring period after optimization.

[0102] The specific steps of S4 include:

[0103] S41, based on the optimized temperature data set and combined with the target temperature , analyze the temperature stability of the pancake machine during heating to obtain the temperature deviation index Wcb, which is obtained by the following formula:

[0104] ;

[0105] In the formula, represents the temperature in the mth monitoring period after optimization, M represents the optimized monitoring period, and m=1, 2, 3, ..., M.

[0106] The specific steps of S4 also include:

[0107] S42, pre-set the deviation threshold Q, and compare and evaluate it with the temperature deviation index Wcb to determine whether the heating power of the pancake machine needs to be optimized again. The specific comparison content is as follows:

[0108] If the temperature deviation index Wcb exceeds the deviation threshold Q, it means that the heating process in the pancake machine is not in a stable state after the current optimized heating power P. At this time, the target area will be set according to the target temperature. To adjust the center, spread out to the surroundings to adjust the target temperature The value is then iterated through steps S32, S33 and S4 until the heating process in the pancake machine is in a stable state, and the iterative analysis is stopped;

[0109] If the temperature deviation index Wcb does not exceed the deviation threshold Q, it means that the heating process in the pancake maker is not in a stable state at the currently optimized heating power P, and the heating temperature data in the pancake maker will be continuously monitored.

[0110] In this embodiment, in S33, by real-time monitoring of the temperature change of the pancake spreading area after optimization, an optimized temperature data group containing multiple monitoring periods is obtained. This real-time monitoring mechanism can reflect the dynamic temperature state of the pancake machine during the optimized heating process, and provide an accurate data basis for the subsequent analysis of temperature control stability and deviation. The optimized temperature data group can effectively capture the changing trend of the internal heating of the pancake machine, and provide real-time feedback on whether the current temperature control meets expectations, ensuring the intelligent response capability of the system. In S41, by comparing the target temperature with the optimized temperature data group, the temperature deviation index is calculated. The index quantifies the degree of temperature fluctuation during the heating process in the form of standard deviation, which can clearly reflect the stability of heating. When its value is small, it indicates that the temperature control system can stably operate around the target temperature; on the contrary, when its value is large, it indicates that the heating power or target temperature needs to be further optimized to form a closed-loop optimization system, so that the temperature control process can be adaptively adjusted in different heating scenarios. Through real-time monitoring and deviation quantification, the system can quickly respond to fluctuation problems in the heating process, adjust the heating power in time, and avoid energy waste or product quality problems caused by unstable temperature. The optimized heating power can accurately match the target temperature requirements, ensuring that the pancakes are heated evenly, significantly reducing the energy consumption of temperature control, and improving the operating efficiency of the system. When the system enters a stable state, the heating power and target temperature will be locked, and iterative optimization will stop.

[0111] Example 7

[0112] Please refer to Figure 2 ,Specifically: A pancake machine heating temperature data analysis and processing system, including a data acquisition module, a data analysis module, an optimization module and an iterative optimization module;

[0113] The data acquisition module is used to monitor the temperature change data of the pancake spreading area in the pancake machine in real time during the heating process of the pancake machine, and generate a relevant temperature rise data group after data preprocessing;

[0114] The data analysis module is used to analyze and fit the temperature distribution based on the relevant temperature rise data group to estimate the optimal estimation parameters, and analyze the probability density of the temperature in each monitoring period based on the optimal estimation parameters. , divide multiple groups of temperature intervals according to equal probability;

[0115] The optimization module is used to set the target temperature according to multiple groups of temperature intervals. , optimize the heating power P of the pancake machine, and after the optimization, monitor the temperature change state of the pancake spreading area in the pancake machine in real time to obtain the optimized temperature data set;

[0116] The iterative optimization module is used to optimize the temperature data set based on the optimized temperature data set and the target temperature , analyze the temperature stability of the pancake machine during heating to obtain the temperature deviation index Wcb, and adjust the target temperature according to the value of the temperature deviation index Wcb And determine whether it is necessary to optimize the heating power P of the pancake machine again.

[0117] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing and processing heating temperature data of a pancake machine, characterized in that: The following steps are included: S1. During the heating process of the pancake machine, the temperature change data of the pancake spreading area in the pancake machine is monitored in real time, and after data preprocessing, a relevant temperature rise data group is generated; S2. Based on the relevant temperature rise data set obtained in S1, analyze and fit the temperature distribution to estimate the optimal estimated parameters, and analyze the probability density of the temperature in each monitoring period based on the optimal estimated parameters. , divide multiple groups of temperature intervals according to equal probability; S3. Set the target temperature according to multiple temperature ranges , optimize the heating power P of the pancake machine, and after the optimization, monitor the temperature change state of the pancake spreading area in the pancake machine in real time to obtain the optimized temperature data set; S4, based on the optimized temperature data set and combined with the target temperature , analyze the temperature stability of the pancake machine during heating to obtain the temperature deviation index Wcb, and adjust the target temperature according to the value of the temperature deviation index Wcb And determine whether it is necessary to optimize the heating power P of the pancake machine again.

2. The method for analyzing and processing heating temperature data of a pancake machine according to claim 1, characterized in that: The specific steps of S1 include: S11, pre-installing a thermocouple in the pancake machine to monitor the temperature change data of the pancake spreading area in the pancake machine in real time, wherein the temperature change data includes the original temperature value Wdz in each monitoring period; S12: Remove noise from the temperature change data obtained in S11 to obtain smoothed temperature values ​​in each monitoring period. The specific acquisition method is as follows: ； In the formula, represents the window size; i represents the time point at which the smoothed temperature value is being calculated; j represents the index of all data points within the window range; represents the original temperature value at the jth moment; S13, smoothing the temperature values ​​in each monitoring period obtained in S12 , and re-statistics are performed to obtain relevant warming data sets.

3. The method for analyzing and processing heating temperature data of a pancake machine according to claim 2, characterized in that: The specific steps of S2 include: S21, based on the relevant temperature rise data group obtained in S1, analyze and fit the temperature distribution to estimate the optimal estimated parameters, wherein the optimal estimated parameters include the optimal temperature mean and the standard deviation of the optimal temperature , which is obtained by the following formula: ； In the formula, represents the logarithmic probability function; represents the smoothed temperature value in the i-th monitoring period; n represents the monitoring period; i=1, 2, 3, ..., n; Represents a mathematical constant; represents the logarithmic function; S22, by calculating the optimal temperature average in the formula involved in S21 and the standard deviation of the optimal temperature Take the derivative and set it to zero to find the log probability function The optimal estimate parameter to maximize.

4. The method for analyzing and processing heating temperature data of a pancake machine according to claim 3, characterized in that: The specific steps of S2 also include: S23. Analyze the probability density of temperature in each monitoring period based on the optimal estimated parameters , which can be obtained by: ； In the formula, represents the natural exponential function.

5. The method for analyzing and processing heating temperature data of a pancake machine according to claim 1, characterized in that: The specific steps of S2 also include: S24. According to the probability density of temperature in each monitoring period , the temperature of each monitoring period is divided into four groups of temperature intervals, and the total probability in each group of temperature intervals is made the same according to the equal probability method, that is, the probability of each group of temperature intervals is 25%, then the target cumulative probability of the first temperature interval is determined to be =0.25, the target cumulative probability in the second temperature interval is =0.50, the target cumulative probability in the third temperature interval is = 0.75 and the target cumulative probability for the fourth temperature interval is =1.0, where , , and are respectively a temperature boundary value of the first temperature interval, a temperature boundary value of the second temperature interval, a temperature boundary value of the third temperature interval, and a temperature boundary value of the fourth temperature interval; S25. Target cumulative probability using corresponding temperature range The inverse function of , combined with the standard normal distribution table, is used to find the target cumulative probability of the corresponding temperature range. The corresponding quantile z is calculated based on the quantile z to find the temperature boundary value in each temperature range, specifically: ; where x represents the temperature boundary value of the corresponding temperature interval, Indicates the target cumulative probability for the corresponding temperature range The corresponding quantile point, represents the optimal temperature mean, represents the standard deviation of the optimal temperature.

6. The method for analyzing and processing heating temperature data of a pancake machine according to claim 5, characterized in that: The specific steps of S3 include: S31, based on the four groups of temperature intervals obtained in S25, identify and extract the peak area in the four groups of temperature intervals as the target area, and take the median in the target area as the target temperature ; The peak area refers to the difference between the two boundary values ​​of the temperature range; S32, according to the relevant temperature rise data group and the target temperature , adjust and optimize the heating power P of the pancake machine, which can be obtained according to the following formula: ； In the formula, is the maximum power, is the smoothed temperature value, is the target temperature.

7. The method for analyzing and processing heating temperature data of a pancake machine according to claim 6, characterized in that: The specific steps of S3 also include: S33. Based on the content of S32, after optimization, the temperature change state of the pancake spreading area in the pancake machine is monitored in real time to obtain an optimized temperature data set, wherein the optimized temperature data set includes the temperatures in each monitoring period after optimization.

8. The method for analyzing and processing heating temperature data of a pancake machine according to claim 1, characterized in that: The specific steps of S4 include: S41, based on the optimized temperature data set and combined with the target temperature , analyze the temperature stability of the pancake machine during heating to obtain the temperature deviation index Wcb, which is obtained by the following formula: ； In the formula, represents the temperature in the mth monitoring period after optimization, M represents the optimized monitoring period, and m=1, 2, 3, ..., M.

9. The method for analyzing and processing heating temperature data of a pancake machine according to claim 1, characterized in that: The specific steps of S4 also include: S42, pre-set the deviation threshold Q, and compare and evaluate it with the temperature deviation index Wcb to determine whether the heating power of the pancake machine needs to be optimized again. The specific comparison content is as follows: If the temperature deviation index Wcb exceeds the deviation threshold Q, it means that the heating process in the pancake machine is not in a stable state after the current optimized heating power P. At this time, the target area will be set according to the target temperature. To adjust the center, spread out to the surroundings to adjust the target temperature The value is then iterated through steps S32, S33 and S4 until the heating process in the pancake machine is in a stable state, and the iterative analysis is stopped; If the temperature deviation indicator Wcb does not exceed the deviation threshold Q, it means that the heating process in the pancake maker is not in a stable state at the currently optimized heating power P, and the heating temperature data in the pancake maker will be continuously monitored.

10. A pancake machine heating temperature data analysis and processing system, used to implement the pancake machine heating temperature data analysis and processing method according to any one of claims 1 to 9, characterized in that: It includes data acquisition module, data analysis module, optimization module and iterative optimization module; The data acquisition module is used to monitor the temperature change data of the pancake spreading area in the pancake machine in real time during the heating process of the pancake machine, and generate a relevant temperature rise data group after data preprocessing; The data analysis module is used to analyze and fit the temperature distribution based on the relevant temperature rise data group to estimate the optimal estimation parameters, and analyze the probability density of the temperature in each monitoring period based on the optimal estimation parameters. , divide multiple groups of temperature intervals according to equal probability; The optimization module is used to set the target temperature according to multiple groups of temperature intervals. , optimize the heating power P of the pancake machine, and after the optimization, monitor the temperature change state of the pancake spreading area in the pancake machine in real time to obtain the optimized temperature data set; The iterative optimization module is used to optimize the temperature data set based on the optimized temperature data set and the target temperature , analyze the temperature stability of the pancake machine during heating to obtain the temperature deviation index Wcb, and adjust the target temperature according to the value of the temperature deviation index Wcb And determine whether it is necessary to optimize the heating power P of the pancake machine again.

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