CN117055652B - Intelligent temperature regulation and control method for food processing baking oven based on big data - Google Patents
Intelligent temperature regulation and control method for food processing baking oven based on big data Download PDFInfo
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Abstract
The invention relates to the technical field of intelligent temperature control, in particular to an intelligent temperature regulation and control method for a food processing baking oven based on big data. According to the method, temperature data in a preset sampling period are obtained, temperature change rates of different preset sampling periods are analyzed to obtain a temperature change threshold value, and then constant time for starting heat balance and reaching preset temperature is obtained; and further analyzing the temperature change characteristics before and after power adjustment in the heat balance process to obtain temperature fluctuation influence parameters and adjustment power influence parameters, and combining the two parameters to improve the PID algorithm. The improved algorithm is more fit with the working scene of the baking oven, the temperature regulation effect is improved, the temperature of the baking oven is enabled to be stably close to the highest temperature, the temperature is kept constant, overshoot oscillation is reduced, the working stability of the baking oven is improved while the product quality is improved, and energy waste is avoided.
Description
Technical Field
The invention relates to the technical field of intelligent temperature control, in particular to an intelligent temperature regulation and control method for a food processing baking oven based on big data.
Background
In the food processing process, the temperature control of the oven for heating the food directly affects the quality of the food product and the use safety of the equipment. Some food production processes require controlling the temperature in the oven to within a target temperature range, maintaining a constant temperature state, to ensure the quality and effect of the toasting.
In the prior art, by collecting the temperature of a food processing baking furnace and combining a data prediction algorithm, an accurate model can be established to predict the heating time. However, when the temperature of the oven is actually adjusted, the actual temperature change rate often fluctuates due to food reasons or thermal inertia reasons, and an overshoot effect is generated when the power is reduced to adjust the temperature, so that the temperature adjustment system needs to repeatedly adjust the power to enable the temperature to be stably close to the highest temperature and keep the temperature constant.
Disclosure of Invention
In order to solve the technical problems of inaccurate power regulation and low smoothness of a temperature regulation system, which results in unsatisfactory temperature control effect and resource waste in the prior art, the invention aims to provide an intelligent temperature regulation and control method for a food processing baking oven based on big data, and the adopted technical scheme is as follows:
acquiring a data matrix of the temperature in the food processing baking oven in real time according to a preset sampling period; the preset sampling period comprises at least two preset sampling periods;
acquiring the temperature change rate and the temperature change threshold of the data matrix; acquiring a heat balance starting matrix according to the temperature change threshold and the temperature change rate of the data matrix; obtaining constant time required for reaching a preset temperature according to the thermal balance starting matrix;
acquiring an adjustment starting time according to the constant time and a preset adjustment time; the power of the baking furnace is reduced according to preset power adjustment parameters at the adjustment starting time, and a data matrix before adjustment and an adjustment feedback data matrix are obtained; acquiring a first period temperature change rate of each sampling period in the data matrix before adjustment, acquiring a fitting straight line of the first period temperature change rate, and acquiring a temperature fluctuation influence parameter according to a fitting residual error of the first period temperature change rate below the fitting straight line; acquiring a second period temperature change rate of each sampling period in the adjustment feedback data matrix, analyzing the change trend fluctuation characteristic of the second period temperature change rate, and acquiring adjustment power influence parameters;
obtaining an improved integral parameter according to the temperature fluctuation influence parameter and the adjustment power influence parameter and an integral parameter of a PID algorithm; and regulating and controlling power through a PID algorithm according to the improved integral parameters, and intelligently regulating and controlling the temperature of the baking furnace.
Further, the method for acquiring the data matrix comprises the following steps:
collecting data points according to preset sampling times in a preset sampling period; taking data points of a preset sampling period as one row of a data matrix, and arranging all the data points of the preset sampling period in the preset sampling period to obtain the data matrix; and the data in the data matrix are subjected to normalization processing.
Further, the method for acquiring the temperature change threshold value comprises the following steps:
taking the first two data matrixes at the beginning of heating as threshold data matrixes; taking the absolute value of the difference value of the temperature change rate of the threshold data matrix as a threshold parameter; and obtaining a temperature change threshold according to a preset constant parameter and the threshold parameter.
Further, the method for acquiring the thermal balance start matrix comprises the following steps:
taking the absolute value of the difference value of the temperature change rates of the data matrix and the next adjacent data matrix as a temperature change parameter; in the two adjacent data matrices, if the temperature change parameter corresponding to the previous data matrix is greater than or equal to the temperature change threshold and the temperature change parameter corresponding to the next data matrix is less than the temperature change threshold, defining the next data matrix as a heat balance start matrix.
Further, the constant time acquisition method includes:
obtaining an expected temperature change rate according to the average value of the temperature change rates of the heat balance start matrix and the next data matrix adjacent to the heat balance start matrix; and obtaining constant time required for reaching a preset temperature according to the expected temperature change rate and the sampling time of the first data point in the thermal balance starting matrix.
Further, the method for acquiring the pre-adjustment data matrix and the adjustment feedback data matrix comprises the following steps:
taking the adjustment starting time as a demarcation point, taking a data matrix constructed by data points of the preset sampling period before the adjustment starting time as a data matrix before adjustment, and taking a data matrix constructed by data points of the preset sampling period after the adjustment starting time as an adjustment feedback data matrix.
Further, the method for acquiring the temperature fluctuation influence parameter comprises the following steps:
acquiring temperature fluctuation influence parameters according to a temperature fluctuation influence parameter calculation formula; the temperature fluctuation influence parameter calculation formula comprises:
wherein Wd represents a temperature fluctuation affecting parameter; when the first period temperature change rate is below a fitting straight line, the corresponding fitting residual error is smaller than zero; r represents the first periodic temperature change rate quantity with a fitting residual less than zero; m is m Wd A total number of the first periodic temperature change rates;a mean value representing the first periodic temperature change rate with all fitting residuals less than zero; r is (r) min Representing the minimum of the first periodic temperature change rates with all fitting residuals less than zero; r is (r) max Representing the maximum of the first periodic temperature change rates with all fitting residuals less than zero; i represents the first periodic temperature change rate with the ith fitting residual less than zero; w (w) i And representing the sequence of the preset sampling periods corresponding to the first period temperature change rate of which the ith fitting residual error is smaller than zero in the preset sampling period.
Further, the method for acquiring the adjustment power influence parameter comprises the following steps:
obtaining a difference data vector according to all the second period temperature change rates; obtaining an adjusting power influence parameter according to an adjusting power influence parameter calculation formula, wherein the adjusting power influence parameter calculation formula comprises:
wherein Sg represents the adjustment power influence parameter; bc (Bc) i Representing an ith temperature change rate difference in the difference data vector;representing the average value of all the temperature change rate differences; m is m Sg Indicating the total number of second cycle temperature change rates.
Further, the method for acquiring the difference data vector comprises the following steps:
taking the difference value of two adjacent second period temperature change rates of the regulation feedback data matrix as a temperature change rate difference value according to the acquisition sequence of the second period temperature change rates; and according to the acquisition sequence corresponding to the temperature change rate difference values, all the temperature change rate difference values are arranged into a difference data vector.
Further, the method for acquiring the improved integral parameter comprises the following steps:
multiplying the temperature fluctuation influence parameter and the adjustment power influence parameter to obtain a parameter product; and (3) mapping and normalizing the parameter product in a negative correlation way, multiplying the parameter product by an integral parameter of a PID algorithm, and taking the product as an improved integral parameter.
The invention has the following beneficial effects:
according to the invention, the data matrix of the preset sampling period is obtained, so that the characteristics in the baking process can be extracted, a prediction model can be established, the change rules in different time periods can be analyzed, and the dynamic characteristics of temperature change can be obtained; further obtaining a temperature change rate and a temperature change threshold value, selecting a heat balance starting matrix, obtaining constant time required for reaching a preset temperature, and facilitating analysis of temperature change characteristics in a heat balance process, so as to optimize a regulating and controlling method; further acquiring an adjustment starting time, and then acquiring a data matrix before adjustment and a data matrix after adjustment, so as to prepare for analyzing the temperature change characteristics before and after adjusting the power; further obtain temperature fluctuation influence parameter and regulation power influence parameter, represent the temperature change characteristic before the regulation power with temperature fluctuation influence parameter, represent the temperature change characteristic after the regulation power with regulation power influence parameter, improve the PID algorithm through temperature fluctuation influence parameter and regulation power influence parameter for the PID algorithm is more laminated the actual operational scenario of oven, promotes temperature regulation and control effect, reduces the oscillation of overshooting, improves the stability of oven work, avoids the energy extravagant.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for intelligently controlling the temperature of a food processing baking oven based on big data according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects of the intelligent temperature regulating method for the food processing baking oven based on big data according to the invention with reference to the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the intelligent temperature regulation method of the food processing baking oven based on big data provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a method for intelligently controlling temperature of a food processing oven based on big data according to an embodiment of the present invention is shown.
Step S1: acquiring a data matrix of the temperature in the food processing baking oven in real time according to a preset sampling period; the preset sampling period comprises at least two preset sampling periods.
The temperature is one of the key factors influencing the quality of the product in food processing, and the power of the baking oven can be adjusted according to the temperature data so as to obtain the ideal food baking effect. The temperature data is constructed into the data matrix, which is favorable for extracting the characteristics in the baking process and establishing a prediction model, analyzing the change rule in different time periods, further knowing the dynamic characteristics of temperature change, further optimizing the temperature regulation and control method, improving the production quality, enhancing the stability of the baking furnace and reducing the resource waste.
In the embodiment of the invention, in a preset sampling period, data points are acquired according to preset sampling times; taking the data points of one preset sampling period as one row of the data matrix, and arranging the data points of all preset sampling periods in the preset sampling period to obtain the data matrix; the data in the data matrix is normalized. In one embodiment of the present invention, the preset sampling period takes 1S, the preset sampling times takes 10 times, the preset sampling period takes 30S, that is, 10 times per second, the sampling data of 30S is used as a data matrix, and the size of the data matrix is 30×10. In other embodiments of the present invention, the practitioner may select other data matrix parameter settings to obtain data matrices of other sizes.
Step S2: acquiring the temperature change rate and the temperature change threshold of the data matrix; acquiring a heat balance starting matrix according to the temperature change threshold value and the temperature change rate of the data matrix; and obtaining constant time required for reaching a preset temperature according to the thermal balance starting matrix.
During food processing, because various heat conduction, heat capacity, heat radiation and other factors can be balanced gradually along with the temperature rise, the temperature rise rate shows nonlinear change, and the higher the temperature is, the slower the rise rate is. If the same power is used from the beginning of heating to the preset temperature of the baking furnace, the actual working highest temperature is higher than the preset temperature due to the existence of thermal inertia, and overshoot and oscillation are generated by adjusting the power. Therefore, the temperature change rate of the data matrix capable of representing the temperature increase rate of a period of time is obtained, one temperature change threshold value is selected from the temperature change process by observing the temperature change rate, a heat balance starting matrix with the temperature change approaching to heat balance is selected according to the temperature change threshold value, the temperature balance change characteristics between the starting acquisition time point of the heat balance starting matrix and the expected time point reaching the preset temperature are analyzed, the power of the baking oven is conveniently adjusted according to the temperature balance change characteristics, the temperature can reach the preset temperature stably, overshoot and oscillation are reduced, the energy consumption is reduced, and the product quality is improved.
In the embodiment of the invention, subtracting the first row and first column data values from the last row and last column data values in the data matrix to obtain the temperature variation; the temperature change rate is obtained by dividing the temperature change amount by the time length of the preset sampling period.
The obtained temperature change rate can represent the temperature increase rate in the preset sampling period, and the larger the temperature change rate is, the larger the temperature change in the period corresponding to the data matrix is, so that the temperature change threshold can be obtained according to the temperature change rate, and the heat balance starting matrix is selected through the temperature change threshold.
Preferably, in one embodiment of the present invention, the first two data matrices at the start of heating are taken as threshold data matrices; taking the absolute value of the difference value of the temperature change rate of the threshold data matrix as a threshold parameter; and obtaining a temperature change threshold according to the preset constant parameter and the threshold parameter. In one embodiment of the invention, the preset constant parameter is 0.1; in other embodiments of the present invention, the practitioner may choose to obtain the temperature change threshold based on empirical values of the temperature change threshold, analyzing slope change characteristics of the temperature profile, and the like.
The temperature change threshold can judge whether the temperature change approaches the heat balance, and if the rate of temperature increase characteristic is greater than or equal to the temperature change threshold, the temperature in the baking oven is still in a faster rising stage; if the rate characteristic of temperature increase is smaller than the temperature change threshold, the temperature in the baking furnace is indicated to start to enter a heat balance stage, and the stage of heat balance can be determined, so that the subsequent analysis of the heat balance process is facilitated, and the regulation and control method is optimized.
Preferably, in one embodiment of the present invention, an absolute value of a difference value of the temperature change rates of the data matrix and the adjacent next data matrix is taken as a temperature change parameter; in the two adjacent data matrices, if the temperature change parameter corresponding to the previous data matrix is greater than or equal to the temperature change threshold value and the temperature change parameter corresponding to the next data matrix is less than the temperature change threshold value, the next data matrix is defined as a thermal balance starting matrix.
The initial acquisition time point corresponding to the thermal balance initial matrix is the initial time point when the temperature in the baking oven enters the thermal balance, and the expected temperature change rate can be obtained by combining the temperature change rates of other data matrices in the adjacent preset acquisition time periods of the thermal balance initial matrix, so that the expected constant time reaching the preset temperature is obtained, the temperature change characteristics in the baking oven from the initial time point when the temperature enters the thermal balance to the expected constant time can be analyzed, and the adjustment and control method is optimized.
Preferably, in one embodiment of the present invention, the expected temperature change rate is obtained from the average value of the temperature change rates of the next data matrix adjacent to the thermal equilibrium starting matrix; and obtaining constant time required for reaching the preset temperature according to the expected temperature change rate and the sampling time of the first data point in the thermal balance starting matrix. It should be noted that the preset temperature is related to the actual processed food and process, and in one embodiment of the present invention, the preset temperature is 250 ℃.
After the constant time is obtained, the temperature balance change characteristic in the heat balance process can be analyzed, so that the power of the baking furnace is adjusted according to the temperature balance change characteristic, the temperature can reach the preset temperature stably, overshoot and oscillation are reduced, the energy consumption is reduced, and the product quality is improved.
Step S3: acquiring an adjustment starting time according to the constant time and the preset adjustment time; the power of the baking furnace is reduced according to preset power adjustment parameters at the beginning of adjustment, and a data matrix before adjustment and an adjustment feedback data matrix are obtained; acquiring a first period temperature change rate of each sampling period in a data matrix before adjustment, acquiring a fitting straight line of the first period temperature change rate, and acquiring a temperature fluctuation influence parameter according to a fitting residual error of the first period temperature change rate below the fitting straight line; and acquiring a second period temperature change rate of each sampling period in the adjustment feedback data matrix, analyzing the change trend fluctuation characteristics of the second period temperature change rate, and acquiring adjustment power influence parameters.
When the heat balance starts, the temperature change rate tends to be stable, but when the difference between the temperature in the baking furnace and the preset temperature is small, the overshoot phenomenon is easy to occur when the power is adjusted, and in order to reduce the adverse effect caused by the overshoot effect due to improper power adjustment, the analysis of the temperature change characteristics before and after the power adjustment is very important in the heat balance process. Firstly, acquiring an adjustment starting time, and adjusting the power of a baking furnace at the adjustment starting time so as to analyze the temperature change characteristics before and after adjustment; the data matrix before adjustment records temperature data before adjustment, the characteristics of the first period temperature change rate of each sampling period in the data matrix before adjustment are analyzed through fitting straight lines, temperature fluctuation influence parameters are obtained according to fitting residual errors, and the larger the temperature fluctuation influence parameters are, the larger the fitting residual errors are, the larger the data fluctuation in the data matrix before adjustment is; the temperature data after adjustment is recorded by the adjustment feedback data matrix, and the adjustment power influence parameter is obtained by analyzing the change trend fluctuation characteristic of the temperature change rate of the second period, wherein the larger the fluctuation of the change trend of the temperature change rate of the second period is, the larger the temperature fluctuation caused by the adjustment power is, and the larger the adjustment power influence parameter is. And the temperature fluctuation influence parameters before and after power adjustment and the adjustment power influence parameters are obtained, so that the subsequent optimization of the temperature adjustment method is facilitated, and the adjustment stability of the temperature adjustment method is improved. In one embodiment of the present invention, the preset adjustment time is 120S, the adjustment start time is 120S before the constant time, and the power operation of the oven is adjusted to be 0.9 times of the original power.
In order to facilitate analysis of the temperature change characteristics before and after power adjustment, a pre-adjustment data matrix and an adjustment feedback data matrix are respectively established before and after the adjustment start time.
Preferably, in one embodiment of the present invention, with the adjustment start time as a demarcation point, a data matrix constructed by data points of a preset sampling period before the adjustment start time is used as a data matrix before adjustment, and a data matrix constructed by data points of a preset sampling period after the adjustment start time is used as an adjustment feedback data matrix.
When the baking oven is ready for power adjustment and keeps constant temperature, the adjusting and controlling unit can frequently adjust and output according to the temperature fluctuation error, so that the power fluctuation in the baking oven is caused, positive feedback is further formed, and the control effect is reduced; fluctuation below the fitting straight line indicates that the temperature is lower, constant temperature precision can be directly affected, the amplitude of power to be reduced is increased by the regulating and controlling system to compensate, the regulating and controlling fluctuation is larger, the stability is worse, the risk is higher, and the temperature fluctuation characteristic before power adjustment is needed to be combined for optimization, so that the temperature fluctuation influence parameters are obtained, and further the stability of the baking oven is improved and the quality of processed foods is improved through a regulating and controlling method of a temperature fluctuation influence parameter optimization regulating and controlling unit.
Preferably, in one embodiment of the present invention, the temperature fluctuation influencing parameter is obtained according to a temperature fluctuation influencing parameter calculation formula; the temperature fluctuation influence parameter calculation formula comprises:
wherein Wd represents a temperature fluctuation affecting parameter; when the first period temperature change rate is below the fitting straight line, the corresponding fitting residual error is smaller than zero; r represents the number of first periodic temperature change rates with fitting residuals less than zero; m is m Wd A total number of first period temperature change rates;a mean value of the first period temperature change rate of all fitting residuals smaller than zero is represented; r is (r) min Representing the minimum of the first periodic temperature change rates with all the fit residuals less than zero; r is (r) max Representing the maximum of the first periodic temperature change rates with all the fit residuals less than zero; i represents a first periodic temperature change rate with the ith fitting residual less than zero; w (w) i And representing the sequence of preset sampling periods corresponding to the first period temperature change rate of which the ith fitting residual error is smaller than zero in the preset sampling period.
In the temperature fluctuation influence parameter calculation formula, the higher the proportion of the number of the fitting residual errors smaller than zero to the total number of the fitting residual errors is, the greater the deviation degree of the first period temperature change rate above the fitting straight line is, the greater the temperature fluctuation degree is, and the greater the temperature fluctuation influence parameter is;the average value of the fitting residual errors smaller than zero is normalized, so that the difference characteristic between the whole first period temperature change rate below the fitting straight line and the fitting straight line can be represented, and the larger the value is, the larger the difference between the whole first period temperature change rate below the fitting straight line and the fitting straight line is, the larger the temperature fluctuation degree is, and the larger the temperature fluctuation influence parameter is; />Representing the randomness of the fluctuations, (w) i+1 -w i ) The larger the sequence interval of the preset sampling period corresponding to the first period temperature change rate of which the fitting residual error is smaller than zero is, the larger the randomness is, the less concentrated the sequence interval is, the larger the influence on the control unit is, and the larger the temperature fluctuation influence parameter is.
The temperature oscillation is caused by the power down-regulation, and the temperature change rate is nonlinear due to the temperature rising rate and thermal inertia before the power regulation, so that the characteristic trend of the temperature change before the power regulation is changed, and the change characteristic of the temperature change rate after the power regulation is analyzed to obtain the regulation power influence parameter.
Preferably, in one embodiment of the present invention, the difference data vector is obtained from all second period temperature change rates; acquiring an adjusting power influence parameter according to an adjusting power influence parameter calculation formula, wherein the adjusting power influence parameter calculation formula comprises:
wherein Sg represents the adjustment power influence parameter; bc (Bc) i Representing an ith temperature change rate difference in the difference data vector;representing the average value of all the temperature change rate differences; m is m Sg Indicating the total number of second cycle temperature change rates.
In the calculation formula of the adjustment power influence parameter,the larger the difference degree reflecting the temperature change rate of the adjacent second period is, the larger the influence of the adjusting power on the temperature change is, and the larger the adjusting power influence parameter is; />The relative change amplitude of the temperature change rate difference value is represented, and the total change amplitude of the temperature change rate difference value, [ the ] is represented by calculating the difference average value of all adjacent relative change amplitudes>The larger the value of (c) is, the larger the overall change amplitude of the temperature change rate difference value is, the larger the influence caused by adjusting the power is, and the larger the adjusting power influence parameter is.
Preferably, in one embodiment of the present invention, a difference between two adjacent second period temperature change rates of the adjustment feedback data matrix is taken as a temperature change rate difference value in the order of acquisition of the second period temperature change rates; and according to the acquisition sequence corresponding to the temperature change rate difference values, all the temperature change rate difference values are arranged into a difference data vector.
The influence of the adjusting power on the temperature change can be intuitively seen through the difference value data vector formed by the temperature change rate difference values, and the larger the fluctuation of the temperature change rate difference value in the difference value data vector is, the larger the influence of the adjusting power on the temperature change is, so that the adjusting power influence parameter can be obtained according to the difference value data vector.
Step S4: obtaining improved integral parameters according to the temperature fluctuation influence parameters and the integral parameters of the adjusting power influence parameters and the PID algorithm; and regulating and controlling the power through a PID algorithm according to the improved integral parameters, and intelligently regulating and controlling the temperature of the baking furnace.
In the PID algorithm, the proportional control component can provide instant adjustment according to the temperature error, the integral control component can process static errors and eliminate residual errors, the differential control component can predict the future temperature change trend, and the temperature can be accurately controlled by combining the proportional control component, the integral control component and the differential control component; however, when the temperature in the oven is smaller than the preset temperature, if the original calculation method of the integral parameter in the PID algorithm is used, the output power is reduced too much, so that the overshoot phenomenon occurs, and therefore, the PID algorithm needs to be adjusted by combining the temperature fluctuation influencing parameter and the adjusting power influencing parameter.
Preferably, in one embodiment of the present invention, the temperature fluctuation influencing parameter and the regulated power influencing parameter are multiplied to obtain a parameter product; and (3) mapping and normalizing the parameter product in a negative correlation way, multiplying the parameter product by an integral parameter of a PID algorithm, and taking the product as an improved integral parameter. The calculation formula for improving the integral parameter comprises:
ki new ＝(e -Wd*Sg )*ki
wherein ki is new Representing an improved integral parameter; wd represents a temperature fluctuation affecting parameter; sg denotes an adjustment power influence parameter; ki denotes PID calculationIntegrating parameters of the method; wd Sg denotes the parameter product.
In the calculation formula for improving the integral parameter, the larger the temperature fluctuation influence parameter is, the larger the influence of fluctuation of the first period temperature change rate below the fitting straight line on the temperature change is, the integral parameter is reduced to eliminate the influence of lower fluctuation, so that the integral parameter is improved to be in negative correlation with the temperature fluctuation parameter; the larger the regulating power influence parameter is, the larger the influence of the down regulating power on the temperature change is, and when the temperature is close to the preset temperature, the temperature can be reduced too quickly due to excessive power reduction, so that the effect of the integral parameter needs to be reduced, and the integral parameter is improved to be in negative correlation with the regulating power influence parameter. The PID algorithm is adjusted by combining the temperature fluctuation influence parameter and the adjustment power influence parameter, so that the improved PID algorithm is more suitable for the working scene of the food processing baking oven, the temperature of the baking oven is enabled to be stably close to the highest temperature and kept constant, the temperature oscillation can be restrained, the working stability of the baking oven is improved, and the product quality is improved.
It should be noted that the integral parameters calculated by Ziegler-Nichols rule, ziegler-Nichols rule and PID algorithm are well known to those skilled in the art, and will not be described here. In other embodiments of the invention, the practitioner may adjust the PID algorithm based on temperature fluctuations affecting parameters and other mathematical relationships used to adjust the power affecting parameters.
In summary, the invention considers that when the existing algorithm regulates the temperature of the baking oven, the overshoot effect is easy to be generated by continuously regulating the power, so that the stability of the baking oven is poor, and the production is influenced. Firstly, acquiring a data matrix of a preset sampling period, further acquiring a temperature change rate and a temperature change threshold, selecting a heat balance starting matrix, and acquiring constant time required for reaching a preset temperature, so as to be convenient for analyzing the temperature change characteristics in the heat balance process; further acquiring an adjustment starting time, and then acquiring a data matrix before adjustment and a data matrix after adjustment, so as to prepare for analyzing the temperature change characteristics before and after adjusting the power; further, the characteristic of the fitting residual error of the first period temperature change rate is analyzed in a straight line fitting mode to obtain a temperature fluctuation influence parameter, the change characteristic of the second period change rate difference value is analyzed to obtain a regulating power influence parameter, the temperature fluctuation influence parameter is used for representing the temperature change characteristic before regulating power, the regulating power influence parameter is used for representing the temperature change characteristic after regulating power, and the PID algorithm is improved through the temperature fluctuation influence parameter and the regulating power influence parameter, so that the PID algorithm is more attached to the actual working scene of the baking oven, the temperature regulation effect is improved, the temperature of the baking oven is enabled to be stably close to the highest temperature and kept constant, overshoot oscillation is reduced, the working stability of the baking oven is improved, and energy waste is avoided.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (7)
1. The intelligent temperature regulation and control method for the food processing baking oven based on big data is characterized by comprising the following steps:
acquiring a data matrix of the temperature in the food processing baking oven in real time according to a preset sampling period; the preset sampling period comprises at least two preset sampling periods;
acquiring the temperature change rate and the temperature change threshold of the data matrix; acquiring a heat balance starting matrix according to the temperature change threshold and the temperature change rate of the data matrix; obtaining constant time required for reaching a preset temperature according to the thermal balance starting matrix;
acquiring an adjustment starting time according to the constant time and a preset adjustment time; the power of the baking furnace is reduced according to preset power adjustment parameters at the adjustment starting time, and a data matrix before adjustment and an adjustment feedback data matrix are obtained; acquiring a first period temperature change rate of each sampling period in the data matrix before adjustment, acquiring a fitting straight line of the first period temperature change rate, and acquiring a temperature fluctuation influence parameter according to a fitting residual error of the first period temperature change rate below the fitting straight line; acquiring a second period temperature change rate of each sampling period in the adjustment feedback data matrix, analyzing the change trend fluctuation characteristic of the second period temperature change rate, and acquiring adjustment power influence parameters;
obtaining an improved integral parameter according to the temperature fluctuation influence parameter and the adjustment power influence parameter and an integral parameter of a PID algorithm; regulating and controlling power through a PID algorithm according to the improved integral parameters, and intelligently regulating and controlling the temperature of the baking furnace;
the method for acquiring the temperature fluctuation influence parameter comprises the following steps:
acquiring temperature fluctuation influence parameters according to a temperature fluctuation influence parameter calculation formula; the temperature fluctuation influence parameter calculation formula comprises:
wherein Wd represents a temperature fluctuation affecting parameter; when the first period temperature change rate is below a fitting straight line, the corresponding fitting residual error is smaller than zero; r represents the first periodic temperature change rate quantity with a fitting residual less than zero; m is m Wd A total number of the first periodic temperature change rates;a mean value representing the first periodic temperature change rate with all fitting residuals less than zero; r is (r) min Representing the minimum of the first periodic temperature change rates with all fitting residuals less than zero; r is (r) max Representing the maximum of the first periodic temperature change rates with all fitting residuals less than zero; i represents the first periodic temperature change rate with the ith fitting residual less than zero; w (w) i Representing the ith fitting residual is less than zeroThe sequence of the preset sampling period corresponding to the period temperature change rate in the preset sampling period;
the method for acquiring the adjustment power influence parameter comprises the following steps:
obtaining a difference data vector according to all the second period temperature change rates; obtaining an adjusting power influence parameter according to an adjusting power influence parameter calculation formula, wherein the adjusting power influence parameter calculation formula comprises:
wherein Sg represents the adjustment power influence parameter; bc (Bc) i Representing an ith temperature change rate difference in the difference data vector;representing the average value of all the temperature change rate differences; m is m Sg Representing the total number of second period temperature change rates;
the method for acquiring the improved integral parameter comprises the following steps:
multiplying the temperature fluctuation influence parameter and the adjustment power influence parameter to obtain a parameter product; mapping and normalizing the parameter product negative correlation, and multiplying the parameter product negative correlation with an integral parameter of a PID algorithm, wherein the product is used as an improved integral parameter; the calculation formula for improving the integral parameter comprises:
ki new ＝(e -Wd*Sg )*ki
wherein ki is new Representing an improved integral parameter; wd represents a temperature fluctuation affecting parameter; sg denotes an adjustment power influence parameter; ki denotes the integral parameter of the PID algorithm; wd Sg denotes the parameter product.
2. The intelligent temperature regulation and control method for a food processing baking oven based on big data as claimed in claim 1, wherein the data matrix acquisition method comprises the following steps:
collecting data points according to preset sampling times in a preset sampling period; taking data points of a preset sampling period as one row of a data matrix, and arranging all the data points of the preset sampling period in the preset sampling period to obtain the data matrix; and the data in the data matrix are subjected to normalization processing.
3. The intelligent temperature regulation and control method for a food processing baking oven based on big data according to claim 1, wherein the method for obtaining the temperature change threshold comprises the following steps:
taking the first two data matrixes at the beginning of heating as threshold data matrixes; taking the absolute value of the difference value of the temperature change rate of the threshold data matrix as a threshold parameter; and obtaining a temperature change threshold according to a preset constant parameter and the threshold parameter.
4. The intelligent temperature control method for a food processing baking oven based on big data according to claim 3, wherein the method for acquiring the heat balance start matrix comprises the following steps:
taking the absolute value of the difference value of the temperature change rates of the data matrix and the next adjacent data matrix as a temperature change parameter; in the two adjacent data matrices, if the temperature change parameter corresponding to the previous data matrix is greater than or equal to the temperature change threshold and the temperature change parameter corresponding to the next data matrix is less than the temperature change threshold, defining the next data matrix as a heat balance start matrix.
5. The intelligent regulation and control method for temperature of a food processing baking oven based on big data according to claim 1, wherein the constant time obtaining method comprises the following steps:
obtaining an expected temperature change rate according to the average value of the temperature change rates of the heat balance start matrix and the next data matrix adjacent to the heat balance start matrix; and obtaining constant time required for reaching a preset temperature according to the expected temperature change rate and the sampling time of the first data point in the thermal balance starting matrix.
6. The intelligent regulation and control method for temperature of a food processing baking oven based on big data according to claim 2, wherein the method for acquiring the pre-regulation data matrix and the regulation feedback data matrix comprises the following steps:
taking the adjustment starting time as a demarcation point, taking a data matrix constructed by data points of the preset sampling period before the adjustment starting time as a data matrix before adjustment, and taking a data matrix constructed by data points of the preset sampling period after the adjustment starting time as an adjustment feedback data matrix.
7. The intelligent temperature regulation and control method for a food processing baking oven based on big data according to claim 1, wherein the method for acquiring the difference data vector comprises the following steps:
taking the difference value of two adjacent second period temperature change rates of the regulation feedback data matrix as a temperature change rate difference value according to the acquisition sequence of the second period temperature change rates; and according to the acquisition sequence corresponding to the temperature change rate difference values, all the temperature change rate difference values are arranged into a difference data vector.
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Effective date of registration: 20240830 Address after: 276000 students from Mengtai Road to Chengzhong Middle School in Xiangcheng Town, Lanling County, Linyi City, Shandong Province Patentee after: Shandong Sansheng New Energy Co.,Ltd. Country or region after: China Address before: 276000 No. 88 Mengtai Road, Xiangcheng Economic Development Zone, Lanling County, Linyi City, Shandong Province Patentee before: Shandong Shengxing Food Technology Co.,Ltd. Country or region before: China |