Intelligent temperature control method for supercritical extraction of plant essential oil

Abstract

The invention relates to the field of industrial monitoring scheduling, in particular to an intelligent temperature control method for supercritical extraction of plant essential oil, which comprises the following steps of: clustering historical temperature monitoring time sequence data windows in an extraction kettle in the extraction process of the plant essential oil to obtain a hidden state corresponding to historical temperature monitoring time sequence data; establishing a hidden Markov prediction model according to the hidden state of the historical temperature monitoring time sequence data of the extraction kettle, predicting the real-time temperature monitoring time sequence data of the extraction kettle according to the hidden Markov prediction model, and obtaining a state transition probability matrix of the real-time temperature monitoring data; obtaining a parameter optimization factor of the real-time temperature monitoring data according to the state transition probability matrix of the real-time temperature monitoring data; according to the parameter optimization factor, a PID control model for real-time temperature control of the extraction kettle is obtained, more accurate extraction kettle temperature control model parameters can be obtained, and the precision of the temperature control model in the plant essential oil extraction process is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial monitoring and scheduling, and in particular to a temperature intelligent control method for supercritical extraction of plant essential oils. Background Art

[0002] Supercritical carbon dioxide extraction (SCTE) is an efficient and environmentally friendly separation method widely used in the extraction of essential oils. Its principle is based on the unique properties of CO2 in a supercritical state, which combines the diffusivity of a gas with the solubility of a liquid. By controlling temperature and pressure, SCTE is brought into contact with plant materials in a supercritical state, dissolving the essential oil components. The essential oils are then separated and collected in a separation vessel by reducing the pressure or temperature. This technology offers significant advantages: no solvent residue, high extraction efficiency, gentle operation, and strong selectivity. It can effectively extract heat-sensitive components and ensure product purity. However, practical applications present challenges such as temperature and pressure control accuracy, equipment energy consumption, and optimization of process parameters for different raw materials.

[0003] Regarding the temperature control of the extraction kettle for supercritical extraction of plant essential oils, in the existing technology, after the plant essential oil extraction kettle obtains all historical temperature monitoring time series data, the PID control model parameters are fitted according to the output power time series data of the heating device during the plant essential oil extraction process, and iterative optimization is performed according to the real-time temperature monitoring time series data to obtain the parameters of the PID control model, thereby obtaining the PID model for temperature control of the plant essential oil extraction kettle.

[0004] Among them, in the process of controlling the temperature of the plant essential oil extraction kettle through the PID model, since the temperature of the extraction kettle may fluctuate, temperature control through the PID control model alone will cause overshoot or oscillation in the temperature control of the extraction kettle when the temperature fluctuates.

[0005] Therefore, how to ensure the accuracy of temperature control during the supercritical extraction of plant essential oils and thereby improve the stability of plant essential oil extraction has become an urgent problem to be solved. Summary of the Invention

[0006] In view of this, the present invention provides an intelligent temperature control method for supercritical extraction of plant essential oils to solve the problem of how to ensure the accuracy of temperature control under temperature fluctuation conditions during the temperature control of the extraction kettle, thereby improving the temperature control accuracy of the extraction kettle.

[0007] An embodiment of the present invention provides a temperature intelligent control method for supercritical extraction of plant essential oils. The temperature intelligent control method for supercritical extraction of plant essential oils includes the following steps:

[0008] Collecting temperature monitoring time series data in a supercritical extraction kettle of plant essential oils, obtaining historical temperature monitoring time series data of the extraction kettle, and dividing all historical temperature monitoring time series data into windows to obtain all temperature time series windows;

[0009] For any extraction kettle, cluster analysis is performed based on the optimized distance metric between the historical temperature monitoring time series data windows to obtain clustering results; based on the clustering results of the historical temperature monitoring time series data windows, the hidden state of each historical temperature monitoring time series data window is obtained;

[0010] Establishing a hidden Markov prediction model based on the historical temperature monitoring time series data and the hidden state of the historical temperature monitoring time series data window; performing predictions based on the real-time temperature monitoring time series data of the extraction kettle using the hidden Markov prediction model to obtain a state transition probability matrix of the real-time temperature monitoring time series data of the extraction kettle; and obtaining a parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle based on the state transition probability matrix and the state fluctuation evaluation of the clusters in the clustering result;

[0011] According to the historical temperature monitoring time series data of the extraction kettle, a PID control model for real-time temperature control of the extraction kettle temperature is obtained, including the converged parameters of the PID control model; according to the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle, the PID control model of the real-time temperature monitoring data of the extraction kettle is parameter optimized, and the temperature of supercritical extraction of plant essential oil is controlled according to the PID control model after the parameter optimization.

[0012] Preferably, the cluster analysis is performed based on the optimized distance metric between the historical temperature monitoring time series data windows to obtain cluster classification results, including:

[0013] For any of the historical temperature monitoring time series data windows, obtain the timestamp distance between the data point in the historical temperature monitoring time series data window and the center point of the window, add the timestamp distance to the constant 1 as the first influencing parameter, and obtain the timestamp distance between the data point in the historical temperature monitoring time series data window and the center point of the window. Outlier detection, obtain the outlier factor of the data in the historical temperature monitoring time series data window, and normalize the absolute value of the outlier factor minus the constant 1 as the second influencing parameter. The normalized result is used as the contribution degree of the fluctuation analysis corresponding to the data in the historical temperature time series data window;

[0014] For any of the historical temperature monitoring time series data windows, based on the fluctuation analysis contribution of the data in the historical temperature monitoring time series data window, the temperature mean of the historical temperature monitoring time series data window and the historical temperature monitoring time series data window, the difference between the data in the historical temperature monitoring time series data window and the temperature mean of the historical temperature monitoring time series data window is used as a first fluctuation measure. Based on the fluctuation analysis contribution corresponding to the data in the historical temperature time series data window, all first fluctuation measures are weightedly summed and divided by the window length of the historical temperature time series data window to obtain a corresponding fluctuation calculation result;

[0015] Obtain the slope of the least squares fitting result of the historical temperature time series data window, and use the absolute value of the difference between the fluctuation calculation result of the historical temperature monitoring time series data window and the fluctuation calculation result of the historical temperature monitoring time series data window corresponding to the cluster center in the clustering process as the first metric of the difference optimization factor. Use the absolute value of the difference between the slope of the least squares fitting result of the historical temperature monitoring time series data window and the slope of the least squares fitting result of the historical temperature monitoring time series data window corresponding to the cluster center in the clustering process as the second metric of the difference optimization factor; normalize the product of the first metric of the difference optimization factor and the second metric of the difference optimization factor to obtain the corresponding normalized value as the difference optimization factor between the historical temperature monitoring time series data window and the historical temperature monitoring time series data window corresponding to the cluster center in the clustering process;

[0016] For any of the historical temperature monitoring time series data windows in the clustering process, the Euclidean distance between the historical temperature monitoring time series data window and the historical temperature monitoring time series data window corresponding to the cluster center in the clustering process is used as the first Euclidean distance; the product of the difference optimization factor between the historical temperature monitoring time series data window and the historical temperature monitoring time series data window corresponding to the cluster center in the clustering process and the first Euclidean distance is used as the optimized distance metric between the historical temperature monitoring time series data window in the clustering process and the historical temperature monitoring time series data window corresponding to the cluster center in the clustering process;

[0017] According to the optimized distance measurement between the historical temperature monitoring time series data window and the historical temperature monitoring time series data window corresponding to the cluster center of the clustering process, The clustering algorithm completes the clustering process and obtains the clustering result of the historical temperature monitoring time series data window.

[0018] Preferably, obtaining the hidden state of each historical temperature monitoring time series data window according to the clustering result of the historical temperature monitoring time series data window includes:

[0019] The clustering result of the historical temperature monitoring time series data window is obtained, and the cluster number of the historical temperature monitoring time series data window is used as the hidden state of each historical temperature monitoring time series data window.

[0020] Preferably, the state transition probability matrix of the real-time temperature monitoring time series data of the extraction kettle is obtained based on the hidden Markov prediction model of the historical temperature monitoring time series data, including:

[0021] Obtain the hidden Markov model of the historical temperature monitoring time series data, obtain the real-time temperature monitoring time series data of the extraction kettle, and obtain the state transition probability matrix corresponding to the real-time temperature monitoring time series data of the extraction kettle based on the hidden Markov model of the historical temperature monitoring time series data.

[0022] Preferably, obtaining the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle according to the state transition probability matrix and the state fluctuation evaluation of the clusters in the cluster division result includes:

[0023] Obtain a state transition probability matrix of the real-time temperature monitoring time series data of the extraction kettle, and use the state transition probability of the target state in the state transition probability matrix of the real-time temperature monitoring time series data of the extraction kettle as a fluctuation weight; obtain a clustering result of the historical temperature monitoring time series data window of the clustering process, and obtain a state fluctuation evaluation of each cluster in the clustering result based on the first fluctuation measure of the historical temperature monitoring time series data window in each cluster; perform weighted summation on the state fluctuation evaluations of all the clusters using the fluctuation weights of the real-time temperature monitoring time series data of the extraction kettle to obtain a corresponding weighted summation result; substitute the opposite of the weighted summation result into an exponential function with a natural constant e as the base, and use the subtraction result between the constant 1 and the first exponential function result as the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle.

[0024] Preferably, obtaining a state fluctuation assessment of each cluster in the cluster division result includes:

[0025] The clustering result of the historical temperature monitoring time series data window is obtained, and the mean value of the fluctuation calculation of the historical temperature monitoring time series data window in each cluster in the clustering result of the historical temperature monitoring time series data window is used as the state fluctuation evaluation of the cluster.

[0026] Preferably, the parameter optimization of the PID control model of the real-time temperature monitoring data of the extraction kettle is performed according to the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle to obtain the PID control model of the real-time temperature monitoring time series data of the extraction kettle, including:

[0027] The integral gain parameter of the real-time temperature PID control model of the extraction kettle and the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle are respectively obtained; a constant 1 is added to the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle as a first parameter weight, and the integral gain parameter of the real-time temperature PID control model of the extraction kettle is weighted according to the first parameter weight, and the result is used as the optimized integral gain parameter.

[0028] The optimized integral gain parameter is used as the real-time integral gain parameter in the real-time temperature PID control model of the extraction kettle to obtain the PID control model of the real-time temperature control time series data of the extraction kettle.

[0029] Preferably, the window division of all historical temperature monitoring time series data to obtain all temperature time series windows includes:

[0030] The historical temperature monitoring time series data of the extraction kettle and a preset window length are respectively obtained; the historical temperature monitoring time series data of the extraction kettle are traversed continuously with a constant step length of 1 according to the preset window length, and the subsequence contained in each movement of the window of the predicted window length is used as the historical temperature monitoring time series data window corresponding to the center point of the window.

[0031] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0032] The temperature monitoring time series data in the supercritical extraction kettle of plant essential oil is collected to obtain the historical temperature monitoring time series data of the extraction kettle. All the historical temperature monitoring time series data are divided into windows to obtain all temperature time series windows. For any extraction kettle, cluster analysis is performed based on the optimized distance metric between the windows of the historical temperature monitoring time series data to obtain cluster division results. According to the clustering result of the historical temperature monitoring time series data window, the hidden state of each historical temperature monitoring time series data window is obtained; according to the historical temperature monitoring time series data and the hidden state of the historical temperature monitoring time series data window, a hidden Markov prediction model is established; according to the real-time temperature monitoring time series data of the extraction kettle, prediction is performed through the hidden Markov prediction model to obtain the state transition probability matrix of the real-time temperature monitoring time series data of the extraction kettle; according to the state transition probability matrix and the state fluctuation evaluation of the clusters in the clustering result, the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle is obtained; according to the historical temperature monitoring time series data of the extraction kettle, a PID control model for the real-time temperature control of the extraction kettle temperature is obtained, including the convergence parameters of the PID control model; according to the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle, the parameters of the PID control model of the real-time temperature monitoring data of the extraction kettle are optimized, and the temperature of the supercritical extraction of plant essential oil is controlled according to the PID control model after the parameter optimization. Among them, by performing fluctuation analysis on all historical temperature monitoring time series data windows of extraction kettles, an optimized distance measurement between the historical temperature monitoring time series data windows is obtained, clustering is performed through the optimized distance measurement to obtain clustering results, and each cluster is regarded as a temperature fluctuation state to determine the hidden state of each temperature time series data window. A hidden Markov prediction model is established through the temperature monitoring time series data and the hidden state of the temperature time series data window, and the state transition probability matrix of the real-time temperature monitoring time series data of the extraction kettle is obtained through the hidden Markov prediction model. The parameter optimization factor is obtained through the state transition probability matrix and the state fluctuation evaluation of each state, which is used to optimize the integral gain parameter of the PID model when the real-time temperature of the extraction kettle is controlled by the PID model to obtain more rigorous PID model parameters, thereby avoiding temperature overshoot or oscillation during the temperature control process, and improving the control accuracy of the temperature control model. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0034] Figure 1 This is a method flow chart of a temperature intelligent control method for supercritical extraction of plant essential oils provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0035] The following describes in detail embodiments of the present disclosure, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0036] It should be noted that the terms "first," "second," and the like in the specification of the present disclosure and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0037] In order to illustrate the technical solution of the present invention, specific embodiments are provided below.

[0038] The specific scenario targeted by the present invention is: intelligent control of the temperature of the extraction kettle during the supercritical carbon dioxide extraction of plant essential oils.

[0039] See also Figure 1 , is a flow chart of a method for intelligent temperature control of supercritical extraction of plant essential oils provided in Example 1 of the present invention, such as Figure 1 As shown, a temperature intelligent control method for supercritical extraction of plants may include:

[0040] Step S101 , collecting the temperature monitoring time series data in the supercritical extraction kettle of the plant essential oil, obtaining the historical temperature monitoring time series data of the extraction kettle, dividing all the historical temperature monitoring time series data into windows, and obtaining all temperature time series windows.

[0041] For the supercritical extraction process of plant essential oils, the temperature sensor in the extraction kettle can be used to collect all historical temperature monitoring time series data in the extraction kettle. Preferably, the sampling frequency of the temperature sensor in the extraction kettle is preset to The data is collected once every second, that is, every The temperature of the extraction kettle is recorded once every second and the temperature time series data is formed as the corresponding extraction kettle temperature monitoring time series data.

[0042] Based on the above data collection method, the historical temperature monitoring time series data of the extraction kettle is obtained. After the historical temperature monitoring time series data of the extraction kettle is obtained, it is necessary to divide the window of the historical temperature monitoring time series data according to the preset time series window length. Preferably, the window length is set in the embodiment of the present invention. , in the extraction kettle historical temperature time series data, from data points, based on the length The window of all historical temperature monitoring time series data of the extraction kettle is Each time the window moves, the data in the window is used as the temperature time series data window corresponding to the center point of the current window.

[0043] Step S102: For any extraction kettle, cluster analysis is performed based on the optimized distance metric between the historical temperature monitoring time series data windows to obtain clustering results. Based on the clustering results of the historical temperature monitoring time series data windows, the hidden state of each historical temperature monitoring time series data window is obtained.

[0044] After obtaining the historical temperature monitoring time series data windows of the extraction kettle, all the historical temperature monitoring time series data windows of the extraction kettle can be clustered through a clustering process, thereby obtaining the hidden state of the historical temperature monitoring time series data windows. Considering that the distance between the temperature monitoring time series data windows of the extraction kettle is measured by Euclidean distance during the clustering process, however, since the historical temperature monitoring time series data of the extraction kettle have different temperature change states, the different temperature change states refer to the existence of temperature rise, fall and continuous fluctuation in the historical temperature monitoring time series data of the extraction kettle. When measuring the distance between the temperature time series data windows during the clustering process, temperature time series data windows with different change patterns but similar distances will appear. For example, in the first temperature time series data window, the temperature change shows a slow upward trend, in the second temperature time series data window, the temperature change shows a slow downward trend, and in the third window, the temperature shows a frequent jitter state. The result of measuring the distance between the first temperature time series data window and the second temperature time series data window by Euclidean distance may be similar to the result of measuring the distance between the first temperature time series data window and the third temperature time series data window by Euclidean distance. Therefore, for the distance measurement between temperature time series data windows, it is necessary to evaluate the characteristics presented by the different fluctuation states between the two temperature time series data windows, so as to optimize the distance measurement between the temperature time series data windows by the difference in fluctuation characteristics presented between the windows. At the same time, considering the possibility of abnormal temperature monitoring data in the temperature time series data window, and in the distance measurement between the extraction kettle historical temperature monitoring time series data windows, because the temperature change of the extraction kettle exists in a transition stage between different fluctuation modes, in the distance measurement process between the extraction kettle historical temperature monitoring time series data windows, if the distance measurement between the windows is performed for each data point in the time series data window using the same weight, it will lead to inaccurate clustering of the data windows in the transition stage. Therefore, for the distance measurement between the historical temperature monitoring time series data windows, it is necessary to optimize the evaluation of the difference in fluctuation characteristics by evaluating the contribution degree of fluctuation analysis of the temperature data in the historical temperature time series data windows. Therefore, in an embodiment of the present invention, first, according to the timestamp difference between the temperature data in any of the historical temperature monitoring time series data windows and the degree of outlier of the data point itself, the degree of fluctuation analysis contribution corresponding to the data in the historical temperature time series data window is obtained, including:

[0045] For any of the historical temperature monitoring time series data windows, obtain the timestamp distance between the data point in the historical temperature monitoring time series data window and the center point of the window, add the timestamp distance to the constant 1 as the first influencing parameter, and obtain the timestamp distance between the data point in the historical temperature monitoring time series data window and the center point of the window. Outlier detection, obtain the outlier factor of the data in the historical temperature monitoring time series data window, and normalize the absolute value of the outlier factor minus the constant 1 as the second influencing parameter. The normalized result is used as the contribution degree of the fluctuation analysis corresponding to the data in the historical temperature time series data window.

[0046] In one embodiment, the The first extraction kettle historical temperature monitoring time series data window The calculation expression corresponding to the contribution degree of fluctuation analysis of temperature data is:

[0047]

[0048] in, Indicates the The first in the historical temperature monitoring time series data window The contribution of temperature data fluctuation analysis to represents the normalization function, represents the linear normalization function, Indicates the absolute value symbol, represents a constant, Indicates the In the historical temperature monitoring time series data window The number of timestamp intervals between each temperature data point and the center point of the window. Indicates the In the historical temperature monitoring time series data window Temperature data in itself The distance neighborhood is of Outlier factor, The size of the distance neighborhood can be adjusted according to the real-time scenario and is not required.

[0049] It should be noted that in the process of measuring the distance between temperature time series data windows, the first The larger the timestamp interval between a temperature data and the center point of the window, the farther the position of the temperature data in the window is from the center point of the window, and the smaller its contribution to the center point of the window in the fluctuation analysis. The smaller the value of ; At the same time, in the distance measurement process between temperature time series data windows, the Temperature data The absolute value of the outlier factor minus the constant 1 The larger the value is, the higher the abnormality of the temperature data is, and the smaller the contribution of the temperature data is in the process of fluctuation analysis, thereby preventing the temperature time series data window from being affected by abnormal data in the process of fluctuation analysis.

[0050] After determining the fluctuation analysis contribution of each temperature data in the historical temperature time series data window, obtaining a difference optimization factor between the historical temperature monitoring time series data window and the historical temperature monitoring time series data window corresponding to the cluster center in the clustering process according to the fluctuation analysis contribution of the data in the historical temperature monitoring time series data window, the temperature mean of the historical temperature monitoring time series data window, and the historical temperature monitoring time series data window, including:

[0051] The difference between the data in the historical temperature monitoring time series data window and the temperature mean of the historical temperature monitoring time series data window is used as the first fluctuation metric. Based on the fluctuation analysis contribution of the data in the historical temperature time series data window, a weighted sum of all first fluctuation metrics is taken and divided by the window length of the historical temperature time series data window to obtain the corresponding fluctuation calculation result;

[0052] Obtain the slope of the least squares fitting result of the historical temperature time series data window, and use the absolute value of the difference between the fluctuation calculation result of the historical temperature monitoring time series data window and the fluctuation calculation result of the historical temperature monitoring time series data window corresponding to the cluster center in the clustering process as the first metric of the difference optimization factor. Use the absolute value of the difference between the slope of the least squares fitting result of the historical temperature monitoring time series data window and the slope of the least squares fitting result of the historical temperature monitoring time series data window corresponding to the cluster center in the clustering process as the second metric of the difference optimization factor. Normalize the product of the first metric of the difference optimization factor and the second metric of the difference optimization factor to obtain the corresponding normalized value as the difference optimization factor between the historical temperature monitoring time series data window and the historical temperature monitoring time series data window corresponding to the cluster center in the clustering process.

[0053] In one embodiment, it is assumed that the clustering process The first in the historical temperature monitoring time series data window The temperature data is , No. The cluster center point of each cluster corresponds to the first cluster in the historical temperature monitoring time series data window. The temperature data is , then The historical temperature monitoring time series data window and the The calculation expression corresponding to the difference optimization factor between the historical temperature monitoring time series data windows corresponding to the cluster center points of the clusters is:

[0054]

[0055] in, Indicates the The historical temperature monitoring time series data window and the The difference optimization factor between the historical temperature monitoring time series data windows corresponding to the cluster center points of the clusters, , Respectively represent In the historical temperature monitoring time series data window The contribution of the fluctuation analysis of the temperature data is the same as that of the The cluster center point of each cluster corresponds to the first cluster in the historical temperature monitoring time series data window. The contribution of temperature data fluctuation analysis to Respectively represent The average temperature in the historical temperature monitoring time series data window is The mean temperature in the historical temperature monitoring time series data window corresponding to the cluster center point of each cluster, Indicates the window length, Respectively represent the historical temperature monitoring time series data window and the The fitting slope of the time series data in the historical temperature monitoring time series data window corresponding to the cluster center point of each cluster is obtained by least square fitting. represents the linear normalization function, Indicates the absolute value symbol, It represents the difference in the weighted variance of the volatility analysis contribution between the two windows, which is also the first measure of the difference optimization factor. It represents the difference in the fitting slope between the two windows, which is also the second measure of the difference optimization factor.

[0056] It should be noted that, in the distance measurement between the windows of historical temperature monitoring time series data in the clustering process, the fluctuation difference between windows needs to be measured by the difference between the fluctuation calculation results between windows, so as to distinguish the different fluctuation amplitudes between windows. Therefore, the difference in the degree of fluctuation is analyzed by the first measurement of the difference optimization factor. The greater the difference in the fluctuation calculation results between the two windows, the greater the difference optimization factor of the corresponding two windows. At the same time, because there are differences in fluctuation trends between windows, the difference in fluctuation trends is analyzed by the second measurement of the difference optimization factor. The greater the difference in the fluctuation trends between the two windows, the greater the difference optimization factor of the corresponding two windows.

[0057] After obtaining the difference optimization factor between the historical temperature monitoring time series data window and the historical temperature monitoring time series data window corresponding to the cluster center in the clustering process, obtaining the optimized distance metric between the historical temperature monitoring time series data window in the clustering process and the historical temperature monitoring time series data window corresponding to the cluster center in the clustering process according to the difference optimization factor, including:

[0058] For any of the historical temperature monitoring time series data windows in the clustering process, the Euclidean distance between the historical temperature monitoring time series data window and the historical temperature monitoring time series data window corresponding to the cluster center in the clustering process is used as a first Euclidean distance. The product of a difference optimization factor between the historical temperature monitoring time series data window and the historical temperature monitoring time series data window corresponding to the cluster center in the clustering process and the first Euclidean distance is used as an optimized distance metric between the historical temperature monitoring time series data window in the clustering process and the historical temperature monitoring time series data window corresponding to the cluster center in the clustering process.

[0059] In one embodiment, the Take the historical temperature time series data window corresponding to the cluster center of the cluster as an example, and obtain the The historical temperature monitoring time series data window and the The calculation expression of the distance measurement between the historical temperature time series data windows corresponding to the cluster centers of clusters is:

[0060]

[0061] in, Indicates the The historical temperature monitoring time series data window and the The distance measurement between the historical temperature time series data windows corresponding to the cluster centers of the clusters, Indicates the The historical temperature monitoring time series data window and the The first Euclidean distance between the historical temperature time series data windows corresponding to the cluster centers of the clusters, Indicates the The historical temperature monitoring time series data window and the The difference optimization factor between the historical temperature monitoring time series data windows corresponding to the cluster center points of the clusters.

[0062] After obtaining the optimized distance metric between the historical temperature monitoring time series data window and the historical temperature monitoring time series data window corresponding to the cluster center of the clustering process, the optimized distance metric between the historical temperature monitoring time series data window and the historical temperature monitoring time series data window corresponding to the cluster center of the clustering process is obtained. The clustering algorithm completes the clustering process and obtains the clustering result of the historical temperature monitoring time series data window.

[0063] It should be noted that, in one embodiment, after obtaining the optimized distance measurement between the historical temperature monitoring time series data window and the historical temperature monitoring time series data window corresponding to the cluster center of the clustering process, of Clustering completes the clustering process, The number of clusters can be adjusted according to the real-time scenario and is not required.

[0064] After obtaining the clustering result of the historical temperature monitoring time series data window, obtaining the hidden state of each historical temperature monitoring time series data window according to the clustering result of the historical temperature monitoring time series data window includes:

[0065] The clustering result of the historical temperature monitoring time series data window is obtained, and the cluster number of the historical temperature monitoring time series data window is used as the hidden state of each historical temperature monitoring time series data window.

[0066] Step S103: Establish a hidden Markov prediction model based on the historical temperature monitoring time series data and the hidden state of the historical temperature monitoring time series data window; perform predictions based on the real-time temperature monitoring time series data of the extraction kettle using the hidden Markov prediction model to obtain a state transition probability matrix of the real-time temperature monitoring time series data of the extraction kettle; and obtain a parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle based on the state transition probability matrix and the state fluctuation evaluation of the clusters in the clustering result.

[0067] After obtaining the hidden state of each window of historical temperature monitoring time series data, a hidden Markov prediction model is established based on the historical temperature monitoring time series data and the hidden state of the historical temperature monitoring time series data window. The hidden Markov prediction model is used to perform predictions based on the real-time temperature monitoring time series data of the extraction kettle to obtain a state transition probability matrix for the real-time temperature monitoring time series data of the extraction kettle. Establishing the hidden Markov prediction model and obtaining the state transition probability matrix using the Viterbi algorithm are both well-known techniques and will not be further described in this embodiment.

[0068] After obtaining the state transition probability matrix of the real-time temperature monitoring time series data of the extraction kettle, obtaining a state fluctuation evaluation of each cluster in the cluster division result according to the cluster division result includes:

[0069] The clustering result of the historical temperature monitoring time series data window is obtained, and the mean value of the fluctuation calculation of the historical temperature monitoring time series data window in each cluster in the clustering result of the historical temperature monitoring time series data window is used as the state fluctuation evaluation of the cluster.

[0070] In one embodiment, the Take the cluster class as an example, get the The calculation expression for the state fluctuation evaluation of a cluster is:

[0071]

[0072] in, Indicates the The state fluctuation evaluation of clusters, Indicates the In the historical temperature monitoring time series data window The contribution of temperature data fluctuation analysis to Indicates the The first in the historical temperature monitoring time series data window The temperature data is , Indicates the The average temperature in the historical temperature monitoring time series data window, Indicates the time series data window length, Indicates the The number of historical temperature time series monitoring data windows in a cluster. 1 represents a constant of 1.

[0073] It should be noted that in the cluster state fluctuation assessment process, the cluster state fluctuation assessment is performed using the average of the fluctuation calculation results of all historical temperature monitoring time series data windows in the cluster. The fluctuation pattern corresponding to the cluster can be characterized by the overall state of the historical temperature monitoring time series data in the cluster. The higher the average fluctuation of the historical temperature monitoring time series data window in the cluster, the higher the cluster state fluctuation assessment, correspondingly The larger the value of , the higher the state fluctuation assessment of the cluster.

[0074] After obtaining the state fluctuation evaluation of each cluster, obtaining the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle according to the state transition probability matrix and the state fluctuation evaluation of the cluster in the cluster division result, including:

[0075] Obtain a state transition probability matrix of the real-time temperature monitoring time series data of the extraction kettle, and use the state transition probability of the target state in the state transition probability matrix of the real-time temperature monitoring time series data of the extraction kettle as a fluctuation weight; obtain a clustering result of the historical temperature monitoring time series data window of the clustering process, and obtain a state fluctuation evaluation of each cluster in the clustering result based on the first fluctuation measure of the historical temperature monitoring time series data window in each cluster; perform weighted summation on the state fluctuation evaluations of all the clusters using the fluctuation weights of the real-time temperature monitoring time series data of the extraction kettle to obtain a corresponding weighted summation result; substitute the opposite of the weighted summation result into an exponential function with a natural constant e as the base, and use the subtraction result between the constant 1 and the first exponential function result as the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle.

[0076] In one embodiment, the real-time temperature monitoring time series data window of the extraction kettle is obtained and recorded as , then the calculation expression of the parameter optimization factor of the extraction kettle real-time temperature monitoring time series data is:

[0077]

[0078] in, represents the parameter optimization factor of the extraction kettle real-time temperature monitoring time series data, Indicates that the extraction kettle real-time temperature monitoring time series data is transferred to the corresponding state in the state transition probability matrix. The probability of a cluster corresponding to a hidden state, Indicates the The state fluctuation evaluation of clusters, Expressed as a natural constant The exponential function with base 1 represents the constant 1. Indicates the number of clusters in the clustering process.

[0079] It should be noted that the parameter optimization factor is measured based on the state transition probability matrix corresponding to the real-time temperature monitoring data of the extraction kettle and the state fluctuation evaluation of all clusters. The state fluctuation evaluation corresponding to the state is weighted summed by the transition probability of the target state. When there is a high probability of transition to a high fluctuation state, the corresponding The larger the value, the greater the parameter optimization factor of the extraction kettle real-time temperature monitoring time series data.

[0080] Step S104: obtaining a PID control model for real-time temperature control of the extraction kettle temperature, including converged parameters of the PID control model, based on the historical temperature monitoring time series data of the extraction kettle; performing parameter optimization on the PID control model of the real-time temperature monitoring data of the extraction kettle based on the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle, and performing temperature control for supercritical extraction of plant essential oils based on the parameter-optimized PID control model.

[0081] After obtaining the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle, the integral gain parameter in the PID control model of the real-time temperature control of the extraction kettle temperature is optimized using the parameter optimization factor, thereby obtaining an accurate extraction kettle temperature control model. Therefore, in an embodiment of the present invention, based on the historical temperature monitoring time series data of the extraction kettle, a PID control model for the real-time temperature control of the extraction kettle temperature is obtained, including the converged parameters of the PID control model; based on the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle temperature, the PID control model of the real-time temperature monitoring data of the extraction kettle temperature is parameter optimized; and based on the parameter optimized PID control model, temperature control of supercritical extraction of plant essential oil is performed, including:

[0082] The integral gain parameter of the real-time temperature PID control model of the extraction kettle and the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle are respectively obtained; a constant 1 is added to the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle as a first parameter weight, and the integral gain parameter of the real-time temperature PID control model of the extraction kettle is weighted according to the first parameter weight, and the result is used as the optimized integral gain parameter; the optimized integral gain parameter is used as the real-time integral gain parameter in the real-time temperature PID control model of the extraction kettle, and the PID control model after the parameter optimization is obtained.

[0083] In one embodiment, it is assumed that the initial integral gain parameter in the real-time temperature PID control model of the extraction kettle is , then the calculation expression for real-time optimization of the integral gain parameter through the parameter optimization factor of the extraction kettle real-time temperature monitoring time series data window is:

[0084]

[0085] in, It represents the real-time integral gain parameter of the real-time temperature PID control model of the extraction kettle. Indicates the parameter optimization factor of the extraction kettle real-time temperature monitoring time series data, 1 represents constant 1, Represents the initial integral gain parameter in the real-time temperature PID control model of the extraction kettle.

[0086] It should be noted that the real-time temperature monitoring time series data window of the extraction kettle in the embodiment of the present invention is the window before the latest temperature monitoring time series data collected by the temperature sensor in the extraction kettle. A window of temperature data is used as a real-time data window to ensure the integrity of the temperature data in the temperature time series data window. In the PID model used for the temperature control of the extraction kettle, its initial parameters are iterated through the regulation process of the historical temperature monitoring time series data until the PID model parameters converge, and the converged PID control model parameters are used as the initial parameters. The establishment of the PID model and the acquisition of parameters belong to the prior art and will not be described in detail here. In the PID control model, the higher the integral gain parameter, the lower the sensitivity of the control system. Therefore, in the real-time temperature control process of the extraction kettle, the higher the parameter optimization factor corresponding to the real-time temperature monitoring time series data window of the extraction kettle, the higher the possibility of temperature fluctuation in the subsequent process, and the corresponding integral gain parameter is higher, thereby reducing the sensitivity of the control system and avoiding overshoot or oscillation due to temperature fluctuations during the temperature control process due to the sensitivity of the control system.

[0087] After obtaining the optimized parameter optimization factors, the temperature control of the supercritical extraction of plant essential oils is performed according to the PID control model after the parameter optimization.

[0088] In summary, the embodiment of the present invention collects temperature monitoring time series data in a supercritical extraction kettle of plant essential oil, obtains historical temperature monitoring time series data of the extraction kettle, and divides all historical temperature monitoring time series data into windows to obtain all temperature time series windows; for any extraction kettle, cluster analysis is performed based on the optimized distance metric between the historical temperature monitoring time series data windows to obtain cluster division results. According to the clustering result of the historical temperature monitoring time series data window, the hidden state of each historical temperature monitoring time series data window is obtained; according to the historical temperature monitoring time series data and the hidden state of the historical temperature monitoring time series data window, a hidden Markov prediction model is established; according to the real-time temperature monitoring time series data of the extraction kettle, prediction is performed through the hidden Markov prediction model to obtain the state transition probability matrix of the real-time temperature monitoring time series data of the extraction kettle; according to the state transition probability matrix and the state fluctuation evaluation of the clusters in the clustering result, the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle is obtained; according to the historical temperature monitoring time series data of the extraction kettle, a PID control model for the real-time temperature control of the extraction kettle temperature is obtained, including the convergence parameters of the PID control model; according to the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle, the parameters of the PID control model of the real-time temperature monitoring data of the extraction kettle are optimized, and the temperature of the supercritical extraction of plant essential oil is controlled according to the PID control model after the parameter optimization. Among them, by performing fluctuation analysis on all historical temperature monitoring time series data windows of extraction kettles, an optimized distance measurement between the historical temperature monitoring time series data windows is obtained, clustering is performed through the optimized distance measurement to obtain clustering results, and each cluster is regarded as a temperature fluctuation state to determine the hidden state of each temperature time series data window. A hidden Markov prediction model is established through the temperature monitoring time series data and the hidden state of the temperature time series data window, and the state transition probability matrix of the real-time temperature monitoring time series data of the extraction kettle is obtained through the hidden Markov prediction model. The parameter optimization factor is obtained through the state transition probability matrix and the state fluctuation evaluation of each state, which is used to optimize the integral gain parameter of the PID model when the real-time temperature of the extraction kettle is controlled by the PID model to obtain more rigorous PID model parameters, thereby avoiding temperature overshoot or oscillation during the temperature control process, and improving the control accuracy of the temperature control model.

[0089] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A temperature intelligent control method for supercritical extraction of plant essential oils, characterized in that: The method comprises: The temperature monitoring time series data in the supercritical extraction kettle of the plant essential oil is collected to obtain the historical temperature monitoring time series data of the extraction kettle, and all the historical temperature monitoring time series data are divided into windows to obtain all temperature time series windows; For any extraction kettle, cluster analysis is performed according to the optimized distance measurement between the historical temperature monitoring time series data windows to obtain clustering results; according to the clustering results of the historical temperature monitoring time series data windows, the hidden state of each historical temperature monitoring time series data window is obtained; A hidden Markov prediction model is established according to the historical temperature monitoring time series data and the hidden state of the historical temperature monitoring time series data window; a state transition probability matrix of the real-time temperature monitoring time series data of the extraction kettle is obtained by predicting the real-time temperature monitoring time series data of the extraction kettle through the hidden Markov prediction model; a parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle is obtained according to the state transition probability matrix and the state fluctuation evaluation of the clusters in the clustering result; According to the historical temperature monitoring time series data of the extraction kettle, a PID control model for real-time temperature control of the extraction kettle temperature is obtained, including the converged parameters of the PID control model; according to the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle, the PID control model of the real-time temperature monitoring data of the extraction kettle is parameter optimized, and the temperature control of supercritical extraction of plant essential oil is performed according to the PID control model after the parameter optimization.

2. A temperature intelligent control method for supercritical extraction of plant essential oils according to claim 1, characterized in that: The cluster analysis is performed based on the optimized distance measurement between the historical temperature monitoring time series data windows to obtain the cluster classification results, including: For any of the historical temperature monitoring time series data windows, obtain the timestamp distance between the data point and the center point of the window in the historical temperature monitoring time series data window, and use the inverse of the result of adding the timestamp distance to the constant 1 as the first influencing parameter, obtain the COF outlier detection of the data point in the historical temperature monitoring time series data window, and obtain the outlier factor of the data in the historical temperature monitoring time series data window. Normalize the absolute value of the outlier factor subtracted from the constant 1 as the second influencing parameter; multiply the first influencing parameter by the constant 1 and perform softmax normalization on the result of the second influencing parameter as the fluctuation analysis contribution degree corresponding to the data in the historical temperature time series data window; For any of the historical temperature monitoring time series data windows, according to the fluctuation analysis contribution of the data in the historical temperature monitoring time series data window, the temperature mean of the historical temperature monitoring time series data window and the historical temperature monitoring time series data window, the difference between the data in the historical temperature monitoring time series data window and the temperature mean of the historical temperature monitoring time series data window is used as the first fluctuation measure; according to the fluctuation analysis contribution corresponding to the data in the historical temperature time series data window, all the first fluctuation measures are weightedly summed and divided by the window length of the historical temperature time series data window to obtain the corresponding fluctuation calculation result; Obtain the slope in the least squares fitting result of the historical temperature time series data window, and use the absolute value of the difference between the fluctuation calculation result of the historical temperature monitoring time series data window and the fluctuation calculation result of the historical temperature monitoring time series data window corresponding to the cluster center in the clustering process as the first measure of the difference optimization factor; use the absolute value of the difference between the slope in the least squares fitting result of the historical temperature monitoring time series data window and the slope in the least squares fitting result of the historical temperature monitoring time series data window corresponding to the cluster center in the clustering process as the second measure of the difference optimization factor; normalize the product of the first measure of the difference optimization factor and the second measure of the difference optimization factor to obtain the corresponding normalized value as the difference optimization factor between the historical temperature monitoring time series data window and the historical temperature monitoring time series data window corresponding to the cluster center in the clustering process; For any of the historical temperature monitoring time series data windows in the clustering process, the Euclidean distance between the historical temperature monitoring time series data window and the historical temperature monitoring time series data window corresponding to the cluster center in the clustering process is used as the first Euclidean distance; the product of the difference optimization factor between the historical temperature monitoring time series data window and the historical temperature monitoring time series data window corresponding to the cluster center in the clustering process and the first Euclidean distance is used as the optimized distance metric between the historical temperature monitoring time series data window in the clustering process and the historical temperature monitoring time series data window corresponding to the cluster center in the clustering process; According to the optimized distance measurement between the historical temperature monitoring time series data window and the historical temperature monitoring time series data window corresponding to the cluster center of the clustering process, The clustering algorithm completes the clustering process and obtains the clustering result of the historical temperature monitoring time series data window.

3. A temperature intelligent control method for supercritical extraction of plant essential oils according to claim 1, characterized in that: The step of obtaining the hidden state of each historical temperature monitoring time series data window according to the clustering result of the historical temperature monitoring time series data window includes: The clustering result of the historical temperature monitoring time series data window is obtained, and the cluster number of the historical temperature monitoring time series data window is used as the hidden state of each historical temperature monitoring time series data window.

4. A temperature intelligent control method for supercritical extraction of plant essential oils according to claim 1, characterized in that: The method of obtaining the state transition probability matrix of the real-time temperature monitoring time series data of the extraction kettle according to the hidden Markov prediction model of the historical temperature monitoring time series data includes: Obtain the hidden Markov model of the historical temperature monitoring time series data, and obtain the real-time temperature monitoring time series data of the extraction kettle; obtain the state transition probability matrix corresponding to the real-time temperature monitoring time series data of the extraction kettle according to the hidden Markov model of the historical temperature monitoring time series data.

5. A temperature intelligent control method for supercritical extraction of plant essential oils according to claim 1, characterized in that: The step of obtaining the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle according to the state transition probability matrix and the state fluctuation evaluation of the clusters in the cluster division result includes: The state transition probability matrix of the real-time temperature monitoring time series data of the extraction kettle is obtained, and the state transition probability of the target state in the state transition probability matrix of the real-time temperature monitoring time series data of the extraction kettle is used as the fluctuation weight; the clustering result of the historical temperature monitoring time series data window of the clustering process is obtained, and the state fluctuation evaluation of each cluster in the clustering result is obtained according to the first fluctuation measure of the historical temperature monitoring time series data window in each cluster; the fluctuation weight of the real-time temperature monitoring time series data of the extraction kettle is weighted summed for the state fluctuation evaluation of all the clusters to obtain the corresponding weighted summation result; the opposite number of the weighted summation result is substituted into the exponential function with the natural constant e as the base, and the corresponding first exponential function result is obtained, and the subtraction result between the constant 1 and the first exponential function result is used as the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle.

6. A temperature intelligent control method for supercritical extraction of plant essential oils according to claim 5, characterized in that: The obtaining of a state fluctuation assessment of each cluster in the cluster division result includes: The clustering result of the historical temperature monitoring time series data window is obtained, and the mean value of the fluctuation calculation of the historical temperature monitoring time series data window in each cluster in the clustering result of the historical temperature monitoring time series data window is used as the state fluctuation evaluation of the cluster.

7. The temperature intelligent control method for supercritical extraction of plant essential oil according to claim 1, characterized in that: The parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle is used to optimize the parameters of the PID control model of the real-time temperature monitoring data of the extraction kettle, including: The integral gain parameter of the real-time temperature PID control model of the extraction kettle and the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle are respectively obtained; a constant 1 is added to the parameter optimization factor of the real-time temperature monitoring time series data of the extraction kettle as a first parameter weight, and the integral gain parameter of the real-time temperature PID control model of the extraction kettle is weighted according to the first parameter weight, and the result is used as the optimized integral gain parameter; the optimized integral gain parameter is used as the real-time integral gain parameter in the real-time temperature PID control model of the extraction kettle, and the PID control model after the parameter optimization is obtained.

8. The temperature intelligent control method for supercritical extraction of plant essential oils according to claim 1, characterized in that: The window division of all historical temperature monitoring time series data to obtain all temperature time series windows includes: The historical temperature monitoring time series data of the extraction kettle and the preset window length are respectively obtained; the historical temperature monitoring time series data of the extraction kettle are traversed continuously with a constant step length of 1 according to the preset window length, and the subsequence contained in each movement of the window of the predicted window length is used as the historical temperature monitoring time series data window corresponding to the center point of the window.

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