Intelligent temperature control system of high-temperature and high-pressure sterilization container

Abstract

The application relates to the technical field of temperature regulation, and provides an intelligent temperature regulation system of a high-temperature high-pressure sterilization container, which comprises a data processor, and the data processor is used for executing the following intelligent temperature regulation strategy: mode decomposition is performed on pressure time sequence data of a current time period of the high-temperature high-pressure sterilization container in a high-temperature high-pressure sterilization stage, low-frequency mode signals, medium-frequency mode signals and high-frequency mode signals are obtained, proportional parameter adjustment coefficients, integral time parameter adjustment coefficients and differential time parameter adjustment coefficients in PID parameters are obtained according to the low-frequency mode signals, the medium-frequency mode signals and the high-frequency mode signals respectively, adjustment of the PID parameters is realized, the PID parameters are adapted to the actual situation of the high-temperature high-pressure sterilization container, the accuracy of PID parameter setting is improved, and then accurate and reliable temperature regulation of the high-temperature high-pressure sterilization container is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of temperature regulation, and particularly relates to an intelligent temperature regulation system of a high-temperature and high-pressure sterilization container. BACKGROUND

[0002] The high-temperature and high-pressure sterilization container is a physical disinfection and sterilization method that uses high temperature and high pressure, and is a commonly used physical disinfection and sterilization method. The sterilization is achieved by using steam and high pressure for a certain period of time. Only when the temperature reaches a certain requirement and is maintained for a period of time, can the sterilization be effective. However, too high temperature can cause damage to the sterilization container, accelerate the aging and deformation of the material, and affect the service life. At the same time, it can also cause the pressure in the sterilization container to rise sharply, exceeding the limit of the container. And too low temperature is not enough to achieve sterilization of the container, which has a use risk. Therefore, controlling the temperature during the high-temperature and high-pressure sterilization process is an important factor to ensure the sterilization effect of the sterilization container.

[0003] The existing temperature control of the high-temperature and high-pressure sterilization process relies on monitoring the temperature of the sterilization process of the container, analyzing the sterilization effect of the container, and thus obtaining the optimal sterilization temperature curve. Based on the obtained sterilization temperature curve, the PID temperature control method is used to realize the regulation and control of the temperature of the container. In the PID temperature control, the PID parameters are usually set artificially, or only set according to the temperature change of the container in the sterilization process, which may not be suitable for the actual situation of the high-temperature and high-pressure sterilization container, thereby causing inaccurate setting of the PID parameters and affecting the accurate and reliable regulation and control of the temperature of the high-temperature and high-pressure sterilization container. SUMMARY

[0004] In order to solve the technical problem of inaccurate setting of the PID parameters in the process of regulating and controlling the temperature of the high-temperature and high-pressure sterilization container by using the PID temperature control method, the purpose of the present application is to provide an intelligent temperature regulation system of a high-temperature and high-pressure sterilization container, and the technical scheme adopted is as follows:

[0005] The present application provides an intelligent temperature regulation system of a high-temperature and high-pressure sterilization container, comprising a data processor, which is used to execute the following intelligent temperature regulation strategy:

[0006] The pressure time series data of the high-temperature and high-pressure sterilization container in the current time period in the high-temperature and high-pressure sterilization stage is subjected to modal decomposition, and low-frequency modal signals, medium-frequency modal signals and high-frequency modal signals are obtained;

[0007] The stability of the low-frequency modal signals is determined, and the proportional parameter adjustment coefficient is obtained in combination with the association with the temperature time series data of the current time period, so as to adjust the proportional parameter in the PID parameter;

[0008] According to the difference between the frequency distribution of the medium frequency modal signal and the normal distribution corresponding thereto, and in combination with the proportional parameter adjustment coefficient, an integral time parameter adjustment coefficient is obtained to adjust the integral time parameter in the PID parameter;

[0009] According to the fluctuation of the high frequency modal signal and the overall level of the signal, in combination with the integral time parameter adjustment coefficient, a differential time parameter adjustment coefficient is obtained to adjust the differential time parameter in the PID parameter.

[0010] In an exemplary embodiment, the process of obtaining the stability of the low frequency modal signal comprises:

[0011] The fluctuation degree of the low frequency modal signal and the first correlation coefficient between the low frequency modal signal and the pressure time series data are obtained.

[0012] According to the fluctuation degree and the first correlation coefficient, the stability of the low frequency modal signal is obtained; the stability is inversely related to the fluctuation degree and positively related to the first correlation coefficient.

[0013] In an exemplary embodiment, the correlation between the low frequency modal signal and the temperature time series data is the second correlation coefficient between the low frequency modal signal and the temperature time series data.

[0014] The process of obtaining the proportional parameter adjustment coefficient comprises:

[0015] The temperature overshoot of the current time period is obtained.

[0016] According to the temperature overshoot and the second correlation coefficient, the temperature credibility of the current time period is obtained; the temperature credibility is positively related to the second correlation coefficient and inversely related to the temperature overshoot.

[0017] The adjustment direction is determined by the size relationship between the temperature at the end of the current time period and the preset temperature, and the proportional parameter adjustment coefficient is obtained in combination with the adjustment degree; the adjustment degree is inversely related to the temperature credibility and the stability.

[0018] In an exemplary embodiment, the adjustment of the proportional parameter in the PID parameter comprises:

[0019] The value 1 is added to the proportional parameter adjustment coefficient, and the proportional parameter of the current time period is multiplied to obtain the proportional parameter of the next time period.

[0020] In an exemplary embodiment, the process of obtaining the integral time parameter adjustment coefficient comprises:

[0021] The frequency distribution curve and the center frequency of the medium frequency modal signal are obtained.

[0022] fitting the frequency distribution curve based on a normal distribution to obtain a fitted normal distribution;

[0023] obtaining a medium frequency signal regularity of the current time period according to the frequency distribution curve and the association of the center frequency with the fitted normal distribution;

[0024] obtaining a reduction amount of the medium frequency signal regularity of the current time period compared with a previous time period thereof;

[0025] obtaining an integral time parameter adjustment coefficient according to the reduction amount and the proportional parameter adjustment coefficient.

[0026] In an exemplary embodiment, the process of obtaining the medium frequency signal regularity comprises:

[0027] obtaining a similarity of the frequency distribution curve and the fitted normal distribution;

[0028] determining a frequency difference of a mean value of the fitted normal distribution and the center frequency;

[0029] obtaining the medium frequency signal regularity according to the similarity, the frequency difference and a standard deviation of the fitted normal distribution; the medium frequency signal regularity is positively correlated with the similarity and is negatively correlated with the frequency difference and the standard deviation.

[0030] In an exemplary embodiment, the process of obtaining the integral time parameter adjustment coefficient according to the reduction amount and the proportional parameter adjustment coefficient comprises:

[0031] calculating a product of a normalized value of the reduction amount and a negative correlation normalized value of the proportional parameter adjustment coefficient as the integral time parameter adjustment coefficient.

[0032] In an exemplary embodiment, the process of obtaining the differential time parameter adjustment coefficient comprises:

[0033] obtaining a reduction amount of a fluctuation degree of the high frequency modal signal of the current time period compared with a previous time period thereof;

[0034] obtaining an adjustment amplitude of a differential time parameter of the current time period according to the reduction amount and the signal overall level; the adjustment amplitude is negatively correlated with the reduction amount and is positively correlated with the signal overall level;

[0035] obtaining a differential time parameter adjustment coefficient according to the adjustment amplitude and the integral time parameter adjustment coefficient; the differential time parameter adjustment coefficient is positively correlated with both the adjustment amplitude and the integral time parameter adjustment coefficient.

[0036] In an example embodiment, the adjusting coefficient of the integral time parameter according to the adjusting amplitude and the integral time parameter adjusting coefficient comprises:

[0037] The product of the normalized value of the adjusting amplitude and the integral time parameter adjusting coefficient is calculated as the differential time parameter adjusting coefficient.

[0038] In an example embodiment, the modal decomposition of the pressure time series data is specifically: a variational modal decomposition (VMD) algorithm is used to decompose the pressure time series data.

[0039] The present application has the following beneficial effects: the present application decomposes the pressure time series data of the high-temperature and high-pressure sterilization container in the current time period in the high-temperature and high-pressure sterilization stage by modal decomposition, obtains low-frequency modal signals, medium-frequency modal signals and high-frequency modal signals, and then determines the proportional parameter, the integral time parameter and the differential time parameter based on the influence of the low-frequency modal signals, the medium-frequency modal signals and the high-frequency modal signals on the proportional parameter, the integral time parameter and the differential time parameter in the PID parameter, respectively, so that the PID parameter is adapted to the actual situation of the high-temperature and high-pressure sterilization container, thereby improving the accuracy of the PID parameter setting, and further realizing accurate and reliable temperature control of the high-temperature and high-pressure sterilization container. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 is a module composition schematic diagram of the intelligent temperature control system of the high-temperature and high-pressure sterilization container provided by an embodiment of the present application;

[0041] Figure 2 is a flowchart of the intelligent temperature control strategy corresponding to the intelligent temperature control system of the high-temperature and high-pressure sterilization container provided by an embodiment of the present application;

[0042] Figure 3 is a stability acquisition flowchart provided by an embodiment of the present application;

[0043] Figure 4 is a proportional parameter adjusting coefficient acquisition flowchart provided by an embodiment of the present application;

[0044] Figure 5 is an integral time parameter adjusting coefficient acquisition flowchart provided by an embodiment of the present application;

[0045] Figure 6 is a medium-frequency signal regularity acquisition flowchart provided by an embodiment of the present application;

[0046] Figure 7 is a differential time parameter adjusting coefficient acquisition flowchart provided by an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to further clarify the technical means and effects taken by the present application to achieve the predetermined inventive objectives, the specific embodiments, structures, features and effects of the present application are described in detail below in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0048] Unless otherwise defined, 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 application belongs. The data information collected by the present application is obtained with the full consent of the authorization.

[0049] The embodiment provides an intelligent temperature regulation system of a high-temperature and high-pressure sterilization container, which is suitable for a high-temperature and high-pressure sterilization container. In a specific application scenario, the required sterilization articles are placed in the high-temperature and high-pressure sterilization container. The sterilization process can be divided into three stages, which are an initial heating stage, a high-temperature and high-pressure sterilization stage and a cooling stage. Among them, the initial heating stage and the cooling stage are in the temperature change stage, and a large amplitude change of the temperature can be allowed in the two stages, so that the traditional PID control strategy can be used to realize temperature control, and the present application does not make specific limitation and description. For the high-temperature and high-pressure sterilization stage, the stability of the temperature environment needs to be ensured to realize the stability of the sterilization effect. Therefore, the present application focuses on the PID temperature regulation of the high-temperature and high-pressure sterilization stage.

[0050] PID temperature regulation is to determine PID parameters by using temperature difference, and the regulation of temperature needs a certain reaction time to be reflected. Because the traditional PID temperature regulation is affected by environmental factors, it is easy to lead to inaccurate temperature difference, and thus inaccurate PID temperature regulation. In the high-temperature and high-pressure sterilization stage of the high-temperature and high-pressure sterilization container, there are often many factors of interference, such as vibration generated by normal operation of the machine equipment, change of the external environment temperature, fluctuation of the true gas pressure and other factors, which can lead to inaccurate acquisition of the temperature difference, and thus incorrect PID temperature regulation. To solve this problem, the present embodiment divides the high-temperature and high-pressure sterilization stage into several time periods, and the PID parameters remain unchanged in each time period. The present application determines the PID parameters of the next time period according to the data information of the current time period, so as to realize real-time dynamic adjustment of the PID parameters. Therefore, the PID parameters of different time periods can be different. In an exemplary embodiment, the high-temperature and high-pressure sterilization stage is equally divided into several time periods, and the length of each time period is set by actual needs, such as 3 minutes.

[0051] The intelligent temperature regulation system of the high-temperature and high-pressure sterilization container provided in the embodiment comprises a pressure sensor, a temperature sensor and a data processor. The pressure sensor and the temperature sensor are connected to the data processor, as shown in Figure 1 The pressure sensor and the temperature sensor are arranged in the high-temperature and high-pressure sterilization container. In an exemplary embodiment, the temperature sensor and the pressure sensor are embedded on the inner wall of the high-temperature and high-pressure sterilization container and are powered by a high-density small-volume battery, such as a button cell. In addition, the temperature sensor and the pressure sensor can be integrated with a wireless communication module for wireless communication with the data processor, thereby eliminating the need for wiring. The pressure sensor is used to detect the pressure of the internal sterilization space of the high-temperature and high-pressure sterilization container, and the temperature sensor is used to detect the temperature of the internal sterilization space of the high-temperature and high-pressure sterilization container. The sampling frequency of the pressure sensor and the temperature sensor is set according to actual needs, such as 0.5 seconds per time. The pressure sensor and the temperature sensor are synchronously collected. Therefore, each time period includes multiple sampling time points (referred to as time points).

[0052] The data processor acquires the pressure and temperature of each time point in each time period in the high-temperature and high-pressure sterilization stage. For any time period, the pressure of the high-temperature and high-pressure sterilization container at each time point in the time period in the high-temperature and high-pressure sterilization stage is acquired, thereby forming the pressure time series data of the time period. Similarly, the temperature of the high-temperature and high-pressure sterilization container at each time point in the time period in the high-temperature and high-pressure sterilization stage is acquired, thereby forming the temperature time series data of the time period.

[0053] The data processor can be a conventional data processing chip, such as a central processing unit (CPU). The data processor performs data processing according to the acquired pressure and temperature to execute the intelligent temperature regulation strategy as shown in Figure 2

[0054] Step 1: modal decomposition is performed on the pressure time series data of the current time period of the high-temperature and high-pressure sterilization container in the high-temperature and high-pressure sterilization stage to obtain a low-frequency modal signal, a medium-frequency modal signal and a high-frequency modal signal;

[0055] Step 2: the stability of the low-frequency modal signal is determined, and the proportional parameter adjustment coefficient is obtained in combination with the association with the temperature time series data of the current time period, so as to adjust the proportional parameter in the PID parameter;

[0056] Step 3: the integral time parameter adjustment coefficient is obtained in combination with the proportional parameter adjustment coefficient according to the difference between the frequency distribution of the medium-frequency modal signal and the normal distribution corresponding thereto, so as to adjust the integral time parameter in the PID parameter;

[0057] ​Step 4: Based on the fluctuation of the high-frequency modal signal and the overall signal level, and combined with the integral time parameter adjustment coefficient, obtain the derivative time parameter adjustment coefficient to adjust the derivative time parameter in the PID parameters.

[0058] The following detailed explanation of each step, in conjunction with the accompanying drawings, is provided.

[0059] Step 1: Perform modal decomposition on the pressure time series data of the high-temperature and high-pressure sterilization container during the current time period of the high-temperature and high-pressure sterilization stage to obtain low-frequency mode signals, medium-frequency mode signals and high-frequency mode signals.

[0060] Obtain the pressure time series data and temperature time series data of the high-temperature and high-pressure sterilization container during the high-temperature and high-pressure sterilization stage.

[0061] Modal decomposition is performed on the pressure time-series data to obtain low-frequency, mid-frequency, and high-frequency mode signals. In an exemplary embodiment, the variational mode decomposition (VMD) algorithm is used to perform modal decomposition on the pressure time-series data. Fluctuations in the high-frequency mode signal reflect temperature disturbances during the high-temperature, high-pressure sterilization stage, such as vibration and noise generated by the equipment. The mid-frequency mode signal reflects the main dynamic response characteristics of the high-temperature, high-pressure sterilization stage, such as the rate of pressure change within the container and the response speed of the sterilization reaction. Changes in the low-frequency mode signal reflect the steady-state characteristics of the high-temperature, high-pressure sterilization stage, demonstrating the trend of pressure changes. Different decomposed signals are used to reflect different PID parameter controls.

[0062] It should be understood that, to further facilitate data processing, this embodiment can also normalize the pressure time series data, low-frequency modal signals, medium-frequency modal signals, high-frequency modal signals, and temperature time series data to eliminate dimensions. In an exemplary embodiment, a maximum-minimum value normalization method can be used. Taking pressure time series data as an example, the maximum and minimum values ​​in the pressure time series data are obtained, and then the maximum-minimum value normalization method is used to normalize the pressure at each time point in the pressure time series data.

[0063] Step 2: Determine the stability of the low-frequency mode signal, and combine it with the temperature time series data of the current time period to obtain the proportional parameter adjustment coefficient, so as to adjust the proportional parameter in the PID parameters.

[0064] In the autoclave, the low-frequency modal signal mainly reflects the long-term trend of temperature and pressure. At this time, the proportional coefficient Kp in the PID parameter is mainly used to adjust the response of the system to the large temperature deviation. When the low-frequency modal signal shows that the temperature is lower than the set value and changes slowly, increasing the proportional coefficient Kp can speed up the heating speed of the system, so that the temperature approaches the set value faster. Conversely, when the low-frequency modal signal shows that the temperature is close to or exceeds the set value, reducing the proportional coefficient Kp can avoid overshoot caused by excessive adjustment, and keep the system stable.

[0065] When the low-frequency modal signal presents a stable trend, it means that the pressure in the current time period is basically stable, and the adjustment of the temperature will change the pressure. At this time, there is no need to adjust the temperature. At the same time, the low-frequency modal signal is compared with the pressure time series data to determine whether the actual pressure in the current time period and the low-frequency modal signal obtained by the current decomposition have high consistency. If they show strong consistency, it means that the authenticity of the pressure data in the current time period is high, and the low-frequency modal signal can be used to judge the stability of the monitored pressure data. If the pressure data has high stability, the authenticity of the data is higher, and the control range of the PID parameter is smaller. The current PID parameter can well meet the high-temperature and high-pressure sterilization process.

[0066] First, the stability of the low-frequency modal signal is obtained, as shown in Figure 3 A specific process for obtaining the stability is as follows:

[0067] Step 21: Obtain the fluctuation degree of the low-frequency modal signal, and the first correlation coefficient of the low-frequency modal signal and the pressure time series data.

[0068] The fluctuation degree of the low-frequency modal signal is obtained. In this embodiment, the standard deviation is used to represent the fluctuation degree, so the standard deviation of the low-frequency modal signal is obtained to represent the fluctuation degree, or the consistency, of the low-frequency modal signal.

[0069] The first correlation coefficient of the low-frequency modal signal and the pressure time series data is obtained. In an exemplary embodiment, the DTW (Dynamic Time Warping) distance of the low-frequency modal signal and the pressure time series data is obtained, and then the DTW distance is negatively correlated and normalized to obtain the first correlation coefficient of the low-frequency modal signal and the pressure time series data.

[0070] Step 22: Obtain the stability of the low-frequency modal signal according to the fluctuation degree and the first correlation coefficient.

[0071] The higher the fluctuation degree of the low-frequency modal signal is, the worse the stability of the low-frequency modal signal is, and thus the two are inversely related. The greater the first correlation coefficient between the low-frequency modal signal and the pressure time series data is, the higher the consistency between the actual pressure in the current time period and the low-frequency modal signal obtained by the current decomposition is, and the stronger the stability of the low-frequency modal signal is, and thus the stability is positively related to the first correlation coefficient.

[0072] In an exemplary embodiment, a specific quantification of the stability of the low-frequency modal signal is given as follows:

[0073] a = exp(-σ(P1)) × exp(-DTW(P1, P));

[0074] wherein a represents the stability of the low-frequency modal signal, P1 represents the low-frequency modal signal, σ(P1) represents the standard deviation of the low-frequency modal signal, exp represents an exponential function with a natural constant as the base, P represents the pressure time series data, DTW(P1, P) represents the DTW distance between the low-frequency modal signal and the pressure time series data, exp(-DTW(P1, P)) is a negative correlation normalization of DTW(P1, P), and the negative correlation normalization in the present embodiment can all be in this manner, and thus exp(-DTW(P1, P)) represents the first correlation coefficient between the low-frequency modal signal and the pressure time series data.

[0075] If there is a large fluctuation in the pressure in the current time period, the next time period of the current time period can appropriately increase the regulation amplitude; if the pressure is maintained stable, the regulation of the temperature needs to be reduced, because if the temperature is greatly regulated at this time, it will lead to a quality problem due to a sharp temperature change in the closed sterilization container. Therefore, the regulation amplitude of the PID temperature control needs to be synchronized with the pressure. However, in the PID parameter regulation process, although the PID parameter does not change, the normal regulation change of the temperature will also occur in the process, the change of the temperature affects the stability of the pressure, thereby leading to inaccurate judgment of the pressure fluctuation, and the small pressure fluctuation in the current time period does not necessarily reflect the abnormality of the temperature, and the temperature regulation needs to be performed.

[0076] Based on the normal regulation process, the change of the temperature should be consistent with the change of the pressure in the container at this time, and thus the regulation of the temperature tends to be stable; if the change of the temperature is inconsistent with the change of the pressure, the regulation amplitude of the PID parameter needs to be adjusted to increase the regulation amplitude of the temperature, so as to accelerate the reaching of the stable state of the regulation, for example, if the change of the temperature is completely opposite to the change of the pressure, the PID parameter can be boldly adjusted.

[0077] Then, the association between the low-frequency modal signal of the current time period and the temperature time series data of the current time period is obtained, and in an exemplary embodiment, the association between the low-frequency modal signal and the temperature time series data is specifically the second correlation coefficient of the low-frequency modal signal and the temperature time series data. As described above for the first correlation coefficient, the DTW distance between the low-frequency modal signal and the temperature time series data is obtained, and then the DTW distance is negatively correlated and normalized to obtain the second correlation coefficient of the low-frequency modal signal and the temperature time series data. The greater the second correlation coefficient, the more similar the changes of the low-frequency modal signal and the temperature time series data, indicating that the change of the temperature is a normal change, and the container is in a balanced state.

[0078] Finally, based on the stability of the low-frequency modal signal and the association with the temperature time series data of the current time period, a proportional parameter adjustment coefficient is obtained. As shown in Figure 4 , a specific process for obtaining the proportional parameter adjustment coefficient is given:

[0079] Step 23: Obtain the temperature overshoot of the current time period.

[0080] The temperature overshoot of the current time period is obtained. The temperature overshoot is a very important parameter in temperature control, which refers to the maximum value of the temperature exceeding the set value as the measurement standard, and the amount exceeding the set value is the overshoot. The calculation method can be: calculate the difference between the maximum temperature in the current time period and the target temperature set value (i.e. the preset temperature, which specifically refers to the theoretical temperature value of the high-temperature high-pressure sterilization stage), and then divide by the target temperature set value to obtain the percentage, which is the temperature overshoot. The temperature overshoot refers to the maximum deviation percentage of the actual temperature value exceeding the set value. Although theoretically, the temperature overshoot has the possibility of being greater than 100%, it is impossible in actual engineering, because it means an extremely serious fault. Therefore, this embodiment does not consider the case where the temperature overshoot is greater than or equal to 100%, and if this case exists, the subsequent steps are not executed, and the system immediately alarms. The greater the temperature overshoot of the current time period, the more the temperature control needs to be limited in the PID temperature control process of the next time period, and at this time, the proportional parameter needs to be quickly adjusted in the opposite direction. Moreover, if the maximum temperature in the current time period is less than the target temperature set value, the numerical range of the temperature overshoot is -1 to 0, and it should be understood that if the maximum temperature in the current time period is less than the target temperature set value, the proportional parameter can be further increased.

[0081] Step 24: Obtain the temperature credibility of the current time period according to the temperature overshoot and the second correlation coefficient.

[0082] The greater the temperature overshoot of the current time period, the less credible the temperature of the current time period, and the lower the temperature credibility. The greater the second correlation coefficient of the low-frequency modal signal and the temperature time series data, the more similar the changes of the low-frequency modal signal and the temperature time series data, which indicates that the change of the temperature is normal, and the higher the temperature credibility of the current time period, and the temperature credibility is positively correlated with the second correlation coefficient. The temperature credibility is used to reflect the accuracy of the temperature, and also reflects the accuracy of the PID parameters in the regulation process.

[0083] In an exemplary embodiment, a specific quantification of the temperature credibility of the current time period is given as follows:

[0084] b = (1 - T over ) × exp(-DTW(P1, T));

[0085] Wherein, b represents the temperature credibility of the current time period, T over represents the temperature overshoot of the current time period, T represents the temperature time series data of the current time period, DTW(P1, T) represents the DTW distance of the low-frequency modal signal and the temperature time series data, and exp(-DTW(P1, T)) represents the second correlation coefficient of the low-frequency modal signal and the temperature time series data.

[0086] Step 25: Determine the adjustment direction according to the size relationship between the temperature at the end of the current time period and the preset temperature, and obtain the proportional parameter adjustment coefficient combined with the adjustment degree.

[0087] Determine the temperature at the end of the current time period, and determine the adjustment direction according to the size relationship between the temperature at the end of the current time period and the preset temperature, wherein when the temperature at the end of the current time period is higher than the preset temperature, the proportional parameter needs to be adjusted, and the adjustment direction is to reduce the proportional parameter; when the temperature at the end of the current time period is lower than the preset temperature, the proportional parameter needs to be increased, and the adjustment direction is to increase the proportional parameter.

[0088] According to the temperature credibility of the current time period and the stability of the low-frequency modal signal of the current time period, the adjustment degree of the current time period is obtained. The higher the temperature credibility, the less the proportional parameter needs to be adjusted, and the higher the stability of the low-frequency modal signal, the less the proportional parameter needs to be adjusted. Therefore, the adjustment degree is inversely related to the temperature credibility and the stability.

[0089] In an exemplary embodiment, a specific quantification of the proportional parameter adjustment coefficient is given as follows:

[0090] q = sign(T theory - T end ) × (1 - b × a);

[0091] wherein q represents a proportional parameter adjustment coefficient, T end represents the temperature at the end of the current time period; T theory represents a preset temperature; sign is a sign function, sign(T theory -T end represents the adjustment direction of the PID parameter, when T theory -T end is greater than 0, sign(T theory -T end ) is equal to 1, when T theory -T end is less than 0, sign(T theory -T end ) is equal to -1, and when T theory -T end is equal to 0, sign(T theory -T end ) is equal to 0. (1-bxa) represents the adjustment degree.

[0092] According to the proportional parameter adjustment coefficient of the current time period and the proportional parameter of the current time period, the proportional parameter in the PID parameter is adjusted to obtain the proportional parameter of the next time period, specifically as follows:

[0093] KP' = KP x (1+q);

[0094] wherein KP' is the proportional parameter of the next time period, and KP is the proportional parameter of the current time period.

[0095] The different parameters of the PID have different degrees of influence on the temperature regulation, and a slight change in the proportional parameter KP has a greater impact on the temperature than other PID parameters. At the same time, the adjustment of one parameter will also affect the change of the remaining parameters. Therefore, there is a correlation between the actual PID parameters. After obtaining the proportional parameter by the above method, it is judged whether there is a large amplitude change in the proportional parameter, and then the adjustment of the remaining PID parameters should be appropriately reduced to avoid large temperature regulation changes, sharp fluctuations in temperature, and obvious parameter overshoot phenomenon, missing the optimal control temperature corresponding to the parameter. Therefore, if the adjustment amplitude of the proportional parameter exceeds 2% (i.e., the adjustment proportion of the proportional parameter is greater than 2%), it is considered that there is a large amplitude adjustment, and at this time, correction is needed on the basis of the adjustment of the remaining PID parameters. It should be understood that the above judgment can also not be performed, and after the adjustment of the proportional parameter, the adjustment of the other PID parameters is continued directly.

[0096] Step 3: According to the difference between the frequency distribution of the intermediate frequency modal signal and the normal distribution corresponding thereto, and in combination with the proportional parameter adjustment coefficient, an integral time parameter adjustment coefficient is obtained to adjust the integral time parameter in the PID parameter.

[0097] The intermediate frequency modal signal usually reflects periodic fluctuations of temperature and pressure, which are caused by periodic operation of the heating element, pulsation of the fluid or other periodic interference. In the sterilization container, these periodic fluctuations cause regular deviations of temperature and pressure around the set value. The role of the integral time parameter is to eliminate the steady-state error of the system by adjusting the accumulation of deviations.

[0098] In an exemplary embodiment, as shown in Figure 5 A specific acquisition process of the integral time parameter adjustment coefficient is given as follows:

[0099] Step 31: Obtain the frequency distribution curve of the intermediate frequency modal signal and the center frequency.

[0100] The intermediate frequency modal signal is converted to the frequency domain to obtain a frequency graph, thereby obtaining the frequency distribution curve of the intermediate frequency modal signal. At the same time, the center frequency of the frequency distribution curve is obtained.

[0101] Step 32: Fit the frequency distribution curve based on the normal distribution to obtain a fitted normal distribution.

[0102] The frequency distribution curve of the intermediate frequency modal signal is fitted based on the normal distribution to obtain a fitted normal distribution. Since the center frequency belongs to the middle position of the frequency distribution curve under normal circumstances, the number of center frequencies has a concentration, and the number of frequencies gradually decreases as the frequency changes to both sides, thus conforming to the normal distribution characteristics. If there is an anomaly in the temperature of the current time period, it will be reflected in the frequency distribution curve of the intermediate frequency modal signal, and the pressure in the container will be affected. The anomaly will destroy the normal distribution and cause a significant change in frequency, i.e., a significant multi-peak phenomenon, so there will be obvious periodic fluctuations.

[0103] After obtaining the fitted normal distribution, the fitting parameters of the fitted normal distribution are obtained, including the mean of the fitted normal distribution and the standard deviation of the fitted normal distribution. The smaller the standard deviation of the fitted normal distribution, the more concentrated the frequency distribution, and the closer the mean of the fitted normal distribution to the center frequency, indicating that the pressure data of the current time period reflects a normal high-temperature high-pressure sterilization process, and the integral time parameter does not need to be adjusted. Therefore, the smaller the standard deviation of the fitted normal distribution, the more regular the frequency of the intermediate frequency modal signal, i.e., the higher the regularity of the intermediate frequency signal, and the two are positively correlated.

[0104] Step 33: Obtain the regularity of the intermediate frequency signal of the current time period according to the correlation between the frequency distribution curve and the center frequency and the fitted normal distribution.

[0105] The regularity of the intermediate frequency signal of the current time period is obtained according to the correlation between the frequency distribution curve and the center frequency and the fitted normal distribution. In an exemplary embodiment, as shown inFigure 6 An example process of obtaining the regularity of the intermediate frequency signal is given as follows:

[0106] Step 331: Obtain the similarity between the frequency distribution curve and the fitted normal distribution.

[0107] In an example embodiment, the DTW distance between the frequency distribution curve and the fitted normal distribution is obtained, and then the DTW distance is negatively correlated and normalized to obtain the similarity between the frequency distribution curve and the fitted normal distribution. The higher the similarity between the frequency distribution curve and the fitted normal distribution, the higher the regularity of the frequency of the intermediate frequency modal signal, i.e., the higher the regularity of the intermediate frequency signal, and the two are positively correlated.

[0108] Step 332: Determine the frequency difference between the mean of the fitted normal distribution and the center frequency.

[0109] The absolute value of the difference between the mean of the fitted normal distribution and the center frequency is calculated as the frequency difference between the two. The greater the frequency difference between the mean of the fitted normal distribution and the center frequency, the greater the difference between the frequency distribution curve and the fitted normal distribution, indicating that the frequency of the intermediate frequency modal signal is less regular, i.e., the lower the regularity of the intermediate frequency signal, and the two are inversely related.

[0110] Step 333: Obtain the regularity of the intermediate frequency signal according to the similarity, the frequency difference, and the standard deviation of the fitted normal distribution.

[0111] Based on the above analysis, the regularity of the intermediate frequency signal is obtained according to the similarity between the frequency distribution curve and the fitted normal distribution, the frequency difference between the mean of the fitted normal distribution and the center frequency, and the standard deviation of the fitted normal distribution. In an example embodiment, a quantitative method for the regularity of the intermediate frequency signal is given as follows:

[0112] ft = R x exp(-|D-Zμ| x Zσ);

[0113] Where ft represents the regularity of the intermediate frequency signal in the current time period, R represents the similarity between the frequency distribution curve and the fitted normal distribution, D represents the center frequency, Zμ represents the mean of the fitted normal distribution, and Zσ represents the standard deviation of the fitted normal distribution.

[0114] The regularity of the intermediate frequency signal in the current time period reflects the necessity of adjusting the integral time parameter, and the greater the regularity of the intermediate frequency signal, the less the necessity of adjusting the integral time parameter.

[0115] Step 34: Obtain the reduction of the regularity of the intermediate frequency signal in the current time period compared to the previous time period.

[0116] In this way, the regularity of the intermediate frequency signal of the current time period is obtained, and the regularity of the intermediate frequency signal of the previous time period of the current time period is obtained in the same way.

[0117] The reduction amount of the regularity of the intermediate frequency signal of the current time period compared with the previous time period thereof is obtained, that is, the regularity of the intermediate frequency signal of the previous time period of the current time period is subtracted from the regularity of the intermediate frequency signal of the current time period, and the difference value is the reduction amount of the regularity of the intermediate frequency signal of the current time period compared with the previous time period thereof. Then, the reduction amount can be a positive value, a negative value or 0. When the reduction amount is a positive value, it indicates that the regularity of the intermediate frequency signal of the current time period is less than that of the previous time period thereof, the regularity of the intermediate frequency signal is in a decreasing trend, the necessity of adjusting the integral time parameter is greater, and the greater the value of the positive number, the greater the necessity of adjusting the integral time parameter, and the greater the integral time parameter adjustment coefficient, so as to reduce the periodic signal fluctuation and weaken the influence of the integral time parameter. When the reduction amount is a negative value, it indicates that the regularity of the intermediate frequency signal of the current time period is greater than that of the previous time period thereof, the regularity of the intermediate frequency signal is in an increasing trend, the necessity of adjusting the integral time parameter is smaller, and the smaller the value of the negative number, the smaller the necessity of adjusting the integral time parameter, and the smaller the integral time parameter adjustment coefficient. When the reduction amount is 0, it indicates that the regularity of the intermediate frequency signal of the current time period is equal to that of the previous time period thereof, and the regularity of the intermediate frequency signal is unchanged.

[0118] It should be understood that if the current time period is the first time period in time sequence, there is no time period before it, and therefore the reduction amount of the regularity of the intermediate frequency signal can not be calculated, so that the integral time parameter adjustment coefficient of the second time period is not obtained according to the first time period, that is, the integral time parameters of the first time period and the second time period in time sequence are not adjusted; or the regularity of the intermediate frequency signal of the time period before the first time period is directly set as an initial empirical value, for example, 0.7, so as to calculate the reduction amount of the two.

[0119] Step 35: obtaining the integral time parameter adjustment coefficient according to the reduction amount and the proportional parameter adjustment coefficient.

[0120] The positive or negative of the reduction amount determines the positive or negative of the integral time parameter adjustment coefficient, that is, determines the adjustment direction of the integral time parameter. Since the proportional parameter adjustment coefficient will affect the adjustment of the integral time parameter and the differential time parameter, according to the regularity reduction amount of the intermediate frequency signal in the current time period and the proportional parameter adjustment coefficient in the current time period, the integral time parameter adjustment coefficient of the next time period is obtained, so as to adjust the integral time parameter of the next time period. The product of the normalized value of the reduction amount and the negative correlation normalized value of the proportional parameter adjustment coefficient is taken as the integral time parameter adjustment coefficient. Since it can be known through the above calculation manner that the numerical range of the intermediate frequency signal regularity is 0-1, then the numerical range of the reduction amount of the intermediate frequency signal regularity is -1 to 1, and the normalized value of the reduction amount is itself.

[0121] In an exemplary embodiment, a specific quantization manner of the integral time parameter of the next time period is given as follows:

[0122] TI' = TI x (1 + p x (1 - |q|));

[0123] Wherein, TI' is the integral time parameter of the next time period, TI is the integral time parameter of the current time period, p represents the reduction amount of the intermediate frequency signal regularity of the current time period compared with the previous time period, (1 - |q|) represents the negative correlation normalized value of the proportional parameter adjustment coefficient. p x (1 - |q|) is the integral time parameter adjustment coefficient.

[0124] It should be noted that during the temperature regulation process in the current time period, the pressure data has normal fluctuations, at this time, these factors do not need to be considered, because the influence of these factors is considered in the adjustment of the proportional parameter, so that the adjustment of the proportional parameter occupies the main part, and the adjustment of the integral time parameter is realized through the adjustment of the proportional parameter, and the adjustment of the differential time parameter is the same.

[0125] Step 4: According to the fluctuation of the high frequency modal signal and the overall level of the signal, combined with the integral time parameter adjustment coefficient, the differential time parameter adjustment coefficient is obtained to adjust the differential time parameter in the PID parameter.

[0126] The high frequency modal signal mainly contains high frequency noise and rapid fluctuation information of temperature and pressure, which may be caused by random disturbance of the system, turbulent flow of the fluid or measurement noise. The role of the differential time parameter is to predict the future trend of the system and adjust the control amount in advance to suppress rapid changes. Therefore, the more significant the high frequency modal signal is, the greater the influence of the monitoring noise is, and the differential time parameter needs to be increased to suppress the temperature data regulation in advance.

[0127] In an exemplary embodiment, a specific quantization manner of the differential time parameter of the next time period is given as follows: Figure 7As shown, a specific acquisition process of the differential time parameter adjustment coefficient is given as follows:

[0128] Step 41: Obtain the reduction of the fluctuation degree of the high-frequency modality signal of the current time period compared with the previous time period.

[0129] The fluctuation degree of the high-frequency modality signal of the current time period is obtained, and the standard deviation is used to represent the fluctuation degree, i.e., the standard deviation of the high-frequency modality signal of the current time period is obtained, and the standard deviation of the high-frequency modality signal of the previous time period of the current time period is obtained. Then, the standard deviation of the high-frequency modality signal of the previous time period of the current time period is subtracted from the standard deviation of the high-frequency modality signal of the current time period, and the difference is the reduction of the fluctuation degree of the high-frequency modality signal of the current time period compared with the previous time period. The reduction can be a positive value, a negative value, or 0. When the reduction is a positive value, it indicates that the fluctuation degree of the high-frequency modality signal of the current time period is less than that of the previous time period, the fluctuation degree of the high-frequency modality signal is in a decreasing trend, and the greater the positive value, the smaller the fluctuation degree of the high-frequency modality signal of the current time period compared with that of the previous time period, and the more obvious the decreasing trend of the fluctuation degree of the high-frequency modality signal, which indicates that the interference on the high-frequency modality signal is smaller, and the adjustment range of the differential time parameter of the current time period is smaller, and the differential time parameter does not need to be adjusted. When the reduction is a negative value, it indicates that the fluctuation degree of the high-frequency modality signal of the current time period is greater than that of the previous time period, the fluctuation degree of the high-frequency modality signal is in an increasing trend, and the smaller the negative value, the greater the fluctuation degree of the high-frequency modality signal of the current time period compared with that of the previous time period, and the more obvious the increasing trend of the fluctuation degree of the high-frequency modality signal, which indicates that the interference on the high-frequency modality signal is greater, and the adjustment range of the differential time parameter of the current time period is greater, and the differential time parameter needs to be adjusted. Therefore, the adjustment range of the differential time parameter of the current time period is inversely related to the reduction of the fluctuation degree of the high-frequency modality signal.

[0130] Step 42: Obtain the adjustment range of the differential time parameter of the current time period according to the reduction and the signal overall level.

[0131] The signal overall level of the high-frequency modality signal of the current time period is obtained, and in an exemplary embodiment, the average value of the high-frequency modality signal of the current time period is calculated as the signal overall level. The smaller the signal overall level, the smaller the interference on the high-frequency modality signal, and the smaller the adjustment range of the differential time parameter of the current time period, and the differential time parameter does not need to be adjusted. The adjustment range of the differential time parameter of the current time period is positively related to the signal overall level.

[0132] In an exemplary embodiment, the quantification of the adjustment amplitude of the differential time parameter of the current time period is given as follows:

[0133]

[0134] wherein w represents the adjustment amplitude of the differential time parameter of the current time period, P3 represents the high-frequency modality signal, σ(P3) front represents the standard deviation of the high-frequency modality signal of the previous time period of the current time period, σ(P3) represents the standard deviation of the high-frequency modality signal of the current time period, E(P3) represents the signal overall level of the high-frequency modality signal of the current time period. sigmoid represents the sigmoid function, since may be positive or negative, and is normalized by the sigmoid function.

[0135] Step 43: obtaining the differential time parameter adjustment coefficient according to the adjustment amplitude and the integral time parameter adjustment coefficient.

[0136] Since in the high-temperature and high-pressure sterilization process, the global stability of the temperature data needs to be ensured first, if the proportional parameter is too small, the system will not respond to the deviation, which may cause the temperature to fail to reach the set value for a long time. At this time, the adjustment effect of the differential time parameter is limited; similarly, the integral action can accumulate and continuously adjust the deviation, and the priority is higher than that of the differential time parameter adjustment. For example, in the holding stage, there may be a small steady-state deviation in the temperature due to system characteristics and external interference. At this time, appropriately reducing the integral time parameter can enhance the integral action, gradually eliminate the deviation, and make the temperature stable around the set value. If the differential time parameter is adjusted at this time, although it can suppress part of the fluctuations, it cannot effectively solve the steady-state error problem. Therefore, the adjustment of the differential time parameter should prioritize the accuracy of the proportional parameter and the integral time parameter.

[0137] According to the adjustment amplitude of the differential time parameter of the current time period and the integral time parameter adjustment coefficient, the differential time parameter adjustment coefficient is obtained, and the differential time parameter adjustment coefficient is positively correlated with the adjustment amplitude and the integral time parameter adjustment coefficient.

[0138] Since the adjustment amplitude of the differential time parameter of the current time period is in the numerical range of 0-1 through the above calculation method, the normalized value of the adjustment amplitude is itself. The product of the adjustment amplitude and the integral time parameter adjustment coefficient is calculated as the differential time parameter adjustment coefficient, which is used to adjust the differential time parameter of the next time period. One specific quantification of the adjustment is given as follows:

[0139] TD' = TD x (1 + w x p x (1 - |q|));

[0140] TD' = TD + w x p x (1-|q|) x (TD-TD'), wherein TD' is the differential time parameter of the next time period, TD is the differential time parameter of the current time period, and w represents the adjustment range of the differential time parameter of the current time period. w x p x (1-|q|) is the differential time parameter adjustment coefficient.

[0141] Therefore, through the above process, the proportional parameter, the integral time parameter and the differential time parameter in the PID parameter of the next time period are obtained, which are brought into the PID control to obtain the temperature regulation value of the next time period, so as to realize the temperature regulation of the next time period. In addition, the PID parameter of the next time period can also be continuously regulated according to the current time period, so as to realize the real-time dynamic adjustment of the PID parameter, and further realize the intelligent dynamic regulation of the temperature.

[0142] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0143] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment mainly describes the difference from other embodiments.

Claims

1. An intelligent temperature control system for a high-temperature, high-pressure sterilization container, characterized in that, Includes a data processor, which is used to execute the following intelligent temperature control strategy: Modal decomposition was performed on the pressure time series data of the high-temperature and high-pressure sterilization container during the high-temperature and high-pressure sterilization stage to obtain low-frequency mode signals, medium-frequency mode signals and high-frequency mode signals; The stability of the low-frequency mode signal is determined, and combined with the correlation with the temperature time series data of the current time period, the proportional parameter adjustment coefficient is obtained to adjust the proportional parameter in the PID parameters; Based on the difference between the frequency distribution of the intermediate frequency mode signal and its corresponding normal distribution, and in conjunction with the proportional parameter adjustment coefficient, the integral time parameter adjustment coefficient is obtained to adjust the integral time parameter in the PID parameters; Based on the fluctuations of the high-frequency modal signal and the overall signal level of the high-frequency modal signal, and combined with the integral time parameter adjustment coefficient, the derivative time parameter adjustment coefficient is obtained to adjust the derivative time parameter in the PID parameters.

2. The intelligent temperature control system for a high-temperature, high-pressure sterilization container as described in claim 1, characterized in that, The process of obtaining the stability of the low-frequency mode signal includes: The fluctuation level of the low-frequency modal signal and the first correlation coefficient between the low-frequency modal signal and the pressure time series data are obtained. The stability of the low-frequency mode signal is obtained based on the fluctuation level and the first correlation coefficient; the stability is inversely correlated with the fluctuation level and positively correlated with the first correlation coefficient.

3. The intelligent temperature control system for a high-temperature, high-pressure sterilization container as described in claim 1, characterized in that, The correlation between the low-frequency modal signal and the temperature time series data is the second correlation coefficient between the low-frequency modal signal and the temperature time series data; The process of obtaining the proportional parameter adjustment coefficient includes: Get the temperature overshoot for the current time period; The temperature confidence level for the current time period is obtained based on the temperature overshoot and the second correlation coefficient; the temperature confidence level is positively correlated with the second correlation coefficient and negatively correlated with the temperature overshoot. The adjustment direction is determined by the relationship between the temperature at the end of the current time period and the preset temperature, and the proportional parameter adjustment coefficient is obtained by combining the adjustment degree; the adjustment degree is inversely correlated with the temperature reliability and the stability.

4. The intelligent temperature control system for a high-temperature, high-pressure sterilization container as described in claim 3, characterized in that, The adjustment of the proportional parameter in the PID parameters includes: Add the value 1 to the proportional parameter adjustment coefficient, and multiply by the proportional parameter of the current time period to obtain the proportional parameter of the next time period.

5. The intelligent temperature control system for a high-temperature, high-pressure sterilization container as described in claim 1, characterized in that, The process of obtaining the integral time parameter adjustment coefficient includes: Obtain the frequency distribution curve and center frequency of the intermediate frequency mode signal; The frequency distribution curve is fitted based on a normal distribution to obtain a fitted normal distribution; Based on the frequency distribution curve and the correlation between the center frequency and the fitted normal distribution, the regularity of the mid-frequency signal in the current time period is obtained; Obtain the regular decrease in the intermediate frequency signal in the current time period compared to the previous time period; The integral time parameter adjustment coefficient is obtained based on the reduction amount and the proportional parameter adjustment coefficient.

6. The intelligent temperature control system for a high-temperature, high-pressure sterilization container as described in claim 5, characterized in that, The process of obtaining the regularity of the intermediate frequency signal includes: Obtain the similarity between the frequency distribution curve and the fitted normal distribution; Determine the frequency difference between the mean of the fitted normal distribution and the center frequency; The regularity of the intermediate frequency signal is obtained based on the similarity, frequency difference, and standard deviation of the fitted normal distribution; the regularity of the intermediate frequency signal is positively correlated with the similarity and negatively correlated with the frequency difference and the standard deviation.

7. The intelligent temperature control system for a high-temperature, high-pressure sterilization container as described in claim 5, characterized in that, The step of obtaining the integral time parameter adjustment coefficient based on the reduction amount and the proportional parameter adjustment coefficient includes: The product of the normalized value of the reduction and the negatively correlated normalized value of the proportional parameter adjustment coefficient is calculated and used as the integral time parameter adjustment coefficient.

8. The intelligent temperature control system for a high-temperature, high-pressure sterilization container as described in claim 1, characterized in that, The process of obtaining the differential time parameter adjustment coefficient includes: Obtain the amount of reduction in the fluctuation of the high-frequency modal signal in the current time period compared to the previous time period; The adjustment range of the differential time parameter for the current time period is obtained based on the reduction amount and the overall signal level; the adjustment range is inversely correlated with the reduction amount and positively correlated with the overall signal level. The differential time parameter adjustment coefficient is obtained based on the adjustment amplitude and the integral time parameter adjustment coefficient; the differential time parameter adjustment coefficient is positively correlated with both the adjustment amplitude and the integral time parameter adjustment coefficient.

9. The intelligent temperature control system for a high-temperature, high-pressure sterilization container as described in claim 8, characterized in that, Based on the adjustment amplitude and the integral time parameter adjustment coefficient, the derivative time parameter adjustment coefficient is obtained, including: The product of the normalized value of the adjustment amplitude and the integral time parameter adjustment coefficient is calculated and used as the differential time parameter adjustment coefficient.

10. The intelligent temperature control system for a high-temperature, high-pressure sterilization container as described in claim 1, characterized in that, The modal decomposition of the pressure time series data specifically involves using the variational mode decomposition (VMD) algorithm to perform modal decomposition on the pressure time series data.

Images