A temperature gradient intelligent control method for a traditional Chinese medicine ointment stirring process

Abstract

The present application relates to the technical field of traditional Chinese medicine ointment stirring, in particular to a temperature gradient intelligent control method for traditional Chinese medicine ointment stirring process, comprising: in the constant temperature stirring reaction stage in the traditional Chinese medicine ointment stirring process, determining the thermal imbalance performance value of each measuring point at the current time, then determining the temperature field confusion of each temperature control area at the current time, so as to determine the area comprehensive temperature control instability degree of each temperature control area at the current time, then determining the interval coupling correction factor at the current time, so as to obtain the control weight of each temperature control area at the current time, and then obtaining the effective temperature of each temperature control area at the current time, using a fuzzy controller to output the temperature control instruction of each temperature control area in real time. The present application improves the accuracy, stability and anti-interference ability of temperature gradient control in the traditional Chinese medicine ointment stirring process by adopting a partition weighting fuzzy control strategy.

Description

Technical Field

[0001] This invention relates to the field of traditional Chinese medicine ointment stirring technology, specifically to a method for intelligent temperature gradient control during the stirring process of traditional Chinese medicine ointment. Background Technology

[0002] Traditional Chinese medicine ointments are a core dosage form for external treatment in TCM, possessing advantages such as long-lasting efficacy, convenient use, and good patient compliance. They are widely used in dermatology, orthopedics, and other fields. Their preparation involves heating, stirring, and boiling, requiring processes such as extraction, concentration, and the addition of excipients to form an ointment. This process involves complex physicochemical changes, and the temperature gradient and stirring parameters directly determine the uniformity, stability, and retention rate of active ingredients in the ointment.

[0003] Existing problems: Modern Chinese medicine ointments have complex compositions (containing herbal extracts, oils, emulsifiers, polymer matrices, etc.), and the system exhibits multiphase, non-homogeneous, and time-varying nonlinear characteristics. High-viscosity ointments are prone to problems such as localized overheating, gelatinization, degradation of active ingredients, and uneven dispersion during stirring. Therefore, the efficacy, stability, and finished product quality of Chinese medicine ointments are highly dependent on the precision of temperature control during the stirring process. However, due to the nonlinearity and time lag of heat conduction caused by the dynamic changes in material viscosity with temperature and shear rate, the strong coupling interference of process parameters such as heating power and stirring speed, the uneven distribution of temperature gradients in the reactor space, and the influence of flow dead zones, improper temperature control can easily occur, leading to problems such as destruction of effective components, uneven mixing, or scorching in the Chinese medicine ointment. Summary of the Invention

[0004] This invention provides a method for intelligent control of temperature gradient during the stirring process of traditional Chinese medicine ointment, in order to solve existing problems.

[0005] The present invention provides a method for intelligent temperature gradient control during the stirring process of a traditional Chinese medicine ointment, which adopts the following technical solution: One embodiment of the present invention provides a method for intelligent control of temperature gradient during the stirring process of traditional Chinese medicine ointment, the method comprising the following steps: During the isothermal stirring reaction stage of the traditional Chinese medicine ointment stirring process, the temperature time sequence of each measuring point in the upper, middle and lower temperature control areas of the stirring vessel, as well as the characterization temperature time sequence of each temperature control area, are obtained. Based on the temperature changes within a local time period in the temperature time series of each measuring point, determine the thermal imbalance performance value of each measuring point at the current moment; based on the inconsistency of the temperatures of all measuring points in the same temperature control area at the same moment, determine the temperature field disorder of each temperature control area at the current moment; based on the thermal imbalance performance value and the magnitude of the temperature field disorder, determine the regional comprehensive temperature control instability of each temperature control area at the current moment. Based on the temperature difference at the same moment in the time series of the characterized temperatures of different temperature control regions, the interval coupling correction factor at the current moment is determined; based on the interval coupling correction factor, the comprehensive temperature control instability of the region is corrected to obtain the control weight of each temperature control region at the current moment. The effective temperature of each temperature control zone at the current moment is obtained by weighting the characteristic temperature with the control weight; based on the real-time effective temperature of each temperature control zone, a fuzzy controller is used to output the temperature control command of each temperature control zone in real time.

[0006] Furthermore, the specific steps for determining the thermal imbalance performance value of each measuring point at the current moment are as follows: Set the current time as the end point, and select a number of consecutive sampling times of a preset window length to form a fixed-length time series analysis window; In the temperature time series of each measuring point, the temperature fluctuation performance value of each measuring point at the current moment is determined based on the temperature change within the time window of the time series analysis. Calculate the absolute value of the difference between the temperature at each moment in the temperature time series of each measuring point and the preset constant temperature target temperature of the temperature control zone where each measuring point is located, and record it as the temperature deviation value of each measuring point at each moment; Based on the magnitude of the temperature deviation value of each measuring point at each moment within the time window of the time series analysis, determine the temperature overshoot performance value of each measuring point at the current moment; The product of the temperature overshoot performance value and the preset overshoot weight is recorded as the first product. The product of the temperature fluctuation problem performance value and the preset fluctuation weight is recorded as the second product. The sum of the first product and the second product is recorded as the thermal imbalance performance value of each measuring point at the current moment.

[0007] Furthermore, the specific steps for determining the temperature fluctuation performance value of each measuring point at the current moment are as follows: In the temperature time series of each measuring point, the standard deviation of all temperatures within the time window of the time series analysis is calculated and recorded as the degree of temperature oscillation of each measuring point at the current moment. Among the temperature difference values ​​of all adjacent moments within the time window of the time series analysis, the number of negative temperature difference values ​​and the number of positive temperature difference values ​​are counted respectively. The normalized value of the maximum value of the two types of values ​​is selected and recorded as the unidirectional temperature variation of each measuring point at the current moment. Calculate the sum of the unidirectional temperature variation of each measuring point at the current moment and the preset constant, and denote it as the first coefficient. Multiply the first coefficient by the temperature oscillation degree of each measuring point at the current moment, and denote it as the temperature fluctuation performance value of each measuring point at the current moment.

[0008] Furthermore, the specific steps for determining the temperature overshoot performance value of each measuring point at the current moment are as follows: For each measuring point, the moment when the temperature deviation value is greater than the preset deviation threshold is recorded as the overshoot moment, and the normalized value of the number of overshoot moments within the time window of the time series analysis is recorded as the temperature overshoot persistence of each measuring point at the current moment. The sum of the temperature overshoot duration of each measuring point at the current moment and the preset constant is recorded as the second coefficient. The product of the second coefficient and the temperature deviation value of each measuring point at the current moment is recorded as the temperature overshoot performance value of each measuring point at the current moment.

[0009] Furthermore, the specific steps for determining the temperature field disorder of each temperature control zone at the current moment are as follows: Within each temperature control region, calculate the standard deviation of the temperature at the same moment in the temperature time series of all measuring points, and denote it as the temperature field disorder of each temperature control region at each moment; Within the time window of the time series analysis, the temperature field disorder time series is constructed by taking the temperature field disorder of each temperature control zone at each moment in chronological order. The least squares method is used to perform linear fitting on the temperature field disorder time series to obtain the temperature field disorder fitting line. The normalized value of the slope of the fitted line of temperature field disorder is calculated and the sum of the slope and the preset constant is denoted as the third coefficient. The product of the third coefficient and the temperature field disorder of each temperature control region at the current moment is denoted as the comprehensive temperature field disorder performance of each temperature control region at the current moment.

[0010] Furthermore, the specific steps for determining the overall temperature control instability of each temperature control zone at the current moment are as follows: Within each temperature control zone, the mean and maximum values ​​of the thermal imbalance performance of all measuring points at the current moment are obtained. The arithmetic mean and maximum value are then calculated to obtain the comprehensive thermal imbalance performance value of each temperature control zone at the current moment. The arithmetic mean of the comprehensive temperature field disorder performance and comprehensive thermal imbalance performance values ​​of each temperature control region at the current moment is used to obtain the regional comprehensive temperature control instability of each temperature control region at the current moment.

[0011] Furthermore, the specific steps for determining the interval coupling correction factor at the current moment are as follows: In the time series of the characterization temperatures of the upper, middle and lower temperature control regions, the difference between the characterization temperature of the lower temperature control region and the characterization temperature of the middle temperature control region at the same time is calculated and recorded as the lower-middle temperature difference at each time. The difference between the characterization temperature of the middle temperature control region and the characterization temperature of the upper temperature control region at the same time is calculated and recorded as the middle-upper temperature difference at each time. Within the time window of the time series analysis, the lower and middle temperature difference time series is constructed by taking the lower and middle temperature difference at each time point in chronological order, and the middle and upper temperature difference time series is constructed by taking the middle and upper temperature difference at each time point in chronological order. Calculate the standard deviation of the lower and middle temperature difference time series, and denote it as the first standard deviation. Calculate the standard deviation of the middle and upper temperature difference time series, and denote it as the second standard deviation. The mean of the first standard deviation and the second standard deviation is denoteed as the temperature gradient instability of the stirred tank at the current moment. Based on the difference between the lower and middle temperature differences and the middle and upper temperature differences at the same time, it is determined that the temperature gradient of the stirred tank is inconsistent at the current time. The sum of the normalized value of the temperature gradient inconsistency of the stirred tank and the preset constant is denoted as the fourth coefficient. The product of the fourth coefficient and the temperature gradient inconsistency of the stirred tank is denoted as the interval coupling correction factor at the current moment.

[0012] Furthermore, the specific steps for determining the inconsistency of the temperature gradient in the stirred tank at the current moment are as follows: Calculate the absolute value of the difference between the lower and middle temperature differences and the middle and upper temperature differences at the same time, and denot it as the temperature gradient inconsistency at each time. The average value of the temperature gradient inconsistency at all times within the time window of the time series analysis is denoted as the temperature gradient inconsistency of the stirred tank at the current time.

[0013] Furthermore, the specific steps for obtaining the control weight of each temperature control zone at the current moment are as follows: Calculate the product of the preset coupling correction weight coefficient and the interval coupling correction factor at the current time, and sum the product with the regional comprehensive temperature control instability of each temperature control region at the current time, and record it as the control weight of each temperature control region at the current time.

[0014] Furthermore, the specific steps for obtaining the effective temperature of each temperature control zone at the current moment are as follows: Within the time window of the time series analysis, the representative temperature of each temperature control region at all times is weighted and averaged according to the control weight of each temperature control region at all times to obtain the effective temperature of each temperature control region at the current time.

[0015] The beneficial effects of the technical solution of the present invention are: In this embodiment of the invention, a partitioned weighted fuzzy control strategy is adopted, dividing the stirred tank into three independent temperature control zones: upper, middle, and lower. During the isothermal stirring reaction stage in the stirring process of the traditional Chinese medicine ointment, based on the temperature changes in local time periods in the temperature time series of each measuring point, and combined with the inconsistency of the temperatures of all measuring points in the same temperature control zone at the same time, the regional comprehensive temperature control instability of each temperature control zone at the current time is determined. Based on the temperature difference performance of the characterized temperature at the same time in the characterized temperature time series of different temperature control zones, the interval coupling correction factor at the current time is determined to correct the regional comprehensive temperature control instability, thereby obtaining the control weight of each temperature control zone at the current time, and thus obtaining the effective temperature of each temperature control zone at the current time. This assigns greater weight to the temperature data at important moments of temperature gradient control and less weight to the temperature data at steady-state moments, making the calculated effective temperature more consistent with the actual temperature gradient change trend, improving the temperature characterization accuracy and temperature control response sensitivity of the isothermal stirring reaction process, suppressing the calculation lag and deviation caused by steady-state redundant data, and ensuring the uniform and stable temperature field of the reaction system. Based on the effective temperature of each temperature control zone at the current moment, a fuzzy controller is used to output temperature control commands for each temperature control zone in real time. Thus, this invention uses a weighted fusion of the average temperature measurements from multiple points in each zone combined with time weights, and uses the weighted effective temperature as the fuzzy control input. This strengthens the dominant role of temperature gradient control during critical process moments while suppressing ineffective adjustments caused by small temperature fluctuations during stable periods, thereby improving the accuracy, stability, and anti-interference capability of temperature gradient control during the stirring process of traditional Chinese medicine ointments. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the steps of an intelligent temperature gradient control method for the stirring process of a traditional Chinese medicine ointment according to the present invention. Figure 2 This is a schematic diagram of the mixing stage of traditional Chinese medicine ointment. Detailed Implementation

[0017] The following description, in conjunction with the accompanying drawings, details the specific scheme of the intelligent temperature gradient control method for the stirring process of traditional Chinese medicine ointment provided by this invention.

[0018] Please see Figure 1 The diagram illustrates a flowchart of a method for intelligent temperature gradient control during the stirring process of a traditional Chinese medicine ointment, according to an embodiment of the present invention. The method includes the following steps: Step S001: During the constant temperature stirring reaction stage of the traditional Chinese medicine ointment stirring process, obtain the temperature time sequence of each measuring point in the upper, middle and lower temperature control areas of the stirring vessel, as well as the characterization temperature time sequence of each temperature control area.

[0019] In this embodiment, the herbal ointment mixing vessel is divided into three temperature control zones along the axial direction: upper, middle, and lower. Each temperature control zone is equipped with an independent adjustable power electric heating module to achieve independent temperature control for each zone. Four high-precision PT100 sensors are evenly distributed around the circumference of the vessel wall in each temperature control zone as measuring points to collect real-time temperature data inside the vessel. The sampling frequency is 1Hz, and the temperature measurement accuracy can reach ±0.1℃.

[0020] It should be noted that in this embodiment, the stirring process of the traditional Chinese medicine ointment is divided into three stages: preheating and fusion stage, constant temperature stirring reaction stage, and cooling and maturation stage. A schematic diagram of the stirring stages of the traditional Chinese medicine ointment is shown below. Figure 2 As shown. The preheating and fusion stage involves only matrix melting, with simple operating conditions, weak nonlinearity, and minimal interference. The cooling and ripening stage only requires gradual cooling and maintaining a simple reverse gradient, leading to stable physical properties and gradual changes. Therefore, these two stages only require conventional fixed gradients and stable heating and cooling. The isothermal stirring reaction stage is the most critical stage in the entire ointment process, characterized by the greatest changes in physical properties, the strongest viscosity fluctuations, and a high risk of localized overheating and component loss. Furthermore, the temperature field and material rheological characteristics are time-varying, lag-dependent, and highly nonlinear, requiring weighted fuzzy temperature gradient control. Specifically: (1) Preheating and fusion stage: The preset preheating target temperatures of the upper, middle and lower temperature control zones are 55℃, 60℃ and 65℃ respectively. The gradient temperature zone layout with higher temperature at the bottom and lower temperature at the top can effectively prevent the material at the bottom of the vessel from overheating and scorching. The system is set to a constant heating rate of 1℃ / min and starts heating synchronously from room temperature. The preheating and fusion stage ends when the upper, middle and lower temperature control zones reach their respective preset preheating target temperatures at the same time.

[0021] (2) Isothermal stirring reaction stage: The preset isothermal target temperatures for the upper, middle and lower temperature control zones are 68℃, 70℃ and 72℃ respectively. A small temperature gradient of higher at the bottom and lower at the top is adopted to enhance the convection circulation of materials in the reactor. The preset isothermal maintenance time is 90min. Subsequent weighted fuzzy independent temperature control is implemented for the upper, middle and lower zones respectively. The isothermal stirring is continued. After 90min, the isothermal stirring reaction stage ends.

[0022] (3) Cooling and maturation stage: The preset cooling target temperatures for the upper, middle, and lower temperature control zones are 42℃, 40℃, and 38℃ respectively, using a reverse temperature gradient of higher at the top and lower at the bottom to prevent premature solidification and skin formation on the top of the paste. The system performs segmented temperature control at a constant cooling rate of 0.5℃ / min. The cooling and maturation stage ends when all three zones reach their respective preset cooling target temperatures. Subsequently, the material naturally cools to room temperature, and the material is discharged.

[0023] It should be noted that the average temperature of four measuring points in each temperature control area at the same time is taken as the real-time characteristic temperature of each temperature control area, and this is used as the basis for judging whether the area temperature is controlled and whether the set target temperature has been reached.

[0024] During the isothermal stirring reaction stage of the traditional Chinese medicine ointment mixing process, the temperature time sequence of each measuring point in each temperature control area of ​​the mixing vessel, as well as the characterization temperature time sequence of each temperature control area, are obtained.

[0025] It should be noted that in this embodiment, within the initial 10 seconds after the preheating and fusion stage ends and the constant temperature stirring reaction stage begins, the control system does not implement temperature gradient adjustment or heating power intervention. It only collects the temperature of multiple measuring points in the upper, middle, and lower temperature control areas. After the 10-second data collection is completed, the weighted fuzzy temperature gradient control is started in the constant temperature stirring reaction stage.

[0026] Step S002: Based on the temperature changes within a local time period in the temperature time series of each measuring point, determine the thermal imbalance performance value of each measuring point at the current moment; based on the inconsistency of the temperatures of all measuring points in the same temperature control area at the same moment, determine the temperature field disorder of each temperature control area at the current moment; based on the thermal imbalance performance value and the magnitude of the temperature field disorder, determine the regional comprehensive temperature control instability of each temperature control area at the current moment.

[0027] It should be noted that, since traditional Chinese medicine ointments are high-viscosity multiphase systems, stirring causes material convection, shear heat transfer, and heating module start-stop regulation. Under normal operating conditions, the temperature during the isothermal stirring reaction stage fluctuates slightly and frequently within a small range around the target temperature. However, when there are uneven heat transfer, local heat accumulation, or material sticking to the walls forming flow dead zones in certain areas of the stirred tank, the temperature time series of the corresponding measuring points will show abnormal characteristics such as significantly increased fluctuation amplitude, large temperature overshoot, and deviation from the set isothermal value. This allows us to determine the real-time thermal imbalance performance value of each measuring point during the isothermal stirring reaction stage.

[0028] During the isothermal stirring reaction stage: First, based on the temperature changes within a local time period in the temperature time series of each measuring point, determine the thermal imbalance performance value of each measuring point at the current moment.

[0029] It should be noted that the larger the thermal imbalance performance value of each measuring point at the current moment, the more problems exist at each measuring point at the current moment, such as large temperature deviation from the target value, long duration of exceeding the threshold, violent temperature fluctuation, and obvious unidirectional heating or cooling trend. The more serious the local heat transfer imbalance, dead zone of medium flow, and temperature control gradient mismatch at the measuring point, the more important it is to identify and prioritize the control of the temperature control system.

[0030] It should be further explained that, based on the thermal imbalance performance value of a single measuring point at the current moment, in order to achieve a comprehensive assessment of the overall temperature control status and a global identification of regional heat transfer imbalance and flow dead zones, it is necessary to perform an aggregated analysis of the temperature characteristics and thermal imbalance performance of all measuring points within the same temperature control area. By integrating the quantitative indicators of thermal imbalance of each measuring point within the area, the evaluation is elevated from a single-point micro-level assessment to a macro-level overall assessment of the temperature control area, thereby enabling the assessment of regional temperature gradient uniformity, the location of abnormal measuring points, and precise zoning control decisions.

[0031] Then, based on the inconsistency of the temperatures at all measuring points within the same temperature control area at the same time, the temperature field disorder of each temperature control area at the current time is determined.

[0032] Finally, based on the magnitude of the thermal imbalance performance of all measuring points in each temperature control area at the current moment, and the magnitude of the temperature field disorder in each temperature control area at the current moment, the overall regional temperature control instability of each temperature control area at the current moment is determined.

[0033] It should be noted that the greater the overall temperature control instability of each temperature control zone at the current moment, the more static problems such as uneven overall temperature gradient, local heat transfer imbalance at measuring points, and continuous temperature overshoot exist in each temperature control zone at the current moment. At the same time, there is also a dynamic trend of continuously increasing temperature field disorder and worsening temperature fluctuation. The risk of regional temperature control instability, thermal balance disruption, uneven heat transfer, and exacerbation of flow dead zones is higher. This is the core control basis for intelligent control of multi-zone temperature gradient and should be prioritized for corrective control.

[0034] Step S003: Based on the temperature difference performance of the characterized temperature at the same moment in the characterized temperature time series of different temperature control regions, determine the interval coupling correction factor at the current moment; based on the interval coupling correction factor, correct the comprehensive temperature control instability of the region to obtain the control weight of each temperature control region at the current moment.

[0035] It should be noted that the above method only solves for the regional comprehensive temperature control instability of each temperature control zone individually, and can only achieve a quantitative evaluation of the temperature control quality within a single zone. However, in a real temperature control system, there are heat conduction, heat convection, and medium flow coupling effects between the upper, middle, and lower temperature control zones. Their temperature fields disturb and influence each other, and the instability of one zone can propagate and induce increased temperature control deviations in other zones. Therefore, to balance the degree of instability within each zone with the coupling interference effect between the three zones, it is necessary to further analyze the temperature correlation characteristics and mutual influence between the upper, middle, and lower temperature control zones. Based on the temporal correlation of temperature, gradient difference, and disturbance transmission law between the zones, the initially solved regional comprehensive temperature control instability of each zone is coupled, corrected, and adaptively fine-tuned. This eliminates the evaluation bias caused by inter-zone thermal coupling interference, obtaining a corrected regional comprehensive temperature control instability considering the mutual influence of multiple zones, providing a more accurate quantitative basis for subsequent multi-zone collaborative decoupled temperature control.

[0036] During the isothermal stirring reaction stage: First, based on the temperature difference at the same moment in the time series of characterization temperatures in different temperature control regions, the interval coupling correction factor at the current moment is determined.

[0037] Then, based on the interval coupling correction factor at the current moment, the regional comprehensive temperature control instability of each temperature control region at the current moment is corrected to obtain the control weight of each temperature control region at the current moment.

[0038] It should be noted that: the interval coupling correction factor is used to correct the overall temperature control instability of each temperature control zone. This not only retains the temperature control instability characteristics of each zone, but also incorporates the influence of mutual interference among the three zones and vertical temperature gradient fluctuations. The corrected overall temperature control instability of the zone after considering the interlayer coupling effect is obtained and used as a control weight. This makes the evaluation results more consistent with the actual heat transfer and flow field coupling characteristics of the stirred tank, and provides a more accurate quantitative benchmark for multi-zone decoupled temperature control.

[0039] Step S004: Calculate the weighted average of the represented temperatures using the control weights to obtain the effective temperature of each temperature control zone at the current moment; based on the real-time effective temperature of each temperature control zone, use a fuzzy controller to output the temperature control command for each temperature control zone in real time.

[0040] During the isothermal stirring reaction stage: Based on the control weight of each temperature control zone at each time point, the characteristic temperature of each temperature control zone at each time point is weighted and averaged to obtain the effective temperature of each temperature control zone at the current time point.

[0041] In this embodiment, each temperature control zone is equipped with an independent adjustable power electric heating module. Based on the real-time effective temperature of each temperature control zone, a fuzzy controller is used to output temperature control commands for each zone in real time through fuzzy control rule inference. Specifically: For any temperature control zone, calculate the difference between the preset constant temperature target temperature of the temperature control zone and the effective temperature at the current moment, and record it as the error at the current moment. Subtract the error at the previous moment from the error at the current moment, and record it as the error change rate at the current moment. Use the real-time error and error change rate as the input of the fuzzy controller, and output the real-time temperature control command for the temperature control zone, that is, the heating power correction amount.

[0042] The fuzzy controller is a well-known technology, and the specific method will not be described here. This enables independent temperature control of the upper, middle and lower zones of the isothermal stirring reaction stage.

[0043] Preferably, in some possible implementations of the embodiments of the present invention, the preset window length is 11 sampling times, which will be described as an example. The current time is set as the time endpoint, and 11 consecutive sampling times are selected to form a fixed-length time series analysis window.

[0044] Preferably, in some possible implementations of the embodiments of the present invention, in the first... In the temperature time series of n measuring points, calculate the standard deviation of all temperatures within the time window of the time series analysis, denoted as the i-th... The degree of temperature fluctuation at each measuring point at the current moment. Calculate the temperature difference between any two adjacent moments within the time window of the time series analysis (the difference between the temperature at the previous moment and the temperature at the next moment). Among all the temperature differences between adjacent moments within the time window of the time series analysis, count the number of negative temperature differences. Quantities with positive temperature differences Select the maximum value among the two types of quantities. normalized value , denoted as the Unidirectional temperature variation at each measuring point at the current moment .

[0045] It should be noted that in this embodiment, the time window length for time series analysis is 11, therefore the number of temperature differences between all adjacent time points within the time window is 10. As The normalized value.

[0046] The default constant is 1, and this will be used as an example for explanation.

[0047] Calculate the first Unidirectional temperature variation at each measuring point at the current moment The sum of the preset constants Let be the first coefficient, and then combine the first coefficient with the second coefficient. The degree of temperature fluctuation at each measuring point at the current moment. The product of is denoted as the i. The performance value of temperature fluctuation at each measuring point at the current moment. The specific calculation formula is as follows: It should be noted that: The larger the value, the greater the temperature fluctuation within the time window of the time series analysis. The larger the value, the more likely it is to indicate a sustained unidirectional temperature increase or decrease within the time window of the time series analysis. right Adjustments were made to obtain the performance values ​​for the temperature fluctuation problem. , The larger the value, the higher the value at the current moment. There may be heat transfer imbalances or flow dead zones at some measurement points, which need to be identified and controlled.

[0048] Preferably, in some possible implementations of the embodiments of the present invention, in the first... In the temperature time series of each measuring point, calculate the temperature at each time point and the temperature at the [missing value]. The absolute value of the difference between the preset constant temperature target temperature of each measuring point in the temperature control zone is denoted as the i-th. The temperature deviation value of each measuring point at each moment.

[0049] It should be noted that: if the first Temperature deviation value at each measuring point at the current moment If it is 0, then let the first... Temperature overshoot performance value at each measuring point at the current moment It is 0.

[0050] Preferably, in some possible implementations of the embodiments of the present invention, the preset deviation threshold is 1°C, which will be used as an example for description. For the first... For each measuring point, the moment when the temperature deviation exceeds a preset deviation threshold is recorded as an overshoot moment. The number of overshoot moments within the time window of the time series analysis is recorded. normalized value , denoted as the Temperature overshoot persistence at each measuring point at the current moment .

[0051] It should be noted that in this embodiment, the time window length for time series analysis is 11, therefore... As The normalized value.

[0052] The first Temperature overshoot persistence at each measuring point at the current moment The sum of the preset constants Let be the second coefficient, and then combine the second coefficient with the first coefficient. Temperature deviation value at each measuring point at the current moment The product of is denoted as the i. Temperature overshoot performance value at each measuring point at the current moment The specific calculation formula is as follows: It should be noted that: The larger it is, the more likely it is to be the first The greater the deviation of the instantaneous temperature of a measuring point from the target constant temperature value at the current moment, and the longer the duration of the over-threshold fluctuation and the worse the temperature stability, the more attention needs to be paid to it when using temperature control gradient control.

[0053] Preferably, in some possible implementations of the embodiments of the present invention, a preset overshoot weight is used. The value is 0.7, used to focus on characterizing the impact of the instantaneous temperature deviation from the set value at the measuring point and the duration of overshoot, with a preset fluctuation weight. The value is 0.3, which is used to focus on characterizing the influence of the temperature oscillation amplitude and the unidirectional continuous temperature change trend at the measuring point. This will be used as an example for the description.

[0054] The first Temperature overshoot performance value at each measuring point at the current moment With preset overshoot weights product , denoted as the first product, and the th The performance value of temperature fluctuation at each measuring point at the current moment. With preset fluctuation weight product Let the product be the second product, and let the sum of the first and second products be the third product. Thermal imbalance performance value at each measuring point at the current moment The specific calculation formula is as follows: Preferably, in some possible implementations of the embodiments of the present invention, in the first... Within a temperature-controlled area, calculate the standard deviation of the temperature at the same moment in the temperature time series of all measuring points, denoted as the i-th... The temperature field disorder of each temperature control zone at every moment.

[0055] Within the time window of the time series analysis, the data is processed sequentially according to the chronological order, starting with the [number]th [time]. The temperature field disorder of each temperature control region at each moment constitutes a time series of temperature field disorder. The least squares method is used to perform linear fitting on the time series of temperature field disorder to obtain the fitted line of temperature field disorder.

[0056] Calculate the slope of the fitted line for the disorder of the temperature field. normalized value The sum of the preset constants Let be the third coefficient, and then combine the third coefficient with the first coefficient. The temperature field disorder in each temperature-controlled region at the current moment The product of is denoted as the i. The overall chaotic performance of the temperature field in each temperature-controlled region at the current moment. The specific calculation formula is as follows: It should be noted that the maximum slope is preset in this embodiment. The minimum slope is preset to 0.5. If it is -0.5, then ,like Greater than Then let If it is 1, Less than Then let It is 0. Among them, for The purpose of setting a lower threshold is to actively suppress the adjustment intensity of the heating module when the system detects that the disorder is rapidly improving (i.e., the slope is below -0.5) by setting the correction term to zero. This design utilizes the thermal conductivity inertia of the ointment itself to achieve a smooth transition, effectively avoiding secondary overshoot caused by adjustment allergies during system convergence. Within a temperature control area, if the temperature field is more chaotic at the current moment, and the temperature field chaos shows an increasing trend, then the overall chaotic performance of the temperature field at the current moment is greater, and more attention needs to be paid to it during control.

[0057] Preferably, in some possible implementations of the embodiments of the present invention, in the first... Within a temperature-controlled area, obtain the average value of the thermal imbalance performance of all measuring points at the current moment. With the maximum value The mean With that maximum value arithmetic mean , denoted as the The overall thermal imbalance performance of each temperature-controlled zone at the current moment. .

[0058] in, It represents the overall average temperature control level of the entire temperature control area. It characterizes the worst thermal imbalance state at a single point within the region. The combination of the two can not only quantitatively evaluate the overall temperature gradient uniformity and steady-state performance of the temperature control area, but also accurately pinpoint abnormal locations.

[0059] The first The overall thermal imbalance performance of each temperature-controlled zone at the current moment. With the The overall chaotic performance of the temperature field in each temperature-controlled region at the current moment. arithmetic mean , denoted as the The overall temperature control instability of each temperature control zone at the current moment. .

[0060] Preferably, in some possible implementations of the embodiments of the present invention, in the time sequence of the characterization temperatures of the upper, middle and lower temperature control regions, the difference between the characterization temperature of the lower temperature control region and the characterization temperature of the middle temperature control region at the same time is calculated and recorded as the lower-middle temperature difference at each time. The difference between the characterization temperature of the middle temperature control region and the characterization temperature of the upper temperature control region at the same time is calculated and recorded as the middle-upper temperature difference at each time.

[0061] Within the time window of the time series analysis, the lower and middle temperature differences are sequentially used to form the lower and middle temperature difference time series, and then the middle and upper temperature differences are sequentially used to form the middle and upper temperature difference time series.

[0062] Calculate the standard deviation of the temperature difference time series and denote it as the first standard deviation. Next, calculate the standard deviation of the upper temperature difference time series, and denot it as the second standard deviation. The first standard deviation Compared with the second standard deviation mean Let be the temperature gradient instability of the stirred tank at the current moment. .

[0063] It should be noted that the standard deviation characterizes the degree of dispersion of interlayer temperature difference over time. It reflects the magnitude of the temporal fluctuations in the temperature gradient in the lower and middle layers. This value reflects the magnitude of the temporal fluctuations in the upper and middle layers of the temperature gradient. The average of these two values ​​comprehensively characterizes the overall intensity of fluctuations in the vertical temperature gradient across the entire upper, middle, and lower regions. The larger this value, the more pronounced the temporal oscillations of the vertical interlayer temperature difference within the vessel, the more unstable the temperature gradient, and the stronger the interlayer thermal coupling disturbance.

[0064] Preferably, in some possible implementations of the embodiments of the present invention, the absolute value of the difference between the lower and middle temperature difference and the middle and upper temperature difference at the same time is calculated and recorded as the temperature gradient inconsistency at each time.

[0065] The average of the temperature gradient inconsistencies across all time points within the time window of the time series analysis is denoted as the temperature gradient inconsistency of the stirred tank at the current time. .

[0066] It should be noted that the preset constant temperature target temperatures for the upper, middle, and lower temperature control zones are 68℃, 70℃, and 72℃ respectively. Under ideal operating conditions, the theoretical difference between the middle temperature difference and the upper temperature difference is constant and equal in magnitude. Therefore, the greater the inconsistency in the temperature gradient of the stirred tank, the more unstable the temperature gradient becomes.

[0067] Preferably, in some possible implementations of the embodiments of the present invention, the inconsistency of the temperature gradient of the stirred tank at the current moment is addressed. normalized value The sum of the preset constants This is denoted as the fourth coefficient, and the fourth coefficient is compared with the temperature gradient instability of the stirred tank at the current moment. The product of these two factors is denoted as the interval coupling correction factor at the current time. The specific calculation formula is as follows: It should be noted that in this embodiment, For the preset temperature coefficient, It is 1℃. Therefore, As The normalized value, In the range inside, when When it is 0, When it is 0, When it is larger, The value is infinitely close to 1. Temperature gradient instability characterizes the degree of dispersion of temperature difference between the upper, middle, and lower layers over time. The larger the value, the more severe the vertical temperature gradient oscillation and the stronger the interlayer thermal coupling disturbance. Temperature gradient inconsistency characterizes the degree of deviation between the actual interlayer temperature difference and the ideal arithmetic distribution. The larger the value, the more the temperature grade ratio of the upper, middle, and lower zones deviates from the design conditions and the worse the gradient regularity. Therefore, by simultaneously integrating the gradient temporal fluctuation characteristics and the gradient ideal regularity deviation characteristics, the degree of mutual interference caused by heat conduction and medium flow coupling between the upper, middle, and lower zones is comprehensively reflected, serving as the global correction coefficient for subsequent correction of temperature control evaluation indicators in each region, i.e., the interval coupling correction factor.

[0068] Preferably, in some possible implementations of the embodiments of the present invention, a preset coupling correction weight coefficient is used. The value is 0.6, and we will use this as an example to illustrate the calculation. Calculate the preset coupling correction weight coefficient. Interval Coupling Correction Factor at the Current Time The product of the product and the product of ... The overall temperature control instability of each temperature control zone at the current moment. The sum of is denoted as the th . The control weight of each temperature control zone at the current moment The specific calculation formula is as follows: Preferably, in some possible implementations of the embodiments of the present invention, within the time window of time series analysis, the characteristic temperature of each temperature control region at all times is weighted and averaged according to the control weight of each temperature control region at all times to obtain the effective temperature of each temperature control region at the current time. The effective temperature of each temperature control zone at the current moment. The calculation formula is: In the formula, For the time window of time series analysis The temperature control zone is in the first The characterization temperature at that time, For the time window of time series analysis The temperature control zone is in the first The control weight at time point 11 represents the time window length for time series analysis.

[0069] It should be noted that in this embodiment, within the initial 10 seconds after the preheating and fusion stage ends and the isothermal stirring reaction stage begins, the control weight value corresponding to each moment is uniformly set to 1. If A value of 0 indicates that the first... If the temperature in the first temperature control zone is stable at the current temperature, then directly set the first... The characteristic temperature of the temperature-controlled region at the current moment is used as the... The effective temperature of each temperature control zone at the current moment. .

[0070] This invention is now complete.

[0071] In summary, in this embodiment of the invention, during the isothermal stirring reaction stage of the traditional Chinese medicine ointment stirring process, the thermal imbalance performance value of each measuring point at the current moment is determined based on the temperature change within a local time period in the temperature time series of each measuring point. Based on the temperature inconsistency of all measuring points within the same temperature control area at the same moment, the temperature field disorder of each temperature control area at the current moment is determined, thereby determining the regional comprehensive temperature control instability of each temperature control area at the current moment. Based on the temperature difference performance of the characterized temperatures at the same moment in the characterized temperature time series of different temperature control areas, the interval coupling correction factor at the current moment is determined, thereby obtaining the control weight of each temperature control area at the current moment, which is used to obtain the effective temperature of each temperature control area at the current moment. A fuzzy controller is used to output the temperature control commands of each temperature control area in real time. This invention improves the accuracy, stability, and anti-interference ability of temperature gradient control during the stirring process of traditional Chinese medicine ointment by adopting a partitioned weighted fuzzy control strategy.

Claims

1. A method for intelligent control of temperature gradient during the stirring process of traditional Chinese medicine ointment, characterized in that, The method includes the following steps: During the isothermal stirring reaction stage of the traditional Chinese medicine ointment stirring process, the temperature time sequence of each measuring point in the upper, middle and lower temperature control areas of the stirring vessel, as well as the characterization temperature time sequence of each temperature control area, are obtained. Based on the temperature changes within a local time period in the temperature time series of each measuring point, determine the thermal imbalance performance value of each measuring point at the current moment; based on the inconsistency of the temperatures of all measuring points in the same temperature control area at the same moment, determine the temperature field disorder of each temperature control area at the current moment; based on the thermal imbalance performance value and the magnitude of the temperature field disorder, determine the regional comprehensive temperature control instability of each temperature control area at the current moment. Based on the temperature difference at the same moment in the time series of the characterized temperatures of different temperature control regions, the interval coupling correction factor at the current moment is determined; based on the interval coupling correction factor, the comprehensive temperature control instability of the region is corrected to obtain the control weight of each temperature control region at the current moment. The effective temperature of each temperature control zone at the current moment is obtained by weighting the characteristic temperature with the control weight; based on the real-time effective temperature of each temperature control zone, a fuzzy controller is used to output the temperature control command of each temperature control zone in real time.

2. The intelligent temperature gradient control method for the stirring process of a traditional Chinese medicine ointment according to claim 1, characterized in that, The specific steps for determining the thermal imbalance performance value of each measuring point at the current moment are as follows: Set the current time as the end point, and select a number of consecutive sampling times of a preset window length to form a fixed-length time series analysis window; In the temperature time series of each measuring point, the temperature fluctuation performance value of each measuring point at the current moment is determined based on the temperature change within the time window of the time series analysis. Calculate the absolute value of the difference between the temperature at each moment in the temperature time series of each measuring point and the preset constant temperature target temperature of the temperature control zone where each measuring point is located, and record it as the temperature deviation value of each measuring point at each moment; Based on the magnitude of the temperature deviation value of each measuring point at each moment within the time window of the time series analysis, determine the temperature overshoot performance value of each measuring point at the current moment; The product of the temperature overshoot performance value and the preset overshoot weight is recorded as the first product. The product of the temperature fluctuation problem performance value and the preset fluctuation weight is recorded as the second product. The sum of the first product and the second product is recorded as the thermal imbalance performance value of each measuring point at the current moment.

3. The intelligent temperature gradient control method for the stirring process of a traditional Chinese medicine ointment according to claim 2, characterized in that, The specific steps involved in determining the temperature fluctuation performance value of each measuring point at the current moment are as follows: In the temperature time series of each measuring point, the standard deviation of all temperatures within the time window of the time series analysis is calculated and recorded as the degree of temperature oscillation of each measuring point at the current moment. Among the temperature difference values ​​of all adjacent moments within the time window of the time series analysis, the number of negative temperature difference values ​​and the number of positive temperature difference values ​​are counted respectively. The normalized value of the maximum value of the two types of values ​​is selected and recorded as the unidirectional temperature variation of each measuring point at the current moment. Calculate the sum of the unidirectional temperature variation of each measuring point at the current moment and the preset constant, and denote it as the first coefficient. Multiply the first coefficient by the temperature oscillation degree of each measuring point at the current moment, and denote it as the temperature fluctuation performance value of each measuring point at the current moment.

4. The intelligent temperature gradient control method for the stirring process of a traditional Chinese medicine ointment according to claim 2, characterized in that, The specific steps for determining the temperature overshoot performance value of each measuring point at the current moment are as follows: For each measuring point, the moment when the temperature deviation value is greater than the preset deviation threshold is recorded as the overshoot moment, and the normalized value of the number of overshoot moments within the time window of the time series analysis is recorded as the temperature overshoot persistence of each measuring point at the current moment. The sum of the temperature overshoot duration of each measuring point at the current moment and the preset constant is recorded as the second coefficient. The product of the second coefficient and the temperature deviation value of each measuring point at the current moment is recorded as the temperature overshoot performance value of each measuring point at the current moment.

5. The intelligent temperature gradient control method for the stirring process of a traditional Chinese medicine ointment according to claim 2, characterized in that, The specific steps involved in determining the temperature field disorder of each temperature control zone at the current moment are as follows: Within each temperature control region, calculate the standard deviation of the temperature at the same moment in the temperature time series of all measuring points, and denote it as the temperature field disorder of each temperature control region at each moment; Within the time window of the time series analysis, the temperature field disorder time series is constructed by taking the temperature field disorder of each temperature control zone at each moment in chronological order. The least squares method is used to perform linear fitting on the temperature field disorder time series to obtain the temperature field disorder fitting line. The normalized value of the slope of the fitted line of temperature field disorder is calculated and the sum of the slope and the preset constant is denoted as the third coefficient. The product of the third coefficient and the temperature field disorder of each temperature control region at the current moment is denoted as the comprehensive temperature field disorder performance of each temperature control region at the current moment.

6. The intelligent temperature gradient control method for the stirring process of a traditional Chinese medicine ointment according to claim 1, characterized in that, The specific steps for determining the overall temperature control instability of each temperature control zone at the current moment are as follows: Within each temperature control zone, the mean and maximum values ​​of the thermal imbalance performance of all measuring points at the current moment are obtained. The arithmetic mean and maximum value are then calculated to obtain the comprehensive thermal imbalance performance value of each temperature control zone at the current moment. The arithmetic mean of the comprehensive temperature field disorder performance and comprehensive thermal imbalance performance values ​​of each temperature control region at the current moment is used to obtain the regional comprehensive temperature control instability of each temperature control region at the current moment.

7. The intelligent temperature gradient control method for the stirring process of a traditional Chinese medicine ointment according to claim 2, characterized in that, The specific steps for determining the interval coupling correction factor at the current time are as follows: In the time series of the characterization temperatures of the upper, middle and lower temperature control regions, the difference between the characterization temperature of the lower temperature control region and the characterization temperature of the middle temperature control region at the same time is calculated and recorded as the lower-middle temperature difference at each time. The difference between the characterization temperature of the middle temperature control region and the characterization temperature of the upper temperature control region at the same time is calculated and recorded as the middle-upper temperature difference at each time. Within the time window of the time series analysis, the lower and middle temperature difference time series is constructed by taking the lower and middle temperature difference at each time point in chronological order, and the middle and upper temperature difference time series is constructed by taking the middle and upper temperature difference at each time point in chronological order. Calculate the standard deviation of the lower and middle temperature difference time series, and denote it as the first standard deviation. Calculate the standard deviation of the middle and upper temperature difference time series, and denote it as the second standard deviation. The mean of the first standard deviation and the second standard deviation is denoteed as the temperature gradient instability of the stirred tank at the current moment. Based on the difference between the lower and middle temperature differences and the middle and upper temperature differences at the same time, it is determined that the temperature gradient of the stirred tank is inconsistent at the current time. The sum of the normalized value of the temperature gradient inconsistency of the stirred tank and the preset constant is denoted as the fourth coefficient. The product of the fourth coefficient and the temperature gradient inconsistency of the stirred tank is denoted as the interval coupling correction factor at the current moment.

8. The intelligent temperature gradient control method for the stirring process of a traditional Chinese medicine ointment according to claim 7, characterized in that, The specific steps involved in determining the inconsistency of the temperature gradient in the stirred tank at the current moment are as follows: Calculate the absolute value of the difference between the lower and middle temperature differences and the middle and upper temperature differences at the same time, and denot it as the temperature gradient inconsistency at each time. The average value of the temperature gradient inconsistency at all times within the time window of the time series analysis is denoted as the temperature gradient inconsistency of the stirred tank at the current time.

9. The intelligent temperature gradient control method for the stirring process of a traditional Chinese medicine ointment according to claim 1, characterized in that, The specific steps for obtaining the control weight of each temperature control zone at the current moment are as follows: Calculate the product of the preset coupling correction weight coefficient and the interval coupling correction factor at the current time, and sum the product with the regional comprehensive temperature control instability of each temperature control region at the current time, and record it as the control weight of each temperature control region at the current time.

10. The intelligent temperature gradient control method for the stirring process of a traditional Chinese medicine ointment according to claim 2, characterized in that, The specific steps for obtaining the effective temperature of each temperature control zone at the current moment are as follows: Within the time window of the time series analysis, the representative temperature of each temperature control region at all times is weighted and averaged according to the control weight of each temperature control region at all times to obtain the effective temperature of each temperature control region at the current time.

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