A precise quantitative intelligent control method and system for a pneumatic plastic regulating valve

Abstract

The present application relates to the field of quantitative control, and particularly relates to a precise quantitative intelligent control method and system for a pneumatic plastic regulating valve, which comprises the following steps: collecting an upstream pressure sequence and a downstream pressure sequence; constructing a time window for the upstream pressure sequence, clustering the time window to obtain sub-sections, calculating the drug content cumulative value of all sub-sections, and taking the last sub-section as the quantitative end section in response to the drug content cumulative value being not less than a preset required drug amount; calculating a first quantitative marker value, obtaining a second quantitative marker value in the same way, calculating the similarity between the first quantitative marker value and any second quantitative marker value to obtain a matching time; correcting the drug content cumulative value according to the calculated actual flow rate to obtain a corrected drug content cumulative value, and completing quantitative control according to the corrected drug content cumulative value. Through the technical scheme of the present application, the accuracy and efficiency of the quantitative control result can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of quantitative control. In particular, it relates to a precise quantitative intelligent control method and system for a pneumatic plastic regulating valve. BACKGROUND

[0002] In the plastic processing, chemical reaction, food manufacturing and pharmaceutical production industries, accurate material delivery and flow control have a key impact on production efficiency, product quality and safety. Among them, the pneumatic plastic regulating valve, due to its simple structure, fast response, convenient maintenance and other advantages, has become a commonly used actuator in various automated production systems.

[0003] In large-capacity solution delivery scenarios, especially for difficult-to-dissolve solutes, fluid viscosity can deviate the actual flow rate from the standard preset flow rate, further changing the flow characteristics. In the prior art, simply relying on empirical parameters and fixed control strategies can lead to inaccurate control results. SUMMARY

[0004] To solve the above technical problems, the present application provides solutions in the following aspects.

[0005] In a first aspect, a precise quantitative intelligent control method for a pneumatic plastic regulating valve is provided, comprising: collecting an upstream pressure sequence at the inlet of the flow channel and a downstream pressure sequence at the outlet of the flow channel; constructing a time window for the upstream pressure sequence, clustering the pressure values within the time window to obtain sub-sections, calculating the drug content of any sub-section, and calculating the cumulative value of the drug content of all sub-sections; in response to the cumulative value of the drug content being not less than a preset required drug amount, taking the last sub-section as the quantitative end section; taking the center point of the quantitative end section as the upstream center time, calculating the average pressure of all sampling times in the quantitative end section, obtaining the upper rail and lower rail of the quantitative end section, and calculating the first quantitative marker value based on the average pressure, the pressure value at the upstream center time, the upper rail and the lower rail; obtaining the downstream center time based on the upstream center time, constructing a downstream window with the downstream center time as the midpoint, and obtaining the second quantitative marker value of any sampling time in the downstream window according to the calculation method of the first quantitative marker value, wherein the size of the downstream window is equal to that of the quantitative end section; calculating the similarity between the first quantitative marker value and any second quantitative marker value, taking the maximum value of the similarity as the matching time of the upstream center time; calculating the actual flow rate based on the matching time, the upstream center time and the obtained flow channel length, correcting the cumulative value of the drug content according to the actual flow rate, obtaining the corrected cumulative value of the drug content, and completing the quantitative control according to the corrected cumulative value of the drug content.

[0006] Preferably, the time window for the upstream pressure sequence is constructed by taking the time length required to obtain the preset required drug amount in the history as the marker length, and constructing the time window with the first sampling time of the upstream pressure sequence as the starting point and the marker length as the size.

[0007] Preferably, the clustering of the pressure values in the time window to obtain the sub-sections comprises: obtaining the viscosity of the liquid medicine, calculating the standard deviation of the pressure in the time window; mapping the viscosity using a hyperbolic tangent function to obtain a mapping value, calculating the sum of 1 and the mapping value, and taking the product of the sum and the standard deviation of the pressure as the neighborhood radius; calculating the reciprocal of the sum of 1 and the standard deviation of the pressure, and taking the result of the reciprocal, the marked length, and the product of the sampling frequency of the upstream pressure sequence as an integer to obtain the minimum number; clustering the pressure values in the time window based on the neighborhood radius and the minimum number to obtain a plurality of effective clustering clusters, and one effective clustering cluster corresponds to one sub-section, and the number of sampling time points in the effective clustering cluster is not less than the minimum number; wherein, in response to the difference between the pressure values of any two sampling time points in the clustering cluster being not greater than the neighborhood radius and the number of sampling time points in the clustering cluster being less than the minimum number, a next sampling time point adjacent to the clustering cluster is added to obtain a new clustering cluster, and in response to the difference between the pressure values of any two sampling time points in the new clustering cluster being greater than the neighborhood radius and the number of sampling time points in the new clustering cluster being less than the minimum number, the sampling time points are added in time sequence until the number of sampling time points in the new clustering cluster is equal to the minimum number to obtain the effective clustering cluster; in response to the difference between the pressure values of any two sampling time points in the new clustering cluster being not greater than the neighborhood radius and the number of sampling time points in the new clustering cluster being not less than the minimum number, the sampling time points are continuously added in time sequence until the difference between the pressure values of any two sampling time points in the new clustering cluster is greater than the neighborhood radius, and the addition is stopped to obtain the effective clustering cluster.

[0008] Preferably, the calculation of the drug content of any sub-section comprises: obtaining the cross-sectional area of the flow channel, the standard flow rate; calculating the density of the liquid medicine at each sampling time point in any sub-section, and calculating the average density of the liquid medicine at all sampling time points in any sub-section; taking the product of the average density, the cross-sectional area, the standard flow rate, and the length of any sub-section as the drug content.

[0009] Preferably, the obtaining of the upper rail and the lower rail of the quantitative end section comprises: obtaining the viscosity of the liquid medicine, calculating the standard deviation of all pressures in the quantitative end section as the quantitative standard deviation; mapping the viscosity using a hyperbolic tangent function to obtain a mapping value, and taking the sum of 1.5 and the mapping value as the viscosity coefficient; calculating a first product of the viscosity coefficient and the quantitative standard deviation; taking the sum of the average pressure and the first product as the upper rail, and taking the difference between the average pressure and the first product as the lower rail.

[0010] Preferably, the first quantitative marker value comprises: taking the average of the upper rail and the lower rail as the middle rail, taking the difference between the pressure value at the upstream center time point and the middle rail as the first term, taking the reciprocal of the difference between the upper rail and the lower rail as the second term, and taking the product of the first term and the second term as the first quantitative marker value.

[0011] Preferably, the acquiring the downstream center time according to the upstream center time comprises: acquiring the distance between the flow channel entrance and the flow channel exit, taking the ratio of the distance and the standard flow rate as the time interval, and taking the sum of the upstream center time and the time interval as the downstream center time.

[0012] Preferably, the actual flow rate comprises: calculating the difference between the matching time and the upstream center time as the first difference, and taking the ratio of the distance and the first difference as the actual flow rate.

[0013] Preferably, the correcting the drug content accumulation value according to the actual flow rate to acquire the drug content accumulation correction value comprises: acquiring the cross-sectional area of the flow channel; calculating the density of the drug liquid at each sampling time in any sub-section, and calculating the average density of the drug liquid at all sampling times in any sub-section; taking the product of the average density, the cross-sectional area, the actual flow rate and the length of any sub-section as the drug content correction value, and traversing to acquire the drug content correction value of each sub-section, and taking the accumulation value of the drug content correction values of all sub-sections as the drug content accumulation correction value.

[0014] The second aspect is a precise quantitative intelligent control system of a pneumatic plastic regulating valve, comprising: a processor and a memory, the memory stores computer program instructions, when the computer program instructions are executed by the processor, any one of the pneumatic plastic regulating valve precise quantitative intelligent control method is realized.

[0015] The present application has the following effects:

[0016] The present application provides a dynamic and real-time drug liquid quantitative control method by using the opening and closing state of the pneumatic plastic regulating valve, by accurately monitoring the flow and distribution state of the drug liquid in the flow channel, combining factors such as pressure data, viscosity and flow rate. Through the analysis of the upstream and downstream pressure sequence, the pressure loss and flow unevenness in the flow channel can be identified, and the drug content can be accurately calculated and corrected accordingly. Especially when considering the influence of the viscosity of the fluid on the pressure change, by introducing an adjustable viscosity coefficient, different drug liquid characteristics can be adapted. Through the establishment of a time window, cluster analysis, quantitative marker value and other methods, the opening and closing time of the pneumatic plastic regulating valve can be accurately judged by the drug liquid quantitative control, so as to achieve precise quantitative control. The drug content can also be corrected according to the actual flow rate, so as to ensure the accurate amount of drug liquid. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 It is a flow chart of a pneumatic plastic regulating valve precise quantitative intelligent control method of an embodiment of the present application. DETAILED DESCRIPTION

[0018] Clearly, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0019] The specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0020] Referring to Figure 1 A precise quantitative intelligent control method for a pneumatic plastic regulating valve includes steps S1-S4, which are as follows:

[0021] S1: Collect an upstream pressure sequence at the inlet of the flow channel and a downstream pressure sequence at the outlet of the flow channel.

[0022] In one embodiment, a pressure sensor is placed at the inlet of the flow channel to collect the upstream pressure sequence, and a pressure sensor is placed at the outlet of the flow channel to collect the downstream pressure sequence. The upstream pressure generally reflects the state of the liquid medicine before it enters the flow channel, while the downstream pressure reflects the changes of the liquid medicine after it passes through the flow channel. By comparing the pressure data at the inlet and the outlet, the pressure loss in the flow channel, the flow resistance of the fluid, and the possible flow unevenness can be obtained.

[0023] S2: Construct a time window for the upstream pressure sequence, cluster the pressure values in the time window to obtain sub-sections, calculate the drug content of any sub-section, and calculate the cumulative value of the drug content of all sub-sections. In response to the cumulative value of the drug content being not less than the preset required drug amount, the last sub-section is taken as the quantitative end section.

[0024] In one embodiment, the length of time required to obtain the preset required drug amount in the history is taken as the marker length, and a time window is constructed with the first sampling time of the upstream pressure sequence as the starting point and the marker length as the size.

[0025] The viscosity of the liquid medicine is obtained, and the pressure standard deviation of the time window is calculated. The viscosity is mapped to obtain a mapping value using a hyperbolic tangent function, the sum of 1 and the mapping value is calculated, and the product of the sum and the pressure standard deviation is taken as the neighborhood radius. The hyperbolic tangent function can map the viscosity to between 0 and 1 and eliminate the dimension. The neighborhood radius satisfies the relationship:

[0026] , represents the neighborhood radius, represents the viscosity, represents the hyperbolic tangent function, represents the pressure standard deviation of the time window. The pressure standard deviation reflects the fluctuation of the pressure values in the time window, i.e., the deviation of the pressure value at each sampling time from the mean value of all pressure values in the time window.

[0027] The sum of the calculation 1 and the pressure standard deviation is inverted, and the result of the inversion, the product of the mark length and the sampling frequency of the upstream pressure sequence is rounded up to be the minimum number. The minimum number satisfies the relationship:

[0028] , represents the minimum number, represents the sampling frequency of the upstream pressure sequence, represents the mark length, represents the pressure standard deviation of the time window, represents rounding up.

[0029] Based on the neighborhood radius and the minimum number, the pressure values in the time window are clustered to obtain a plurality of effective clustering clusters, and one effective clustering cluster corresponds to one sub-section, and the number of sampling time points in the effective clustering cluster is not less than the minimum number.

[0030] Wherein, in response to the difference between the pressure values of any two sampling time points in the clustering cluster being not greater than the neighborhood radius, the number of sampling time points in the clustering cluster being less than the minimum number, a new clustering cluster is obtained by adding the next sampling time point adjacent to the clustering cluster, in response to the difference between the pressure values of any two sampling time points in the new clustering cluster being greater than the neighborhood radius, the number of sampling time points in the new clustering cluster being less than the minimum number, the sampling time points are added in time sequence until the number of sampling time points in the new clustering cluster is equal to the minimum number to obtain the effective clustering cluster; in response to the difference between the pressure values of any two sampling time points in the new clustering cluster being not greater than the neighborhood radius, the number of sampling time points in the new clustering cluster being not less than the minimum number, the sampling time points are continuously added in time sequence until the difference between the pressure values of any two sampling time points in the new clustering cluster is greater than the neighborhood radius, and the addition is stopped to obtain the effective clustering cluster.

[0031] It should be noted that the process of clustering is to complete an effective clustering cluster before constructing the second effective clustering cluster, so each sampling time point is clustered in time sequence. The condition that the effective clustering cluster must satisfy is that the number of sampling time points in the clustering cluster is not less than the minimum number.

[0032] When the difference between the pressure values of any two sampling time points in the clustering cluster is not greater than the neighborhood radius, and the number of sampling time points in the clustering cluster is less than the minimum number, it indicates that the clustering is not completed at this time, and the next time point adjacent to the last sampling time point in the clustering cluster is added to the clustering cluster in time sequence to obtain a new clustering cluster, at this time, there are two cases in the new clustering cluster.

[0033] Case one: the difference between the pressure values of any two sampling time points in the new cluster is greater than the neighborhood radius, and the number of sampling time points in the new cluster is less than the minimum number. In this case, the difference between the pressure values of any two sampling time points is greater than the neighborhood radius, indicating that the newly added sampling time point does not belong to the new cluster. However, since the number of sampling time points in the new cluster is less than the minimum number, the continuous sampling time points are directly added to the new cluster according to the minimum number, which can avoid data discontinuity.

[0034] Case two: the difference between the pressure values of any two sampling time points in the new cluster is not greater than the neighborhood radius, and the number of sampling time points in the new cluster is not less than the minimum number. At this time, not only does the newly added sampling time point belong to the new cluster, but also the newly added sampling time point causes the new cluster to meet the minimum number. At this time, the next sampling time point is added in time sequence, and the difference between the pressure values of any two sampling time points is calculated, until the difference between the pressure values of any two sampling time points in the cluster is greater than the neighborhood radius, and the addition is stopped to obtain an effective cluster. In this way, the sampling time points of the same cluster can be clustered together as much as possible to prevent the sampling time points of the same cluster from being divided into two or even multiple clusters, increasing the redundancy of the calculation process.

[0035] The drug content of any sub-section is calculated by: obtaining the cross-sectional area of the flow channel and the standard flow rate; calculating the density of the drug solution at each sampling time point in any sub-section, and calculating the average density of the drug solution at all sampling time points in any sub-section; and taking the product of the average density, the cross-sectional area, the standard flow rate, and the length of any sub-section as the drug content.

[0036] The drug content accumulation value of all sub-sections is calculated, and the last sub-section is taken as the end-of-dosing segment in response to the drug content accumulation value being not less than the preset required drug amount.

[0037] S3: taking the center point of the end-of-dosing segment as the upstream center time point, calculating the pressure average of all sampling time points in the end-of-dosing segment, obtaining the upper rail and the lower rail of the end-of-dosing segment, and calculating the first dosing marker value based on the pressure average, the pressure value of the upstream center time point, the upper rail, and the lower rail; obtaining the downstream center time point based on the upstream center time point, constructing a downstream window with the downstream center time point as the midpoint, and obtaining the second dosing marker value of any sampling time point in the downstream window according to the calculation method of the first dosing marker value, wherein the size of the downstream window is equal to that of the end-of-dosing segment; calculating the similarity between the first dosing marker value and any second dosing marker value, and taking the maximum value of the similarity as the matching time point of the upstream center time point.

[0038] In one embodiment, the center point of the end of dosing section is taken as the upstream center time, the average pressure of all sampling times in the end of dosing section is calculated, and the upper track and the lower track of the end of dosing section are obtained. The prior art is obtained by a Bollinger line chart, and the calculation method is generally two times of the standard deviation. In the present embodiment, the viscosity coefficient is used to replace the fixed two times. Specifically, the viscosity of the liquid medicine is obtained, the standard deviation of all pressures in the end of dosing section is calculated as the dosing standard deviation, the sum of 1.5 and the mapped value obtained by mapping the viscosity by using the hyperbolic tangent function is taken as the viscosity coefficient, the first product of the viscosity coefficient and the dosing standard deviation is calculated, and the sum of the average pressure and the first product is taken as the upper track, and the difference between the average pressure and the first product is taken as the lower track.

[0039] The viscosity coefficient plays a regulating role in this calculation, and is specifically determined by the sum of 1.5 and the mapped viscosity. When the viscosity of the liquid medicine is high, the viscosity coefficient increases, thereby amplifying the influence on the calculation of the upper track and the lower track.

[0040] The upper track satisfies the relationship: , wherein the upper track is represented by , the average pressure of all pressures in the end of dosing section is represented by , the viscosity coefficient is represented by , and the standard deviation of all pressures in the end of dosing section is represented by

[0041] The lower track satisfies the relationship: , wherein the lower track is represented by , the average pressure of all pressures in the end of dosing section is represented by , the viscosity coefficient is represented by , and the standard deviation of all pressures in the end of dosing section is represented by

[0042] Then, the first dosing marker value is calculated based on the average pressure, the pressure value at the upstream center time, the upper track and the lower track, including:

[0043] The average of the upper track and the lower track is taken as the middle track, the difference between the pressure value at the upstream center time and the middle track is taken as the first term, the reciprocal of the difference between the upper track and the lower track is taken as the second term, and the product of the first term and the second term is taken as the first dosing marker value.

[0044] The first dosing marker value satisfies the relationship: , wherein the first dosing marker value at the upstream center time is represented by , the pressure value at the upstream center time is represented by , the upper track is represented by , and the lower track is represented by

[0045] In order to calculate the flow time of the liquid medicine in the flow channel from the inlet to the outlet, the distance between the inlet and the outlet of the flow channel needs to be obtained first, and the time required for the fluid flow is calculated in combination with the standard flow rate. Specifically, the ratio of the distance of the flow channel to the standard flow rate is taken as the time interval, that is, the flow time of the liquid medicine from the inlet to the outlet. Then, the upstream center time is added to the calculated time interval to obtain the downstream center time. In this way, the time efficiency of the liquid medicine in the flow channel can be calculated, and accurate time information can be provided for dynamic monitoring and control.

[0046] The downstream window is constructed with the downstream center time as the midpoint, and the second quantitative marker value at any sampling time in the downstream window is obtained according to the calculation method of the first quantitative marker value, wherein the size of the downstream window is equal to the length of the quantitative end section. The similarity between the first quantitative marker value of the upstream center time and the second quantitative marker value at any sampling time in the downstream window is calculated, and the maximum value of the similarity is taken as the matching time of the upstream center time. It should be noted that the similarity can be represented by the reciprocal of the Euclidean distance.

[0047] S4: Calculate the actual flow rate based on the matching time, the upstream center time and the obtained flow channel length, correct the drug content accumulation value according to the actual flow rate, obtain the corrected drug content accumulation value, and complete the quantitative control according to the corrected drug content accumulation value.

[0048] In one embodiment, the difference between the matching time and the upstream center time is calculated as a first difference, and the ratio of the distance to the first difference is taken as the actual flow rate.

[0049] The actual flow rate is substituted into the standard flow rate in the drug content formula, that is, the product of the average density, the cross-sectional area, the actual flow rate and the length of any sub-section is taken as the drug content correction value, and the drug content correction value of each sub-section is obtained by traversal. The cumulative value of the drug content correction values of all sub-sections is taken as the corrected drug content accumulation value.

[0050] When the corrected drug content accumulation value is not less than the preset required drug amount, it indicates that the liquid medicine meets the requirements, at this time, the pneumatic plastic regulating valve needs to be closed, and no supplementary liquid medicine is needed, so as to complete the quantitative control.

[0051] The system comprises a processor and a memory, and the memory stores computer program instructions which, when executed by the processor, implement the intelligent control method for precise quantitative control of the pneumatic plastic regulating valve according to the first aspect of the present application.

[0052] The system also comprises a communication bus and a communication interface and other components familiar to those skilled in the art, the settings and functions of which are known in the art, and therefore will not be described here.

[0053] It should be noted that, for the person of ordinary skill in the art, several modifications and improvements can be made without departing from the inventive concept, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application patent should be subject to the appended claims.

Claims

1. A precise quantitative intelligent control method for a pneumatic plastic regulating valve, characterized in that, The method comprises the following steps: Collecting an upstream pressure sequence at the inlet of the flow channel and a downstream pressure sequence at the outlet of the flow channel; Constructing a time window for the upstream pressure sequence, clustering the pressure values in the time window to obtain sub-sections, calculating the drug content of any sub-section, and calculating the cumulative value of the drug content of all sub-sections; in response to the cumulative value of the drug content being not less than a preset required drug amount, taking the last sub-section as a quantitative end section; Taking the center point of the quantitative end section as an upstream center time, calculating the average pressure of all sampling times in the quantitative end section, obtaining the upper rail and the lower rail of the quantitative end section, and calculating a first quantitative marker value based on the average pressure, the pressure value at the upstream center time, the upper rail and the lower rail; taking the downstream center time as the midpoint to construct a downstream window, and obtaining a second quantitative marker value of any sampling time in the downstream window according to the calculation method of the first quantitative marker value, wherein the size of the downstream window is equal to that of the quantitative end section; calculating the similarity between the first quantitative marker value and any second quantitative marker value, and taking the maximum value of the similarity as a matching time of the upstream center time; Calculating the actual flow rate based on the matching time, the upstream center time and the obtained length of the flow channel, correcting the cumulative value of the drug content according to the actual flow rate, obtaining a corrected cumulative value of the drug content, and completing the quantitative control according to the corrected cumulative value of the drug content; The method comprises the following steps: Obtaining the viscosity of the liquid medicine, calculating the standard deviation of all pressures in the quantitative end section as a quantitative standard deviation; Mapping the viscosity using a hyperbolic tangent function to obtain a mapping value, and taking the sum of 1.5 and the mapping value as a viscosity coefficient; Calculating a first product of the viscosity coefficient and the quantitative standard deviation; Taking the sum of the average pressure and the first product as the upper rail, and taking the difference between the average pressure and the first product as the lower rail.

2. The method of claim 1, wherein, The method comprises the following steps: Taking the length of time required to obtain the preset required drug amount in the history as a marker length, and constructing a time window with the first sampling time of the upstream pressure sequence as the starting point and the marker length as the size.

3. The method of claim 2, wherein the method further comprises: The method comprises the following steps: Obtaining the viscosity of the liquid medicine, calculating the pressure standard deviation of the time window; Mapping the viscosity using a hyperbolic tangent function to obtain a mapping value, calculating the sum of 1 and the mapping value, and taking the product of the sum and the pressure standard deviation as a neighborhood radius; Calculating the reciprocal of the sum of 1 and the pressure standard deviation, and taking the integral result of the product of the reciprocal, the marker length and the sampling frequency of the upstream pressure sequence as the minimum number; Clustering the pressure values in the time window based on the neighborhood radius and the minimum number to obtain a plurality of effective clustering clusters, one effective clustering cluster corresponding to one sub-section, and the number of sampling times in the effective clustering cluster being not less than the minimum number; Specifically, in response to the condition that the difference in pressure values ​​between any two sampling moments in a cluster is not greater than the neighborhood radius and the number of sampling moments in the cluster is less than the minimum number, a new cluster is obtained by adding the next sampling moment adjacent to the cluster. In response to the condition that the difference in pressure values ​​between any two sampling moments in the new cluster is greater than the neighborhood radius and the number of sampling moments in the new cluster is less than the minimum number, sampling moments are added in chronological order until the number of sampling moments in the new cluster equals the minimum number, thus obtaining a valid cluster. In response to the condition that the difference in pressure values ​​between any two sampling moments in the new cluster is not greater than the neighborhood radius and the number of sampling moments in the new cluster is not less than the minimum number, sampling moments are added in chronological order until the difference in pressure values ​​between any two sampling moments in the new cluster is greater than the neighborhood radius, at which point the addition stops, and a valid cluster is obtained.

4. The method of claim 1, wherein the method further comprises: The calculation of the drug content of any sub-segment includes: Obtain the cross-sectional area and standard flow velocity of the flow channel; Calculate the density of the liquid medicine at each sampling time in any sub-segment, and calculate the average density of the liquid medicine at all sampling times in any sub-segment; The drug content is calculated as the product of the mean density, cross-sectional area, standard flow rate, and length of any segment.

5. The method of claim 1, wherein the method further comprises: The first quantitative marker value includes: The average of the upper and lower rails is taken as the middle rail. The difference between the pressure value at the upstream center moment and the middle rail is taken as the first term. The reciprocal of the difference between the upper and lower rails is taken as the second term. The product of the first and second terms is taken as the first quantitative marker value.

6. The method of claim 1, wherein the method further comprises: The process of obtaining the downstream center time based on the upstream center time includes: Obtain the distance between the flow channel inlet and outlet, use the ratio of the distance to the standard flow velocity as the time interval, and use the sum of the upstream center time and the time interval as the downstream center time.

7. The method of claim 6, wherein the method further comprises: The actual flow velocity includes: The difference between the matching time and the upstream center time is calculated as the first difference, and the ratio of the distance to the first difference is taken as the actual flow velocity.

8. The method of claim 1, wherein the method further comprises: The step of correcting the cumulative drug content value based on the actual flow rate to obtain the corrected cumulative drug content value includes: Obtain the cross-sectional area of ​​the flow channel; Calculate the density of the liquid medicine at each sampling time in any sub-segment, and calculate the average density of the liquid medicine at all sampling times in any sub-segment; The product of the mean density, cross-sectional area, actual flow velocity, and length of any segment is used as the drug content correction value. The drug content correction value of each segment is obtained by iterating through the segments, and the cumulative value of the drug content correction values ​​of all segments is used as the cumulative drug content correction value.

9. A precision quantitative intelligent control system for a pneumatic plastic regulating valve, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement a precise quantitative intelligent control method for a pneumatic plastic regulating valve according to any one of claims 1-8.

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