Intelligent digital dosing method and system based on edge calculation

Abstract

The invention belongs to the technical field of intelligent control, and particularly relates to an intelligent digital dosing method and system based on edge computing, and the method comprises the following steps: calculating a system dynamic inertia period representing the comprehensive time lag of a dosing process through the pipeline volume, the real-time flow velocity and the reference reaction time, and calculating the pH value of the dosing process according to the inertia period and the real-time change rate of the pH value. The static deviation between the pH value and the target set value is corrected to obtain a prospective state deviation, a dynamic control gain modulation factor is calculated through a nonlinear mapping function, the dynamic control gain modulation factor is applied to a feedback control algorithm, and the accurate control quantity of the digital dosing pump is generated and driven. According to the invention, oscillation in the control process is inhibited, and the stability of the system, the dosing accuracy and the self-adaptive ability to the change of working conditions are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control. More particularly, the present application relates to an intelligent digital dosing method and system based on edge computing. BACKGROUND

[0002] In industrial processes such as chemical production and water treatment, accurate control of the dosage of chemical agents is a key link to ensure product quality, treatment effect and cost control.

[0003] Traditional dosing systems mostly use manual timing and quantitative or simple open-loop control, which is difficult to cope with real-time changes in working conditions, leading to waste of chemicals or substandard treatment.

[0004] To solve this problem, an intelligent digital dosing system integrating sensors and control algorithms has emerged, especially a dosing system based on edge computing, which realizes low delay and high autonomy of control by processing data and making decisions on edge devices close to the data source; such systems usually obtain fluid parameters in real time through sensors, calculate the required amount of chemicals by algorithms in the edge controller, and then drive digital dosing pumps to execute dosing.

[0005] However, the existing technology in implementing intelligent control mainly relies on snapshot data collected by sensors at the current time, which ignores the inherent physical and chemical hysteresis of the dosing process itself, such as the transmission delay from the dosing point to the downstream sensor detection point, and the mixing and reaction time required for the chemical agents to react with the fluid and reach a stable state. If the controller cannot effectively distinguish between the two states of the drug effect not yet being reflected and the drug amount being insufficient, and only adjusts according to the instantaneous error, it is easy to cause frequent adjustment of control commands, leading to serious overshoot and sustained oscillation of the system near the target set point, not only causing additional consumption of chemicals, but also making it difficult to maintain stable water quality. SUMMARY

[0006] To solve the above technical problem of how to accurately model the timing dynamic characteristics of the dosing process at the edge to overcome the control instability caused by system inertia, the present application provides solutions in the following aspects.

[0007] In a first aspect, the present application provides an intelligent digital dosing method based on edge computing, comprising: acquiring real-time flow rate and pH value of fluid in a dosing pipeline; calculating system dynamic inertia period representing comprehensive time lag of the dosing process through pipeline volume, the real-time flow rate and a preset reference reaction time; calculating a prospective state deviation for predicting future state of the system according to static deviation of the pH value from a target set value of the pH value and product of variation rate of the pH value and the system dynamic inertia period; calculating a dynamic control gain modulation factor through a nonlinear mapping function based on absolute value of the prospective state deviation, the nonlinear mapping function making the dynamic control gain modulation factor positively correlated with the absolute value of the prospective state deviation; combining the dynamic control gain modulation factor into a feedback control algorithm of the system to obtain control amount of the digital dosing pump at next time, and driving the digital dosing pump to perform dosing operation.

[0008] The present application dynamically quantifies comprehensive lag of medicament transmission and chemical reaction by introducing system dynamic inertia period, calculates a prospective state deviation capable of predicting future state of the system based thereon, dynamically adjusts gain of a control algorithm according to the predicted deviation size by using a nonlinear function, realizes intelligent adjustment of strong intervention for large deviation, fine adjustment for small deviation, and early deceleration for good trend, and fundamentally solves the problem of control instability caused by lag by combining the prospective state deviation and adaptive gain, so that the dosing process is more stable and accurate, and medicament waste and water quality fluctuation are effectively avoided.

[0009] Preferably, the system dynamic inertia period representing comprehensive time lag of the dosing process is calculated through pipeline volume, the real-time flow rate and a preset reference reaction time, comprising: ; wherein: represents system dynamic inertia period at the time t; represents pipeline volume from the medicament dosing point to installation position of the downstream sensor; represents real-time flow rate at the time t; , respectively represent reference reaction time and reference flow rate; is a mixing efficiency influence coefficient; represents a natural exponential function.

[0010] The present application determines system dynamic inertia period representing comprehensive time lag of the dosing process according to physical transmission delay and chemical reaction delay, which can more accurately reflect different influences of flow rate variation on transmission time and mixing reaction efficiency, and provides a more reliable basis for subsequent predictive control.

[0011] Preferably, the step of calculating the forward-looking state deviation for predicting the future state of the system based on the static deviation between the pH value and the target set value, and the product of the rate of change of the pH value and the dynamic inertia period of the system, includes: In the formula: express Foresight bias at any given moment; This indicates the target setpoint for pH value; , They represent time, pH value after fluid mixing reaction at a given time; This indicates the static deviation of the pH value from the target pH setting. Indicates the time interval for data collection; express The rate of change of pH value after the fluid mixing reaction at a given time; express The system's dynamic inertial period at any given moment.

[0012] This invention obtains a forward-looking state deviation for predicting the future state of the system by subtracting a predicted value representing the future trend from the current static deviation. When the system pH value is rapidly approaching the target value, the forward-looking deviation is made much smaller than the static deviation, thereby weakening the control effect in advance and effectively suppressing overshoot.

[0013] Preferably, the step of calculating the dynamic control gain modulation factor based on the absolute value of the forward state deviation using a nonlinear mapping function includes: In the formula: express Dynamic control gain modulation factor at any given time; express Foresight bias at any given moment; Indicates taking the absolute value; Represents the natural exponential function; This represents the gain sensitivity coefficient.

[0014] This invention provides a smooth and continuous curve from 0 to 1 through a nonlinear mapping function, mapping the forward state deviation to a dynamic control gain modulation factor. This makes the adjustment process of the control force smooth, enabling rapid response when the deviation is large and fine-tuning when the deviation is small. It effectively avoids system jitter caused by sudden gain changes and ensures the smoothness and stability of the control process.

[0015] Preferably, the step of incorporating the dynamic control gain modulation factor into the system's feedback control algorithm to obtain the control quantity of the digital dosing pump at the next moment includes: In the formula: express The control quantity of the digital dosing pump at any given time; express The basic feedforward dosage at any given time; , These are the proportional gain coefficient and integral gain coefficient in the PID controller, respectively. express Dynamic control gain modulation factor at any given time; express The static deviation of pH value after fluid mixing reaction at time t, and , express pH value after fluid mixing reaction at time [time]. This indicates the target set value for pH.

[0016] This invention adds feedforward control based on real-time flow rate to the feedback control, which enables the system to respond more quickly and with smaller deviations when dealing with changes in major operating conditions such as flow rate, thereby enhancing the system's anti-disturbance capability and dynamic performance.

[0017] Preferably, the basic feedforward dosage , express Real-time flow rate at any given moment This indicates the target concentration that the drug needs to achieve in the pipeline. This indicates the concentration of the pharmaceutical stock solution itself.

[0018] Preferably, the driving of the digital dosing pump to perform the dosing operation includes: transmitting the calculated control quantity of the digital dosing pump through an edge computing device. The signal is converted into a hardware control signal, which drives the digital dosing pump to precisely execute the dosing operation, thereby completing a closed-loop control.

[0019] Preferably, the preset baseline reaction time, target pH value, and pipe volume are... The target concentration of the agent to be achieved in the pipeline and reference flow rate These are all system calibration parameters; among them, the target setpoint for pH and the reference flow rate are... The target concentration of the agent to be achieved in the pipeline The system's human-machine interface allows operators to set parameters according to process requirements; and to consider the pipeline distance between the reagent dosing point and the downstream sensor installation location. and pipe diameter Calculate the pipe volume from the reagent dosing point to the downstream sensor installation location. The baseline reaction time was obtained through experimental calibration.

[0020] Preferably, the method for obtaining the reference reaction time is: at a reference flow rate , a dosing operation is performed once according to the process requirements; the change curve of the pH value is continuously monitored and recorded by the downstream pH sensor; the time elapsed from the start of the dosing of the reagent to the reading of the downstream pH sensor reaching and maintaining the target set value is measured as the reaction time obtained in one experiment; a plurality of reaction times are obtained by multiple implementations, and the mean value of the plurality of reaction times is taken as the reference reaction time .

[0021] In a second aspect, the present application provides an intelligent digital dosing system based on edge computing, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned intelligent digital dosing method based on edge computing is realized.

[0022] By adopting the above technical solution, the above-mentioned intelligent digital dosing method based on edge computing is generated into a computer program and stored in the memory to be loaded and executed by the processor, so that a terminal device is manufactured according to the memory and the processor, and the use is facilitated.

[0023] The present application has the following beneficial effects: The present application dynamically quantifies the comprehensive lag of the reagent transmission and the chemical reaction by introducing the system dynamic inertia period, calculates the prospective state deviation capable of predicting the future state of the system based thereon, dynamically adjusts the gain of the control algorithm according to the predicted deviation size by using a nonlinear function, realizes the intelligent adjustment of strong intervention when the deviation is large, fine adjustment when the deviation is small, and early deceleration when the trend is good, and fundamentally solves the control instability problem caused by the lag by combining the prospective and adaptive gain, so that the dosing process is more stable and accurate, and the waste of reagents and the water quality fluctuation are effectively avoided. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 is a flowchart schematically showing an intelligent digital dosing method based on edge computing in the present application; Figure 2 is a pH curve schematically showing the pH values at multiple time points; Figure 3 is a flow rate curve schematically showing the real-time flow rates at multiple time points; Figure 4 is a curve schematically showing the control amount of the digital dosing pump at each time point. DETAILED DESCRIPTION

[0025] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative work are within the protection scope of the present application.

[0026] The specific implementation of the present application will be described in detail below with reference to the drawings.

[0027] The embodiments of the present application disclose an intelligent digital dosing method based on edge computing, referring to Figure 1 , comprising steps S1-S5: S1, through a plurality of sensors connected with the edge computing device, real-time flow rate and pH value in the dosing process are collected in real time.

[0028] It should be noted that, in order to realize the precise closed-loop control of the dosing process, the real-time state information and basic physical parameters of the system need to be obtained first.

[0029] Specifically, through the flow sensor deployed on the dosing pipeline, the real-time flow rate of the fluid in the pipeline at each time is obtained; through the pH sensor deployed at the specified position downstream of the dosing point, the real-time chemical parameter after the fluid mixing reaction, i.e. the pH value, is obtained.

[0030] Further, the calibration parameters of the system are obtained, including the target set value of the pH value , the pipeline volume between the dosing point and the installation position of the downstream sensor , the target concentration of the reagent required to be reached in the pipeline , the reference reaction time , and the reference flow rate .

[0031] Among them, the target set value of the pH value , the reference flow rate , and the target concentration of the reagent required to be reached in the pipeline are set by the operator according to the process requirements through the man-machine interface of the system.

[0032] In addition, according to the pipeline distance between the dosing point and the installation position of the downstream sensor and the pipeline diameter , the pipeline volume between the dosing point and the installation position of the downstream sensor is calculated .

[0033] And the reference reaction time These are inherent system parameters obtained through experimental calibration, and the baseline reaction time represents the time required for the chemical reaction to reach stability at a baseline flow rate. The specific method is as follows: at the baseline flow rate... Next, perform a single dosing operation according to process requirements; continuously monitor and record the pH value change curve using a downstream pH sensor; measure the time elapsed from the start of reagent dosing until the downstream pH sensor reading reaches and remains at the target set value, and use this as the reaction time obtained in one experiment; obtain multiple reaction times through multiple trials, and use the average of the multiple reaction times as the baseline reaction time. .

[0034] S2. Calculate the system dynamic inertial period, which characterizes the overall time lag of the dosing process, using the pipeline volume, real-time flow rate, and preset baseline reaction time.

[0035] It should be noted that the total system delay is not a fixed constant; it is closely related to both the physical transport velocity of the fluid and the chemical reaction kinetics. Higher flow rates result in shorter times for the reagent to reach the detection point, but increased fluid turbulence may promote mixing, thus shortening the reaction settling time. Therefore, an index that dynamically reflects the overall effect, namely the system dynamic inertial period, is needed to characterize the overall time lag effect during the dosing process.

[0036] Specifically, by using the pipeline volume, real-time flow rate at each moment, and baseline reaction time, the physical transport delay caused by changes in flow rate and the chemical reaction delay caused by changes in mixing reaction efficiency are obtained. Then, the system dynamic inertial period at each moment is calculated, characterizing the comprehensive time lag effect during the dosing process. The formula for calculating the system dynamic inertial period at each moment is as follows: ; In the formula: express The system dynamic inertia period at any given moment, in seconds, represents the estimated total time from the application of control to the generation of a stable response under the current operating conditions; This indicates the pipe volume from the reagent dosing point to the downstream sensor installation location; express Real-time flow rate at any given moment; , These represent the baseline reaction time and the baseline flow rate, respectively. The mixing efficiency influence coefficient is used to characterize the degree of influence of flow rate change on reaction time, and the value range of the mixing efficiency influence coefficient is [0.1, 0.5]. When the value is small, the effect of flow rate changes on the correction of reaction delay is weak. When the value is large, the correction effect is too sensitive, therefore, Setting it to 0.2 can achieve good dynamic response under most operating conditions; This represents the natural exponential function.

[0037] It should be noted that when real-time flow rate is detected... When the value is zero, the system suspends the output update of the control quantity of the digital dosing pump until the flow rate returns to normal.

[0038] Among them, based on real-time flow rate The inertial period, which characterizes the speed of the entire system's response, is calculated. System dynamic inertia period It consists of two parts: Part 1 This represents the physical transmission delay, when the flow rate... As the flow rate increases, this term decreases; the second part represents the chemical reaction delay, which occurs when the real-time flow rate increases. greater than the reference flow rate At that time, the exponent term Reducing the flow rate decreases the reaction delay, simulating a scenario where mixing efficiency is improved and the reaction is accelerated at high flow rates; conversely, when the flow rate decreases, the reaction delay will increase accordingly.

[0039] S3. Calculate the forward-looking state deviation used to predict the future state of the system based on the static deviation between the pH value and the target pH value, and the product of the rate of change of pH value and the dynamic inertial period of the system.

[0040] It should be noted that the pH value collected by the sensor after the fluid mixing reaction is actually the result of control action at a certain point in the past. To make accurate current control decisions, the controller needs to anticipate the future state of the system and consider the system state in inertial time. By analyzing the internal trends and combining them with the system's dynamic inertial period, we can calculate the system's forward-looking state deviation and avoid overreacting based on lagging information.

[0041] Specifically, based on the difference between the pH value after the fluid mixing reaction at each time point and the target pH value, and the product of the rate of change of the pH value after the fluid mixing reaction and the system's dynamic inertial period, the forward-looking state deviation at each time point is calculated to predict the future state of the system. The specific calculation formula is as follows: ; In the formula: express Foresight bias at any given moment; This indicates the target setpoint for pH value; , They represent time, pH value after fluid mixing reaction at a given time; denotes the time interval of data collection; denotes the rate of change of pH value after fluid mixing reaction at the moment; denotes the system dynamic inertia period at the moment.

[0042] wherein, denotes the static deviation of pH value after fluid mixing reaction at the moment; denotes the trend prediction of pH value after fluid mixing reaction at the moment, which estimates the amount of change of pH value caused by the current change trend in the future time; the present application subtracts the trend prediction term from the static deviation ; when is rapidly approaching , is a larger positive value, so that the prospective state deviation at the moment is much smaller than the current static deviation, thereby sending an early deceleration signal to the controller, thereby effectively suppressing overshoot.

[0043] S4, based on the prospective state deviation, the dynamic control gain modulation factor is calculated through a nonlinear mapping function.

[0044] It should be noted that the adjustment strength of the controller should match the estimated future deviation: when the prospective state deviation is large, strong control should be adopted to quickly respond to the deviation; when the prospective state deviation is small or the system is rapidly approaching the target, soft control should be adopted to ensure stability.

[0045] Specifically, according to the prospective state deviation at each moment, the dynamic control gain modulation factor at each moment is calculated through a nonlinear mapping function, and the nonlinear mapping function makes the dynamic control gain modulation factor positively correlated with the absolute value of the prospective state deviation.

[0046] The specific calculation formula is as follows: ; In the formula, denotes the dynamic control gain modulation factor at the moment, which takes a value in the range of [0, 1); denotes the prospective state deviation at the moment; denotes taking the absolute value; denotes the natural exponential function; This represents the gain sensitivity coefficient, used to adjust the dynamic control gain modulation factor. With forward-looking state bias The degree of drastic change, ranging from [1, 5], with smaller values... The value makes the gain change gradual, the system response is soft but slow, and a larger value... The value makes the gain response more sensitive, therefore, Setting it to 2.5 allows for a faster response time while maintaining stability.

[0047] Among them, by forward state deviation The input is fed into a nonlinear mapping function to obtain the dynamic control gain modulation factor. :when When the absolute value of the denominator approaches 0, it means that the system state, after prediction, is very close to the target setpoint. Approaching 2, making Approaching 0, the controller's output will be significantly reduced, thus playing a role in fine-tuning and stabilizing the system; when When the absolute value of is large, it indicates that the system state, after prediction, will still be far from the target setpoint. In this case, the denominator approaches 1, making As the value approaches 1, the controller will adjust more aggressively to respond quickly to deviations.

[0048] S5. Integrate the dynamic control gain modulation factor into the system's feedback control algorithm to obtain the control quantity of the digital dosing pump at the next moment, and drive the digital dosing pump to perform the dosing operation.

[0049] Specifically, the dynamic control gain modulation factor is incorporated into the system's feedback control algorithm to obtain the control quantity of the digital dosing pump at the next moment; then, the calculated control quantity of the digital dosing pump is transmitted through an edge computing device. The signal is converted into a hardware control signal, which drives the digital dosing pump to precisely execute the dosing operation, thereby completing a closed-loop control.

[0050] The formula for calculating the control quantity of the digital dosing pump at the next moment is as follows: ; In the formula: express The control quantity of the digital dosing pump at any given time; express The base feedforward dosage at each time step, and the base feedforward dosage , express Real-time flow rate at any given moment This indicates the target concentration that the drug needs to achieve in the pipeline. represents the self-concentration of the medicament stock solution; 、 respectively represent the proportional gain coefficient and the integral gain coefficient in the PID controller; represents the dynamic control gain modulation factor at the moment of t; represents the static deviation of the pH value after the fluid mixing reaction at the moment of t; represents the dynamic control gain modulation factor at the moment of t; represents the static deviation of the pH value after the fluid mixing reaction at the moment of t; , represents the pH value after the fluid mixing reaction at the moment of t; represents the target set value of the pH value.

[0051] wherein the finally obtained control amount of the digital dosing pump is composed of a basic feedforward part and a dynamically modulated feedback part, wherein the feedback part includes the proportional term and the integral term of the PID, and the overall action strength of the feedback part is adjusted in real time: when the system tends to be stable, approaches 0, the feedback regulation action is inhibited, avoiding unnecessary control actions and oscillations caused by small disturbances near the set point, and when the system deviation is large, approaches 1, the feedback regulation action is fully released, ensuring the rapid response capability of the system.

[0052] wherein in the PID controller, the proportional gain coefficient and the integral gain coefficient directly affect the response speed, stability and steady-state accuracy of the system, wherein the proportional gain coefficient directly outputs the control amount according to the current error size, reacts quickly, and the larger the proportional gain coefficient is, the faster the response is, but an excessively large proportional gain coefficient is easy to cause overshoot, oscillation or even instability, the integral gain coefficient eliminates steady-state error by accumulating historical errors, and the larger the integral gain coefficient is, the faster the steady-state error is eliminated, but an excessively large integral gain coefficient is easy to cause overshoot, integral saturation or system oscillation; therefore, the proportional gain coefficient and the integral gain coefficient are determined by the critical proportional method (Ziegler-Nichols method), and the specific steps are as follows: the integral gain coefficient = 0, only the proportional gain coefficient is used for control, the proportional gain coefficient is gradually increased until the system output appears equal-amplitude oscillation, the critical gain and the oscillation period are recorded, and , are set according to the Z-N formula.​​

[0053] For example, when the system calibration parameters include: the target setpoint for pH value =8.5, Pipeline volume from the reagent dosing point to the downstream sensor installation location =9.8m 3 The target concentration of the agent to be achieved in the pipeline =0.05mol / m 3 Reference reaction time =2min and baseline flow rate =10m 3 / min, the intrinsic concentration of the reagent stock solution =1000 mol / m 3 And the proportional gain coefficient of the PID controller =0.08 and integral gain coefficient When the pH value is 0.02, the pH curve composed of pH values ​​at multiple moments throughout the entire regulation process is as follows: Figure 2 As shown, the obtained pH curve smoothly approaches the target pH value, and the oscillations near the target value are greatly reduced; the flow velocity curve composed of real-time flow velocities at multiple times is shown in the figure. Figure 3 As shown, the curves composed of the control quantities of the digital dosing pump at each moment are as follows: Figure 4 As shown, the dosage can be adjusted quickly and smoothly during flow rate changes, rather than reacting drastically after deviations occur.

[0054] This invention also discloses an intelligent digital dosing system based on edge computing, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement an intelligent digital dosing method based on edge computing according to the present invention.

[0055] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

Claims

1. A smart digital dosing method based on edge computing, characterized in that, include: Obtain the real-time flow rate and pH value of the fluid in the dosing pipeline; The system dynamic inertial period, which characterizes the overall time lag of the dosing process, is calculated using the pipeline volume, the real-time flow rate, and the preset baseline reaction time. Based on the static deviation between the pH value and the target pH value, and the product of the rate of change of the pH value and the dynamic inertial period of the system, a forward-looking state deviation for predicting the future state of the system is calculated. Based on the absolute value of the prospective state deviation, the dynamic control gain modulation factor is calculated through a nonlinear mapping function, wherein the nonlinear mapping function makes the dynamic control gain modulation factor positively correlated with the absolute value of the prospective state deviation. By incorporating the dynamic control gain modulation factor into the system's feedback control algorithm, the control quantity of the digital dosing pump at the next moment is obtained, driving the digital dosing pump to perform the dosing operation.

2. The intelligent digital dosing method based on edge computing according to claim 1, characterized in that, The calculation of the system dynamic inertia period, which characterizes the overall time lag of the dosing process, using the pipeline volume, the real-time flow rate, and the preset baseline reaction time, includes: ； In the formula: express The system's dynamic inertial period at any given moment; This indicates the pipe volume from the reagent dosing point to the downstream sensor installation location; express Real-time flow rate at any given moment; , These represent the baseline reaction time and the baseline flow rate, respectively. The coefficient representing the influence of mixing efficiency; This represents the natural exponential function.

3. The intelligent digital dosing method based on edge computing according to claim 2, characterized in that, The calculation of the forward-looking state deviation for predicting the future state of the system, based on the static deviation between the pH value and the target pH value, and the product of the rate of change of the pH value and the dynamic inertial period of the system, includes: ； In the formula: express Foresight bias at any given moment; This indicates the target setpoint for pH value; , They represent time, pH value after fluid mixing reaction at a given time; This indicates the static deviation of the pH value from the target pH setting. Indicates the time interval for data collection; express The rate of change of pH value after the fluid mixing reaction at a given time; express The system's dynamic inertial period at any given moment.

4. The intelligent digital dosing method based on edge computing according to claim 1, characterized in that, The dynamic control gain modulation factor, calculated using a nonlinear mapping function based on the absolute value of the forward-looking state deviation, includes: ； In the formula: express Dynamic control gain modulation factor at any given time; express Foresight bias at any given moment; Indicates taking the absolute value; Represents the natural exponential function; This represents the gain sensitivity coefficient.

5. The intelligent digital dosing method based on edge computing according to claim 3, characterized in that, The step of incorporating the dynamic control gain modulation factor into the system's feedback control algorithm to obtain the control quantity of the digital dosing pump at the next moment includes: ； In the formula: express The control quantity of the digital dosing pump at any given time; express The basic feedforward dosage at any given time; , These are the proportional gain coefficient and integral gain coefficient in the PID controller, respectively. express Dynamic control gain modulation factor at any given time; express The static deviation of pH value after fluid mixing reaction at time t, and , express pH value of the fluid after the reaction at a given time. This indicates the target set value for pH.

6. The intelligent digital dosing method based on edge computing according to claim 5, characterized in that, The basic feedforward dosage , express Real-time flow rate at any given moment This indicates the target concentration that the drug needs to achieve in the pipeline. This indicates the concentration of the pharmaceutical stock solution itself.

7. The intelligent digital dosing method based on edge computing according to claim 1, characterized in that, The drive digital dosing pump performs dosing operations, including: The control quantity of the digital dosing pump is calculated through edge computing devices. The signal is converted into a hardware control signal, which drives the digital dosing pump to precisely execute the dosing operation, thereby completing a closed-loop control.

8. The intelligent digital dosing method based on edge computing according to any one of claims 6, characterized in that, The preset baseline reaction time, target pH value, and pipe volume are... The target concentration of the agent to be achieved in the pipeline and reference flow rate These are all system calibration parameters; Among them, the target set value of pH value and the reference flow rate The target concentration of the agent to be achieved in the pipeline The system's human-machine interface allows operators to set parameters according to process requirements; Based on the pipeline distance between the reagent dosing point and the downstream sensor installation location and pipe diameter Calculate the pipe volume from the reagent dosing point to the downstream sensor installation location. ; The baseline reaction time was obtained through experimental calibration.

9. The intelligent digital dosing method based on edge computing according to claim 8, characterized in that, The method for obtaining the reference reaction time is as follows: at a reference flow rate Next, perform a single dosing operation according to process requirements; continuously monitor and record the pH value change curve using a downstream pH sensor; measure the time elapsed from the start of reagent dosing until the downstream pH sensor reading reaches and remains at the target set value, and use this as the reaction time obtained in one experiment; obtain multiple reaction times through multiple trials, and use the average of the multiple reaction times as the baseline reaction time. .

10. An intelligent digital dosing system based on edge computing, characterized in that, include: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement an intelligent digital dosing method based on edge computing according to any one of claims 1-9.

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