High-precision automatic mixing and concentration closed-loop control system
The solution concentration control system, which integrates multi-sensor fusion and adaptive closed-loop control, solves the problems of accuracy and consistency in concentration control during traditional solution mixing processes. It achieves high-precision automated mixing and concentration closed-loop control, thereby improving production efficiency and product quality.
Patent Information
- Application Number
- CN202511229204.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-29
- Publication Date
- 2025-10-17
AI Technical Summary
In traditional solution mixing processes, it is difficult to achieve high precision in controlling solution concentration. This is affected by environmental changes and differences in operator skills, making it difficult to guarantee product quality and consistency.
By employing a multi-sensor fusion detection module, combined with an adaptive closed-loop control module and a fully automatic hybrid execution system, real-time data acquisition and fusion from conductivity, weight, and temperature sensors are used to dynamically adjust PID controller parameters, thereby achieving precise solution preparation and concentration control.
It improves solution mixing accuracy and system stability, reduces the risk of manual operation, ensures product quality and batch consistency, optimizes production efficiency, and reduces errors and waste.
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Figure CN120802600A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application relates to the technical field of solution concentration control, in particular to a high-precision automatic mixing and concentration closed-loop control system. BACKGROUND
[0002] With the continuous development of industrial production, especially in the fields of chemical industry, food, pharmaceuticals and the like, the demand for accurate control of the mixing process is increasing, and the traditional solution preparation and mixing process usually relies on manual monitoring and experience adjustment, which is easily affected by environmental changes, skill differences of operators and the like, so that the quality and consistency of the final product are difficult to guarantee, therefore, the high-precision automatic mixing and concentration closed-loop control technology has become an important direction for improving production efficiency and guaranteeing product quality.
[0003] In the solution mixing process, the solution concentration is a key quality control parameter, in order to accurately control the solution concentration, it is usually necessary to monitor the physical parameters of the solution in real time, and to adjust the raw material feeding and stirring and the like through appropriate control algorithms, however, due to the mutual influence of multiple physical parameters, the detection accuracy and response speed of a single sensor are difficult to meet the high-precision control demand, in order to overcome these problems, the multi-sensor fusion technology, real-time data acquisition and analysis, and the combination of the adaptive control algorithm are used for concentration closed-loop control, which is an effective means for improving the mixing precision and system stability. SUMMARY
[0004] To solve the above technical problems, a high-precision automatic mixing and concentration closed-loop control system is provided, which solves the above problems.
[0005] To achieve the above purposes, the technical scheme adopted by the application is as follows: A high-precision automatic mixing and concentration closed-loop control system, comprising: A multi-sensor fusion detection module: used for collecting real-time data through integrated conductivity sensors, weight sensors and temperature sensors, and performing fusion processing on the real-time data to generate high-confidence solution concentration values and physical parameter data; An adaptive closed-loop control module: based on the high-confidence solution concentration values and physical parameter data, a mechanism optimization control model is established, the parameters of the PID controller are dynamically adjusted based on the real-time concentration deviation and the system state, and the control model is optimized in combination with an abnormal correction mechanism to generate accurate raw material feeding rate and dilution pump flow adjustment instructions to control the solution concentration in the mixing process; a full-automatic mixing execution system module: according to the instructions from the adaptive closed-loop control algorithm, the raw material feeding, stirring and dilution operations are automatically executed to realize accurate solution preparation, and the final solution concentration meets the preset target, and emergency measures are taken to restore to the set range when the concentration exceeds the standard.
[0006] Preferably, the multi-sensor fusion detection module specifically comprises: The data acquisition unit: measures the conductivity of the solution in real time based on the conductivity sensor, and further obtains the concentration and component changes of the solution, measures the mass of the solution in the container based on the weight sensor, monitors the feeding amount of raw materials and the total mass change of the solution, and detects the temperature of the solution in real time based on the temperature sensor; The data preprocessing unit: processes and filters the original signals from the sensors, removes noise, and calimits and standardizes the signals from different sensors; The data fusion unit: fuses the real-time data from the conductivity, weight, and temperature sensors based on the Kalman filtering algorithm, generates a comprehensive solution concentration value, and through data fusion, comprehensively considers the different characteristics and measurement accuracy of the sensors to obtain a high-confidence solution concentration estimation value; The solution concentration estimation unit: combines the fused data, calculates the final concentration value of the solution through a mathematical model, and based on the conductivity data, weight data, and temperature data, uses a solution concentration-physical quantity relationship model to calculate the solution concentration. The physical parameter generation unit: generates physical parameter data related to the solution concentration, including solution density, viscosity, and conductivity.
[0007] Preferably, the data fusion unit specifically comprises: According to the dynamic equation of the solution concentration change, the state at the next moment is predicted, and the error covariance of the predicted state is updated through the error model of the system; Receive new measurement data from the sensor, calculate the Kalman gain, balance the contribution of the predicted value and the observed value, and make the system able to correct the estimation error; Update the solution concentration estimation value at the current moment using the Kalman gain, combine the predicted value with the actual measurement value, and generate the final solution concentration estimation through weighted average.
[0008] Preferably, the dynamic equation of the solution concentration change is used to predict the state at the next moment, and the error covariance of the predicted state is updated through the error model of the system, specifically comprising: Wherein, the state prediction formula is: In the formula, is the predicted value of the state at the next moment, is the state estimation value at the current moment, A k is the state transition matrix, B k is the control input matrix, ∪ k is the control input; Wherein, the error covariance update formula is: In the formula, Pk+1|k is the covariance matrix of the predicted state, representing the uncertainty of the predicted value, P k|k is the state estimation error covariance matrix at the current time, A k is the state transition matrix, describing the change of the state from time k to k+1, Q k process noise covariance matrix, state transition matrix A k transpose of the state transition matrix.
[0009] Preferably, the receiving of new measurement data from the sensor, the calculation of the Kalman gain, and the balancing of the contributions of the predicted value and the observed value to enable the system to correct the estimation error specifically include: wherein the formula for calculating the Kalman gain is: wherein P k|k-1 is the error covariance of the prediction, H k is the observation matrix, R k is the measurement noise covariance, K k is the Kalman gain, is the transpose of the observation matrix.
[0010] Preferably, the adaptive closed-loop control module specifically includes: PID controller parameter dynamic adjustment unit: dynamically adjusting the proportional, integral, and derivative coefficients of the PID controller according to the real-time concentration deviation and system state, and correcting the solution concentration deviation by optimizing the PID parameters; abnormal correction mechanism: when the system detects an abnormality, including sensor failure and concentration exceeding the set range, starting the abnormal correction mechanism to trigger the control algorithm and adjust the controller parameters; instruction generation and output unit: generating precise raw material feeding rate and dilution pump flow adjustment instructions based on the adjustment amount output by the PID controller, and transmitting these instructions to the full-automatic mixing execution system module; real-time feedback and closed-loop correction unit: adjusting the control strategy according to the real-time concentration information fed back by the execution system, and re-optimizing the control system through the closed-loop correction process if the control does not achieve the expected result.
[0011] Preferably, the PID controller parameter dynamic adjustment unit specifically includes: wherein the basic formula of the PID controller is: wherein uk is the control amount, representing the output of the PID controller, K p is the proportional coefficient, K i is the integral coefficient, representing the response degree of the control cumulative error, K dis the differential coefficient, controlling the error change rate, the response degree of e k is the error at time k, and Δt is the time interval, e k -e k-1 is the difference between the current error and the error at the previous time; According to the real-time concentration deviation and the system state, the PID parameters are dynamically adjusted.
[0012] Preferably, the dynamically adjusting the PID parameters according to the real-time concentration deviation and the system state specifically comprises: According to the current concentration deviation, the proportional coefficient is adjusted, according to the historical cumulative error, the integral coefficient is adjusted, and according to the error change rate, the differential coefficient is adjusted. An adaptive control algorithm is used to automatically adjust the proportional coefficient, the integral coefficient and the differential coefficient, a tolerance threshold of the error is set, and if the concentration error exceeds the set threshold, the PID parameter adjustment is started.
[0013] Preferably, the automatically adjusting the proportional coefficient, the integral coefficient and the differential coefficient by using the adaptive control algorithm specifically comprises: The adaptive control algorithm formula is: In the formula, e(t) is the error at time t, w(t) is a weighting factor, n is the order of the error, T is the time interval, and J is the fitness function.
[0014] Preferably, the full-automatic mixing execution system module specifically comprises: The raw material feeding control unit automatically calculates the amount of raw material to be added according to the instructions generated by the control algorithm and the concentration error, and the automatic feeding unit controls the automatic feeding device to feed the precise amount of raw material into the mixing container. The raw material monitoring unit monitors the feeding process in real time to ensure that each raw material is added according to the set proportion, avoids errors, automatically adjusts the stirring speed according to the properties of the solution and the amount of raw material, ensures uniform mixing of the solution, and monitors the state of the solution through a sensor; the dilution ratio calculation unit calculates the amount of diluent to be added according to the concentration error and the properties of the solution, and automatically adjusts the amount of diluent to be added to make the concentration of the solution reach the target value. The feedback adjustment unit adjusts the feeding and stirring operations in real time when the concentration does not reach the set target.
[0015] Compared with the prior art, the beneficial effects of the present application are: The application proposes to accurately estimate the solution concentration and physical parameters through multi-sensor fusion and Kalman filtering algorithm, improve the mixing accuracy, dynamically adjust the PID parameters of the adaptive closed-loop control module, respond to environmental changes and abnormal situations, ensure stable operation of the system, reduce the risk of manual operation of the automatic mixing execution system, adjust the solution concentration in real time, ensure safety and product quality, optimize production efficiency, reduce errors and waste, ensure batch consistency, provide decision support for multi-parameter collaborative control, improve production efficiency through intelligent management, and improve the accuracy and economy of the production process. BRIEF DESCRIPTION OF DRAWINGS
[0016] Figure 1 The system framework diagram of the application is shown in the figure. Figure 2 The internal system framework diagram of the adaptive closed-loop control module in the application is shown in the figure. DETAILED DESCRIPTION
[0017] The following description is used to disclose the application so that those skilled in the art can implement the application. The preferred embodiments in the following description are only as examples, and other obvious modifications can be made by those skilled in the art.
[0018] Referring to Figure 1 The high-precision automatic mixing and concentration closed-loop control system shown in the figure comprises: A multi-sensor fusion detection module is used to collect real-time data by integrating conductivity sensors, weight sensors and temperature sensors, fuse the real-time data, and generate high-confidence solution concentration values and physical parameter data. An adaptive closed-loop control module is based on high-confidence solution concentration values and physical parameter data, establishes a mechanism optimization control model, dynamically adjusts the parameters of the PID controller based on real-time concentration deviation and system state, and combines an abnormal correction mechanism to optimize the control model, generates accurate raw material feeding rate and dilution pump flow adjustment instructions, and controls the solution concentration during the mixing process. An automatic mixing execution system module automatically executes raw material feeding, stirring and dilution operations according to the instructions from the adaptive closed-loop control algorithm, realizes accurate solution preparation, and obtains a final solution concentration that meets the preset target. When the concentration exceeds the standard, emergency measures are taken to restore it to the set range.
[0019] The multi-sensor fusion detection module specifically comprises: A data acquisition unit measures the conductivity of the solution in real time based on the conductivity sensor, and then obtains the concentration and composition changes of the solution, measures the mass of the solution in the container based on the weight sensor, monitors the feeding amount of the raw material and the total mass change of the solution, and detects the temperature of the solution in real time based on the temperature sensor. Data preprocessing unit: processing and filtering raw signals from various sensors, removing noise, calibrating and normalizing signals from different sensors; Data fusion unit: based on Kalman filtering algorithm, real-time data from conductivity, weight and temperature sensors are fused to generate a comprehensive solution concentration value. Through data fusion, the different characteristics and measurement accuracy of sensors are considered to obtain a high confidence solution concentration estimate; Solution concentration estimation unit: combined with the fused data, the final concentration value of the solution is calculated through a mathematical model. Based on conductivity data, weight data and temperature data, a solution concentration-physical quantity relationship model is used to calculate the solution concentration; Physical parameter generation unit: generates physical parameter data related to solution concentration, including solution density, viscosity and conductivity. This module not only integrates conductivity, weight and temperature sensors, but also introduces data preprocessing and fusion technology. Through advanced algorithms such as Kalman filtering, real-time comprehensive processing of data from different sensors is performed to generate high-precision solution concentration values. This multi-sensor fusion method can significantly improve the estimation accuracy of solution concentration and reduce system bias caused by single sensor error.
[0020] The data fusion unit specifically includes: According to the dynamic equation of solution concentration change, the state at the next moment is predicted, and the error covariance of the predicted state is updated through the error model of the system; Receive new measurement data from sensors, calculate Kalman gain, balance the contribution of predicted value and observed value, so that the system can correct the estimation error; Update the solution concentration estimate at the current time using Kalman gain, combine the predicted value with the actual measurement value, and generate the final solution concentration estimate through weighted average.
[0021] According to the dynamic equation of solution concentration change, the state at the next moment is predicted, and the error covariance of the predicted state is updated through the error model of the system specifically includes: Wherein, the state prediction formula is: In the formula, is the predicted value of the state at the next moment, is the state estimate at the current moment, A k is the state transition matrix, B k is the control input matrix, ∪ k is the control input; Wherein, the error covariance update formula is: In the formula, P k+1|kis the covariance matrix of the predicted state, which represents the uncertainty of the predicted value, P k|k is the state estimation error covariance matrix at the current moment, A k is the state transition matrix, which describes the change of state from time k to k+1, Q k The process noise covariance matrix, State transition matrix A k The transpose of By calculating the Kalman gain and correcting the system error, the system can ensure that the error in the solution concentration estimation is corrected in real time. Compared with traditional control algorithms, this method can more effectively balance the observed and predicted values when facing rapidly changing system states, significantly improving the response speed and stability of the control system.
[0022] Receive new measurement data from the sensor, calculate the Kalman gain, balance the contribution of the predicted value and the observed value, and enable the system to correct the estimation error. Specifically, it includes: Among them, the Kalman gain calculation formula is: Where, P k|k-1 is the predicted error covariance, H k is the observation matrix, R k is the measurement noise covariance, K k is the Kalman gain, is the transpose of the observation matrix.
[0023] Reference Figure 2 As shown, the adaptive closed-loop control module specifically includes: PID controller parameter dynamic adjustment unit: According to the real-time concentration deviation and system status, the proportional, integral and differential coefficients of the PID controller are dynamically adjusted to correct the solution concentration deviation by optimizing the PID parameters; Abnormal correction mechanism: When the system detects an abnormality, including sensor failure and concentration exceeding the set range, the abnormal correction mechanism is activated, the control algorithm is triggered, and the controller parameters are adjusted; Instruction generation and output unit: Based on the adjustment amount output by the PID controller, it generates accurate raw material feeding rate and dilution pump flow adjustment instructions, and transmits these instructions to the fully automatic mixing execution system module; Real-time feedback and closed-loop correction unit: adjusts the control strategy based on the real-time concentration information fed back by the execution system. If the control fails to meet expectations, the control system is optimized again through the closed-loop correction process.
[0024] The PID controller parameter dynamic adjustment unit specifically includes: Among them, the basic formula of PID controller is: where uk is the control variable, representing the output of the PID controller, K p is the proportional coefficient, K i is the integral coefficient, controlling the response degree of cumulative error, K d is the derivative coefficient, controlling the response degree of error change rate, e k is the error at time k, Δt is the time interval, e k -e k-1 is the difference between the current error and the error at the previous time; Adjusting the PID parameters dynamically according to the real-time concentration deviation and system state; Through real-time adjustment of PID parameters, this module can respond to real-time changes in solution concentration, reduce over-adjustment and under-adjustment, and improve control accuracy. By dynamically adjusting the PID parameters, the system can more effectively respond to concentration deviations and reduce the time the solution concentration spends outside the target range.
[0025] Adjusting the PID parameters dynamically according to the real-time concentration deviation and system state specifically includes: Adjusting the proportional coefficient according to the current concentration deviation, adjusting the integral coefficient according to the historical cumulative error, and adjusting the derivative coefficient according to the error change rate; Using an adaptive control algorithm to automatically adjust the proportional coefficient, integral coefficient, and derivative coefficient, and setting a tolerance threshold for error. If the concentration error exceeds the set threshold, the PID parameter adjustment is started.
[0026] Using an adaptive control algorithm to automatically adjust the proportional coefficient, integral coefficient, and derivative coefficient specifically includes: where the adaptive control algorithm formula is: where e(t) is the error at time t, w(t) is the weighting factor, n is the order of error, T is the time interval, and J is the fitness function; Using an adaptive control algorithm, PID parameters are automatically adjusted according to error size, time interval, and other key parameters, so that the control system can always maintain excellent performance in different operating environments. This innovation enables the system to automatically optimize its control strategy and dynamically adjust response speed and stability at different operating stages.
[0027] The fully automatic mixing execution system module specifically includes: Raw material feeding control unit: automatically calculating the amount of raw materials to be added according to the instructions generated by the control algorithm and the concentration error, and automatically feeding unit: controlling the automatic feeding equipment to feed the precise amount of raw materials into the mixing container; The raw material monitoring unit monitors the feeding process in real time, ensures that each raw material is added according to the set proportion, avoids errors, automatically adjusts the stirring speed according to the properties of the solution and the feeding amount, ensures uniform mixing of the solution, and monitors the state of the solution through a sensor; the dilution ratio calculation unit calculates the amount of diluent to be added according to the concentration error and the properties of the solution, automatically adjusts the amount of diluent added, and makes the solution concentration reach the target value; The feedback adjustment unit adjusts the feeding and stirring operation in real time when the concentration does not reach the set target.
[0028] In summary, the advantages of the present application are: The present technology utilizes conductivity sensors, weight sensors and temperature sensors through a multi-sensor fusion detection module, collects and fuses data in real time, can more accurately estimate the solution concentration and related physical parameters, the application of Kalman filter algorithm can effectively reduce the sensor error and improve the accuracy of concentration estimation, and further improve the solution mixing precision; The adaptive closed-loop control module dynamically adjusts the parameters of the PID controller based on the real-time concentration deviation and system state, optimizes the proportional, integral and differential coefficients through adaptive algorithm, thereby effectively dealing with environmental changes and system disturbances, especially in the case of sensor failure or abnormal concentration, the system can start the abnormal correction mechanism in time, automatically adjust the control strategy, and ensure stable operation of the mixing process; The use of a fully automatic mixing execution system can accurately control the feeding, stirring and dilution operations of raw materials, avoid the uncertainty and risk brought by manual operation, and quickly start the emergency mechanism when the system detects that the solution concentration exceeds the standard, thereby restoring the solution concentration to the set range through adjusting the feeding rate and dilution pump flow, ensuring product quality and production safety; The present technology can accurately adjust the input amount of raw materials and diluent through high-precision control and automatic execution, reduce manual intervention, improve production efficiency, and in addition, the real-time concentration monitoring and feedback adjustment system can ensure the quality consistency of each batch of solution, avoid waste or rework caused by unstable concentration; The use of multi-sensor data fusion of conductivity, weight and temperature can not only better reflect the change of solution concentration, but also calculate other key parameters of the solution based on the physical model, providing more decision basis for subsequent production and quality control; The system has the ability of automatic feedback adjustment and closed-loop correction, can realize continuous monitoring and optimization during production, avoid human errors, reduce errors and resource waste in the production process, and thereby improve the intelligent degree and economic benefit of production.
[0029] The basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above examples, the above examples and descriptions in the specification are only the principles of the present application, and various changes and improvements can be made without departing from the spirit and scope of the present application, which fall within the scope of the claimed present application. The scope of protection claimed by the present application is defined by the appended claims and their equivalents.
Claims
1. A high-precision automatic mixing and concentration closed-loop control system, characterized in that: include: Multi-sensor fusion detection module: used to collect real-time data by integrating conductivity sensors, weight sensors, and temperature sensors, and fuse the real-time data to generate high-confidence solution concentration values and physical parameter data; Adaptive closed-loop control module: Based on high-confidence solution concentration values and physical parameter data, a mechanism-optimized control model is established. Based on real-time concentration deviations and system status, the parameters of the PID controller are dynamically adjusted. Combined with the abnormality correction mechanism, the control model is optimized to generate precise raw material feed rate and dilution pump flow adjustment instructions to control the solution concentration during the mixing process. Fully automatic mixing execution system module: Based on instructions from the adaptive closed-loop control algorithm, it automatically performs raw material feeding, stirring and dilution operations to achieve accurate solution preparation, so that the final solution concentration meets the preset target. If the concentration exceeds the target, emergency measures are taken to restore it to the set range.
2. A high-precision automatic mixing and concentration closed-loop control system according to claim 1, characterized in that: The multi-sensor fusion detection module specifically includes: Data acquisition unit: Based on the conductivity sensor, the conductivity of the solution is measured in real time to obtain the concentration and composition changes of the solution. The weight sensor is used to measure the mass of the solution in the container to monitor the amount of raw materials and the total mass change of the solution. The temperature sensor is used to detect the temperature of the solution in real time. Data preprocessing unit: processes and filters the raw signals from each sensor, removes noise, and calibrates and standardizes the signals from different sensors; Data fusion unit: Based on the Kalman filter algorithm, it fuses the real-time data from the conductivity, weight, and temperature sensors to generate a comprehensive solution concentration value. Through data fusion, it comprehensively considers the different characteristics and measurement accuracy of the sensors to obtain a high-confidence solution concentration estimate. Solution concentration estimation unit: Combines the fused data and uses a mathematical model to deduce the final concentration of the solution. Based on the conductivity data, weight data, and temperature data, the solution concentration is calculated using a solution concentration-physical quantity relationship model. Physical parameter generation unit: generates physical parameter data related to solution concentration, including solution density, viscosity and conductivity.
3. A high-precision automatic mixing and concentration closed-loop control system according to claim 2, characterized in that: The data fusion unit specifically includes: According to the dynamic equation of solution concentration change, the state at the next moment is predicted, and the error covariance of the predicted state is updated through the system error model; Receive new measurement data from the sensor and calculate the Kalman gain to balance the contribution of the predicted value and the observed value, so that the system can correct the estimation error; The Kalman gain is used to update the estimated solution concentration at the current moment, the predicted value is combined with the actual measured value, and the final solution concentration estimate is generated through weighted averaging.
4. A high-precision automatic mixing and concentration closed-loop control system according to claim 3, characterized in that: The dynamic equation of solution concentration change is used to predict the state at the next moment, and the error covariance of the predicted state is updated through the system error model. include: Among them, the state prediction formula is: Where, is the predicted value of the state at the next moment, is the estimated value of the state at the current moment, A k is the state transition matrix, B k is the control input matrix, ∪ k is the control input; Among them, the error covariance update formula is: Where, P k+1|k is the covariance matrix of the predicted state, which represents the uncertainty of the predicted value, P k|k is the state estimation error covariance matrix at the current moment, A k is the state transition matrix, which describes the change of state from time k to k+1, Q k The process noise covariance matrix, State transition matrix A k The transpose of .
5. A high-precision automatic mixing and concentration closed-loop control system according to claim 3, characterized in that: The system receives new measurement data from the sensor, calculates the Kalman gain, balances the contribution of the predicted value and the observed value, and enables the system to correct the estimation error. include: Among them, the Kalman gain calculation formula is: Where, P k|k-1 is the predicted error covariance, H k is the observation matrix, R k is the measurement noise covariance, K k is the Kalman gain, is the transpose of the observation matrix.
6. A high-precision automatic mixing and concentration closed-loop control system according to claim 1, characterized in that: The adaptive closed-loop control module specifically includes: PID controller parameter dynamic adjustment unit: According to the real-time concentration deviation and system status, the proportional, integral and differential coefficients of the PID controller are dynamically adjusted to correct the solution concentration deviation by optimizing the PID parameters; Abnormal correction mechanism: When the system detects an abnormality, including sensor failure and concentration exceeding the set range, the abnormal correction mechanism is activated, the control algorithm is triggered, and the controller parameters are adjusted; Instruction generation and output unit: Based on the adjustment amount output by the PID controller, it generates accurate raw material feeding rate and dilution pump flow adjustment instructions, and transmits these instructions to the fully automatic mixing execution system module; Real-time feedback and closed-loop correction unit: adjusts the control strategy based on the real-time concentration information fed back by the execution system. If the control fails to meet expectations, the control system is optimized again through the closed-loop correction process.
7. A high-precision automatic mixing and concentration closed-loop control system according to claim 6, characterized in that: The PID controller parameter dynamic adjustment unit specifically include: Among them, the basic formula of PID controller is: Where uk is the control quantity, which represents the output of the PID controller, K p is the proportionality coefficient, K i is the integral coefficient, which controls the response degree of the cumulative error, K d is the differential coefficient, which controls the response degree of the error change rate, e k is the error at time k, Δt is the time interval, e k -e k-1 is the difference between the current error and the error at the previous moment; Dynamically adjust PID parameters according to real-time concentration deviation and system status.
8. A high-precision automatic mixing and concentration closed-loop control system according to claim 6, characterized in that: The dynamic adjustment of PID parameters according to the real-time concentration deviation and system status specifically includes: Adjust the proportional coefficient according to the current concentration deviation, adjust the integral coefficient according to the historical accumulated error, and adjust the differential coefficient according to the error change rate; Adaptive control algorithm is used to automatically adjust the proportional coefficient, integral coefficient and differential coefficient, set the error tolerance threshold, and start PID parameter adjustment if the concentration error exceeds the set threshold.
9. A high-precision automatic mixing and concentration closed-loop control system according to claim 8, characterized in that: The adaptive control algorithm is used to automatically adjust the proportional coefficient, integral coefficient and differential coefficient. include: Among them, the adaptive control algorithm formula is: Where e(t) is the error at time t, w(t) is the weighting factor, n is the order of the error, T is the time interval, and J is the fitness function.
10. A high-precision automatic mixing and concentration closed-loop control system according to claim 1, characterized in that: The fully automatic hybrid execution system module specifically includes: Raw material feeding control unit: automatically calculates the amount of raw materials to be added based on the instructions and concentration error generated by the control algorithm Automatic feeding unit: controls the automatic feeding equipment to feed the precise amount of raw materials into the mixing container; Raw material monitoring unit: monitors the feeding process in real time to ensure that each raw material is added according to the set ratio to avoid errors. According to the properties of the solution and the feeding amount, the stirrer speed is automatically adjusted to ensure uniform mixing of the solution. The state of the solution is monitored by sensors. Dilution ratio calculation unit: calculates the amount of diluent required based on the concentration error and the properties of the solution, and automatically adjusts the amount of diluent added to make the solution concentration reach the target value. Feedback adjustment unit: If the concentration does not reach the set target, the control system will adjust the feeding and stirring operations in real time.