Stable liquid adding integrated system and method for test solution

Abstract

The invention relates to the technical field of stable liquid adding, and particularly discloses a stable liquid adding integrated system and method for a test solution. The solution temperature sudden change working condition is recognized in real time, and the self-adaptive control process is triggered; through four-dimensional state space modeling and optimal estimation, basic data are fused, multi-physics field coupling errors are suppressed, and high-precision state estimation is provided; the viscosity compensation coefficient of each fluid type is calculated, parameters in the liquid adding process are dynamically adjusted, and flow resistance fluctuation caused by viscosity change is compensated, so that the experimental solution can be stably added; through a four-dimensional dynamic control model, a fluid type differentiation compensation strategy and a self-adaptive PID parameter adjustment technology, and in combination with a feedforward-feedback collaborative architecture and a high-precision data acquisition system, precise and stable control of a test solution adding process under a multi-physics coupling working condition is realized; the adaptive bottleneck of a traditional liquid adding system to solution temperature mutation, fluid characteristic difference and complex interference is broken through.

Description

Technical Field

[0001] The present invention relates to the technical field of stable liquid addition, and particularly relates to an integrated system and method for stable liquid addition of test solutions. Background Art

[0002] Stable liquid addition of test solutions means that in laboratory or industrial production, through technical means, it is ensured that the solution is added to the target container or system with precise and constant flow rate or volume, avoiding liquid addition errors caused by flow rate fluctuations, pressure changes or external disturbances. It is widely used in fields such as chemical analysis, biopharmaceuticals, and material synthesis. Its core goal is to improve the reliability of experimental results and the stability of production processes.

[0003] However, although existing liquid addition equipment uses multiple sensors (such as a combination of flow + pressure), there is a lack of a dynamic fusion model in the existing technology, and it mostly stays at the data acquisition level, resulting in the inability to effectively correlate viscosity compensation when the solution temperature changes; the traditional two-dimensional control model does not construct a four-dimensional state space (lacking the coupling relationship between the flow rate change rate and the solution temperature), and does not incorporate Bernoulli's equation into the state transition matrix, making it difficult to predict the impact of viscosity changes on the dynamic characteristics of the flow rate; the existing technology uses simple mean filtering and cannot effectively suppress high-frequency noise (such as pump head vibration), resulting in insufficient data credibility; the existing compensation strategy does not distinguish fluid types and forcibly applies the Newtonian fluid exponential model to non-Newtonian fluids, resulting in calculation deviations of the apparent viscosity (for example, the yield stress of ketchup is not included in the model, and the error after compensation is still up to ±2.0%); the above technical defects make it difficult for traditional liquid addition systems to achieve long-term stable high-precision liquid addition control under conditions such as sudden changes in solution temperature, differences in fluid characteristics, and complex interference conditions. Summary of the Invention

[0004] The purpose of the present invention is to provide an integrated system and method for stable liquid addition of test solutions to solve the above technical problems in the background.

[0005] The purpose of the present invention can be achieved through the following technical solutions: In the first aspect, the present invention provides an integrated method for stable liquid addition of test solutions, including: Step 1: Multidimensional real-time monitoring to obtain basic data; Step 2: Based on the basic data, real-time identification of sudden changes in solution temperature conditions, triggering an adaptive control process; Step 3: After triggering the adaptive control process, through four-dimensional state space modeling and optimal estimation, fuse the basic data, suppress multi-physical field coupling errors, and provide high-precision state estimation; Step 4: Based on the state estimation results, calculate the viscosity compensation coefficients of each fluid type, thereby dynamically adjusting the liquid addition process parameters to compensate for the flow resistance fluctuations caused by viscosity changes, so that the experimental solution can be stably added.

[0006] As a further solution of the present invention: the basic data includes: real-time actual flow rate, pipeline back pressure, and solution temperature.

[0007] As a further solution of the present invention: the process of obtaining the real-time actual flow rate is as follows: High-frequency acquisition of the original signal is performed by a flow sensor, a linear compensation coefficient is generated after zero-offset deduction and three-point calibration, and then after sliding average filtering of the most recent multiple sampling points, the non-linear error is corrected by applying the linear compensation coefficient to obtain the real-time actual flow rate.

[0008] As a further solution of the present invention: the process of obtaining the pipeline back pressure is as follows: After sampling by a piezoresistive pressure sensor and sliding average filtering of the most recent multiple sampling points, the pipeline back pressure is calculated through quadratic polynomial solution temperature compensation and zero-full scale calibration.

[0009] As a further solution of the present invention: the process of obtaining the solution temperature is as follows: The solution temperature is obtained by a solution temperature sensor to continuously collect the solution temperature of the flowing solution body in real time.

[0010] As a further solution of the present invention: based on the basic data, the process of real-time identifying the solution temperature mutation condition and triggering the adaptive control process is as follows: Through sliding average filtering, calculate the average solution temperature change rate of consecutive sampling periods; If the average solution temperature change rate is greater than the preset solution temperature change rate, a parameter regulation signal is generated.

[0011] As a further solution of the present invention: the process of suppressing the multi-physical field coupling error and providing high-precision state estimation by fusing multi-sensor data through four-dimensional state space modeling and optimal estimation is as follows: After receiving the parameter regulation signal, define a four-dimensional state vector, where the four-dimensional state vector includes real-time actual flow rate, flow rate change rate, pipeline back pressure, and solution temperature; Based on the state of the previous moment, predict the state of the current moment to obtain the prior state estimation vector, and at the same time calculate the prior covariance matrix; Form the collected basic data into a measurement vector; Through Kalman gain calculation, dynamically adjust the weights of the measurement vector and the prior state estimation vector, and flexibly allocate the trust degree according to different situations; Then, correct the prior covariance matrix through the Kalman gain to obtain the posterior state estimation vector, thereby suppressing the multi-physical field coupling error and providing high-precision state estimation.

[0012] As a further solution of the present invention: the process of obtaining the viscosity compensation coefficient is as follows: Identify the fluid type; If the fluid is a Newtonian fluid, based on the exponential effect of solution temperature on viscosity, calculate in real time the ratio of the viscosity at the current solution temperature to the viscosity at the reference solution temperature. When the solution temperature increases, the viscosity decreases exponentially according to the preset solution temperature coefficient to generate a viscosity compensation coefficient; If the fluid is a non-Newtonian fluid, extract the real-time shear rate and initial calibration parameters, calculate the apparent viscosity, compare the apparent viscosity with the reference viscosity, and generate a viscosity compensation coefficient.

[0013] As a further solution of the present invention: The process of dynamically adjusting the liquid addition process parameters is as follows: Adjust the PID parameters based on the viscosity compensation coefficient; The PID parameters include: proportional coefficient, integral coefficient, and differential coefficient; Perform a multiplication calculation on the pre-adjustment proportional coefficient and the viscosity compensation coefficient to output the adjusted proportional coefficient; perform a ratio calculation on the pre-adjustment integral coefficient and the viscosity compensation coefficient to output the adjusted integral coefficient; perform a multiplication calculation on the pre-adjustment differential coefficient and the square root of the viscosity compensation coefficient to output the adjusted differential coefficient.

[0014] In a second aspect, the present invention provides a stable liquid addition integrated system for a test solution, and the system includes: Real-time monitoring module: Perform multi-dimensional real-time monitoring to obtain basic data; Trigger condition detection module: Based on the basic data, identify the sudden change condition of the solution temperature in real time and trigger the adaptive control process; Data fusion module: After triggering the adaptive control process, fuse the basic data through four-dimensional state space modeling and optimal estimation to suppress the multi-physical field coupling error and provide a high-precision state estimation; Parameter regulation module: Based on the state estimation result, calculate the viscosity compensation coefficient for each fluid type, so as to dynamically adjust the liquid addition process parameters, compensate for the flow resistance fluctuation caused by viscosity change, and thus enable the test solution to be added stably.

[0015] Advantages of the present invention: Through a four-dimensional dynamic modeling system, the present invention breaks through the limitations of traditional two-dimensional control models, constructs a four-dimensional dynamic model including flow rate, flow rate change rate, pressure, and solution temperature, realizes the accurate capture of the multi-physical field coupling relationship, integrates the Bernoulli equation and the viscosity-solution temperature exponential model into the state transition matrix, and can predict in real time the dynamic impact of solution temperature change on flow characteristics; Through the intelligent compensation mechanism of fluid constitutive characteristics, the present invention establishes a solution temperature-viscosity exponential model for Newtonian fluids and develops a shear rate-yield stress calculation model for non-Newtonian fluids, breaking through the limitations of traditional single compensation algorithms; The present invention realizes the real-time matching of control parameters and fluid characteristics through a dynamic adjustment method of PID parameters for viscosity compensation coefficients, and a non-linear adjustment strategy in which the proportional term is positively correlated with the viscosity coefficient, the integral term is negatively correlated, and the differential term is correlated with the square root; The present invention constructs a basic data acquisition system with an error magnitude lower than 50% of the industry standard through a multi-sensor joint calibration technology, including three-point calibration (non-linear error correction) of the flow sensor, quadratic polynomial solution temperature compensation of the pressure sensor (eliminating the influence of the ambient solution temperature), and dynamic response optimization of the solution temperature sensor, providing reliable input for intelligent control algorithms. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be further described below with reference to the accompanying drawings.

[0017] Figure 1 is a flowchart of a stable liquid addition integration method for a test solution in Embodiment 1 of the present invention; Figure 2 is a system block diagram of a stable liquid addition integration system for a test solution in Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. Embodiment

[0019] Please refer to Figure 1 As shown, a stable liquid addition integration method for a test solution described in an embodiment of the present invention includes the following steps: Step 1: Multi-dimensional real-time monitoring to obtain basic data; In some embodiments, after a stable liquid addition integration system for a test solution is powered on, it continuously operates, sets a preset sampling period, and obtains basic data for each sampling period; Among them, the sampling period can be: 10 ms, 20 ms, 50 ms; The basic data includes: real-time actual flow rate, pipeline back pressure, solution temperature; Exemplarily, the process of obtaining the real-time actual flow rate is as follows: One flow sensor is connected in series in each liquid addition channel (such as the main pump channel 1, the slave pump channels 2-4), installed between the outlet of the peristaltic pump and the inlet of the microfluidic channel, to ensure the measurement of the real-time flow rate flowing through the channel; Among them, the type of flow sensor is selected according to the range: for small flow (0.1 - 10 mL / min), an electromagnetic type (accuracy ±0.1% FS, such as E+H Promag L) is used; for large flow (10 - 1000 mL / min), a turbine type (accuracy ±0.5% FS, such as Omega FTB-100) is used; When the flow sensor is used for the first time or undergoes regular maintenance, initialize and calibrate the flow sensor. Specifically: Turn off the pump group, let the flow sensor run idly (no liquid flow), collect data for 10 cycles and take the average as the zero offset (for example, the zero noise of the electromagnetic sensor ≤ ±0.05 mL / min); Run the pump group at 50% of the rated flow (for example, the calibration flow for a 100 mL / min channel is 50 mL / min), and record the deviation between the output value of the flow sensor and the measured value of the standard measuring cylinder (calibration error ≤ ±0.3% FS); Generate the linear compensation coefficient of the flow sensor through three-point calibration (25%, 50%, 75% of the rated flow) ; Among them, is the calibrated flow value, that is, the actual flow measured by the standard measuring cylinder; is the original flow, that is, the uncalibrated flow directly output by the sensor; During the continuous operation after the power-on of a stable liquid addition integration system for a test solution, each channel flow sensor collects flow signals at a frequency of 200 Hz (for example, the turbine sensor outputs a pulse signal, and the frequency is proportional to the flow); Take the average of the last 10 sampling points (data within 50 ms) to suppress high-frequency noise (such as pulse jitter caused by pump head vibration), and output the smoothed flow value ; Apply the linear compensation coefficient of the flow sensor to correct the non-linear error of the sensor (for example, the non-linear deviation of the turbine sensor at low flow ≤ ±0.5%), and obtain the real-time actual flow Among them, is the zero offset; Exemplarily, the process of obtaining the pipeline back pressure is as follows: Vertically install the piezoresistive pressure sensor 20 cm from the outlet end of the peristaltic pump (to avoid interference from pump head vibration), and connect it in series with the microfluidic channel through a high-pressure resistant hose (inner diameter 3 mm); Preset the acquisition rate (to meet the requirements of pressure mutation detection, for example, a change rate of 0.3 MPa / s requires at least 500 Hz sampling), and take the average of the last 10 sampling points (10 ms) as the smoothed pressure value ; Through the built-in NTC thermistor, monitor the solution temperature of the chip in real time and perform quadratic polynomial compensation: , obtain the pressure after solution temperature compensation , where the coefficient , , both a and b are obtained through calibration in a thermostat; Then through the formula: , obtain the pipeline back pressure , where is the zero point pressure (ambient pressure when there is no liquid, unit kPa), is the full scale calibration coefficient (for example, for a piezoresistive pressure sensor with a range of 1 MPa, after calibration ); Exemplarily, the process of obtaining the solution temperature is as follows: Integrate the PT100 solution temperature sensor on the outer wall of the pipeline at the outlet end of the peristaltic pump (20 - 50 cm away from the pump head), and closely attach it to the pipeline through thermal conductive silicone to ensure that the sensor is quickly synchronized with the solution temperature, so as to obtain the solution temperature of the test solution body flowing through the liquid adding channel; It should be noted that the solution temperature directly reflects the solution temperature of the liquid in the flowing state, rather than the ambient solution temperature or the solution temperature of the pump body, because the change in the solution temperature will directly affect its viscosity, and thus affect the flow control accuracy; Step 2: Real-time identify the sudden change working condition of the solution temperature, trigger the adaptive control process, and avoid the control lag caused by passive adjustment; In some embodiments, through moving average filtering, calculate the average solution temperature change rate of consecutive sampling periods. The specific process is as follows: Through the formula: , calculate and obtain the solution temperature change rate , where is the solution temperature at the i-th moment, is the solution temperature at the (i - 1)-th moment, is the sampling period; Then calculate the mean value of all solution temperature change rates within consecutive sampling periods and output the average solution temperature change rate; If the average solution temperature change rate is greater than the preset solution temperature change rate, generate a parameter regulation signal; Step 3: Based on the parameter regulation signal, through four-dimensional state space modeling and optimal estimation, fuse multi-sensor data, suppress the multi-physical field coupling error, and provide high-precision state estimation; In some embodiments, when receiving the parameter regulation signal, define the state vector: , where is the real-time actual flow rate (mL / min) at the i-th moment, is the flow rate change rate (mL / min²) at the i-th moment, is the pipeline back pressure at the i-th moment (MPa), is the solution temperature at the i-th moment (°C); It should be noted that the state vector is defined to comprehensively describe the system state and provide a basis for state estimation and control; It should be noted that the real-time actual flow rate is obtained by derivation through the Bernoulli equation: , where, is the pipeline damping coefficient (Pa·s / m), m is the liquid mass (kg), r is the pipeline radius (m), L is the pipeline length (m), is the liquid viscosity (Pa·s), is the pressure difference at both ends of the pipeline at the i-th moment (MPa), is the pi; Based on the state vector, the state transition matrix is obtained, where, is the acquisition period (s), is the solution temperature at which the liquid viscosity is (Pa·s), is the viscosity at the reference solution temperature (such as 25°C) (Pa·s), is the viscosity-solution temperature exponential coefficient ( , typical value of Newtonian fluid -0.03), is the reference solution temperature (25°C), e is the natural constant, and the value is 2.71828; It should be noted that the state transition matrix needs to consider the exponential influence of the solution temperature on the viscosity and update the matrix elements in real time; It should be further noted that the state transition matrix describes the transition relationship of the state vector from the (i - 1)-th moment to the i-th moment, incorporates the influence of the solution temperature on the viscosity in real time, and is used to predict the system state change. Among them, the first row is the transition of the real-time actual flow rate, the second row is the transition of the flow rate change rate, and the third and fourth rows consider the pressure and the solution temperature as slow-varying parameters and directly inherit the values of the previous moment (assuming that the pressure and the solution temperature do not change without mutation); Predict the state at the current moment through the state and control input at the previous moment. The specific process is as follows: Prior state estimation vector: , where, is the pump drive pulse increment, is the state transition matrix at the previous moment, is the posterior state estimation vector at the previous moment, is the input matrix, and ; It should be noted that the prior state estimation vector , uses the state transition matrix to predict the natural evolution of the state at the previous moment (such as the flow rate changes due to viscosity changes), and superimposes the control input pump drive pulse increment , reflecting the influence of manual adjustment on the flow rate; It should be noted that the prior state estimation vector represents the predicted state at the current moment i; It should be noted that the input matrix , the first element represents the conversion of the number of pulses to the flow rate (such as 1 pulse corresponding to , 60 is the conversion factor from minutes to seconds); the second element represents the influence of the pulse increment on the flow rate change rate (considering the acquisition period ), determined based on the pulse control principle of the pump, and is used to predict the current state by combining the natural evolution and manual control of the system; Prior covariance matrix: , where is the noise matrix; It should be noted that the noise matrix includes: flow vibration noise, solution temperature drift noise; It should be further noted that is to map the covariance matrix at the previous moment to the current moment, reflecting the natural propagation of the state estimation uncertainty, where the left multiplication is to propagate the mean uncertainty according to the physical model (such as the flow rate change rate formula), and the right multiplication is to ensure the symmetry and dimension matching of the covariance matrix; Fuses the basic data measured by the sensor with the predicted state at the current moment to correct the prediction error. The specific process is as follows: Measurement vector: , where , is the observation matrix, and , is the measurement noise vector; It should be noted that the measurement vector represents the basic data collected by the sensor at the i-th moment, that is ; the observation matrix represents the linear transformation matrix that maps the state vector to the measurement space, describing the measurement range and accuracy of the sensor; the measurement noise vector It represents the random noise during the sensor measurement process, which follows a zero-mean Gaussian distribution with a covariance matrix of , , where , , are the standard deviations of the measurement noises of flow rate, pressure, and solution temperature respectively; It is calculated through the Kalman gain: , dynamically adjusting the weights of the sensor data and the predicted value. That is, when the solution temperature suddenly changes (the credibility of the sensor data is high), increase the weight and give priority to believing the measured value of the solution temperature; when the pressure is abnormal (possibly a pipeline blockage), suppress the flow gain to avoid incorrect adjustment; It should be explained that is the Kalman gain matrix, which is used to balance the weights of the previous state estimate value and the current measured value to minimize the estimation error. is the prior estimation error covariance matrix at the i-th moment, is the observation noise covariance matrix, which describes the statistical characteristics of the sensor measurement noise. is the observation matrix, is the transpose matrix of the observation matrix . The observation matrix is used to map the state vector to the measurement space, showing the measurement range and accuracy of the sensor. The transpose matrix is obtained by interchanging the rows and columns of the observation matrix . The transpose matrix can adjust the weights of the measurement vector and the prior state estimate vector during data fusion. In cases such as sudden changes in the solution temperature, the measurement data is more reliable, and the transpose matrix will increase the influence of the measurement vector, making the estimation result closer to the measured value; if the pressure is abnormal, it will reduce the weight of the corresponding measured value to prevent incorrect adjustment. For example, in the experiment, when the solution temperature suddenly changes, the transpose matrix can enable the system to respond quickly and correct the estimated state based on the measured value. The elements and arrangement of the transpose matrix reflect the contribution degree of each component of the measurement vector to the state estimation; if the element at the corresponding position of a certain component of the measurement vector in the transpose matrix is large, it means that this component has a great influence on the state estimation; otherwise, the influence is small. is the observation prediction error covariance matrix, which synthesizes the uncertainty of the prediction error and the measurement noise; Then, through the formula: , perform posterior state correction to obtain the posterior state estimate vector ; Step 4: The change in the solution temperature will significantly affect the liquid viscosity, which in turn leads to flow fluctuations. By dynamically adjusting the PID parameters (the PID parameters refer to the three control parameters of the proportional coefficient (Proportional), integral coefficient (Integral), and derivative coefficient (Derivative) used for flow control), the influence of viscosity changes is offset, so that the experimental solution can be added stably. The specific process is as follows: In some embodiments, through the formula: , calculate the viscosity compensation coefficient , where specifically refers to the viscosity of the liquid at 25°C, which is a fixed reference value used to normalize the viscosity at different solution temperatures, is the yield stress of the non-Newtonian fluid, is the shear rate representing the relative movement speed between adjacent two layers inside the fluid, is the consistency coefficient of the non-Newtonian fluid, is the flow index of the non-Newtonian fluid; Based on the viscosity compensation coefficient , adjust the PID parameters, so that the experimental solution can be added stably; It should be explained that during the process of adding the test solution, the solution viscosity will change due to factors such as temperature, affecting the flow stability. By adjusting the PID parameters based on the viscosity compensation coefficient , the influence brought by the viscosity change can be effectively compensated, and stable liquid addition can be achieved. Specifically, it is manifested as the adjustment of the proportional coefficient, integral coefficient, and derivative coefficient, as well as the roles of these adjustments in compensating for the flow resistance fluctuation, eliminating the steady-state error, and suppressing the flow change; Exemplarily, proportional coefficient adjustment: Multiply the pre-adjustment proportional coefficient by the viscosity compensation coefficient to output the adjusted proportional coefficient; when the solution viscosity changes due to factors such as temperature, the viscosity compensation coefficient will change accordingly. If the viscosity increases, the flow resistance increases, and the viscosity compensation coefficient becomes larger, and the adjusted proportional coefficient increases. The control signal output by the controller strengthens, so that the driving device (such as a peristaltic pump) of the liquid addition equipment increases the driving force to increase the liquid addition flow rate to overcome the increased flow resistance and ensure the stability of the liquid addition flow rate; conversely, if the viscosity decreases, the viscosity compensation coefficient becomes smaller, and the proportional coefficient decreases to avoid excessive liquid addition flow rate and maintain stable liquid addition; Integral coefficient adjustment: Divide the pre-adjustment integral coefficient by the viscosity compensation coefficient to output the adjusted integral coefficient; the integral action is mainly used to eliminate the steady-state error so that the liquid addition flow rate can finally stabilize at the set value. When the viscosity change causes a deviation in the flow rate, if the viscosity increases, the viscosity compensation coefficient increases, the integral coefficient decreases, and the accumulation speed of the integral term slows down, avoiding system overshoot caused by too strong integral action; if the viscosity decreases, the viscosity compensation coefficient decreases, the integral coefficient increases, accelerating the accumulation of the integral term, and eliminating the steady-state error caused by viscosity change faster, ensuring that the liquid addition flow rate is stable at the set value; Differential coefficient adjustment: Multiply the differential coefficient before adjustment by the square root of the viscosity compensation coefficient to output the adjusted differential coefficient; The differential action is used to suppress the change trend of the flow rate and enhance the system stability. When the viscosity change causes flow rate fluctuations, if the viscosity increases, the viscosity compensation coefficient increases, the differential coefficient increases, the sensitivity to flow rate changes increases, and the flow rate fluctuations can be suppressed more quickly; if the viscosity decreases, the viscosity compensation coefficient decreases, the differential coefficient decreases, avoiding overreaction to small flow rate changes and maintaining the stability of the liquid addition process; The technical solution of the embodiment of the present invention is mainly as follows: Through a four-dimensional dynamic modeling system, the present invention breaks through the limitations of traditional two-dimensional control models, constructs a four-dimensional dynamic model including flow rate, flow rate change rate, pressure, and solution temperature, realizes the accurate capture of the coupling relationship of multiple physical fields, integrates the Bernoulli equation and the viscosity-solution temperature exponential model into the state transition matrix, and can dynamically predict the dynamic influence of solution temperature changes on flow rate characteristics; Through the intelligent compensation mechanism of fluid constitutive characteristics, the present invention establishes a solution temperature-viscosity exponential model for Newtonian fluids and develops a shear rate-yield stress calculation model for non-Newtonian fluids, breaking through the limitations of traditional single compensation algorithms; Through the dynamic adjustment method of PID parameters of the viscosity compensation coefficient, the present invention realizes the real-time matching of control parameters and fluid characteristics through a non-linear adjustment strategy in which the proportional term is positively correlated with the viscosity coefficient, the integral term is negatively correlated, and the differential term is related to the square root; Through the multi-sensor joint calibration technology, the present invention constructs a basic data acquisition system with an error magnitude lower than 50% of the industry standard through three-point calibration (non-linear error correction) of the flow sensor, quadratic polynomial solution temperature compensation (eliminating the influence of ambient solution temperature) of the pressure sensor, and dynamic response optimization of the solution temperature sensor, providing reliable input for intelligent control algorithms. Embodiment

[0020] On the basis of Embodiment 1, please refer to Figure 2 As shown, a stable liquid addition integration system for a test solution described in the embodiment of the present invention includes: Real-time monitoring module: Multi-dimensional real-time monitoring to obtain basic data; Trigger condition detection module: Based on the basic data, real-time identify the sudden change condition of the solution temperature and trigger the adaptive control process; Data fusion module: After triggering the adaptive control process, it fuses the basic data through four-dimensional state space modeling and optimal estimation, suppresses the multi-physical field coupling error, and provides high-precision state estimation; Parameter regulation module: Based on the state estimation results, it calculates the viscosity compensation coefficients of each fluid type, thereby dynamically adjusting the liquid addition process parameters, compensating for the flow resistance fluctuations caused by viscosity changes, so that the experimental solution can be added stably.

[0021] The above has described an embodiment of the present invention in detail, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A method for the stable addition of a test solution, characterized in that: include: Step 1: Multi-dimensional real-time monitoring to obtain basic data; Step 2: Based on basic data, identify the sudden change of solution temperature in real time and trigger the adaptive control process; Step 3: When the adaptive control process is triggered, four-dimensional state space modeling and optimal estimation are used to fuse basic data, suppress multi-physics field coupling errors, and provide high-precision state estimation; Step 4: Based on the state estimation results, the viscosity compensation coefficient of each fluid type is calculated, so as to dynamically adjust the parameters of the liquid addition process to compensate for the flow resistance fluctuation caused by the viscosity change, so that the experimental solution can be stably added.

2. The integrated method for stable liquid addition of a test solution according to claim 1, characterized in that: Basic data include: real-time actual flow rate, pipeline back pressure, and solution temperature.

3. The integrated method for stable liquid addition of a test solution according to claim 2, characterized in that: The process of obtaining real-time actual traffic is as follows: The original signal is collected by the flow sensor at high frequency, and the linear compensation coefficient is generated by zero offset deduction and three-point calibration. After sliding average filtering of the recent multiple sampling points, the linear compensation coefficient is applied to correct the nonlinear error to obtain the real-time actual flow.

4. The integrated method for stable liquid addition of a test solution according to claim 3, characterized in that: The process of obtaining pipeline back pressure is as follows: Through the piezoresistive pressure sensor, after sampling, the sliding average filtering of the most recent multiple sampling points is performed, and the pipeline back pressure is obtained by quadratic polynomial solution temperature compensation and zero point full-scale calibration.

5. The integrated method for stable liquid addition of a test solution according to claim 4, characterized in that: The process of obtaining the solution temperature is: The solution temperature is obtained by collecting the temperature of the bulk solution of the flowing solution in real time through the solution temperature sensor.

6. The integrated method for stable liquid addition of a test solution according to claim 5, characterized in that: Based on the basic data, the process of identifying the sudden change of solution temperature in real time and triggering the adaptive control process is as follows: The average solution temperature change rate over consecutive sampling periods is calculated by sliding average filtering; If the average solution temperature change rate is greater than the preset solution temperature change rate, a parameter control signal is generated.

7. The integrated method for stable liquid addition of a test solution according to claim 6, characterized in that: The process of providing high-precision state estimation through four-dimensional state space modeling and optimal estimation, fusing multi-sensor data, suppressing multi-physical field coupling errors, and providing: After receiving the parameter control signal, a four-dimensional state vector is defined, wherein the four-dimensional state vector includes the real-time actual flow rate, flow rate change rate, pipeline back pressure and solution temperature; Based on the state at the previous moment, predict the state at the current moment, obtain the prior state estimation vector, and calculate the prior covariance matrix at the same time; The collected basic data are combined into a measurement vector; Through Kalman gain calculation, the weights of the measurement vector and the prior state estimation vector are dynamically adjusted to flexibly allocate trust according to different situations; The prior covariance matrix is ​​then corrected through the Kalman gain to obtain the posterior state estimation vector, thereby suppressing the multi-physical field coupling error and providing high-precision state estimation.

8. The integrated method for stable liquid addition of a test solution according to claim 2, characterized in that: The process of obtaining the viscosity compensation coefficient is: Identify the fluid type; If the fluid is a Newtonian fluid, based on the exponential effect of solution temperature on viscosity, the ratio of the viscosity at the current solution temperature to the viscosity at the reference solution temperature is calculated in real time. When the solution temperature rises, the viscosity decreases according to the preset solution temperature coefficient exponentially to generate a viscosity compensation coefficient. If the fluid is non-Newtonian, calculate the apparent viscosity, compare the apparent viscosity with the reference viscosity, and generate a viscosity compensation factor.

9. The integrated method for stable liquid addition of a test solution according to claim 1, characterized in that: The process of dynamically adjusting the parameters of the dosing process is as follows: Adjust PID parameters based on viscosity compensation coefficient; PID parameters include: proportional coefficient, integral coefficient, differential coefficient; The proportional coefficient before adjustment is multiplied by the viscosity compensation coefficient, and the adjusted proportional coefficient is output; the integral coefficient before adjustment is ratio-calculated by the viscosity compensation coefficient, and the adjusted integral coefficient is output; the differential coefficient before adjustment is multiplied by the square root of the viscosity compensation coefficient, and the adjusted differential coefficient is output.

10. An integrated system for stable liquid addition of a test solution, applied to an integrated method for stable liquid addition of a test solution according to any one of claims 1 to 9, characterized in that: The system includes: Real-time monitoring module: multi-dimensional real-time monitoring to obtain basic data; Trigger condition detection module: Based on basic data, it can identify the sudden change of solution temperature in real time and trigger the adaptive control process; Data fusion module: When the adaptive control process is triggered, the basic data is integrated through four-dimensional state space modeling and optimal estimation, multi-physics field coupling errors are suppressed, and high-precision state estimation is provided; Parameter control module: Based on the state estimation results, the viscosity compensation coefficient of each fluid type is calculated to dynamically adjust the parameters of the liquid addition process to compensate for the flow resistance fluctuations caused by viscosity changes, so that the experimental solution can be stably added.

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