A method for automatically correcting errors of a seawater trace heavy metal on-line monitoring probe

Abstract

The application discloses a kind of seawater trace heavy metal on-line monitoring probe error automatic correction method, belong to marine environment monitoring automatic control technical field, it includes obtaining seawater fluid parameter and probe surface microorganism attachment thickness data;According to the data generation sensitive membrane deformation prediction parameter;Based on the parameter dynamic synthesis seawater osmotic pressure standard solution;Through adsorption efficiency verification obtains in-situ adsorption efficiency coefficient;Comparing benchmark efficiency coefficient calculates probe electrochemistry compensation parameter;Based on microorganism attachment thickness dynamic adjustment deformation parameter generates voltage compensation instruction;Finally through electrochemistry parameter and voltage instruction's correction superposition output probe correction control parameter.The application is combined by constructing feedforward-feedback compound control system, the predictive compensation to physical disturbance and real-time feedback calibration based on dynamic osmotic pressure standard solution are combined, the decoupling inhibition of multiple source disturbance is realized, so as to establish the self-closing loop control ability of probe.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic control of marine environment monitoring, and in particular to a method for automatically correcting errors of a seawater trace heavy metal online monitoring probe. BACKGROUND

[0002] In a complex marine environment, the output stability of a seawater trace heavy metal monitoring probe is affected by both fluid dynamic disturbance and biological fouling deformation, and a dynamic compensation mechanism is needed to maintain the sensing characteristics. Such a system usually uses an electrochemical sensitive film as a detection carrier, and its output drift suppression depends on a coordinated control strategy for multiple physical field coupling interference.

[0003] The prior art mainly maintains the stability of the probe through static calibration and periodic cleaning: one uses a reference solution to calibrate the electrochemical parameters offline, and performs open-loop voltage correction through a compensation table; the other periodically removes biological attachments on the surface of the probe by mechanical scraping to suppress fouling deformation. Some improved schemes introduce a linear feedback mechanism, which adjusts the detection voltage in proportion to a single environmental parameter to reduce the influence of fluid disturbance.

[0004] The above technical means has significant limitations: offline calibration cannot respond to dynamically changing biological fouling stress fields, resulting in the absence of deformation feedforward compensation; periodic mechanical intervention disrupts the continuous monitoring process; linear feedback control is difficult to decouple the coupling effects of fluid-biological fouling double disturbance sources, resulting in a mismatch between electrochemical compensation parameters and actual environmental disturbance states. SUMMARY

[0005] To solve the above problems, the present application provides a method for automatically correcting errors of a seawater trace heavy metal online monitoring probe, which combines predictive compensation for physical disturbance with real-time feedback calibration based on a dynamic osmotic pressure standard solution by constructing a feedforward-feedback composite control system, realizes decoupling and suppression of multiple source disturbances, and thus establishes the self-closed loop control capability of the probe.

[0006] The above object can be achieved by the following scheme:

[0007] The present application discloses a method for automatically correcting errors of a seawater trace heavy metal online monitoring probe, which comprises obtaining seawater fluid parameters and probe surface microbial attachment thickness data; generating sensitive film deformation prediction parameters according to the data; dynamically synthesizing seawater osmotic pressure standard solution based on the parameters; obtaining in-situ adsorption efficiency coefficient through adsorption efficiency verification; calculating probe electrochemical compensation parameters by comparing the reference efficiency coefficient; dynamically adjusting the deformation parameters based on the microbial attachment thickness to generate voltage compensation instructions; and finally outputting probe correction control parameters by superimposing the correction of electrochemical parameters and voltage instructions.

[0008] Optionally, the acquiring seawater fluid parameter comprises: collecting temperature gradient data, salinity fluctuation data and turbulent intensity data; fusing the temperature gradient data and the salinity fluctuation data and the turbulent intensity data into an environmental disturbance feature vector; performing multi-scale decomposition and time-frequency feature extraction on the environmental disturbance feature vector to generate the seawater fluid parameter.

[0009] Optionally, the collecting microbial adhesion thickness data of the probe surface comprises: monitoring a microbial membrane layer thickness; calculating a real-time adhesion thickness based on the microbial membrane layer thickness; performing multi-aperture spectral reflectance feature analysis on the real-time adhesion thickness to obtain a biofouling influence factor; and performing time-varying adjustment on the biofouling influence factor to obtain the microbial adhesion thickness data.

[0010] Optionally, the generating sensitive membrane deformation prediction parameter comprises: inputting the environmental disturbance feature vector into a transfer function library to output a temperature-salinity coupling deformation coefficient; inputting the biofouling influence factor into a membrane layer stress model to output a biological adhesion deformation coefficient; and fusing the temperature-salinity coupling deformation coefficient and the biological adhesion deformation coefficient to generate the sensitive membrane deformation prediction parameter.

[0011] Optionally, the generating seawater osmotic pressure standard liquid based on the sensitive membrane deformation prediction parameter comprises: extracting ion component data of the current seawater; obtaining a multi-modal pore attenuation coefficient based on the sensitive membrane deformation prediction parameter; obtaining a standard liquid formula according to the ion component data and a preset isotonic ratio rule; and performing microfluidic collaborative synthesis on the multi-modal pore attenuation coefficient and the standard liquid formula to generate the seawater osmotic pressure standard liquid.

[0012] Optionally, the calculating the probe electrochemical compensation parameter comprises: performing difference operation on the in-situ adsorption efficiency coefficient and the reference efficiency coefficient to obtain a pore migration index; calling a preset voltage-efficiency mapping table according to the pore migration index to output a compensation voltage value and a current adjustment slope; and performing temperature-deformation double-field coupling correction on the compensation voltage value and the current adjustment slope to obtain the probe electrochemical compensation parameter.

[0013] Optionally, the generating voltage compensation instruction based on the dynamic adjustment of the sensitive membrane deformation parameter according to the microbial adhesion thickness data comprises: obtaining a three-dimensional biological stress tensor based on the microbial adhesion thickness data; dynamically adjusting the sensitive membrane deformation parameter based on the three-dimensional biological stress tensor to obtain a real-time membrane strain matrix; and combining the real-time membrane strain matrix with the compensation voltage value in the probe electrochemical compensation parameter to generate the voltage compensation instruction.

[0014] Optionally, the combining the real-time membrane strain matrix with the compensation voltage value in the probe electrochemical compensation parameter to generate the voltage compensation instruction comprises: obtaining a strain gradient vector field based on the real-time membrane strain matrix; superimposing the compensation voltage value in the probe electrochemical compensation parameter based on the strain gradient vector field to obtain a directional deformation voltage; and performing pulse width-biological activity cooperative coding on the directional deformation voltage to generate the voltage compensation instruction.

[0015] Optionally, the parameter correction and superimposition processing of the voltage compensation instruction based on the probe electrochemical compensation parameter to obtain the probe correction control parameter comprises: key parameter extraction and application processing of the probe electrochemical compensation parameter to obtain a compensation voltage value; biological fouling related deformation correction and dynamic adjustment of the voltage compensation instruction to obtain a real-time membrane strain matrix; and parameter correction and superimposition processing of the real-time membrane strain matrix based on the compensation voltage value to obtain the probe correction control parameter.

[0016] Based on the same inventive concept, the application further provides a seawater trace heavy metal online monitoring probe error automatic correction system, which comprises: a multi-source parameter acquisition module for acquiring seawater fluid parameters and collecting microbial adhesion thickness data on the surface of a probe; a sensitive membrane deformation cooperative modeling module for generating sensitive membrane deformation prediction parameters according to the seawater fluid parameters and the microbial adhesion thickness data; a dynamic synthesis module for generating seawater osmotic pressure standard liquid based on the sensitive membrane deformation prediction parameters; an in-situ adsorption efficiency verification module for performing heavy metal adsorption efficiency verification on the seawater osmotic pressure standard liquid to obtain an in-situ adsorption efficiency coefficient; a compensation parameter calculation module for calculating probe electrochemical compensation parameters according to the in-situ adsorption efficiency coefficient and a preset reference efficiency coefficient; a biological fouling compensation instruction module for dynamically adjusting the sensitive membrane deformation parameters based on the microbial adhesion thickness data to generate voltage compensation instructions; and a double-path fusion correction module for parameter correction and superimposition processing of the voltage compensation instructions based on the probe electrochemical compensation parameters to obtain probe correction control parameters.

[0017] Compared with the prior art, the application has the following advantages:

[0018] 1. The application constructs a feedforward-feedback composite control architecture, which improves the dynamic response and robustness of the system. The application innovatively designs a double-channel feedforward-feedback cooperative control architecture. The feedforward control channel establishes a prediction model of disturbance and sensitive membrane deformation by real-time monitoring of fluid parameters and biological fouling thickness, and compensates for predictable disturbances in advance. The feedback control channel generates a standard liquid in-situ and verifies the adsorption efficiency to obtain the real system output error for closed-loop correction. This composite control architecture can effectively suppress various disturbances and is significantly better than traditional open-loop or single feedback control.

[0019] 2、The application realizes decoupling control of multivariate disturbance, improves control accuracy, adjusts physical deformation disturbance and electrochemical characteristic drift through voltage compensation instructions based on fluid-biofouling prediction and electrochemical compensation parameters based on measured efficiency deviation; finally, through fusion algorithm superposition, effective decoupling of physical and chemical disturbance sources is realized, control mismatch caused by disturbance coupling in traditional methods is avoided, and control accuracy is greatly improved;

[0020] 3、The application establishes an adaptive closed-loop correction mechanism to ensure long-term stability, a standard liquid matching the current seawater osmotic pressure is dynamically synthesized to build an adaptive online calibration closed loop; the mechanism can track and compensate the performance degradation caused by slow time-varying factors such as sensitive membrane aging and biofouling accumulation in real time, so that the probe has the ability of self-learning and continuous optimization, thereby ensuring long-term and high-stability online monitoring without human intervention.

[0021] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be realized and achieved by the structure as indicated in the description, claims and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0023] Figure 1 is a flowchart of a seawater trace heavy metal online monitoring probe error automatic correction method according to an embodiment of the present application.

[0024] Figure 2 is a heavy metal adsorption efficiency curve according to an embodiment of the present application.

[0025] Figure 3 is a seawater fluid parameter diagram according to an embodiment of the present application.

[0026] Figure 4 is an environmental disturbance feature vector time-frequency analysis diagram according to an embodiment of the present application.

[0027] Figure 5 is a sensitive membrane deformation prediction parameter generation process diagram according to an embodiment of the present application.

[0028] Figure 6A three-dimensional bio-stress tensor distribution diagram of an embodiment of the present application.

[0029] Figure 7 A structural schematic diagram of a seawater trace heavy metal online monitoring probe error automatic correction system of an embodiment of the present application. DETAILED DESCRIPTION

[0030] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in connection with the drawings of the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0031] With reference to Figure 1 An embodiment of the present application provides a seawater trace heavy metal online monitoring probe error automatic correction method. A feedforward-feedback composite control system is constructed to combine predictive compensation for physical disturbance with real-time feedback calibration based on a dynamic osmotic pressure standard solution, realize decoupling inhibition of multi-source disturbance, and establish autonomous closed-loop control capability of the probe.

[0032] The method of the embodiment specifically comprises the following steps.

[0033] Obtain seawater fluid parameters and collect microbial adhesion thickness data on a probe surface.

[0034] Generate sensitive membrane deformation prediction parameters according to the seawater fluid parameters and the microbial adhesion thickness data.

[0035] Generate seawater osmotic pressure standard solution based on the sensitive membrane deformation prediction parameters.

[0036] Perform heavy metal adsorption efficiency verification on the seawater osmotic pressure standard solution to obtain an in-situ adsorption efficiency coefficient.

[0037] Calculate a probe electrochemical compensation parameter according to the in-situ adsorption efficiency coefficient and a preset reference efficiency coefficient.

[0038] Generate a voltage compensation instruction based on dynamic adjustment of the sensitive membrane deformation parameters according to the microbial adhesion thickness data.

[0039] Perform parameter correction and superposition processing on the voltage compensation instruction based on the probe electrochemical compensation parameter to obtain a probe correction control parameter.

[0040] Specifically, the heavy metal adsorption efficiency curve is as shown in Figure 2As shown, first, the seawater osmotic pressure standard solution generated by the dynamic synthesis module is injected into the microfluidic adsorption verification unit, which is physically connected to the seawater trace heavy metal online monitoring probe. The automatic pipetting arm is configured to extract an aliquot of the standard solution from the sample pool and transfer it to the electrochemical detection cell. The cell is equipped with a working electrode, a reference electrode, and a counter electrode, and the three-electrode system is controlled by a constant potential instrument. Then, the constant potential instrument is set to apply a predetermined step voltage sequence to drive the heavy metal ions in the standard solution to undergo electrochemical adsorption reactions on the surface of the working electrode. The current-time curve during the adsorption process is recorded synchronously, and the real-time adsorption charge is obtained by integral operation. The real-time adsorption charge is compared with the known heavy metal ion concentration in the standard solution to obtain the instantaneous adsorption efficiency data. Second, the adsorption reaction duration is set to be no less than ten minutes, and multiple sets of instantaneous adsorption efficiency data are continuously collected. Linear regression analysis is performed on all data points to obtain the slope value of the adsorption efficiency change curve. When the absolute value of the slope is less than the preset stability threshold, the average efficiency value in the time window is output as the original adsorption efficiency coefficient. Finally, real-time collection of current seawater temperature environmental temperature data and microbial attachment thickness data is performed, and a preconfigured temperature-biofouling correction model is called to compensate the original adsorption efficiency coefficient. The model converts the temperature drift and bioattachment effects into correction factors through a sensitivity matrix, and outputs the final in-situ adsorption efficiency coefficient after compensating the original adsorption efficiency coefficient.

[0041] Optionally, the obtaining the seawater fluid parameter comprises:

[0042] Collecting temperature gradient data, salinity fluctuation data, and turbulent intensity data;

[0043] Fusing the temperature gradient data, the salinity fluctuation data, and the turbulent intensity data into an environmental disturbance feature vector;

[0044] Performing multi-scale decomposition and time-frequency feature extraction on the environmental disturbance feature vector to generate the seawater fluid parameter.

[0045] Specifically, the seawater fluid parameter is as shown in the following formula: Figure 3 As shown, first, the seawater temperature gradient data is collected using the temperature sensor built into the seawater monitoring probe. This data represents the rate of temperature change over time or space, and through continuous time sampling, a temperature gradient vector variable is obtained, where each element corresponds to the temperature change value at the measurement time. The salinity fluctuation data is collected using the salinity sensor, which is obtained by calculating the standard deviation of the salinity time series and reflects the amplitude characteristics of the salinity fluctuations. At the same time, the turbulent intensity data is collected using a turbulence meter, which represents the energy intensity value of the seawater flow. The above three data sets are fused by linear combination to form an environmental disturbance feature vector, which contains temperature gradient, salinity fluctuation, and turbulent intensity data. The formula for the environmental disturbance feature vector is as follows:

[0046] ,

[0047] wherein the parameter symbol represents the environmental disturbance feature vector, the parameter symbol represents the temperature gradient vector from the temperature sensor, for example containing the time-varying temperature difference component, the parameter symbol represents the salinity fluctuation data obtained from the salinity sensor and processed by the standard deviation calculation, the parameter symbol represents the turbulence intensity data obtained from the turbulence meter, the water flow energy intensity measurement, the vector elements are directly superimposed by equal weight to realize vectorization. Then the environmental disturbance feature vector is input into the multi-scale decomposition process, which is decomposed into multiple scale sub-components using the discrete wavelet transform algorithm, which analyzes the signal characteristics of different frequency bands with specific wavelet basis functions. Then the time-frequency feature extraction step is performed, and the short-time Fourier transform algorithm is applied to calculate the time-frequency energy spectrum distribution of each scale component, generating a matrix feature set containing the frequency and time correlation. Finally, the seawater flow parameter is extracted and output from the matrix, which comprehensively represents the dynamic disturbance state of seawater flow and can be used for subsequent correction processing.

[0048] Exemplarily, the seawater trace heavy metal online monitoring probe system is deployed at the offshore monitoring station, and the initial data acquisition conditions are set. The seawater temperature gradient data obtained by the sensor is 0.1 degrees Celsius per second, the standard deviation value of the salinity fluctuation data calculated after being measured by the sensor is 0.01 per unit, and the turbulence intensity data measured by the turbulence meter is 0.2 watts per square meter. The environmental disturbance feature vector element values 0.1, 0.01 and 0.2 are obtained by data fusion, and the vector is decomposed into three scale components by discrete wavelet transform, then the short-time Fourier transform is applied to extract the energy characteristics of the typical frequency band, and finally the seawater flow parameter such as the average disturbance frequency band index value 0.85 is generated.

[0049] Optionally, the collection of the microorganism adhesion thickness data includes:

[0050] Monitoring the thickness of the microorganism membrane layer;

[0051] Based on the thickness of the microorganism membrane layer, the real-time adhesion thickness is calculated;

[0052] The real-time adhesion thickness is analyzed by multi-aperture spectral reflectance characteristics to obtain a biofouling influence factor;

[0053] The biofouling influence factor is adjusted by time variation to obtain the microorganism adhesion thickness data.

[0054] Specifically, the optical thickness sensor is arranged on the surface of the seawater trace heavy metal online monitoring probe, the sensor emits a light beam with a wavelength of 500 nm to the surface of the probe, and the thickness of the film layer formed by microorganisms is monitored according to the comparison between the time difference of the light beam reflection and a preset threshold value; the time difference between the emission and return of the light beam is measured and the film layer thickness is calculated based on the speed of light , the formula is

[0055]

[0056] wherein the parameter symbol represents the speed value of the light beam, and the parameter symbol represents the time difference of the light beam measured by the sensor internal clock. Based on the film layer thickness , the real-time adhesion thickness is calculated , the formula is

[0057]

[0058] The parameter symbol represents the environmental disturbance correction coefficient, and the current temperature value is obtained by the seawater temperature sensor, and the coefficient mapping table is queried to obtain the correlation between the temperature and the coefficient based on the experimental calibration; the parameter symbol represents the basic adhesion offset, which is obtained by calculating the average value from the film layer thickness history database of the past 30 days. Then, when the real-time adhesion thickness is analyzed by multi-aperture spectral reflectance characteristics, a multi-aperture spectral instrument is configured, which includes three light aperture apertures of 50 microns, 100 microns and 200 microns, and the spectral reflectance data is recorded by sequentially scanning the probe surface area; the reflected light intensity value is obtained during the reflection process, and the characteristic points corresponding to different apertures such as peak wavelength and average reflectivity are calculated to form a feature vector ; the vector is input into a preset analysis model, which is a linear regression equation:

[0059]

[0060] is the biological fouling influence factor, the parameter symbol represents the weight vector, represents the bias constant, which is a fixed value preset during manufacturing. Finally, the biological fouling influence factor is adjusted to obtain the microorganism adhesion thickness data , the formula is

[0061] ​​​​​

[0062] Parameter Symbol Represents the time decay coefficient, set to 0.001 per second, the constant is initialized by the system clock, the parameter symbol Represents the accumulated time since the probe was deployed, obtained in real time from the system timer.

[0063] For example, an online monitoring probe system for trace heavy metals in seawater is deployed on an ocean buoy. In the initial step, an optical thickness sensor measures the thickness of the film layer. 4 microns; seawater temperature is 25°C, and the environmental disturbance correction coefficient α obtained from the mapping table is 0.95; the basic attachment offset is calculated based on historical data -0.35; get real-time attachment thickness =0.95×4+(-0.35)=3.45 microns; subsequent multi-aperture spectral reflection feature analysis is performed, and the reflected light intensity of the 50-micron aperture =0.88, peak wavelength =520 nm, average reflectivity =0.75, forming the eigenvector =[0.88,520,0.75], similarly obtain the eigenvalues ​​of 100 and 200 microns and input them into the analytical model =[0.1,0.2,0.05] and =0.3 to calculate the biofouling impact factor =0.1×0.88+0.2×520+0.05×0.75+0.3≈104.88 After normalization adjustment, the standard value is 0.75; the cumulative time t is 5400 seconds. =0.75× (-0.001×5400)≈0.75×0.004≈0.003 is output as the microbial attachment thickness data.

[0064] Optionally, generating the sensitive membrane deformation prediction parameter includes:

[0065] Inputting the environmental disturbance characteristic vector into a transfer function library and outputting a temperature-salinity coupled deformation coefficient;

[0066] Inputting the biofouling influencing factor into the membrane stress model and outputting the biofouling deformation coefficient;

[0067] The temperature-salinity coupled deformation coefficient and the biological attachment deformation coefficient are integrated to generate a sensitive membrane deformation prediction parameter.

[0068] Specifically, the time-frequency analysis of the environmental disturbance characteristic vector is as follows: Figure 4As shown, firstly, the environmental disturbance feature vector is taken as the input data, wherein the environmental disturbance feature vector is composed of the temperature gradient vector, the salinity fluctuation scalar and the turbulence intensity scalar. The vector is input into the transfer function library, which stores the fluid-structure coupling equation set for the seawater environment, for example, the function in the form simplified by the Navier-Stokes equation:

[0069] ,

[0070] wherein the parameter symbol represents the seawater density, the parameter symbol represents the flow velocity component, is the partial derivative of the velocity with respect to time, is the spatial gradient of the velocity. After processing via the function library, the temperature-salinity coupling deformation coefficient is output. Meanwhile, the obtained biofouling influence factor is input into the membrane layer stress model , which is established based on the principle of composite material elasticity mechanics, and the formula is as follows:

[0071] ,

[0072] The parameter symbol represents the Young's modulus of the sensitive membrane, and the parameter symbol represents the strain component, which is linearly mapped from the biofouling influence factor to , and the bioattachment deformation coefficient is calculated.

[0073] The fusion process adopts the following formula:

[0074] ,

[0075] The parameter symbol represents the environmental weight factor, which is obtained by querying the preset table through the temperature gradient value, and the range is 0 to 1, is the bioattachment deformation coefficient, is the temperature-salinity coupling deformation coefficient. Finally, the sensitive membrane deformation prediction parameter is output, which comprehensively reflects the influence value of the joint action of the environment and the biofouling.

[0076] Exemplarily, the environmental disturbance feature vector of a certain gulf monitoring point is processed and output as 0.85, and the transfer function library calculates the temperature-salinity coupling deformation coefficient as 0.62 according to this; the microbial attachment thickness data of the same point is 0.003; the biofouling influence factor is 0.75, and the bioattachment deformation coefficient 0.075; field temperature gradient 0.2 °C per second query weight factor 0.6; final fusion calculation = 0.6 x 0.62 + (1 - 0.6) x 0.075 = 0.65 as a sensitive membrane deformation prediction parameter output.

[0077] Optionally, the seawater osmotic pressure standard solution is generated based on the sensitive membrane deformation prediction parameter, comprising:

[0078] Extracting the ion component data of the current seawater;

[0079] Based on the sensitive membrane deformation prediction parameter, a multi-modal pore attenuation coefficient is obtained;

[0080] According to the ion component data and the preset isotonic matching rule, a standard solution formula is obtained;

[0081] The multi-modal pore attenuation coefficient and the standard solution formula are microfluidically synthesized in cooperation to generate the seawater osmotic pressure standard solution.

[0082] Specifically, the sensitive membrane deformation prediction parameter generation process is as shown in Figure 5 Firstly, the ion chromatograph equipment is deployed in the monitoring probe system to extract the ion component data of the current seawater in real time, which contains the molar concentration values of main ions such as sodium ions, chloride ions and magnesium ions, forming an ion concentration vector. The sensitive membrane deformation prediction parameter (the parameter represents the combined effect of environmental disturbance and biological fouling) is input into the pore response equation to calculate the multi-modal pore attenuation coefficient, which represents the attenuation degree of the sensitive membrane pore permeability, and the formula is:

[0083] ,

[0084] is the multi-modal pore attenuation coefficient, which is the output value; represents the sensitive membrane basic permeability constant; represents the seawater density; the parameter symbol represents the input sensitive membrane deformation prediction parameter. Then, the ion concentration vector is used to query the preset isotonic matching rule library, which contains a formula mapping table based on the target osmotic pressure, and the standard solution formula including the reagent type and concentration value is output. This matching rule ensures that the synthesized liquid has equivalent osmotic properties. Finally, the multi-modal pore attenuation coefficient and the standard solution formula are input into the microfluidic cooperative synthesis unit, which is configured to drive sodium chloride, magnesium chloride and other reagent solutions by using a peristaltic pump. Based on the value, the flow rate is adjusted by a piezoelectric ceramic valve to compensate for the pore attenuation; the proportioning and dynamic balance control are performed during the synthesis process, and the reaction is completed in the microchannel chamber to output the seawater osmotic pressure standard solution meeting the osmotic pressure standard.

[0085] Exemplarily, the offshore buoy platform initiates a correction process, and sets the deformation prediction parameter input of the sensitive membrane to 0.65 mol / L, and the seawater density meter obtains 1.025 , and the multi-modal pore attenuation coefficient formula is obtained, wherein is a fixed value of 0.85, and the calculation obtains = 0.85 x e^(-1.025 x 0.65) ≈ 0.85 x 0.52 ≈ 0.44; the standard liquid formula is obtained after matching the isosmotic matching rule library, including 0.48 mol / L of sodium chloride solution and 0.05 mol / L of magnesium chloride solution; the microfluidic unit drives pump No. 1 to inject sodium chloride solution at 2.4 mL / min, and pump No. 2 injects magnesium chloride solution at 0.25 mL / min, while adjusting the compensation valve opening to 44% according to = 0.44, and the seawater osmotic pressure standard liquid is output after 10 minutes of continuous reaction in the mixing chamber.

[0086] Optionally, the calculation of the probe electrochemical compensation parameter comprises:

[0087] The in-situ adsorption efficiency coefficient is subtracted from the reference efficiency coefficient to obtain a pore migration index;

[0088] A preset voltage-efficiency mapping table is called according to the pore migration index, and a compensation voltage value and a current adjustment slope are output;

[0089] The compensation voltage value and the current adjustment slope are subjected to temperature-deformation double-field coupling correction to obtain a probe electrochemical compensation parameter.

[0090] Specifically, first, an in-situ adsorption efficiency coefficient and a reference efficiency coefficient pre-stored in the system are obtained, and the reference efficiency coefficient is obtained as a reference value under ideal conditions without interference through laboratory calibration. A pore migration index is obtained by subtracting the in-situ adsorption efficiency coefficient from the reference efficiency coefficient, and the formula is:

[0091] ,

[0092] wherein represents the pore migration index, represents the in-situ adsorption efficiency coefficient, which is measured by a heavy metal adsorption test of seawater osmotic pressure standard liquid, represents the reference efficiency coefficient which is fixed. Then, a preset voltage-efficiency mapping table is called, and the mapping table is constructed based on probe electrochemical performance test data under different pore migration indexes, and the input​ value after the value of the output compensation voltage value and current adjustment slope Finally, the and Perform temperature-deformation dual-field coupling correction: real-time acquisition of seawater temperature Obtain Celsius data through temperature sensor, and obtain current sensitive membrane deformation prediction parameter , establish correction equation:

[0093] ,

[0094] ,

[0095] Among them and are the corrected parameters, represent the fixed value of the reference temperature 25℃, and are temperature compensation coefficients determined by the material thermal expansion characteristics, and are fixed values, and are deformation compensation coefficients obtained by film material stress test, and are fixed values. The final output probe electrochemical compensation parameter set is { }.

[0096] Exemplarily, run the correction process on the East China Sea monitoring platform, assuming that the in-situ adsorption efficiency verification measured =0.82, the system preset =0.90, calculate the pore migration index =0.82-0.90=-0.08; query the voltage-efficiency mapping table (if =-0.08, the corresponding relationship is =-0.15V, =0.02A / V); the field seawater temperature =28℃, the sensitive membrane deformation prediction parameter =0.65; the system calls =0.005 / ℃, =0.1, =0.003 / ℃, =0.08, =25℃ into the equation: =-0.15×[1+0.005×(28-25)+0.1×0.65]≈-0.15×1.065≈-0.16V, =0.02×[1+0.003×(28-25)+0.08×0.65]≈0.02×1.052≈0.021A / V, output probe electrochemical compensation parameter = -0.16V, = 0.021A / V.

[0097] Optionally, the dynamic adjustment of the sensitive membrane deformation parameter based on the microbial adhesion thickness data comprises:

[0098] Based on the microbial adhesion thickness data, a three-dimensional biological stress tensor is obtained;

[0099] Based on the three-dimensional biological stress tensor, the sensitive membrane deformation parameter is dynamically adjusted to obtain a real-time membrane strain matrix;

[0100] The compensation voltage value in the probe electrochemical compensation parameter is combined with the real-time membrane strain matrix to generate a voltage compensation instruction.

[0101] Specifically, the three-dimensional biological stress tensor distribution is as shown in Figure 6 First, the microbial adhesion thickness data is obtained, which is generated by optical thickness measurement and multi-spectral analysis to represent the degree of biological fouling. The thickness data is input into a biological stress conversion model, which is converted into a three-dimensional biological stress tensor according to the elastic modulus characteristics of the microbial membrane, and the formula is:

[0102] ,

[0103] Wherein represents the output value of the three-dimensional biological stress tensor, represents the equivalent elastic modulus constant of the microbial membrane, which is obtained by laboratory material testing, is the input microbial adhesion thickness data. Then is input into the sensitive membrane dynamic adjustment system, which is embedded with the finite element equation of elasticity mechanics:

[0104] ,

[0105] Wherein the output is a real-time membrane strain matrix, is the flexibility matrix of the sensitive membrane, which is preset according to the specifications of the composite membrane material on the probe, is the input three-dimensional biological stress tensor. Finally, the compensation voltage value volts in the probe electrochemical compensation parameter is extracted and combined with the real-time membrane strain matrix to perform strain-voltage coupling superposition:

[0106] ,

[0107] Wherein is the final compensation instruction, is the strain-voltage conversion coefficient, which is determined by the piezoelectric characteristics of the membrane material, The absolute value of the main diagonal elements of the strain matrix is taken. The voltage compensation instruction is generated to contain a voltage adjustment value sequence.

[0108] Exemplarily, the microbial adhesion thickness data is measured at a monitoring point in the South China Sea = 0.015 mm; it is assumed that = 0.8 MPa (typical biological membrane modulus), and a three-dimensional biological stress tensor is calculated = 0.8 * 0.015 = 0.012 MPa; a sensitive film flexibility matrix A preset diagonal matrix [0.05, 0.05, 0.03] per MPa is obtained, and its inverse is [20, 20, 33.3]; a real-time film strain matrix is calculated = [20, 20, 33.3] * 0.012 ≈ [0.24, 0.24, 0.4], the sum of the absolute values of the micro-strains = 0.88; it is assumed that the electrochemical compensation parameters = -0.15 V, = 0.1 V , and the sum is = -0.15 + 0.1 * 0.88 ≈ 0.088 V, and a compensation instruction set is output .

[0109] Optionally, the combining the real-time film strain matrix and the compensation voltage value in the probe electrochemical compensation parameter to generate a voltage compensation instruction includes:

[0110] Based on the real-time film strain matrix, a strain gradient vector field is obtained;

[0111] Based on the strain gradient vector field, a compensation voltage value in the probe electrochemical compensation parameter is superimposed to obtain a directional deformation voltage;

[0112] The directional deformation voltage is pulse width-bioactivity synergistically encoded to generate a voltage compensation instruction.

[0113] Specifically, a real-time film strain matrix is first obtained, which is generated by conversion of a three-dimensional biological stress tensor and contains strain components in x, y, and z directions of the film surface. Spatial differentiation operation is performed on the matrix to generate a strain gradient vector field, and the formula is:

[0114] ,

[0115] Wherein is the output value of the strain gradient vector field, represents the intensity of compression along the x-axis, represents the intensity of compression along the y-axis, represents the severity of compression along the z-axis, each partial derivative term is calculated by central difference on the real-time film strain matrix in the probe surface coordinate system, and the spatial step is set to 1 millimeter corresponding to the probe surface grid resolution. At the same time, the compensation voltage value in the probe electrochemical compensation parameter is called , and the vector superposition operation is performed:

[0116] ,

[0117] wherein is the directional deformation voltage vector output value, is the probe normal, a fixed value preset by the probe geometry, is the voltage-gradient coupling coefficient, is the modulus of the strain gradient vector field. Finally, the microbial adhesion thickness data is associated to calculate the bioactivity feedback value:

[0118] ,

[0119] represents the influence coefficient of biofouling on the performance of the probe, represents the measured thickness of the microbial layer on the surface of the probe, is the biological decay constant (fixed value 0.2 per millimeter), the pulse width modulator is input to generate a pulse sequence, and the pulse width formula is:

[0120] ,

[0121] wherein is the modulated pulse width, is the reference width, a fixed value of 10 milliseconds, is the bioactivity gain coefficient, a fixed value of 0.5. The directional deformation voltage vector is pulse coded to generate a compensation instruction set containing voltage amplitude, action direction and pulse width parameters.

[0122] Exemplarily, the real-time film strain matrix =[0.2,−0.1,0.3] is obtained at a monitoring point in a certain gulf, the micro-strain (corresponding to x, y, z directions); the probe curved surface normal vector is set, the strain gradient vector field is calculated by spatial differentiation, and the modulus is obtained. The compensation voltage value =-0.12V is taken, the directional deformation voltage vector is calculated by superposition: , the current microbial adhesion thickness =0.018mm, and the bioactivity feedback value = is calculated. After pulse width modulation The final voltage compensation instruction is generated: .

[0123] Optionally, the parameter correction and superposition processing of the probe electrochemical compensation parameters on the voltage compensation instruction is performed to obtain the probe correction control parameters, including:

[0124] The key parameters of the probe electrochemical compensation parameters are extracted and applied to obtain the compensation voltage value;

[0125] The biological fouling related deformation correction and dynamic adjustment of the voltage compensation instruction is performed to obtain the real-time membrane strain matrix;

[0126] The parameter correction and superposition processing of the compensation voltage value on the real-time membrane strain matrix is performed to obtain the probe correction control parameters.

[0127] Specifically, the probe electrochemical compensation parameter set is first called, which includes the compensation voltage value corrected by the temperature-deformation double field coupling And the current adjustment slope . The key parameter extraction operation is performed, and only the compensation voltage value is extracted as the core electrochemical compensation input. Then the voltage compensation instruction is obtained, which includes the voltage adjustment sequence after biological fouling correction, and the real-time membrane strain matrix is decoded from the instruction set . The biological fouling related deformation correction of the real-time membrane strain matrix is performed: combined with the current microbial adhesion thickness data , the membrane permeability correction coefficient is calculated:

[0128] ,

[0129] is the permeation decay constant (0.15 per millimeter is calibrated by membrane material swelling experiment), and the strain matrix is corrected by scalar multiplication:

[0130] ,

[0131] is the corrected real-time membrane strain matrix. Finally, the parameter superposition processing is performed: the compensation voltage value and the correction matrix are mapped according to the probe surface control area, and the control voltage formula of each partition is: ,

[0132] wherein is the probe correction control sub-parameter of the th partition, is the electrochemical weight distribution factor (preset fixed value by probe electrode layout). strain-potential conversion coefficient (determined by the film piezoelectric constant 1.2 volts per micro-strain), Take the average of the main diagonal elements of the correction matrix. Form the probe correction control parameter matrix by collecting all regional sub-parameters and output.

[0133] Exemplarily, the probe electrochemical compensation parameters are obtained at the Yellow Sea monitoring station =-0.15V, =0.02A / V, extract value. Decode the voltage compensation instruction to obtain the real-time film strain matrix =[0.3,-0.05,0.2] (corresponding to three-axis data). The current microbial adhesion thickness =0.02mm, calculate =e^{-0.15×0.02}≈0.997, the correction matrix ≈[0.299,-0.0498,0.199]. Set the upstream region of the probe =0.8, the middle reaches =1.0, the lower reaches =1.2, each region take the corresponding average. The upstream region calculates =-0.15×0.8+1.2×(|0.299|)≈-0.12+0.3588≈0.2388V, the middle reaches =-0.15×1.0+1.2×(|-0.0498|)≈-0.15+0.06≈-0.09V, the lower reaches =-0.15×1.2+1.2×(|0.199|)≈-0.18+0.2388≈0.0588V, the final output probe correction control parameter matrix [0.24V,-0.09V,0.06V].

[0134] Based on the same inventive concept, as Figure 7 shown, the application also provides an automatic correction system for errors of an online seawater trace heavy metal monitoring probe, the system comprising:

[0135] a multi-source parameter acquisition module for acquiring seawater fluid parameters and collecting microbial adhesion thickness data on the surface of the probe;

[0136] a sensitive film deformation collaborative modeling module for generating sensitive film deformation prediction parameters according to the seawater fluid parameters and the microbial adhesion thickness data;

[0137] a dynamic synthesis module for generating seawater osmotic pressure standard liquid based on the sensitive film deformation prediction parameters;

[0138] An in-situ adsorption efficiency verification module is configured to perform heavy metal adsorption efficiency verification on the seawater osmotic pressure standard solution, and obtain an in-situ adsorption efficiency coefficient;

[0139] A compensation parameter calculation module is configured to calculate a probe electrochemical compensation parameter according to the in-situ adsorption efficiency coefficient and a preset reference efficiency coefficient;

[0140] A biofouling compensation instruction module is configured to dynamically adjust the sensitive membrane deformation parameter based on the microorganism adhesion thickness data, and generate a voltage compensation instruction;

[0141] A dual-path fusion correction module is configured to perform parameter correction and superposition processing on the voltage compensation instruction based on the probe electrochemical compensation parameter, and obtain a probe correction control parameter.

[0142] It should be noted that the electrical connection between the above-mentioned units does not necessarily represent the direct connection of the line, and the indirect connection mode can also be applied to the embodiments of the present application as long as the purpose of the present application is achieved. The above-mentioned is only an exemplary embodiment of the present application, and cannot limit the scope of the present application.

[0143] That is, any equivalent changes and modifications made according to the teachings of the present application are still within the scope of the present application. Other embodiments of the present application will be readily apparent to those skilled in the art upon considering the specification and practice of the true principles disclosed herein. The present application is intended to cover any variations, uses, or adaptive changes of the present application that follow the general principles of the present application and include common knowledge or conventional technical means in the art that are not disclosed in the present application.

Claims

1. A method for automatically correcting probe errors for online monitoring of trace heavy metals in seawater, characterized in that: The method comprises: Obtain seawater fluid parameters and collect microbial attachment thickness data on the probe surface; generating a sensitive membrane deformation prediction parameter according to the seawater fluid parameter and the microbial attachment thickness data; generating a seawater osmotic pressure standard solution based on the sensitive membrane deformation prediction parameter; Performing heavy metal adsorption efficiency verification on the seawater osmotic pressure standard solution to obtain an in-situ adsorption efficiency coefficient; Calculating the probe electrochemical compensation parameter based on the in-situ adsorption efficiency coefficient and a preset benchmark efficiency coefficient; Dynamically adjusting the deformation parameters of the sensitive membrane based on the microorganism attachment thickness data to generate a voltage compensation instruction; Perform parameter correction and superposition processing on the voltage compensation instruction based on the probe electrochemical compensation parameter to obtain the probe correction control parameter; The calculated electrochemical compensation parameters of the probe include: Performing a difference operation on the in-situ adsorption efficiency coefficient and the benchmark efficiency coefficient to obtain a pore migration index; Calling a preset voltage-efficiency mapping table according to the pore migration index, and outputting a compensation voltage value and a current adjustment slope; Performing temperature-deformation dual-field coupling correction on the compensation voltage value and the current adjustment slope to obtain the electrochemical compensation parameters of the probe; The dynamically adjusting the deformation parameters of the sensitive membrane based on the microorganism attachment thickness data to generate a voltage compensation instruction includes: obtaining a three-dimensional biological stress tensor based on the microbial attachment thickness data; Dynamically adjusting the deformation parameters of the sensitive membrane based on the three-dimensional biological stress tensor to obtain a real-time membrane strain matrix; Combine the real-time membrane strain matrix and superimpose the compensation voltage value in the probe electrochemical compensation parameter to generate a voltage compensation instruction; The step of combining the real-time membrane strain matrix with the compensation voltage value in the probe electrochemical compensation parameter to generate a voltage compensation instruction includes: Based on the real-time membrane strain matrix, a strain gradient vector field is obtained; Obtaining a directional deformation voltage based on the strain gradient vector field and superimposing a compensation voltage value in the probe electrochemical compensation parameter; The directional deformation voltage is subjected to pulse width-bioactivity collaborative encoding to generate a voltage compensation instruction.

2. The method for automatically correcting errors of online monitoring probes for trace heavy metals in seawater according to claim 1, characterized in that: The obtaining of seawater fluid parameters comprises: Collect temperature gradient data, salinity fluctuation data and turbulence intensity data; fusing the temperature gradient data, the salinity pulsation data, and the turbulence intensity data into an environmental disturbance feature vector; The environmental disturbance feature vector is subjected to multi-scale decomposition and time-frequency feature extraction to generate seawater fluid parameters.

3. The method for automatically correcting errors of online monitoring probes for trace heavy metals in seawater according to claim 2, characterized in that: The data of microorganism attachment thickness on the surface of the acquisition probe includes: Monitor the thickness of microbial film; Based on the thickness of the microbial film layer, the real-time attachment thickness is calculated; Performing multi-aperture spectral reflectance characteristic analysis on the real-time adhesion thickness to obtain a biofouling influencing factor; The biofouling influencing factor is adjusted time-varyingly to obtain microbial attachment thickness data.

4. The method for automatically correcting errors of online monitoring probes for trace heavy metals in seawater according to claim 3, characterized in that: The generating of the sensitive film deformation prediction parameters includes: Inputting the environmental disturbance characteristic vector into a transfer function library and outputting a temperature-salinity coupled deformation coefficient; Inputting the biofouling influencing factor into the membrane stress model and outputting the biofouling deformation coefficient; The temperature-salinity coupled deformation coefficient and the biological attachment deformation coefficient are integrated to generate a sensitive membrane deformation prediction parameter.

5. The method for automatic error correction of an online monitoring probe for trace heavy metals in seawater according to claim 1, characterized in that: The generating of the seawater osmotic pressure standard solution based on the sensitive membrane deformation prediction parameter includes: Extract the ion composition data of current seawater; Based on the deformation prediction parameters of the sensitive membrane, a multimodal pore attenuation coefficient is obtained; Obtaining a standard solution formula according to the ion component data and a preset isotonic ratio rule; The multimodal pore attenuation coefficient and the standard solution formula are synthesized in a microfluidic collaborative manner to generate a seawater osmotic pressure standard solution.

6. The method for automatically correcting errors of online monitoring probes for trace heavy metals in seawater according to claim 1, characterized in that: The parameter correction and superposition processing of the voltage compensation instruction based on the probe electrochemical compensation parameter to obtain the probe correction control parameter includes: Extracting and applying key parameters of the probe electrochemical compensation parameters to obtain a compensation voltage value; Performing biofouling-related deformation correction and dynamic adjustment on the voltage compensation instruction to obtain a real-time membrane strain matrix; Parameter correction and superposition processing are performed on the real-time membrane strain matrix based on the compensation voltage value to obtain probe correction control parameters.

7. A system for automatically correcting errors of online monitoring probes for trace heavy metals in seawater, applied to a method for automatically correcting errors of online monitoring probes for trace heavy metals in seawater according to any one of claims 1 to 6, characterized in that: The system comprises: Multi-source parameter acquisition module, used to obtain seawater fluid parameters and collect microbial attachment thickness data on the probe surface; A sensitive membrane deformation collaborative modeling module is used to generate sensitive membrane deformation prediction parameters based on the seawater fluid parameters and the microbial attachment thickness data; A dynamic synthesis module, used to generate a seawater osmotic pressure standard solution based on the sensitive membrane deformation prediction parameters; an in-situ adsorption efficiency verification module, configured to verify the heavy metal adsorption efficiency of the seawater osmotic pressure standard solution and obtain an in-situ adsorption efficiency coefficient; A compensation parameter calculation module, configured to calculate the electrochemical compensation parameter of the probe based on the in-situ adsorption efficiency coefficient and a preset reference efficiency coefficient; a biofouling compensation instruction module, configured to dynamically adjust the deformation parameters of the sensitive membrane based on the microbial attachment thickness data and generate a voltage compensation instruction; The dual-path fusion correction module is used to perform parameter correction and superposition processing on the voltage compensation instruction based on the probe electrochemical compensation parameter to obtain the probe correction control parameter.

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