Automatic error correction method for seawater trace heavy metal on-line monitoring probe

Abstract

The invention discloses a seawater trace heavy metal on-line monitoring probe error automatic correction method, and belongs to the technical field of marine environment monitoring automatic control, and the method comprises the following steps: obtaining seawater fluid parameters and probe surface microorganism adhesion thickness data; generating a sensitive film deformation prediction parameter according to the data; dynamically synthesizing a seawater osmotic pressure standard solution based on the parameters; an in-situ adsorption efficiency coefficient is obtained through adsorption efficiency verification; comparing the reference efficiency coefficient to calculate an electrochemical compensation parameter of the probe; dynamically adjusting deformation parameters based on the microorganism adhesion thickness to generate a voltage compensation instruction; and finally, outputting probe correction control parameters through correction and superposition of the electrochemical parameters and the voltage instruction. By constructing a feedforward-feedback composite control system, predictive compensation for physical disturbance and real-time feedback calibration based on dynamic osmotic pressure standard liquid are combined, decoupling suppression for multi-source disturbance is achieved, and therefore the autonomous closed-loop control capacity of a probe is established.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control of marine environment monitoring, in particular to a method for automatically correcting errors of an on-line monitoring probe for trace heavy metals in seawater. Background Art

[0002] In complex marine environments, the output stability of seawater trace heavy metal monitoring probes is affected by both hydrodynamic disturbances and biofouling deformation, requiring dynamic compensation mechanisms to maintain sensing characteristics. These systems typically use electrochemically sensitive membranes as detection carriers, and output drift suppression relies on coordinated control strategies for multi-physics field coupling interference.

[0003] Existing technologies primarily maintain probe stability through static calibration and periodic cleaning. One approach involves offline calibration of electrochemical parameters using a reference solution and open-loop voltage correction using a compensation scale. A second approach involves periodically removing biological deposits from the probe surface through mechanical scraping to prevent deformation. Some improved approaches incorporate linear feedback mechanisms, proportionally adjusting the detection voltage based on a single environmental parameter to mitigate the effects of fluid disturbances.

[0004] The above technical means have significant limitations: offline calibration cannot respond to the dynamically changing biofouling stress field, resulting in the lack of deformation feedforward compensation; periodic mechanical intervention destroys the continuous monitoring process; linear feedback control makes it difficult to decouple the coupling effect of the dual disturbance sources of fluid and biofouling, resulting in a mismatch between the electrochemical compensation parameters and the actual disturbance state of the environment. Summary of the Invention

[0005] To solve the above problems, the present invention provides a method for automatic error correction of online monitoring probes for trace heavy metals in seawater. By constructing a feedforward-feedback composite control system, the predictive compensation for physical disturbances is combined with real-time feedback calibration based on a dynamic osmotic pressure standard solution, thereby achieving decoupling and suppression of multi-source disturbances, thereby establishing the autonomous closed-loop control capability of the probe.

[0006] The above objectives can be achieved through the following solutions: The present invention discloses an automatic error correction method for an on-line monitoring probe for trace heavy metals in seawater. The method comprises the following steps: obtaining seawater fluid parameters and probe surface microbial attachment thickness data; generating sensitive membrane deformation prediction parameters according to the data; dynamically synthesizing a seawater osmotic pressure standard solution based on the parameters; obtaining an in-situ adsorption efficiency coefficient through adsorption efficiency verification; calculating a probe electrochemical compensation parameter by comparing the coefficient with a benchmark efficiency; dynamically adjusting the deformation parameter based on the microbial attachment thickness to generate a voltage compensation instruction; and finally outputting a probe correction control parameter by correcting and superimposing the electrochemical parameter and the voltage instruction.

[0007] Optionally, obtaining seawater fluid parameters includes: collecting temperature gradient data, salinity pulsation 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; performing multi-scale decomposition and time-frequency feature extraction on the environmental disturbance feature vector to generate seawater fluid parameters.

[0008] Optionally, collecting the microbial attachment thickness data on the probe surface includes: monitoring the thickness of the microbial film layer; calculating the real-time attachment thickness based on the microbial film layer thickness; performing multi-aperture spectral reflectance characteristic analysis on the real-time attachment thickness to obtain a biofouling influencing factor; and performing time-varying adjustment on the biofouling influencing factor to obtain the microbial attachment thickness data.

[0009] Optionally, generating the sensitive membrane deformation prediction parameters includes: inputting the environmental disturbance characteristic vector into a transfer function library to output a temperature-salinity coupling deformation coefficient; inputting the biological fouling influencing factor into a membrane stress model to output a biological attachment deformation coefficient; and fusing the temperature-salinity coupling deformation coefficient with the biological attachment deformation coefficient to generate the sensitive membrane deformation prediction parameters.

[0010] Optionally, generating a seawater osmotic pressure standard solution based on the sensitive membrane deformation prediction parameters includes: extracting ion component data of current seawater; obtaining a multimodal pore attenuation coefficient based on the sensitive membrane deformation prediction parameters; obtaining a standard solution formula according to the ion component data and a preset isotonic ratio rule; and performing microfluidic collaborative synthesis of the multimodal pore attenuation coefficient and the standard solution formula to generate a seawater osmotic pressure standard solution.

[0011] Optionally, the calculation of the probe electrochemical compensation parameters includes: 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 probe electrochemical compensation parameters.

[0012] Optionally, the dynamic adjustment of the sensitive membrane deformation parameters based on the microbial 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 sensitive membrane deformation parameters based on the three-dimensional biological stress tensor to obtain a real-time membrane strain matrix; and superimposing the compensation voltage value in the probe electrochemical compensation parameter in combination with the real-time membrane strain matrix to generate a voltage compensation instruction.

[0013] Optionally, the generation of voltage compensation instructions by combining the real-time membrane strain matrix with the compensation voltage value in the probe electrochemical compensation parameter includes: obtaining a strain gradient vector field based on the real-time membrane strain matrix; obtaining a directional deformation voltage based on the strain gradient vector field and superimposing the compensation voltage value in the probe electrochemical compensation parameter; and performing pulse width-bioactivity collaborative encoding on the directional deformation voltage to generate a voltage compensation instruction.

[0014] Optionally, the voltage compensation instruction is subjected to parameter correction and superposition processing based on the probe electrochemical compensation parameter to obtain the probe correction control parameter, including: extracting and applying key parameters of the probe electrochemical compensation parameter 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; and the real-time membrane strain matrix is ​​subjected to parameter correction and superposition processing based on the compensation voltage value to obtain the probe correction control parameter.

[0015] Based on the same inventive concept, the present invention also provides an automatic error correction system for online monitoring probes of trace heavy metals in seawater, the system comprising: a multi-source parameter acquisition module for acquiring seawater fluid parameters and collecting microbial attachment thickness data on the probe surface; a sensitive membrane deformation collaborative modeling module for generating sensitive membrane deformation prediction parameters based on the seawater fluid parameters and the microbial attachment thickness data; a dynamic synthesis module for generating a seawater osmotic pressure standard solution 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 solution to obtain an in-situ adsorption efficiency coefficient; a compensation parameter calculation module for calculating the probe electrochemical compensation parameters based on the in-situ adsorption efficiency coefficient and a preset benchmark efficiency coefficient; a biofouling compensation instruction module for dynamically adjusting the sensitive membrane deformation parameters based on the microbial attachment thickness data to generate a voltage compensation instruction; a dual-path fusion correction module for performing parameter correction and superposition processing on the voltage compensation instruction based on the probe electrochemical compensation parameters to obtain probe correction control parameters.

[0016] Compared with the prior art, the present invention has the following advantages: 1. This invention establishes a feedforward-feedback composite control architecture, improving the system's dynamic response and robustness. The invention innovatively designs a dual-channel feedforward-feedback collaborative control architecture. The feedforward control channel monitors fluid parameters and biofouling thickness in real time to establish a predictive model for disturbances and sensitive membrane deformation, proactively compensating for predictable disturbances. The feedback control channel generates standard solutions in situ and verifies adsorption efficiency to obtain the actual system output error for closed-loop correction. This composite control architecture effectively suppresses various disturbances, significantly outperforming traditional open-loop or single-feedback control. 2. This invention achieves decoupled control of multivariable disturbances, improving control accuracy. By using voltage compensation instructions based on fluid-biofouling predictions and electrochemical compensation parameters based on measured efficiency deviations, adjustments are made to physical deformation disturbances and electrochemical property drift, respectively. Finally, through superposition through a fusion algorithm, effective decoupling of the two major disturbance sources, physical and chemical, is achieved, avoiding the control mismatch problem caused by disturbance coupling in traditional methods and significantly improving control accuracy. 3. The present invention establishes an adaptive closed-loop correction mechanism to ensure long-term stability. By dynamically synthesizing a standard solution that matches the current seawater osmotic pressure, an adaptive online calibration closed loop is constructed. This mechanism can track and compensate in real time for performance degradation caused by slow time-varying factors such as sensitive membrane aging and biological fouling accumulation, enabling the probe to have the ability of autonomous learning and continuous optimization, thereby ensuring long-term, highly stable online monitoring without human intervention.

[0017] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 The present invention is a flowchart of an automatic error correction method for an on-line monitoring probe of trace heavy metals in seawater according to an embodiment of the present invention.

[0020] Figure 2 2 is a heavy metal adsorption efficiency curve diagram of an embodiment of the present invention.

[0021] Figure 3 2 is a diagram of seawater fluid parameters according to an embodiment of the present invention.

[0022] Figure 4 3 is a time-frequency analysis diagram of the environmental disturbance characteristic vector according to an embodiment of the present invention.

[0023] Figure 5 4 is a diagram of the process of generating the prediction parameters of the sensitive film deformation according to an embodiment of the present invention.

[0024] Figure 6 3D biological stress tensor distribution diagram of an embodiment of the present invention.

[0025] Figure 7 The present invention is a schematic structural diagram of an automatic error correction system for online monitoring probes of trace heavy metals in seawater according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0027] Reference Figure 1 One embodiment of the present invention proposes an automatic error correction method for an online monitoring probe for trace heavy metals in seawater. By constructing a feedforward-feedback composite control system, the predictive compensation for physical disturbances is combined with real-time feedback calibration based on a dynamic osmotic pressure standard solution, thereby achieving decoupling and suppression of multi-source disturbances, thereby establishing the autonomous closed-loop control capability of the probe.

[0028] The method of this embodiment specifically includes: 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 electrochemical compensation parameter of the probe according to 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; The voltage compensation instruction is subjected to parameter correction and superposition processing based on the probe electrochemical compensation parameter to obtain a probe correction control parameter.

[0029] Specifically, the heavy metal adsorption efficiency curve is as follows: Figure 2As shown, the seawater osmotic pressure standard solution generated by the dynamic synthesis module is first injected into the microfluidic adsorption verification unit, which is physically connected to the online monitoring probe for trace heavy metals in seawater. An automated pipetting arm is configured to extract aliquots of the standard solution from the sample cell and transfer them to an electrochemical detection cell. This cell is equipped with a working electrode, a reference electrode, and a counter electrode, and the three-electrode system is controlled by a potentiostat. The potentiostat is then programmed to apply a predetermined step voltage sequence to drive the electrochemical adsorption reaction of heavy metal ions in the standard solution on the working electrode surface. The current-time curve of the adsorption process is simultaneously recorded, and the real-time adsorbed charge is obtained by integration. The real-time adsorbed charge is then calculated by comparing it with the known heavy metal ion concentration in the standard solution to obtain instantaneous adsorption efficiency data. The adsorption reaction duration is set to at least ten minutes, and multiple sets of instantaneous adsorption efficiency data are continuously collected. Linear regression analysis is performed on all data points to determine the slope of the adsorption efficiency curve. When the absolute value of this slope falls below a preset stability threshold, the average efficiency value within that time window is output as the raw adsorption efficiency coefficient. Finally, the system collects real-time data on current seawater temperature and microbial adhesion thickness, and uses a pre-set temperature-biofouling correction model to calculate and compensate the original adsorption efficiency coefficient. This model uses a sensitivity matrix to convert temperature drift and biofouling effects into correction factors, compensating the original adsorption efficiency coefficient and outputting the final in-situ adsorption efficiency coefficient.

[0030] Optionally, obtaining seawater fluid parameters includes: 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.

[0031] Specifically, seawater fluid parameters such as Figure 3 As shown, first, the temperature sensor built into the seawater monitoring probe is used to collect seawater temperature gradient data. This data represents the distribution of the temperature change rate over time or space. The temperature gradient vector variable is obtained through continuous time sampling, in which each element corresponds to the temperature change value at the measurement moment. The salinity sensor is used to collect seawater salinity pulsation data. This data is obtained by calculating the standard deviation of the salinity time series, reflecting the amplitude characteristics of salinity fluctuations. At the same time, a turbulence meter is deployed to collect turbulence intensity data, which represents the energy intensity value of seawater flow. The above three data sets are fused, specifically through linear combination to form an environmental disturbance feature vector, which contains data collection of temperature gradient, salinity pulsation and turbulence intensity. The formula for the environmental disturbance feature vector is: , Among them, the parameter symbol represents the environmental disturbance eigenvector, and the parameter symbol Represents the temperature gradient vector from the temperature sensor, such as the temperature difference component containing time changes. The parameter symbol The scalar representing the salinity pulse data obtained from the salinity sensor and processed by standard deviation calculation, parameter symbol The turbulence intensity data represents the water flow energy intensity measurement value obtained by the turbulence meter. The vector elements are directly superimposed with equal weights to achieve vectorization. The environmental disturbance feature vector is then input into the multi-scale decomposition process and decomposed into sub-components at multiple scale levels using the discrete wavelet transform algorithm. This process uses specific wavelet basis functions to analyze the signal characteristics of different frequency bands. The time-frequency feature extraction step is then performed, and the short-time Fourier transform algorithm is applied to calculate the time-frequency energy spectrum distribution of each scale component to generate a matrix feature set containing frequency and time correlations. Finally, the seawater fluid parameters are extracted and output from the matrix. These parameters comprehensively represent the dynamic disturbance state of the seawater flow and can be used for subsequent correction processing.

[0032] For example, an online monitoring probe system for trace heavy metals in seawater was deployed at an offshore monitoring station. Initial data acquisition conditions were set: seawater temperature gradient data was acquired by sensors at a rate of change of 0.1 degrees Celsius per second, salinity pulsation data was measured by sensors with a calculated standard deviation of 0.01 per unit, and turbulence intensity data was measured by a turbulence meter at 0.2 watts per square meter. Data fusion was performed to obtain the element values ​​of the environmental disturbance characteristic vector, 0.1, 0.01, and 0.2. This vector was decomposed into three scale components using a discrete wavelet transform, and then a short-time Fourier transform was applied to extract the energy characteristics of the typical frequency bands. Ultimately, seawater fluid parameters were generated, such as the average disturbance frequency band index value of 0.85.

[0033] Optionally, the collecting of microorganism attachment thickness data on the probe surface 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.

[0034] Specifically, an optical thickness sensor is deployed on the surface of the online monitoring probe for trace heavy metals in seawater. The sensor emits a 500-nanometer wavelength light beam to the probe surface and monitors the thickness of the film formed by microorganisms by comparing the time difference of the light beam reflection with the preset threshold. Based on the speed of light Calculate the film thickness , the formula is: , The parameter symbol Represents the beam velocity value, parameter symbol The round trip time difference of the measuring beam is directly obtained by the sensor's internal clock. Based on the film thickness , calculate the real-time attachment thickness , the formula is: , Parameter Symbol Represents the environmental disturbance correction coefficient. After obtaining the current temperature value through the seawater temperature sensor, the coefficient mapping table is queried to obtain the association between temperature and coefficient based on experimental calibration. Parameter symbol Represents the base attachment offset, which is obtained by calculating the average value from the film thickness history database for the past 30 days. When performing multi-aperture spectral reflection feature analysis, a multi-aperture spectrometer device is configured. The device contains three apertures of 50 microns, 100 microns and 200 microns. The surface area of ​​the probe is scanned in sequence to record the spectral reflection data; the reflected light intensity value is obtained during the reflection process. And calculate the characteristic points corresponding to different apertures such as peak wavelength and average reflectivity Constructing feature vectors ; This vector inputs the preset analytical model, which is a linear regression equation: , is the biofouling influencing factor, parameter symbol represents the weight vector, Represents the deviation constant, which is a fixed value preset during manufacturing. Time-varying adjustment is performed to obtain microbial attachment thickness data , the formula is: , 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.

[0035] 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.

[0036] Optionally, generating the sensitive membrane deformation prediction parameter 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.

[0037] Specifically, the time-frequency analysis of the environmental disturbance characteristic vector is as follows: Figure 4 As shown, the environmental disturbance characteristic vector is first used as input data, where the environmental disturbance characteristic vector is composed of a temperature gradient vector, a salinity fluctuation scalar, and a turbulence intensity scalar. This vector is input into a transfer function library, which stores a set of fluid-structure coupling equations for seawater environments, such as a simplified form function of the Navier-Stokes equations: , The parameter symbol Represents the density of seawater, parameter symbol represents the flow velocity component, is the partial derivative of velocity with respect to time, is the spatial gradient of velocity. After processing by the function library, the output is the temperature-salinity coupled deformation coefficient At the same time, the biofouling impact factor Input membrane stress model , the model is established based on the elastic mechanics principle of composite materials, and the formula is: , Parameter Symbol Represents the Young's modulus of the sensitive film, parameter symbol represents the strain component, which is affected by the biofouling factor The linear mapping is , the biological attachment deformation coefficient is calculated , The fusion process uses the following formula: , Parameter Symbol Represents the environmental weight factor, which is obtained by querying the preset table through the temperature gradient value, ranging from 0 to 1. is the biological attachment deformation coefficient, is the temperature-salinity coupled deformation coefficient. The final output is the sensitive membrane deformation prediction parameter , which comprehensively reflects the impact value of the combined effects of environment and biofouling.

[0038] For example, the environmental disturbance characteristic vector of a bay monitoring point is processed and output as 0.85, and the transfer function library calculates the temperature-salinity coupling deformation coefficient based on this. is 0.62; the microbial attachment thickness data at the same point is 0.003; the biofouling influencing factor is 0.75, and the biological attachment deformation coefficient is calculated by inputting the membrane stress model 0.075; the field temperature gradient is 0.2℃ per second. Query weight factor The corresponding table is 0.6; the final fusion calculation =0.6×0.62+(1-0.6)×0.075≈0.65 is output as the sensitive membrane deformation prediction parameter.

[0039] Optionally, generating a 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.

[0040] Specifically, the process of generating the prediction parameters of the sensitive membrane deformation is as follows: Figure 5As shown, first deploy the ion chromatograph equipment in the monitoring probe system to extract the current seawater ion component data in real time. The data includes the molar concentration values ​​of major ions such as sodium ions, chloride ions, and magnesium ions to form an ion concentration vector. (This parameter represents the combined effect of environmental disturbance and biofouling) Input the pore response equation to calculate the multimodal pore attenuation coefficient, which represents the attenuation degree of the pore permeability of the sensitive membrane. The formula is: , is the multimodal pore attenuation coefficient, which is the output value; Represents the basic permeability constant of the sensitive membrane; Represents the density of seawater; parameter symbol Represents the input sensitive membrane deformation prediction parameter. Then, according to the ion concentration vector, the preset isotonic ratio rule library is queried. The database contains a formula mapping table based on the target osmotic pressure, and the output standard solution formula includes the reagent type and concentration value. This ratio rule ensures that the synthesized liquid has equivalent osmotic characteristics. Finally, the multimodal pore attenuation coefficient The formula of the standard solution is input into the microfluidic collaborative synthesis unit, which is equipped with a peristaltic pump to drive the sodium chloride, magnesium chloride and other reagent solutions. The value is adjusted by piezoelectric ceramic valve to regulate the flow and compensate for pore attenuation; the synthesis process performs proportional mixing and dynamic balance control, completes the reaction in the microchannel chamber, and outputs seawater osmotic pressure standard solution that meets the osmotic pressure standard.

[0041] For example, the calibration process is started on the offshore buoy platform, and the sensitive membrane deformation prediction parameter input is set The ion concentration of seawater measured by ion chromatography was 0.47 mol / L sodium, 0.55 mol / L chloride, and 0.053 mol / L magnesium; the seawater density was obtained by seawater density meter. 1.025 , substitute into the multimodal pore attenuation coefficient formula, where Fixed value 0.85, calculated =0.85×e^(-1.025×0.65)≈0.85×0.52≈0.44; after querying the isotonic ratio rule library, the standard solution formula is obtained, including 0.48 mol / L sodium chloride solution and 0.05 mol / L magnesium chloride solution; the microfluidic unit drives pump 1 to inject sodium chloride solution at 2.4 mL / min, and pump 2 to inject magnesium chloride solution at 0.25 mL / min. At the same time, according to =0.44 Adjust the opening of the compensation valve to 44%, and output the seawater osmotic pressure standard solution after the mixing chamber continues to react for 10 minutes.

[0042] Optionally, the calculating the probe electrochemical compensation parameter includes: 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; The compensation voltage value and the current adjustment slope are corrected by temperature-deformation dual-field coupling to obtain the electrochemical compensation parameters of the probe.

[0043] Specifically, first obtain the in situ adsorption efficiency coefficient and the benchmark efficiency coefficient pre-stored in the system The benchmark efficiency coefficient is obtained through laboratory calibration as a reference value under ideal conditions without interference. The pore migration index is obtained by performing a difference operation, and the formula is: , in represents the pore migration index, Represents the in-situ adsorption efficiency coefficient, which is measured by the heavy metal adsorption test of seawater osmotic pressure standard solution. The base efficiency coefficient is fixed. Then call the preset voltage-efficiency mapping table, which is constructed based on the electrochemical performance test data of the probe under different pore migration indices. Enter Output compensation voltage value after value and current adjustment slope Finally and Perform temperature-deformation dual-field coupling correction: real-time acquisition of seawater temperature Obtain Celsius data through the temperature sensor and obtain the current sensitive membrane deformation prediction parameters , establish the correction equation: , , in and is the corrected parameter, Represents a fixed value of 25°C as the reference temperature. and The temperature compensation coefficient is a fixed value determined by the thermal expansion characteristics of the material. and The deformation compensation coefficient is a fixed value obtained by stress testing the membrane material. The final output probe electrochemical compensation parameter set { }.

[0044] For example, the calibration process is run on the East China Sea monitoring platform, assuming that the in situ adsorption efficiency verification is measured =0.82, 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); on-site seawater temperature =28℃, prediction parameters of sensitive membrane deformation =0.65; system call =0.005 / ℃, =0.1, =0.003 / ℃, =0.08, =25℃Substitute 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 parameters =-0.16V, =0.021A / V.

[0045] Optionally, the dynamically adjusting the deformation parameter 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; The real-time membrane strain matrix is ​​combined with the compensation voltage value in the probe electrochemical compensation parameter to generate a voltage compensation instruction.

[0046] Specifically, the three-dimensional biological stress tensor distribution is as follows Figure 6 As shown, first obtain the microbial attachment thickness data The data is generated by optical thickness measurement and multi-spectral analysis to represent the degree of biofouling. This thickness data is input into the biostress conversion model, which converts it into a three-dimensional biostress tensor based on the elastic modulus characteristics of the microbial membrane. The formula is: , in Represents the three-dimensional biological stress tensor output value, Represents the equivalent elastic modulus constant of the microbial membrane, obtained from laboratory material testing, is the input microbial attachment thickness data. Input the dynamic adjustment system of the sensitive membrane, which has embedded finite element equations of elasticity: , in The output is a real-time membrane strain matrix, The flexibility matrix of the sensitive membrane is preset according to the specifications of the composite membrane material on the probe. The three-dimensional biological stress tensor is input. Finally, the compensation voltage value of the probe electrochemical compensation parameter is extracted. Volts, with real-time membrane strain matrix Perform coupled strain-voltage superposition: , in is the final compensation instruction, is the strain-voltage conversion coefficient, which is determined by the piezoelectric properties of the membrane material. Take the sum of the absolute values ​​of the main diagonal elements of the strain matrix and generate a voltage compensation instruction containing a sequence of voltage adjustment values.

[0047] For example, the microbial attachment thickness data was measured at the South China Sea monitoring point. =0.015 mm; = 0.8 MPa (typical biofilm modulus), calculate the three-dimensional biostress tensor =0.8×0.015=0.012 MPa; sensitive film flexibility matrix The preset diagonal matrix is ​​[0.05, 0.05, 0.03] per MPa, and the inverse is [20, 20, 33.3]; calculate the real-time membrane strain matrix =[20,20,33.3]×0.012≈[0.24,0.24,0.4], the sum of the absolute values ​​of microstrain =0.88; assuming electrochemical compensation parameters = -0.15 volts, =0.1 volt / , superimposed =-0.15+0.1×0.88≈0.088 volts, output compensation instruction set .

[0048] Optionally, combining the real-time membrane strain matrix with the compensation voltage value in the probe electrochemical compensation parameter to generate the 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.

[0049] Specifically, first obtain the real-time membrane strain matrix , which is generated by transforming the three-dimensional biological stress tensor and contains the strain components in the x, y, and z directions of the membrane surface. The spatial differentiation operation on this matrix generates the strain gradient vector field, which is formulated as: , in is the output value of the strain gradient vector field, Indicates the severity of compression along the x-axis, Indicates the severity of compression along the y-axis, Indicates the severity of compression along the z-axis. Each partial derivative is obtained by performing central difference calculation on the real-time membrane strain matrix in the probe surface coordinate system. The spatial step size is set to 1 mm, corresponding to the probe surface grid resolution. At the same time, the compensation voltage value in the probe electrochemical compensation parameter is called , perform a vector overlay operation: , in is the directional deformation voltage vector output value, is the probe normal, which is preset to a fixed value by the probe geometry. is the voltage-gradient coupling coefficient, is the modulus of the strain gradient vector field. Finally, the microbial attachment thickness data is correlated , calculate the bioactivity feedback value: , represents the effect of biofouling on the probe performance, Indicates the measured thickness of the microbial layer on the probe surface, The biological attenuation constant (fixed value 0.2 per millimeter) is input into the pulse width modulator to generate a pulse sequence. The pulse width formula is: , in is the pulse width after modulation, The base width is fixed at 10 milliseconds. is the biological activity gain coefficient, fixed at 0.5. Execute pulse encoding to generate a compensation instruction set including voltage amplitude, action direction and pulse width parameters.

[0050] For example, a real-time membrane strain matrix is ​​obtained at a monitoring point in a bay. =[0.2,−0.1,0.3], microstrain (corresponding to x, y, z directions); set the probe surface normal vector , the strain gradient vector field is calculated by spatial differentiation , mold length . Take the compensation voltage value =-0.12V, superimpose and calculate the directional deformation voltage vector: , current microbial attachment thickness =0.018mm, calculate the bioactivity feedback value = After pulse width modulation . Finally, the voltage compensation instruction is generated: .

[0051] Optionally, performing parameter correction and superposition processing on 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.

[0052] Specifically, first call the probe electrochemical compensation parameter set, which contains the compensation voltage value corrected by temperature-deformation dual field coupling With current adjustment slope . Perform key parameter extraction operation and extract only the compensation voltage value As the core electrochemical compensation input. Then get the voltage compensation instruction, which contains the voltage adjustment sequence after biofouling correction, and decode the real-time membrane strain matrix from the instruction set . Perform biofouling-related deformation correction on the real-time membrane strain matrix: Incorporate current microbial attachment thickness data , calculate the membrane permeability correction coefficient : , is the permeation attenuation constant (calibrated to 0.15 per mm by membrane material swelling experiment), and the strain matrix is ​​corrected by scalar multiplication: , is the corrected real-time membrane strain matrix. Finally, perform parameter superposition processing: the compensation voltage value With the correction matrix The control area of ​​the probe surface is divided into zones and mapped. The control voltage formula for each zone is: , in For the Partitioned probe calibration control sub-parameters, is the electrochemical weight distribution factor (preset to a fixed value by the probe electrode layout), is the strain-to-potential conversion coefficient (determined by the membrane piezoelectric constant of 1.2 volts per microstrain), Take the mean of the main diagonal elements of the correction matrix. Collect all regional sub-parameters to form the probe correction control parameter matrix And output.

[0053] For example, the electrochemical compensation parameters of the probe are obtained at the Yellow Sea monitoring station. =-0.15V, =0.02A / V, extraction Decode the voltage compensation instruction to obtain the real-time membrane strain matrix =[0.3,-0.05,0.2] (corresponding to triaxial data). Current microbial attachment thickness =0.02mm, calculate =e^{-0.15×0.02}≈0.997, correction matrix ≈[0.299,-0.0498,0.199]. Set the probe upstream area =0.8, mid-range =1.0, downstream =1.2, each region Take the corresponding mean. Calculation of upstream area =-0.15×0.8+1.2×(|0.299|)≈-0.12+0.3588≈0.2388V, midstream area =-0.15×1.0+1.2×(|-0.0498|)≈-0.15+0.06≈-0.09V, downstream area =-0.15×1.2+1.2×(|0.199|)≈-0.18+0.2388≈0.0588V, and the final output probe correction control parameter matrix is ​​[0.24V,-0.09V,0.06V].

[0054] Based on the same inventive concept, Figure 7 As shown, the present invention also provides a system for automatically correcting errors of online monitoring probes for trace heavy metals in seawater, the system comprising: 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.

[0055] It should be noted that the electrical connections between the above-mentioned units do not necessarily mean direct connections of lines. Indirect connections are applicable to the embodiments of the present invention as long as the purpose of the present invention is achieved. The above description is only an exemplary embodiment of the present invention and is not intended to limit the scope of the present invention.

[0056] That is, any equivalent changes and modifications made according to the teachings of the present invention are still within the scope of the present invention. Those skilled in the art will readily conceive of other embodiments of the present invention after considering the disclosure of the specification and practical truths. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary technical means in the art not described herein.

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 electrochemical compensation parameter of the probe according to 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; The voltage compensation instruction is subjected to parameter correction and superposition processing based on the probe electrochemical compensation parameter to obtain a probe correction control parameter.

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 calculated probe electrochemical compensation parameters 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; The compensation voltage value and the current adjustment slope are corrected by temperature-deformation dual-field coupling to obtain the electrochemical compensation parameters of the probe.

7. The method for automatically correcting errors of online monitoring probes for trace heavy metals in seawater according to claim 1, characterized in that: The dynamically adjusting the deformation parameter 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; The real-time membrane strain matrix is ​​combined with the compensation voltage value in the probe electrochemical compensation parameter to generate a voltage compensation instruction.

8. The method for automatically correcting errors of online monitoring probes for trace heavy metals in seawater according to claim 7, characterized in that: The generating of the voltage compensation instruction by combining the real-time membrane strain matrix with the compensation voltage value in the probe electrochemical compensation parameter 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.

9. 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.

10. An automatic error correction system for online monitoring probes of trace heavy metals in seawater, applied to an automatic error correction method for online monitoring probes of trace heavy metals in seawater according to any one of claims 1 to 9, 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.

Images