Seawater carbon parameter field detection method using particulate matter polarized light data

Abstract

A seawater carbon parameter on-site detection method using particulate matter polarized light data comprises: performing classification and feature extraction on particulate matters in a seawater sample by using a polarized light technology, and constructing a polarization scattering database (S1); establishing a correlation between the particulate matter polarized light data and the seawater carbon parameter through a statistical method or regression analysis, and forming an analytical formula or a digital model (S2); building a detection algorithm (S3) by considering the data correspondence, the application range and error control based on the association relationship; and executing a detection algorithm, and obtaining seawater carbon parameters from the polarized light data through an analytical formula or digital model calculation (S4). According to the invention, polarized light data of particles in seawater is measured, field detection of seawater carbon parameters is realized by combining a detection algorithm with an analytical formula of a statistical method or a digital model based on a machine learning method, rapid and accurate detection of seawater carbon parameters is realized, and an efficient means is provided for ocean carbon cycle research and environmental monitoring.

Description

Technical Field

[0001] The invention relates to a seawater carbon parameter detection technology, and in particular to a seawater carbon parameter on-site detection method using particulate matter polarized light data. Background Art

[0002] Marine carbon sink refers to the mechanism and process by which the ocean absorbs carbon dioxide (CO2) from the atmosphere and fixes it. It involves biological, chemical and physical processes in the ocean, which together cause carbon dioxide in seawater to be dissolved and stored in the ocean. The ocean is one of the largest carbon sinks on Earth. Through physical and biological processes on the ocean surface, carbon dioxide dissolved in seawater can be absorbed by the ocean. This process helps to mitigate the increase of carbon dioxide in the atmosphere and reduce the impact of global warming. The detection and monitoring of marine carbon sinks is of great significance for understanding the global carbon cycle, climate change and the stability of ecosystems.

[0003] According to Marine Ecology, particulate matter participates in the carbon cycle in seawater. From the perspective of the transfer and transformation of carbon in the marine water layer, in the upper seawater (euphotic layer), phytoplankton uses photosynthesis to convert carbon dioxide dissolved in water into organic matter, which is then transferred and consumed in sequence through the grazing food chain. Non-living particulate carbon is used through the detritus food chain, and some of it sinks to the lower ocean and reaches the seabed. Some dissolved organic carbon is used by the microbial food chain and re-enters organisms (particles). Therefore, it can be seen that carbon parameters in seawater, including total carbon, particulate carbon, dissolved carbon, organic carbon, inorganic carbon, etc., are all related to particles in seawater. Theoretically, it is possible to obtain seawater carbon parameters by measuring data such as the types and concentrations of particulate matter in seawater.

[0004] Existing research methods for detecting ocean carbon content include satellite remote sensing, laboratory chemical analysis, incineration, etc. However, these methods have some drawbacks. For example, satellite remote sensing cannot detect carbon parameters in deep seawater, and chemical analysis or incineration cannot measure quickly on site. Therefore, developing a method that can quickly and on-site detect seawater carbon parameters is of great significance to promote ocean carbon sink research.

[0005] At present, it has been reported that detection instruments based on polarized light technology can collect and analyze information on single particles in the ocean. The instrument obtains information such as the type and concentration of particles based on the change law of the polarization state before and after the scattering process and the characteristics of fluorescence generation, thereby realizing the monitoring of the concentration and type of marine particles. Marine carbon parameters, especially parameters such as particulate carbon and organic carbon, are inseparable from the information of tiny particles in the ocean. At present, there are still deficiencies in the technology of on-site and online detection of marine carbon parameters, mainly in terms of the accuracy, stability and adaptability of on-site detection equipment to complex marine environments.

[0006] It should be noted that the information disclosed in the above background technology section is only used for understanding the background of the present application, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the invention

[0007] The main purpose of the present invention is to solve the problems existing in the above-mentioned background technology and provide a method for on-site detection of seawater carbon parameters using particulate matter polarized light data.

[0008] To achieve the above object, the present invention adopts the following technical solutions:

[0009] A method for on-site detection of seawater carbon parameters using particulate matter polarized light data comprises the following steps:

[0010] S1: Use polarized light technology to classify and extract features of seawater particles in seawater samples and build a polarized scattering database;

[0011] S2: Conduct correlation analysis on the polarization light data of seawater particles and the seawater carbon parameters by statistical methods to obtain an analytical formula containing the correlation between the polarization light parameters of seawater particles and the seawater carbon parameters; or, use the data set of the polarization light parameters of seawater particles and the corresponding seawater carbon parameters to establish a digital model containing the correlation between the polarization light parameters of seawater particles and the seawater carbon parameters;

[0012] S3: using the correlation relationship, and considering the one-to-one correspondence between the output carbon parameter and the input seawater particle polarization data, the applicable scope and the error control, a detection algorithm based on the analytical formula or the digital model is constructed for each carbon parameter;

[0013] S4: Execute the detection algorithm for the polarized light data of seawater particles detected by the instrument, wherein the carbon parameters of seawater are obtained by solving the equation using the analytical formula, or the polarized light data of particles are input into the digital model, and the carbon parameters of seawater are calculated using the digital model.

[0014] Furthermore, the statistical method includes:

[0015] Obtain polarized light data of seawater particles and related seawater carbon parameters and water quality parameters from seawater samples;

[0016] Through correlation analysis, the high correlation parameters and low correlation parameters between the polarized light data of seawater particles and the seawater carbon parameters are identified;

[0017] Based on the correlation analysis results, for the highly correlated parameters, an analytical formula is constructed, wherein the formula describes the relationship between the highly correlated parameters and the seawater carbon parameters;

[0018] Based on the correlation analysis results, the analytical formula is auxiliary constructed for low-correlation parameters to enhance the prediction ability of the analytical formula.

[0019] Furthermore, the construction of the digital model includes:

[0020] Obtain polarized light data of seawater particles and related seawater carbon parameters and water quality parameters from seawater samples;

[0021] Select features that have a significant impact on the prediction of seawater carbon parameters from the pre-processed seawater particle polarization data;

[0022] Using a large amount of seawater sample data from the same source, combined with selected features and known carbon parameters, a digital model is trained to learn the mapping relationship between seawater particle polarization data and seawater carbon parameters;

[0023] Validate the trained numerical model to assess its accuracy and reliability in predicting seawater carbon parameters;

[0024] According to the results of model verification, adjust the model parameters and structure to optimize the model performance;

[0025] The final optimized digital model is saved for use in on-site testing to ensure the repeatability and consistency of the model.

[0026] Furthermore, the characteristics that have a significant impact on the prediction of seawater carbon parameters include size, shape and polarized light intensity.

[0027] Furthermore, the carbon parameter includes one or more of the following carbon content indicators: total carbon (TC), inorganic carbon (IC), non-purgeable organic carbon (NPOC), and total carbon and non-purgeable organic carbon of seawater after filtration using a filter membrane.

[0028] Furthermore, the construction of the digital model also includes:

[0029] A pulse information extraction method is used to extract single-particle scattered pulse information from the polarized light data of seawater particles measured by the instrument, and multi-dimensional data of particles is generated for particle feature identification. The number of particles detected within a given time at a given flow rate is counted to reflect the relative number of particles in seawater. The single pulse characteristics of single-particle information and the total number of specific types of particles in a statistical sense are characterized to reflect the information of seawater from multiple levels. The obtained data are used for training and optimization of the digital model.

[0030] Furthermore, the instrument uses four polarized light detection channels and one fluorescence detection channel to collect one or more information of the amplitude, distribution range, half-maximum full width, protrusion, etc. of the single particle scattered pulse.

[0031] Furthermore, the statistical method adopts linear fitting, and the concentration of different types of particles is c n , the corresponding model coefficient is a n , construct the functional relationship of formula 1-formula 3:

[0032] NPOC=f1(a1c1,a2c2...,a n c n ) (1)

[0033] TC=f2(a1c1,a2c2...,a n c n ) (2)

[0034] IC=f3(a1c1,a2c2...,a n c n ) (3)

[0035] The particle concentration c is obtained by using the instrument to identify the particles n Based on the carbon parameters of a large number of real seawater, the coefficient a is calculated using the least squares method. n .

[0036] Furthermore, a machine learning method is used to construct a digital model based on a regression model, and the regression model is a random forest regression model, a Lasso regression model or a GBDT model.

[0037] A computer-readable storage medium stores a computer program, which implements the method when executed by a processor.

[0038] The present invention has the following beneficial effects:

[0039] The present invention provides a novel on-site detection method for seawater carbon parameters, which measures polarized light data of particles in seawater and uses a detection algorithm combined with a statistical analytical formula or a machine learning digital model to achieve on-site detection of seawater carbon parameters. The method is based on the phenomenon that particles in seawater participate in the ocean carbon cycle, uses polarized light technology to detect and classify marine microparticles, gives full play to the advantages of big data models in correlation analysis, and establishes a relationship between particle polarized light data and seawater carbon parameters; then, through the detection algorithm, the seawater carbon parameters are obtained from the on-site measured particle polarized light data, thereby efficiently and reliably achieving on-site and online detection of the latter, providing strong technical support for marine carbon cycle research and environmental monitoring.

[0040] Other beneficial effects of the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1Schematic diagram of the statistical method of an embodiment of the present invention.

[0042] Figure 2 A schematic diagram is constructed for a digital model of an embodiment of the present invention.

[0043] Figure 3 A schematic diagram of a detection algorithm according to an embodiment of the present invention.

[0044] Figure 4 This is a physical picture of the polarization fluorescence instrument according to an embodiment of the present invention.

[0045] Figure 5 This is a diagram showing the classification effect of typical particles in seawater according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The following is a detailed description of the embodiments of the present invention. It should be emphasized that the following description is only exemplary and is not intended to limit the scope and application of the present invention.

[0047] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0048] See also Figures 1 to 3 The embodiment of the present invention provides a method for on-site detection of seawater carbon parameters using particulate matter polarized light data, comprising the following steps:

[0049] S1: Use polarized light technology to classify and extract features of seawater particles (such as seaweed, microplastics, and sediment) in seawater samples and build a polarized scattering database;

[0050] S2: The correlation between the polarized light data of seawater particles and the carbon parameters of seawater is analyzed by statistical methods, and the analytical formula containing the correlation between the polarized light parameters of seawater particles and the carbon parameters of seawater is obtained (see Figure 1 ); or, using regression analysis and other methods to analyze the polarization parameters of seawater particles and the true values ​​of the corresponding seawater carbon parameters, and constructing a digital model containing the correlation between the polarization parameters of seawater particles and the seawater carbon parameters (see Figure 2 );

[0051] S3: Using the correlation relationship, and considering the one-to-one correspondence between the output carbon parameter and the input seawater particle polarization data, the applicable scope and the error control, a detection algorithm based on the analytical formula or the digital model is constructed for each carbon parameter (see Figure 3 );

[0052] S4: Execute the detection algorithm for the polarized light data of seawater particles detected by the instrument, wherein the carbon parameters of seawater are obtained by solving the equation using the analytical formula, or the polarized light data of particles are input into the digital model, and the carbon parameters of seawater are calculated using the digital model.

[0053] like Figure 1 As shown, in some preferred embodiments, the statistical method includes: obtaining seawater particle polarization data and related seawater carbon parameters and water quality parameters of seawater samples; identifying high-correlation parameters and low-correlation parameters between seawater particle polarization data and seawater carbon parameters through correlation analysis; based on the correlation analysis results, for high-correlation parameters, constructing an analytical formula, the formula describing the relationship between the high-correlation parameters and the seawater carbon parameters; based on the correlation analysis results, for low-correlation parameters, assisting in the construction of the analytical formula to enhance the predictive ability of the analytical formula.

[0054] like Figure 2 As shown, in other preferred embodiments, the construction of the digital model includes: obtaining seawater particle polarization data and related seawater carbon parameters and water quality parameters of seawater samples; selecting features that have a significant impact on the prediction of seawater carbon parameters from the pre-processed seawater particle polarization data, including but not limited to the size, shape, polarization intensity, etc. of the particles; using a large amount of seawater sample data from the same source, combined with the selected features and known carbon parameters, to train the digital model to learn the mapping relationship between seawater particle polarization data and seawater carbon parameters; verifying the trained digital model to evaluate its accuracy and reliability in predicting seawater carbon parameters; adjusting the model parameters and structure according to the results of the model verification to optimize the model performance; and saving the final optimized digital model for use in field detection to ensure the repeatability and consistency of the model.

[0055] In some preferred embodiments, the construction of the digital model also includes: extracting single-particle scattering pulse information using a pulse information extraction method based on the polarized light data of seawater particles measured by the instrument, preferably including using four polarized light detection channels and one fluorescence detection channel to collect the amplitude, distribution range, half-maximum full width, protrusion and other information of the single-particle scattering pulse, to generate multi-dimensional data of particles for particle feature identification; counting the number of particles detected within a certain period of time at a certain flow rate to reflect the relative number of particles in seawater; wherein, the single pulse characteristics of the single particle information and the total number of specific types of particles in a statistical sense are characterized to reflect the information of seawater from multiple levels; and the obtained data are used for training and optimization of the digital model.

[0056] The specific embodiments of the present invention are further described below.

[0057] In order to establish the relationship between the particle polarization data and the seawater carbon parameters, a statistical method can be used for correlation analysis to find specific parameters with a high correlation with the carbon parameters, so as to construct an analytical formula for the carbon parameters with respect to the particle polarization data. The principle is as follows: Figure 1 As shown. From the polarized light data of seawater particles, information about the types and quantities of seawater particles can be obtained. The correlation analysis is performed on the seawater water quality parameters and seawater carbon parameters. The parameters with higher correlation can be used to directly construct the analytical formula, and the parameters with lower correlation can be used to construct or optimize the analytical formula, playing a reference and auxiliary role. Through statistical methods, an analytical formula containing the correlation between the polarized light parameters of seawater particles and the seawater carbon parameters is obtained.

[0058] The composition of seawater particles is complex and has many characteristics. There are more than ten common water quality parameters. The polarized light data of seawater particles contains many dimensions. In addition to statistical methods, artificial intelligence technology can also be used to construct a digital model for the polarized light data and carbon parameters of seawater particles. The principle is as follows Figure 2 As shown. The particle classification detection instrument can detect single particles in seawater, distinguish them according to their characteristics, and build a regression model with the large amount of data generated by the instrument detecting particles and the true value of the carbon parameter of the corresponding seawater sample to obtain a digital model and save it for future use. Through this method, a digital model containing the complex correlation between the polarization light parameters of seawater particles and the carbon parameters of seawater is obtained.

[0059] By using the correlation relationship, taking into account the one-to-one correspondence between the output carbon parameter and the input seawater particle polarization data, the applicable scope and error control, the best detection algorithm is developed for each carbon parameter. Using this detection algorithm, within the applicable scope, the seawater carbon parameter can be obtained from the seawater particle polarization data. The construction principle and use process of the detection algorithm are as follows Figure 3 As shown, by using the existing correlation, screening is performed through one-to-one correspondence, applicable scope, error control and other factors, and finally a detection algorithm with better performance is obtained. When this detection algorithm is used to detect unknown seawater, the carbon parameters can be calculated using the polarized light data of seawater particles.

[0060] For the statistical method, the polarized light data of seawater particles detected by the instrument is input into the analytical formula, and the equation is solved to obtain the carbon parameters of the unknown seawater; for the artificial intelligence digital model, the method of use is to input the polarized light data of various seawater particles of the unknown seawater into the existing digital model, and use the model calculation output to obtain the seawater carbon parameters.

[0061] In order to achieve on-site and rapid measurement of seawater carbon parameters, a portable and intelligent device is needed to accurately detect and analyze the composition of seawater, so as to detect the carbon parameters of seawater. At present, there are instruments that meet the above conditions. The appearance of the instrument is as follows: Figure 4 In order to distinguish the types of particles, a polarization scattering database containing typical marine particles was constructed for the instrument. Typical particles in seawater include seaweed, microplastics, and mud. The instrument's classification effect on different particles is as follows: Figure 5 After the instrument database was built, the instrument was used to monitor the seawater at 76 points in the waters near Guangdong Province for several months. These points covered a wide range of offshore water types.

[0062] At the same time, multiple carbon parameter tests and multiple water quality index tests were conducted on each of the above samples. The carbon parameters involved here are the following carbon content indicators obtained under laboratory conditions based on the National Environmental Protection Standard HJ 501-2009: total carbon (TC), inorganic carbon (IC), non-purgeable organic carbon (NPOC), and total carbon and non-purgeable organic carbon of seawater after filtration using a 0.45 micron PES filter membrane.

[0063] In order to make the model more accurate and have a wider range of applications, a large number of carbon parameters of real seawater samples and corresponding particle information model training are invested. Preferably, for the data measured by the instrument, a pulse information extraction method that can reflect the multi-dimensional information of the particles is adopted, which helps to fully explore the pulse information detected by the instrument, extract as many pulse parameters as possible, identify the characteristics of the particles, and improve the accuracy of particle type identification. This will generate multi-dimensional data containing a large amount of particle information, which can be used to build artificial intelligence digital models. Preferably, the sources of these particle information are: the amplitude, distribution range, half-height full width, protrusion and other information of single-particle scattering pulses collected by four polarized light detection channels and one fluorescence detection channel. At a certain flow rate, the number of particles detected within a certain period of time reflects the relative number of particles in seawater. Characterizing a series of information such as the single pulse characteristics of single particle information and the total number of specific types of particles in a statistical sense, reflects the information of seawater from multiple levels.

[0064] Preferably, the statistical method model can adopt linear fitting, and the concentration of different types of particles can be assumed to be c n , the corresponding model coefficient is a n , then we can construct the functional relationship as shown in equations 1 to 3. Using the instrument's ability to identify the type of particles, we can get the concentration c of the particles. n Based on the carbon parameters of a large number of real seawater, the coefficient a is calculated using the least squares method. n Perform the calculation:

[0065] NPOC=f1(a1c1,a2c2...,a n c n ) (1)

[0066] TC=f2(a1c1,a2c2...,a n c n ) (2)

[0067] IC=f3(a1c1,a2c2...,a n c n ) (3)

[0068] When the amount of data is large, especially when the instrument extracts many features, it is preferred to use artificial intelligence methods to build a digital model. The carbon parameter detection model of the instrument can choose an appropriate regression model, such as random forest regression, Lasso regression, GBDT model, etc. Similar to the aforementioned correlation analysis, characteristic coefficient analysis of the preliminary constructed model can be performed to study the particulate matter parameters that significantly affect parameter carbon, so as to construct a regression model more scientifically and efficiently. Current research shows that pulse parameters such as peak size, full width at half maximum, and some of the particulate matter parameters such as seaweed, microplastics, and major sediment content have the most significant impact on the regression model.

[0069] No less than 200 sets of data are used for model training. This is because more sample data allows the model to avoid overfitting as much as possible, and seawater data from more points allows the model to adapt to more types of sea scenarios during use. The above process completes the construction of the model. In the model application, the instrument can obtain particulate matter information on site, and inputting this data into the model can quickly detect seawater carbon parameters on site.

[0070] An embodiment of the present invention further provides a storage medium for storing a computer program, which at least performs the above method when executed.

[0071] An embodiment of the present invention further provides a control device, comprising a processor and a storage medium for storing a computer program; wherein the processor is configured to execute at least the method described above when executing the computer program.

[0072] An embodiment of the present invention further provides a processor, wherein the processor executes a computer program and at least executes the method described above.

[0073] The storage medium can be implemented by any type of non-volatile storage device, or a combination thereof. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a ferromagnetic random access memory (Flash Memory), a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The storage medium described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0074] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0075] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0076] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0077] A person skilled in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, etc. Various media that can store program codes.

[0078] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0079] The methods disclosed in the several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0080] The features disclosed in several product embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0081] The features disclosed in several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0082] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art of the present invention, several equivalent substitutions or obvious variations can be made without departing from the concept of the present invention, and the performance or use is the same, which should be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for on-site detection of seawater carbon parameters using particulate matter polarized light data, characterized in that: The steps include: S1: Use polarized light technology to classify and extract features of seawater particles in seawater samples and build a polarized scattering database; S2: Conduct correlation analysis on the polarization light data of seawater particles and the seawater carbon parameters by statistical methods to obtain an analytical formula containing the correlation between the polarization light parameters of seawater particles and the seawater carbon parameters; or, use the data set of the polarization light parameters of seawater particles and the corresponding seawater carbon parameters to establish a digital model containing the correlation between the polarization light parameters of seawater particles and the seawater carbon parameters; S3: using the correlation relationship, and considering the one-to-one correspondence between the output carbon parameter and the input seawater particle polarization data, the applicable scope and the error control, a detection algorithm based on the analytical formula or the digital model is constructed for each carbon parameter; S4: Execute the detection algorithm for the polarized light data of seawater particles detected by the instrument, wherein the carbon parameters of seawater are obtained by solving the equation using the analytical formula, or the polarized light data of particles are input into the digital model, and the carbon parameters of seawater are calculated using the digital model.

2. The method for on-site detection of seawater carbon parameters according to claim 1, characterized in that: The statistical methods include: Obtain polarized light data of seawater particles and related seawater carbon parameters and water quality parameters from seawater samples; Through correlation analysis, the high correlation parameters and low correlation parameters between the polarized light data of seawater particles and the seawater carbon parameters are identified; Based on the correlation analysis results, for the highly correlated parameters, an analytical formula is constructed, wherein the analytical formula describes the relationship between the highly correlated parameters and the seawater carbon parameters; Based on the correlation analysis results, the analytical formula is auxiliary constructed for low-correlation parameters to enhance the prediction ability of the analytical formula.

3. The method for on-site detection of seawater carbon parameters according to claim 1, characterized in that: The construction of the digital model includes: Obtain polarized light data of seawater particles and related seawater carbon parameters and water quality parameters from seawater samples; Select features that have a significant impact on the prediction of seawater carbon parameters from the pre-processed seawater particle polarization data; Using seawater sample data from the same source, combined with selected features and known carbon parameters, the digital model is trained to learn the mapping relationship between seawater particle polarization data and seawater carbon parameters; Validate the trained numerical model to assess its accuracy and reliability in predicting seawater carbon parameters; According to the results of model verification, adjust the model parameters and structure to optimize the model performance; The final optimized digital model is saved for use in on-site testing to ensure the repeatability and consistency of the model.

4. The method for on-site detection of seawater carbon parameters according to any one of claims 1 to 3, characterized in that: The features that have a significant impact on the prediction of seawater carbon parameters include size, shape and polarized light intensity.

5. The method for on-site detection of seawater carbon parameters according to any one of claims 1 to 4, characterized in that: The carbon parameter includes one or more of the following carbon content indicators: total carbon (TC), inorganic carbon (IC), non-purgeable organic carbon (NPOC), and total carbon and non-purgeable organic carbon of seawater after filtration using a filter membrane.

6. The method for on-site detection of seawater carbon parameters according to any one of claims 1 to 5, characterized in that: The construction of the digital model also includes: A pulse information extraction method is used to extract single-particle scattered pulse information from the polarized light data of seawater particles measured by the instrument, and multi-dimensional data of particles is generated for particle feature identification. The number of particles detected within a given time at a given flow rate is counted to reflect the relative number of particles in seawater. The single pulse characteristics of single-particle information and the total number of specific types of particles in a statistical sense are characterized to reflect the information of seawater from multiple levels. The obtained data are used for training and optimization of the digital model.

7. The method for on-site detection of seawater carbon parameters according to claim 6, characterized in that: The instrument uses four polarized light detection channels and one fluorescence detection channel to collect one or more of the amplitude, distribution interval, half-maximum full width, and protrusion information of a single particle scattered pulse.

8. The method for on-site detection of seawater carbon parameters according to any one of claims 1 to 5, characterized in that: The statistical method adopts linear fitting, and the concentration of different types of particles is c n , the corresponding model coefficient is a n , construct the functional relationship of formula 1-formula 3: NPOC=f1(a1c1,a2c2...,a n c n ) (1) <h2 style=";text-align:left;direction:ltr">TC = f2(a1c1,a2c2...,a<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> c<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ) (2) <h2 style=";text-align:left;direction:ltr">IC = f3(a1c1,a2c2...,a<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> c<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ) (3) The particle concentration c is obtained by using the instrument to identify the particles n Based on the carbon parameters of a large number of real seawater, the coefficient a is calculated using the least squares method. n .

9. The method for on-site detection of seawater carbon parameters according to any one of claims 1, 3 to 7, characterized in that: A machine learning method is used to construct a digital model based on a regression model, and the regression model is a random forest regression model, a Lasso regression model or a GBDT model.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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